lp_lgbm.py

import lightgbm as lgbm

train_data = lgbm.Dataset(xtrain, ytrain)
test_data = lgbm.Dataset(xtest, ytest)

# define parameters
parameters = {
    'objective': 'binary',
    'metric': 'auc',
    'is_unbalance': 'true',
    'feature_fraction': 0.5,
    'bagging_fraction': 0.5,
    'bagging_freq': 20,
    'num_threads' : 2,
    'seed' : 76
}

# train lightGBM model
model = lgbm.train(parameters,
                   train_data,
                   valid_sets=test_data,
                   num_boost_round=1000,
                   early_stopping_rounds=20)