def level_two_wrapper():
ftype=''
config_instance=Config(ftype)
level='level_two'#in which level right now is
#for normal stacking (call level_data)
clf_name_level_data=[
ftype+'_lr_sag',
ftype+'_lr_newton',
ftype+'_lr_lbfgs',
ftype+'_lr_liblinear',
ftype+'_rf100',
ftype+'_rf200',
ftype+'_rf500',
ftype+'_rf1000',
ftype+'_gbdt20',
ftype+'_gbdt50',
ftype+'_gbdt100',
ftype+'_ada20',
ftype+'_ada50',
ftype+'_ada100',
ftype+'_xgb2000',
ftype+'_xgb2500',
ftype+'_xgb2000_2',
ftype+'_xgb2500_2'
]
#for partial stacking model, call level_data_part, select the BBM, in this case xgb2000
clf_name=[
# ftype+'_lr_sag',
# ftype+'_lr_newton',
# ftype+'_lr_lbfgs',
# ftype+'_lr_liblinear',
# ftype+'_rf100',
# ftype+'_rf200',
# ftype+'_rf500',
# ftype+'_rf1000',
# ftype+'_gbdt20',
# ftype+'_gbdt50',
# ftype+'_gbdt100',
# ftype+'_ada20',
# ftype+'_ada50',
# ftype+'_ada100',
# ftype+'_bag20',
# ftype+'_bag50',
# ftype+'_xgb1000',
ftype+'_xgb2000',
# ftype+'_xgb2500',
# ftype+'_xgb1000_2',
# ftype+'_xgb2000_2',
# ftype+'_xgb2500_2',
# ftype+'_xgb1000_3',
# ftype+'_xgb2000_3',
# ftype+'_xgb2500_3',
# ftype+'_xgb2500_4',
# ftype+'_xgb1000_5',
# ftype+'_xgb2000_5',
# ftype+'_xgb2500_5',
# ftype+'_xgb1000_6',
# ftype+'_xgb2000_6',
# ftype+'_xgb2500_6'
]
###############################################################################
# choose one below to uncomment #
###############################################################################
#normal stacking edition
#load_data_instance=load_train_data.Load_train_data(config_instance,'level_one',clf_name_level_data)#instantiate Load_train_data
#X_0,X_1,uid_0,uid_1=load_data_instance.level_data()#obtain output of level_one as training data for level two
#partial stacking edition
load_data_instance=load_train_data.Load_train_data(config_instance,'level_one',clf_name)#instantiate Load_train_data
X_0,X_1,uid_0,uid_1=load_data_instance.level_data_part()#subsample of X where their risk to default are supposed to be overestimated by BBM
threads=[]
threads.append(Level_train_thread(config_instance,Ridge(),level,ftype+'_ridge',X_0,X_1,uid_0,uid_1))
threads.append(Level_train_thread(config_instance,LogisticRegression(max_iter=1000,solver='sag'),level,ftype+'_lr_sag_1000',X_0,X_1,uid_0,uid_1))
threads.append(Level_train_thread(config_instance,LogisticRegression(solver='sag'),level,ftype+'_lr_sag',X_0,X_1,uid_0,uid_1))
threads.append(Level_train_thread(config_instance,LogisticRegression(solver='newton-cg'),level,ftype+'_lr_newton',X_0,X_1,uid_0,uid_1))
threads.append(Level_train_thread(config_instance,LogisticRegression(solver='lbfgs'),level,ftype+'_lr_lbfgs',X_0,X_1,uid_0,uid_1))
threads.append(Level_train_thread(config_instance,LogisticRegression(solver='liblinear'),level,ftype+'_lr_liblinear',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,RandomForestClassifier(n_estimators=100,max_depth=2,min_samples_split=10),level,ftype+'_rf100',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,RandomForestClassifier(n_estimators=200,max_depth=3,min_samples_split=10),level,ftype+'_rf200',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,RandomForestClassifier(n_estimators=500,max_depth=3,min_samples_split=10),level,ftype+'_rf500',X_0,X_1,uid_0,uid_1))
#threads.append(Level_train_thread(config_instance,RandomForestClassifier(n_estimators=1000,max_depth=8,min_samples_split=9),level,ftype+'_rf1000',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,GradientBoostingClassifier(n_estimators=200,max_depth=3,min_samples_split=15,learning_rate=0.005,subsample=0.7),level,ftype+'_gbdt20',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,GradientBoostingClassifier(n_estimators=50,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,ftype+'_gbdt50',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,GradientBoostingClassifier(n_estimators=100,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,ftype+'_gbdt100',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=10),n_estimators=20,learning_rate=0.001),level,ftype+'_ada20',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=10),n_estimators=50,learning_rate=0.02),level,ftype+'_ada50',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=10),n_estimators=100,learning_rate=0.02),level,ftype+'_ada100',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,BaggingClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=9),n_estimators=20),level,ftype+'_bag20',X_0,X_1,uid_0,uid_1))
# threads.append(Level_train_thread(config_instance,BaggingClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=9),n_estimators=50),level,ftype+'_bag50',X_0,X_1,uid_0,uid_1))
params={
'booster':'gbtree',
'objective': 'binary:logistic',
'scale_pos_weight':40920/3080.0,
'eval_metric': 'auc',
'gamma':0,
'max_depth':8,
'lambda':700,
'subsample':0.7,
'colsample_bytree':1,
'min_child_weight':5,
'eta': 0.001,
'seed':1,
'nthread':10
}
#threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb1000',X_0,X_1,uid_0,uid_1,params,1000))
#threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2000',X_0,X_1,uid_0,uid_1,params,2000))
#threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2500',X_0,X_1,uid_0,uid_1,params,2500))
# params['scale_pos_weight']=40920/2000.0
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb1000_2',X_0,X_1,uid_0,uid_1,params,1000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2000_2',X_0,X_1,uid_0,uid_1,params,2000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2500_2',X_0,X_1,uid_0,uid_1,params,2500))
# params['eta']=0.001
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb1000_3',X_0,X_1,uid_0,uid_1,params,1000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2000_3',X_0,X_1,uid_0,uid_1,params,2000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2500_3',X_0,X_1,uid_0,uid_1,params,2500))
# params['eta']=0.005
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb1000_4',X_0,X_1,uid_0,uid_1,params,1000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2000_4',X_0,X_1,uid_0,uid_1,params,2000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2500_4',X_0,X_1,uid_0,uid_1,params,2500))
# params['eta']=0.002
# params['max_depth']=4
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb1000_5',X_0,X_1,uid_0,uid_1,params,1000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2000_5',X_0,X_1,uid_0,uid_1,params,2000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2500_5',X_0,X_1,uid_0,uid_1,params,2500))
# params['max_depth']=5
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb1000_6',X_0,X_1,uid_0,uid_1,params,1000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2000_6',X_0,X_1,uid_0,uid_1,params,2000))
# threads.append(Xgb_level_train_thread(config_instance,level,ftype+'_xgb2500_6',X_0,X_1,uid_0,uid_1,params,2500))
for thread in threads:
thread.run()
def level_two_predict():
config_instance=Config('')
level='level_two'
ftype=''
clf_name=[
ftype+'_lr_sag',
ftype+'_lr_newton',
ftype+'_lr_lbfgs',
ftype+'_lr_liblinear',
ftype+'_xgb1000',
ftype+'_xgb2000',
ftype+'_xgb2500',
ftype+'_xgb1000_2',
ftype+'_xgb2000_2',
ftype+'_xgb2500_2',
ftype+'_xgb1000_3',
ftype+'_xgb2000_3',
ftype+'_xgb2500_3',
ftype+'_xgb1000_4',
ftype+'_xgb2000_4',
ftype+'_xgb2500_4',
ftype+'_xgb1000_5',
ftype+'_xgb2000_5',
ftype+'_xgb2500_5',
ftype+'_xgb1000_6',
ftype+'_xgb2000_6',
ftype+'_xgb2500_6'
]
#from load_train_data.py module Load Load_train_data class
#this class has three parameters: config,level,clf_name
#initialize by calling the class and asgin parameters vales
#config=config_instance, level='level_one', clf_name=clf_name
load_data_instance=load_train_data.Load_train_data(config_instance,'level_one',clf_name)
#from load_predict_data.py module Load Load_predict_data class
#this class has three parameters: config,level,clf_name
#initialize by calling the class and asgin parameters vales
#config=config_instance, level='level_one', clf_name=clf_name
predict_data_instance=load_predict_data.Load_predict_data(config_instance,'level_one',clf_name)
#for local_verify
X_0,X_1,uid_00,uid_11=load_data_instance.level_data()
predict_X,predict_uid=predict_data_instance.level_data()
threads=[]
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='sag'),level,'_lr_sag',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(max_iter=1000,solver='sag'),level,ftype+'_lr_sag_1000',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(max_iter=1500,solver='sag'),level,ftype+'_lr_sag_1500',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='newton-cg'),level,'_lr_newton',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='lbfgs'),level,'_lr_lbfgs',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='liblinear'),level,'_lr_liblinear',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=100,max_depth=3,min_samples_split=10),level,'_rf100',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=200,max_depth=3,min_samples_split=10),level,'_rf200',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=500,max_depth=3,min_samples_split=10),level,'_rf500',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=100,max_depth=3,min_samples_split=10),level,'_rf1000',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,GradientBoostingClassifier(n_estimators=20,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,'_gbdt20',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,GradientBoostingClassifier(n_estimators=50,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,'_gbdt50',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,GradientBoostingClassifier(n_estimators=100,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,'_gbdt100',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=5,max_depth=3,min_samples_split=10),n_estimators=20,learning_rate=0.02),level,'_ada20',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=5,max_depth=3,min_samples_split=10),n_estimators=50,learning_rate=0.02),level,'_ada50',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=5,max_depth=3,min_samples_split=10),n_estimators=100,learning_rate=0.02),level,'_ada100',X_0,X_1,predict_X,predict_uid))
