def param_search(self, search_method='grid'):
'''
@description: use param search tech to find best param
@param {type}
search_method: two options. grid or bayesian optimization
@return: None
'''
if search_method == 'grid':
logger.info("use grid search")
self.model = Grid_Train_model(self.model, self.X_train,
self.X_test, self.y_train,
self.y_test)
elif search_method == 'bayesian':
logger.info("use bayesian optimization")
trn_data = lgb.Dataset(data=self.X_train,
label=self.y_train,
free_raw_data=False)
tst_data = lgb.Dataset(data=self.X_test,
label=self.y_test,
free_raw_data=False)
param = bayes_parameter_opt_lgb(trn_data)
logger.info("best param", param)
param['objective'] = 'multiclass'
param['metric'] = 'auc'
self.model = lgb.train(param,
trn_data,
valid_sets=[trn_data, tst_data],
verbose_eval=1000,
early_stopping_rounds=100)
def param_search(self, search_method='grid'):
if search_method == 'grid':
logger.info('use grid search ... ')
self.model = grid_train_model(self.model, self.x_train,
self.x_test, self.y_train,
self.y_test)
elif search_method == 'bayesian':
logger.info('use bayesian optimization ... ')
trn_data = lgb.Dataset(data=self.x_train,
label=self.y_train,
free_raw_data=False)
param = bayes_parameter_opt_lgb(trn_data)
logger.info('best param', param)
return param
def param_search(self, search_method='grid'):
'''
@description: use param search tech to find best param
@param {type}
search_method: two options. grid or bayesian optimization
@return: None
'''
if search_method == 'grid':
logger.info("use grid search")
self.model = Grid_Train_model(self.model, self.X_train,
self.X_test, self.y_train,
self.y_test)
elif search_method == 'bayesian':
logger.info("use bayesian optimization")
trn_data = lgb.Dataset(data=self.X_train,
label=self.y_train,
free_raw_data=False)
param = bayes_parameter_opt_lgb(trn_data)
logger.info("best param", param)
return param