【ML算法】集成学习——LightGBM的Python实现

深碍√TFBOYSˉ_ 2022-05-17 11:15 205阅读 0赞

### 前言 ###

LightGBM算法作为Kaggle竞赛的热门算法，具有速度快、精度高、可并行等特点，本文实现了LightGBM算法的简单实现。

### 代码 ###

# coding: utf-8
    import json
    import lightgbm as lgb
    import pandas as pd
    from sklearn.metrics import mean_squared_error
    
    
    # load or create your dataset
    print('Load data...')
    df_train = pd.read_csv('../data/train', header=None, sep='\t')
    df_test = pd.read_csv('../data/test', header=None, sep='\t')
    
    y_train = df_train[0].values
    y_test = df_test[0].values
    X_train = df_train.drop(0, axis=1).values
    X_test = df_test.drop(0, axis=1).values
    
    # create dataset for lightgbm
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
    
    # specify your configurations as a dict
    params = {
        'task': 'train',
        'boosting_type': 'gbdt',
        'objective': 'regression',
        'metric': {
       'l2', 'auc'},
        'num_leaves': 31,
        'learning_rate': 0.05,
        'feature_fraction': 0.9,
        'bagging_fraction': 0.8,
        'bagging_freq': 5,
        'verbose': 0
    }
    
    print('Start training...')
    # train
    gbm = lgb.train(params,
                    lgb_train,
                    num_boost_round=20,
                    valid_sets=lgb_eval,
                    early_stopping_rounds=5)
    
    print('Save model...')
    # save model to file
    gbm.save_model('model.txt')
    
    print('Start predicting...')
    # predict
    y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
    # eval
    print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)