params={
'booster':'gbtree',
'objective': 'binary:logistic',
'scale_pos_weight':40920/3080.0,
'eval_metric': 'auc',
'gamma':15,
'max_depth':3,
'lambda':600,
'subsample':0.40,
'colsample_bytree':0.3,
'min_child_weight':10,
'eta': 0.002,#0.0005
'seed':1,
'nthread':8
}
threads.append(Xgb_level_predict_thread(config_instance,level,'_xgb2000',X_0,X_1,predict_X,predict_uid,params,2000))
threads.append(Xgb_level_predict_thread(config_instance,level,'_xgb2500',X_0,X_1,predict_X,predict_uid,params,2500))
params={
'booster':'gbtree',
'objective': 'binary:logistic',
'scale_pos_weight':40920/3080.0,
'eval_metric': 'auc',
'gamma':0,
'max_depth':3,
'lambda':700,
'subsample':0.6,
'colsample_bytree':0.3,
'min_child_weight':5,
'eta': 0.01,
'seed':1,
'nthread':8
}
threads.append(Xgb_level_predict_thread(config_instance,level,'_xgb2000_2',X_0,X_1,predict_X,predict_uid,params,2000))
threads.append(Xgb_level_predict_thread(config_instance,level,'_xgb2500_2',X_0,X_1,predict_X,predict_uid,params,2500))
for thread in threads:
thread.run()
pass
def level_three_predict():
config_instance=Config('')
level='level_three'
clf_name=[
'log_move_lr_sag',
'log_move_lr_newton',
'log_move_lr_lbfgs',
'log_move_lr_liblinear',
'log_move_rf100',
'log_move_rf200',
'log_move_rf500',
'log_move_rf1000',
'log_move_gbdt20',
'log_move_gbdt50',
'log_move_gbdt100',
'log_move_ada20',
'log_move_ada50',
'log_move_ada100',
'log_move_xgb2000',
'log_move_xgb2500',
'log_move_xgb2000_2',
'log_move_xgb2500_2'
]
load_data_instance=load_train_data.Load_train_data(config_instance,'level_two',clf_name)
predict_data_instance=load_predict_data.Load_predict_data(config_instance,'level_two',clf_name)
X_0,X_1,uid_0,uid_1=load_data_instance.level_data()
predict_X,predict_uid=predict_data_instance.level_data()
threads=[]
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='sag'),level,'log_move_lr_sag',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='newton-cg'),level,'log_move_lr_newton',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='lbfgs'),level,'log_move_lr_lbfgs',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,LogisticRegression(solver='liblinear'),level,'log_move_lr_liblinear',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=100,max_depth=3,min_samples_split=10),level,'log_move_rf100',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=200,max_depth=3,min_samples_split=10),level,'log_move_rf200',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=500,max_depth=3,min_samples_split=10),level,'log_move_rf500',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,RandomForestClassifier(n_estimators=1000,max_depth=3,min_samples_split=10),level,'log_move_rf1000',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,GradientBoostingClassifier(n_estimators=20,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,'log_move_gbdt20',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,GradientBoostingClassifier(n_estimators=50,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,'log_move_gbdt50',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,GradientBoostingClassifier(n_estimators=100,max_depth=3,min_samples_split=15,learning_rate=0.01,subsample=0.7),level,'log_move_gbdt100',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=10),n_estimators=20,learning_rate=0.02),level,'log_move_ada20',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=10),n_estimators=50,learning_rate=0.02),level,'log_move_ada50',X_0,X_1,predict_X,predict_uid))
threads.append(Level_predict_thread(config_instance,AdaBoostClassifier(base_estimator=RandomForestClassifier(n_estimators=50,max_depth=3,min_samples_split=10),n_estimators=100,learning_rate=0.02),level,'log_move_ada100',X_0,X_1,predict_X,predict_uid))
params={
'booster':'gbtree',
'objective': 'binary:logistic',
'scale_pos_weight':28000/4000.0,
'eval_metric': 'auc',
'gamma':0,
'max_depth':3,
'lambda':700,
'subsample':0.9,
'colsample_bytree':0.3,
'min_child_weight':5,
'eta': 0.0005,
'seed':1,
'nthread':8
}
threads.append(Xgb_level_predict_thread(config_instance,level,'log_move_xgb2000',X_0,X_1,predict_X,predict_uid,params,2000))
threads.append(Xgb_level_predict_thread(config_instance,level,'log_move_xgb2500',X_0,X_1,predict_X,predict_uid,params,2500))
params={
'booster':'gbtree',
'objective': 'binary:logistic',
'scale_pos_weight':28000/4000.0,
'eval_metric': 'auc',
'gamma':0,
'max_depth':3,
'lambda':700,
'subsample':0.9,
'colsample_bytree':0.3,
'min_child_weight':5,
'eta': 0.0005,
'seed':1,
'nthread':8
}
threads.append(Xgb_level_predict_thread(config_instance,level,'log_move_xgb2000_2',X_0,X_1,predict_X,predict_uid,params,2000))
threads.append(Xgb_level_predict_thread(config_instance,level,'log_move_xgb2500_2',X_0,X_1,predict_X,predict_uid,params,2500))
for thread in threads:
thread.run()
pass