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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 |
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@memory.cache |
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def InitializeFirstStageCM (RemainingIds, setMaxLoopValue): |
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random.seed(RANDOM_SEED) |
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global XData |
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global yData |
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global addKNN |
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global addLR |
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global addMLP |
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global addRF |
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global addGradB |
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global countAllModels |
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# loop through the algorithms |
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global allParametersPerfCrossMutr |
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global HistoryPreservation |
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global randomSearchVar |
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greater = randomSearchVar*5 |
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KNNIDs = list(filter(lambda k: 'KNN' in k, RemainingIds)) |
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LRIDs = list(filter(lambda k: 'LR' in k, RemainingIds)) |
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MLPIDs = list(filter(lambda k: 'MLP' in k, RemainingIds)) |
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RFIDs = list(filter(lambda k: 'RF' in k, RemainingIds)) |
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GradBIDs = list(filter(lambda k: 'GradB' in k, RemainingIds)) |
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countKNN = 0 |
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countLR = 0 |
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countMLP = 0 |
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countRF = 0 |
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countGradB = 0 |
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paramAllAlgs = PreprocessingParam() |
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KNNIntIndex = [] |
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LRIntIndex = [] |
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MLPIntIndex = [] |
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RFIntIndex = [] |
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GradBIntIndex = [] |
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localCrossMutr = [] |
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allParametersPerfCrossMutrKNNC = [] |
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while countKNN < setMaxLoopValue[16]: |
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for dr in KNNIDs: |
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if (int(re.findall('\d+', dr)[0]) >= greater): |
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KNNIntIndex.append(int(re.findall('\d+', dr)[0])-addKNN) |
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else: |
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KNNIntIndex.append(int(re.findall('\d+', dr)[0])) |
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KNNPickPair = random.sample(KNNIntIndex,2) |
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pairDF = paramAllAlgs.iloc[KNNPickPair] |
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crossoverDF = pd.DataFrame() |
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for column in pairDF: |
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listData = [] |
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randomZeroOne = random.randint(0, 1) |
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valuePerColumn = pairDF[column].iloc[randomZeroOne] |
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listData.append(valuePerColumn) |
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crossoverDF[column] = listData |
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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()): |
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crossoverDF = pd.DataFrame() |
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else: |
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clf = KNeighborsClassifier() |
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params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]} |
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AlgorithmsIDsEnd = countAllModels + countKNN |
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localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_C', AlgorithmsIDsEnd) |
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countKNN += 1 |
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crossoverDF = pd.DataFrame() |
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countAllModels = countAllModels + setMaxLoopValue[16] |
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for loop in range(setMaxLoopValue[16] - 1): |
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localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] |
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localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) |
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localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True) |
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localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True) |
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allParametersPerfCrossMutrKNNC.append(localCrossMutr[0]) |
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allParametersPerfCrossMutrKNNC.append(localCrossMutr[1]) |
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allParametersPerfCrossMutrKNNC.append(localCrossMutr[2]) |
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allParametersPerfCrossMutrKNNC.append(localCrossMutr[3]) |
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HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrKNNC |
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countKNN = 0 |
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KNNIntIndex = [] |
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localCrossMutr.clear() |
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allParametersPerfCrossMutrKNNM = [] |
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while countKNN < setMaxLoopValue[10]: |
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for dr in KNNIDs: |
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if (int(re.findall('\d+', dr)[0]) >= greater): |
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KNNIntIndex.append(int(re.findall('\d+', dr)[0])-addKNN) |
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else: |
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KNNIntIndex.append(int(re.findall('\d+', dr)[0])) |
