Use this for LightGBM parameter optimisation by Bayesian optimisation.

lgb_bo.py

import pandas as pd;
import numpy as np;
import lightgbm as lgb
from bayes_opt import BayesianOptimization
from sklearn.model_selection import cross_val_score

def lgb_evaluate(                
                numLeaves,
                maxDepth,
                scaleWeight,
                minChildWeight,
                subsample,
                colSam
                ):
    
    clf = lgb.LGBMClassifier(
        objective = 'binary',
        metric= 'auc',
        eval_metric= 'auc',
        reg_alpha= 0,
        reg_lambda= 2,
        bagging_fraction= 0.999,
        min_split_gain= 0,
        min_child_samples= 10,
        subsample_freq= 3,
        subsample_for_bin= 50000,
        n_estimators= 9999999,
        num_leaves= int(numLeaves),
        max_depth= int(maxDepth),
        scale_pos_weight= scaleWeight,
        min_child_weight= minChildWeight,
        subsample= subsample,
        colsample_bytree= colSam,
        verbose =-1
    )
    
    scores = cross_val_score(clf, train_x, train_y, cv=5, scoring='roc_auc')
    print(np.mean(scores))

    return np.mean(scores)
   
def bayesOpt(train_x, train_y):
    lgbBO = BayesianOptimization(lgb_evaluate, {                                                
                                                'numLeaves':  (5, 50),
                                                'maxDepth': (2, 63),
                                                'scaleWeight': (1, 10000),
                                                'minChildWeight': (0.01, 70),
                                                'subsample': (0.4, 1),                                                
                                                'colSam': (0.4, 1)
                                            })


    lgbBO.maximize(init_points=5, n_iter=5)

    print(lgbBO.res['max'])
    

bayesOpt(train_x, train_y)

#Run this in one of your Ipynb cells
#Kindly comment if you wish to share anything or improvise

thank you! It's old but this has aged well 🥇