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# coding: utf-8 # pylint: disable = invalid-name, C0111 import json import lightgbm as lgb import pandas as pd from sklearn.metrics import mean_squared_error from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.datasets import make_classification iris = load_iris() data=iris.data target = iris.target X_train,X_test,y_train,y_test =train_test_split(data,target,test_size=0.2) # 加载你的数据 # print('Load data...') # df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t') # df_test = pd.read_csv('../regression/regression.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 # 创建成lgb特征的数据集格式 lgb_train = lgb.Dataset(X_train, y_train) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) # 将参数写成字典下形式 params = { 'task': 'train', 'boosting_type': 'gbdt', # 设置提升类型 'objective': 'regression', # 目标函数 'metric': {'l2', 'auc'}, # 评估函数 'num_leaves': 31, # 叶子节点数 'learning_rate': 0.05, # 学习速率 'feature_fraction': 0.9, # 建树的特征选择比例 'bagging_fraction': 0.8, # 建树的样本采样比例 'bagging_freq': 5, # k 意味着每 k 次迭代执行bagging 'verbose': 1 # <0 显示致命的, =0 显示错误 (警告), >0 显示信息 } print('Start training...') # 训练 cv and train gbm = lgb.train(params,lgb_train,num_boost_round=20,valid_sets=lgb_eval,early_stopping_rounds=5) print('Save model...') # 保存模型到文件 gbm.save_model('model.txt') print('Start predicting...') # 预测数据集 y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration) # 评估模型 print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5) 快把你的结果在评论区里亮出来吧！ |
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