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KNNPickPair = random.sample(KNNIntIndex,1) |
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pairDF = paramAllAlgs.iloc[KNNPickPair] |
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crossoverDF = pd.DataFrame() |
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for column in pairDF: |
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listData = [] |
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if (column == 'n_neighbors'): |
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randomNumber = random.randint(101, math.floor(((len(yData)/crossValidation)*(crossValidation-1)))-1) |
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listData.append(randomNumber) |
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crossoverDF[column] = listData |
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else: |
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valuePerColumn = pairDF[column].iloc[0] |
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listData.append(valuePerColumn) |
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crossoverDF[column] = listData |
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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()): |
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crossoverDF = pd.DataFrame() |
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else: |
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clf = KNeighborsClassifier() |
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params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]} |
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AlgorithmsIDsEnd = countAllModels + countKNN |
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localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_M', AlgorithmsIDsEnd) |
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countKNN += 1 |
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crossoverDF = pd.DataFrame() |
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countAllModels = countAllModels + setMaxLoopValue[10] |
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for loop in range(setMaxLoopValue[10] - 1): |
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localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] |
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localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) |
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localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True) |
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localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True) |
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allParametersPerfCrossMutrKNNM.append(localCrossMutr[0]) |
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allParametersPerfCrossMutrKNNM.append(localCrossMutr[1]) |
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allParametersPerfCrossMutrKNNM.append(localCrossMutr[2]) |
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allParametersPerfCrossMutrKNNM.append(localCrossMutr[3]) |
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HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrKNNM |
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localCrossMutr.clear() |
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allParametersPerfCrossMutrLRC = [] |
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while countLR < setMaxLoopValue[15]: |
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for dr in LRIDs: |
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if (int(re.findall('\d+', dr)[0]) >= greater): |
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LRIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar)) |
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else: |
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LRIntIndex.append(int(re.findall('\d+', dr)[0])) |
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LRPickPair = random.sample(LRIntIndex,2) |
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pairDF = paramAllAlgs.iloc[LRPickPair] |
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crossoverDF = pd.DataFrame() |
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for column in pairDF: |
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listData = [] |
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randomZeroOne = random.randint(0, 1) |
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valuePerColumn = pairDF[column].iloc[randomZeroOne] |
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listData.append(valuePerColumn) |
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crossoverDF[column] = listData |
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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()): |
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crossoverDF = pd.DataFrame() |
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else: |
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clf = LogisticRegression(random_state=RANDOM_SEED) |
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params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]} |
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AlgorithmsIDsEnd = countAllModels + countLR |
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localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_C', AlgorithmsIDsEnd) |
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countLR += 1 |
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crossoverDF = pd.DataFrame() |
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countAllModels = countAllModels + setMaxLoopValue[15] |
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for loop in range(setMaxLoopValue[15] - 1): |
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localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] |
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localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) |
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localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True) |
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localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True) |
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allParametersPerfCrossMutrLRC.append(localCrossMutr[0]) |
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allParametersPerfCrossMutrLRC.append(localCrossMutr[1]) |
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allParametersPerfCrossMutrLRC.append(localCrossMutr[2]) |
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allParametersPerfCrossMutrLRC.append(localCrossMutr[3]) |
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HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrLRC |
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countLR = 0 |
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LRIntIndex = [] |
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localCrossMutr.clear() |
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allParametersPerfCrossMutrLRM = [] |
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while countLR < setMaxLoopValue[9]: |
