Code for optimizing LGBM model for quantile regression

LGBM_BHPT_quantileregression.py

covariates = trainx; target = trainy
def lgb_trainer(num_leaves, learning_rate, 
                max_depth, n_estimators, 
                reg_lambda,
                #alpha, 
                reg_alpha, 
                subsample):
  
  lgb = LGBMRegressor(objective = "quantile",
                      alpha = .95,
                      subsample = subsample, 
                      reg_alpha = reg_alpha, 
                      reg_lambda = reg_lambda,
                      n_estimators = int(n_estimators),
                      max_depth = int(max_depth),
                      learning_rate = learning_rate)
                    
  fit_params = {"categorical_feature": catcols}
  
  rskf = RepeatedKFold(n_splits = 5, n_repeats = 2)
  scores = cross_val_score(lgb, covariates, target, 
                           cv = rskf, 
                           fit_params = fit_params,
                           scoring = "neg_mean_absolute_error"
                           ) 
  return np.mean(scores)

def lgb_opt():
  optimizer = BayesianOptimization(
  f = lgb_trainer,
  pbounds = {"num_leaves":(10, 50),
             "n_estimators" : (200,1000),
             "learning_rate" : (0.05, 0.40),
            #"alpha": (0.45, 0.55),
             "reg_alpha": (0, 10.),
             "reg_lambda": (0, 10.),
             "subsample": (0.8, 1.0),            
             "max_depth": (8,15),
   
   
            }
  )
  optimizer.maximize(n_iter = 5, init_points = 1)
  print("Final result:", optimizer.max)
  return optimizer.max

lgb_results = lgb_opt()
print(lgb_results)