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params.txt
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{'estimators': [('30',
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bylevel=0.6, colsample_bytree=1, eta=0.001, gamma=0, max_depth=6, max_leaves=15, n_estimators=800, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=2.5, subsample=1, tree_method='auto'))],
verbose=False)),
('24',
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=1, eta=0.05, gamma=0, max_depth=6, max_leaves=0, n_estimators=200, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.625, reg_lambda=0.8333333333333334, subsample=0.8, tree_method='auto'))],
verbose=False)),
('21',
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=0.5, eta=0.2, gamma=0, max_depth=7, max_leaves=7, n_estimators=25, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=0.20833333333333334, subsample=1, tree_method='auto'))],
verbose=False)),
('11',
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=0.6, eta=0.3, gamma=0, max_depth=6, max_leaves=0, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.3125, reg_lambda=2.3958333333333335, subsample=1, tree_method='auto'))],
verbose=False)),
('0',
Pipeline(memory=None,
steps=[('maxabsscaler', MaxAbsScaler(copy=True)),
('lightgbmclassifier',
LightGBMClassifier(min_data_in_leaf=20, n_jobs=1, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None))],
verbose=False)),
('13',
Pipeline(memory=None,
steps=[('maxabsscaler', MaxAbsScaler(copy=True)),
('sgdclassifierwrapper',
SGDClassifierWrapper(alpha=7.5510448979591835, class_weight='balanced', eta0=0.001, fit_intercept=True, l1_ratio=0.42857142857142855, learning_rate='constant', loss='modified_huber', max_iter=1000, n_jobs=1, penalty='none', power_t=0.7777777777777777, random_state=None, tol=0.0001))],
verbose=False)),
('3',
Pipeline(memory=None,
steps=[('sparsenormalizer', Normalizer(copy=True, norm='l2')),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=0.7, eta=0.01, gamma=0.01, max_depth=7, max_leaves=31, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=2.1875, reg_lambda=1.0416666666666667, subsample=1, tree_method='auto'))],
verbose=False)),
('22',
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=True)),
('lightgbmclassifier',
LightGBMClassifier(boosting_type='gbdt', colsample_bytree=0.4955555555555555, learning_rate=0.05789894736842106, max_bin=210, max_depth=5, min_child_weight=0, min_data_in_leaf=0.07931241379310346, min_split_gain=0.3684210526315789, n_estimators=600, n_jobs=1, num_leaves=137, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None, reg_alpha=0.5789473684210527, reg_lambda=0.42105263157894735, subsample=0.05))],
verbose=False)),
('12',
Pipeline(memory=None,
steps=[('maxabsscaler', MaxAbsScaler(copy=True)),
('logisticregression',
LogisticRegression(C=719.6856730011514, class_weight=None,
dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None,
max_iter=100, multi_class='multinomial',
n_jobs=1, penalty='l2', random_state=None,
solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False))],
verbose=False))],
'voting': 'soft',
'weights': [0.35714285714285715,
0.07142857142857142,
0.07142857142857142,
0.14285714285714285,
0.07142857142857142,
0.07142857142857142,
0.07142857142857142,
0.07142857142857142,
0.07142857142857142],
'n_jobs': None,
'flatten_transform': None,
'estimators_': [Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bylevel=0.6, colsample_bytree=1, eta=0.001, gamma=0, max_depth=6, max_leaves=15, n_estimators=800, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=2.5, subsample=1, tree_method='auto'))],
verbose=False),
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=1, eta=0.05, gamma=0, max_depth=6, max_leaves=0, n_estimators=200, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.625, reg_lambda=0.8333333333333334, subsample=0.8, tree_method='auto'))],
verbose=False),
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=0.5, eta=0.2, gamma=0, max_depth=7, max_leaves=7, n_estimators=25, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=0.20833333333333334, subsample=1, tree_method='auto'))],
verbose=False),
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=0.6, eta=0.3, gamma=0, max_depth=6, max_leaves=0, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.3125, reg_lambda=2.3958333333333335, subsample=1, tree_method='auto'))],
verbose=False),
Pipeline(memory=None,
steps=[('maxabsscaler', MaxAbsScaler(copy=True)),
('lightgbmclassifier',
LightGBMClassifier(min_data_in_leaf=20, n_jobs=1, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None))],
verbose=False),
Pipeline(memory=None,
steps=[('maxabsscaler', MaxAbsScaler(copy=True)),
('sgdclassifierwrapper',
SGDClassifierWrapper(alpha=7.5510448979591835, class_weight='balanced', eta0=0.001, fit_intercept=True, l1_ratio=0.42857142857142855, learning_rate='constant', loss='modified_huber', max_iter=1000, n_jobs=1, penalty='none', power_t=0.7777777777777777, random_state=None, tol=0.0001))],
verbose=False),
Pipeline(memory=None,
steps=[('sparsenormalizer', Normalizer(copy=True, norm='l2')),
('xgboostclassifier',
XGBoostClassifier(booster='gbtree', colsample_bytree=0.7, eta=0.01, gamma=0.01, max_depth=7, max_leaves=31, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=2.1875, reg_lambda=1.0416666666666667, subsample=1, tree_method='auto'))],
verbose=False),
Pipeline(memory=None,
steps=[('standardscalerwrapper',
StandardScalerWrapper(copy=True, with_mean=False, with_std=True)),
('lightgbmclassifier',
LightGBMClassifier(boosting_type='gbdt', colsample_bytree=0.4955555555555555, learning_rate=0.05789894736842106, max_bin=210, max_depth=5, min_child_weight=0, min_data_in_leaf=0.07931241379310346, min_split_gain=0.3684210526315789, n_estimators=600, n_jobs=1, num_leaves=137, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None, reg_alpha=0.5789473684210527, reg_lambda=0.42105263157894735, subsample=0.05))],
verbose=False),
Pipeline(memory=None,
steps=[('maxabsscaler', MaxAbsScaler(copy=True)),
('logisticregression',
LogisticRegression(C=719.6856730011514, class_weight=None,
dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None,
max_iter=100, multi_class='multinomial',
n_jobs=1, penalty='l2', random_state=None,
solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False))],
verbose=False)],
'_labels': array([0, 1]),
'le_': LabelEncoder(),
'classes_': array([0, 1])}