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for dr in LRIDs: |
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if (int(re.findall('\d+', dr)[0]) >= greater): |
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LRIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar)) |
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else: |
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LRIntIndex.append(int(re.findall('\d+', dr)[0])) |
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LRPickPair = random.sample(LRIntIndex,1) |
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pairDF = paramAllAlgs.iloc[LRPickPair] |
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crossoverDF = pd.DataFrame() |
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for column in pairDF: |
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listData = [] |
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if (column == 'C'): |
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randomNumber = random.randint(101, 1000) |
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listData.append(randomNumber) |
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crossoverDF[column] = listData |
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else: |
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valuePerColumn = pairDF[column].iloc[0] |
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listData.append(valuePerColumn) |
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crossoverDF[column] = listData |
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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()): |
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crossoverDF = pd.DataFrame() |
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else: |
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clf = LogisticRegression(random_state=RANDOM_SEED) |
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params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]} |
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AlgorithmsIDsEnd = countAllModels + countLR |
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localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_M', AlgorithmsIDsEnd) |
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countLR += 1 |
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crossoverDF = pd.DataFrame() |
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countAllModels = countAllModels + setMaxLoopValue[9] |
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for loop in range(setMaxLoopValue[9] - 1): |
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localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] |
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localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) |
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localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True) |
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localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True) |
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allParametersPerfCrossMutrLRM.append(localCrossMutr[0]) |
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allParametersPerfCrossMutrLRM.append(localCrossMutr[1]) |
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allParametersPerfCrossMutrLRM.append(localCrossMutr[2]) |
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allParametersPerfCrossMutrLRM.append(localCrossMutr[3]) |
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HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrLRM |
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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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|
||||||
@ -1 +0,0 @@ |
|||||||
{"duration": 0.31346797943115234, "input_args": {"XData": " Fbs Slope Trestbps Exang Thalach Age Chol Sex Oldpeak Restecg Cp Ca Thal\n0 1 0 145 0 150 63 233 1 2.3 0 3 0 1\n1 0 0 130 0 187 37 250 1 3.5 1 2 0 2\n2 0 2 130 0 172 41 204 0 1.4 0 1 0 2\n3 0 2 120 0 178 56 236 1 0.8 1 1 0 2\n4 0 2 120 1 163 57 354 0 0.6 1 0 0 2\n.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "LogisticRegression(C=187, max_iter=50, penalty='none', random_state=42,\n solver='saga')", "params": "{'C': [187], 'max_iter': [50], 'solver': ['saga'], 'penalty': ['none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "656"}} |
|
||||||
@ -1 +0,0 @@ |
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{"duration": 0.3468780517578125, "input_args": {"XData": " Fbs Slope Trestbps Exang Thalach Age Chol Sex Oldpeak Restecg Cp Ca Thal\n0 1 0 145 0 150 63 233 1 2.3 0 3 0 1\n1 0 0 130 0 187 37 250 1 3.5 1 2 0 2\n2 0 2 130 0 172 41 204 0 1.4 0 1 0 2\n3 0 2 120 0 178 56 236 1 0.8 1 1 0 2\n4 0 2 120 1 163 57 354 0 0.6 1 0 0 2\n.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='kd_tree', metric='euclidean', n_neighbors=221,\n weights='distance')", "params": "{'n_neighbors': [221], 'metric': ['euclidean'], 'algorithm': ['kd_tree'], 'weights': ['distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "564"}} |
|
||||||
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@ -1 +0,0 @@ |
|||||||
{"duration": 0.5366220474243164, "input_args": {"XData": " Fbs Slope Trestbps Exang Thalach Age Chol Sex Oldpeak Restecg Cp Ca Thal\n0 1 0 145 0 150 63 233 1 2.3 0 3 0 1\n1 0 0 130 0 187 37 250 1 3.5 1 2 0 2\n2 0 2 130 0 172 41 204 0 1.4 0 1 0 2\n3 0 2 120 0 178 56 236 1 0.8 1 1 0 2\n4 0 2 120 1 163 57 354 0 0.6 1 0 0 2\n.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='kd_tree', metric='chebyshev', n_neighbors=54)", "params": "{'n_neighbors': [54], 'metric': ['chebyshev'], 'algorithm': ['kd_tree'], 'weights': ['uniform']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "547"}} |
|
||||||
@ -1 +0,0 @@ |
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{"duration": 2.277498245239258, "input_args": {"XData": " Fbs Slope Trestbps Exang Thalach Age Chol Sex Oldpeak Restecg Cp Ca Thal\n0 1 0 145 0 150 63 233 1 2.3 0 3 0 1\n1 0 0 130 0 187 37 250 1 3.5 1 2 0 2\n2 0 2 130 0 172 41 204 0 1.4 0 1 0 2\n3 0 2 120 0 178 56 236 1 0.8 1 1 0 2\n4 0 2 120 1 163 57 354 0 0.6 1 0 0 2\n.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "LogisticRegression(C=418, max_iter=450, penalty='none', random_state=42)", "params": "{'C': [418], 'max_iter': [450], 'solver': ['lbfgs'], 'penalty': ['none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "699"}} |
|
||||||
@ -1 +0,0 @@ |
|||||||
{"duration": 0.5745747089385986, "input_args": {"XData": " Fbs Slope Trestbps Exang Thalach Age Chol Sex Oldpeak Restecg Cp Ca Thal\n0 1 0 145 0 150 63 233 1 2.3 0 3 0 1\n1 0 0 130 0 187 37 250 1 3.5 1 2 0 2\n2 0 2 130 0 172 41 204 0 1.4 0 1 0 2\n3 0 2 120 0 178 56 236 1 0.8 1 1 0 2\n4 0 2 120 1 163 57 354 0 0.6 1 0 0 2\n.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "LogisticRegression(C=207, max_iter=250, random_state=42, solver='sag')", "params": "{'C': [207], 'max_iter': [250], 'solver': ['sag'], 'penalty': ['l2']}", "eachAlgor": "'LR_M'", "AlgorithmsIDsEnd": "515"}} |
|
||||||
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