import pandas as pd # 数据科学计算工具
import numpy as np # 数值计算工具
import matplotlib.pyplot as plt # 可视化
import seaborn as sns # matplotlib的高级API
from sklearn.model_selection import StratifiedKFold #交叉验证
from sklearn.model_selection import GridSearchCV #网格搜索
from sklearn.model_selection import train_test_split #将数据集分开成训练集和测试集
from xgboost import XGBClassifier                     



# lgb_train = lgb.Dataset(x_train, y_train)
# lgb_eval = lgb.Dataset(x_test, y_test, reference=lgb_train)

parameters = {
    'max_depth': range(3,8,2), # max_depth ：设置树深度，深度越大可能过拟合
    'num_leaves': range(50, 170, 30),  
    #num_leaves：因为 LightGBM 使用的是 leaf-wise 的算法，因此在调节树的复杂程度时，使用的是 num_leaves 而不是 max_depth。
    #大致换算关系：num_leaves = 2^(max_depth)，但是它的值的设置应该小于 2^(max_depth)，否则可能会导致过拟合。

    # 这是提高精确度的最重要的参数

}


#gbm = lgb.LGBMClassifier(objective='binary',num_leaves=31,learning_rate=0.05,n_estimators=20) 分类

gbm = lgb.LGBMRegressor(objective = 'regression', # 回归 设置
                         metric = 'binary_logloss,auc', # 评估函数
                         learning_rate = 0.1,
                         feature_fraction = 0.7, #建树的特征选择比例
                         min_child_samples=21,
                         min_child_weight=0.001,
                         bagging_fraction = 1,
                         bagging_freq = 2,
                         reg_alpha = 0.001,
                         reg_lambda = 8,
                         cat_smooth = 0,
                         num_iterations = 200,
                         verbose=-1 ## <0 显示致命的, =0 显示错误 (警告), >0 显示信息
                        )
gsearch = GridSearchCV(gbm, param_grid=parameters, scoring='roc_auc', cv=3)
gsearch.fit(x_train, y_train.astype('str'))


print('参数的最佳取值:{0}'.format(gsearch.best_params_))
print('最佳模型得分:{0}'.format(gsearch.best_score_))
print(gsearch.cv_results_['mean_test_score'])
print(gsearch.cv_results_['params'])

# 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

lgb_train = lgb.Dataset(x_train, y_train)
lgb_eval = lgb.Dataset(x_test, y_test, reference=lgb_train)
# 上边是加载数据比较快
params = {
    'max_depth':3,
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric': {'l2', 'l1'},
    'num_leaves':50,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': 1
}

gbm = lgb.train(params,
                lgb_train,
                num_boost_round=300,
                valid_sets=lgb_eval,
                early_stopping_rounds=5)
y1_pred = gbm.predict(test_x_2020_1_guiyihua, num_iteration=gbm.best_iteration)
# eval

# data是一个以为度得列表 ，这样是把数据写进去一列 
name=['pressure']
test=pd.DataFrame(columns=name,data=data)
test.to_csv('jieguo.csv')