def train(self):
"""
## Auto Grid Search Parameters ##
Model Name:
'lr': Logistic Regression
'rf': Random Forest
'et': Extra Trees
'ab': AdaBoost
'gb': GradientBoosting
'xgb': XGBoost
'xgb_sk': XGBoost using scikit-learn module
'lgb': LightGBM
'lgb_sk': LightGBM using scikit-learn module
'cb': CatBoost
'dnn': Deep Neural Networks
'stack_lgb': LightGBM for stack layer
'christar': Christar1991
'prejudge_b': PrejudgeBinary
'prejudge_m': PrejudgeMultiClass
'stack_t': StackTree
"""
TM = TrainingMode()
"""
Training Arguments
"""
train_args = {
'prescale': False,
'postscale': True,
'use_scale_pos_weight': False,
'use_global_valid': False,
'use_custom_obj': False,
'show_importance': False,
'show_accuracy': False,
'save_final_pred': True,
'save_final_prob_train': False,
'save_cv_pred': False,
'save_cv_prob_train': False,
'save_csv_log': True,
'append_info': 'forward_window_postscale'
}
"""
Cross Validation Arguments
"""
# cv_args = {'n_valid': 4,
# 'n_cv': 20,
# 'n_era': 20}
cv_args = self.get_cv_args('lgb_fi')
"""
Reduced Features
"""
reduced_feature_list = None
"""
Base Parameters
"""
base_parameters = self.get_base_params('xgb_fw')
# base_parameters = None
"""
Auto Grid Search Parameters
"""
pg_list = [[
['max_depth', (8, 9, 10)],
['min_child_weight', (2, 4, 6, 8)],
['subsample', (0.81, 0.84, 0.87, 0.9)],
['colsample_bytree', (0.8, 0.85, 0.9)],
['colsample_bylevel', (0.7, 0.75, 0.8)],
]]
train_seed_list = [999]
cv_seed_list = [95]
# train_seed_list = None
# cv_seed_list = None
TM.auto_grid_search('xgb',
num_boost_round=95,
n_epoch=1,
full_grid_search=True,
use_multi_group=False,
train_seed_list=train_seed_list,
cv_seed_list=cv_seed_list,
parameter_grid_list=pg_list,
base_parameters=base_parameters,
save_final_pred=False,
reduced_feature_list=reduced_feature_list,
train_args=train_args,
cv_args=cv_args)
def train(self):
"""
## Auto Grid Search Parameters ##
Model Name:
'lr': Logistic Regression
'rf': Random Forest
'et': Extra Trees
'gb': GradientBoosting
'xgb': XGBoost
'xgb_sk': XGBoost using scikit-learn module
'lgb': LightGBM
'lgb_sk': LightGBM using scikit-learn module
'cb': CatBoost
"""
TM = TrainingMode()
"""
Training Arguments
"""
train_args = {
'use_global_valid': False,
'use_custom_obj': False,
'show_importance': False,
'save_final_pred': True,
'save_final_pred_train': False,
'save_cv_pred': False,
'save_cv_pred_train': False,
'save_csv_log': True,
'loss_fuc': None,
'append_info': 'forward_window_postscale_mdp-11_sub'
}
"""
Cross Validation Arguments
"""
# cv_args = {'n_splits': 10,
# 'n_cv': 10}
cv_args = self.get_cv_args('xgb')
"""
Base Parameters
"""
# base_parameters = self.get_base_params('xgb')
base_parameters = None
"""
Auto Grid Search Parameters
"""
pg_list = [[
['max_depth', (8, 9, 10)],
['min_child_weight', (2, 4, 6, 8)],
['subsample', (0.81, 0.84, 0.87, 0.9)],
['colsample_bytree', (0.8, 0.85, 0.9)],
['colsample_bylevel', (0.7, 0.75, 0.8)],
]]
train_seed_list = [999]
cv_seed_list = [95]
# train_seed_list = None
# cv_seed_list = None
TM.auto_grid_search('xgb',
num_boost_round=95,
n_epoch=1,
full_grid_search=True,
train_seed_list=train_seed_list,
cv_seed_list=cv_seed_list,
parameter_grid_list=pg_list,
base_parameters=base_parameters,
save_final_pred=False,
train_args=train_args,
cv_args=cv_args)