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【AI实战】应用xgboost.XGBRegressor搭建空气质量预测模型（一）

2022-07-03 03:13:00 【szZack】

1、xgboost.XGBRegressor 详解

xgboost.XGBRegressor 的详细参数可查看 https://xgboost.readthedocs.io/en/latest/python/python_api.html?highlight=XGBRegressor#xgboost.XGBRegressor

XGBRegressor 类：

class xgboost.XGBRegressor(*, objective='reg:squarederror', **kwargs)

核心参数包括：

n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds.

max_depth (Optional[int]) – Maximum tree depth for base learners.

learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)

verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

tree_method (Optional[str]) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from the parameters document tree method

n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.

gamma (Optional[float]) – (min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree.

min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.

subsample (Optional[float]) – Subsample ratio of the training instance.

colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.

scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.

2、应用xgboost.XGBRegressor搭建空气质量预测模型

2.1 依赖的库

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold #k折交叉验证
from sklearn.model_selection import GridSearchCV #网格搜索
from sklearn.metrics import make_scorer
import os
import sys
import time
import math
from sklearn.metrics import r2_score
from sklearn.ensemble import GradientBoostingRegressor
import numpy as np
import warnings
warnings.filterwarnings("ignore", category=FutureWarning, module="sklearn", lineno=193)
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
import joblib
from sklearn.preprocessing import MinMaxScaler

2.2 搭建空气质量预测模型

模型
使用 xgboost.XGBRegressor 作为基础模型，使用 MultiOutputRegressor 包装 XGBRegressor 从而实现多维时间输出（多目标回归 Multi target regression）
模型核心代码如下：

    def fit_model(self, x, y, learning_rate=0.05,
                         n_estimators=500,
                         max_depth=7,
                         min_child_weight=1,
                         gamma=0.0,
                         subsample=0.8,
                         colsample_bytree=0.8,
                         scale_pos_weight=0.8):
        
        model = xgb.XGBRegressor(learning_rate=learning_rate,
                                 n_estimators=n_estimators,
                                 max_depth=max_depth,
                                 min_child_weight=min_child_weight,
                                 gamma=gamma,
                                 subsample=subsample,
                                 colsample_bytree=colsample_bytree,
                                 scale_pos_weight=scale_pos_weight,
                                 seed=42,
                                 tree_method='gpu_hist',
                                 gpu_id=2)
        
        multioutput = MultiOutputRegressor(model).fit(x, y)
        
        return multioutput

输入 x
shape 为（N, W, 24）
其中 N 为数据的天数， W 为特征的维度， 24 为输入数据的小时数
输出 y
shape 为 (N, 24)
其中 N 为数据的天数，24 为输出数据的小时数

2.3 核心代码


# 基于 XGBRegressor 的空气质量模型
class AQXGB():

    def __init__(self, factor, n_input, n_output, version):
        
        self.n_input = n_input
        self.n_output = n_output
        self.version = version
        self.factor = factor#空气因子
        
        if not os.path.exists('./ml_data/'):#保存机器学习的训练数据
            os.mkdir('./ml_data/')
        

    def train(self, train_data_path, test_data_path):
        
        x,y = self.load_data(self.version, 'train', train_data_path, self.n_input, self.n_output)
        train_x,test_x,train_y,test_y = train_test_split(x,y,test_size=0.2,random_state=2022)
        
        model = self.fit_model(train_x, train_y)
        
        pre_y = model.predict(test_x)
        
        #计算决策系数r方
        r2 = self.performance_metric(test_y, pre_y)  
        print('test_r2 = ', r2)
            
        x,y = self.load_data(self.version, 'test', test_data_path, self.n_input, self.n_output)
        pre_y = model.predict(x)
        r2 = self.performance_metric(y, pre_y)
        print('val_r2 = ', r2)
        
        #保存模型
        joblib.dump(model, './ml_data/xgb_%s_%d_%d_%s.model' %(self.factor, self.n_input, self.n_output, self.version)) 

    

    def performance_metric(self, y_true, y_predict):
        # 根据需要选择评估函数
        # r2
        score = r2_score(y_true,y_predict)
        
        # MSE
        MSE=np.mean(( y_predict- y_true)**2)
        print('RMSE: ',MSE**0.5)
        
        #MAE
        MAE=np.mean(np.abs( y_predict- y_true))
        print('MAE: ',MAE)
        
        #SMAPE
        SMAPE=self.smape(y_true, y_predict)
        print('SMAPE: ',SMAPE)

        return score
        
    def smape(self, A, F):
        A = A.reshape(-1)
        F = F.reshape(-1)
        return 1.0/len(A) * np.sum(2 * np.abs(F - A) / (np.abs(A) + np.abs(F)))

    def fit_model(self, x, y, learning_rate=0.05,
                         n_estimators=500,
                         max_depth=7,
                         min_child_weight=1,
                         gamma=0.0,
                         subsample=0.8,
                         colsample_bytree=0.8,
                         scale_pos_weight=0.8):
        
        model = xgb.XGBRegressor(learning_rate=learning_rate,
                                 n_estimators=n_estimators,
                                 max_depth=max_depth,
                                 min_child_weight=min_child_weight,
                                 gamma=gamma,
                                 subsample=subsample,
                                 colsample_bytree=colsample_bytree,
                                 scale_pos_weight=scale_pos_weight,
                                 seed=42,
                                 tree_method='gpu_hist',
                                 gpu_id=2)
        
        multioutput = MultiOutputRegressor(model).fit(x, y)
        
        return multioutput

2.4 模型训练

训练代码

if __name__ == "__main__":

    if len(sys.argv) == 7:
        # 训练模型
        # python3 src/train_xgb_model.py data/train_data.csv data/test_data.csv O3 24 24 v2
        aq_model = AQXGB(sys.argv[3], int(sys.argv[4]), int(sys.argv[5]), sys.argv[6])
        aq_model.train(sys.argv[1], sys.argv[2])

训练脚本
输入过去24小时是特征数据，输出未来24小时的O3的预测结果
```
python3 src/train_xgb_model.py data/train_data.csv data/test_data.csv O3 24 24 v2
```

2.5 数据格式

数据格式
csv文件
示例

air_pressure,CO,humidity,AQI,monitoring_time,NO2,O3,PM10,PM25,SO2,station_number,air_temperature,wind_direction,wind_speed,longitude,latitude,station_type_name
1013.0,0.3,59.0,69.0,2019-02-01 00:00:00,15.0,80.0,88.0,26.0,8.0,xxx监测站,-0.4,205.8,1.1,116.97810856433719,36.61655020673796,shik
1013.0,0.3,58.0,68.0,2019-02-01 01:00:00,15.0,80.0,86.0,26.0,8.0,xxx监测站,-0.5,179.4,1.0,116.97810856433719,36.61655020673796,shik
1012.0,0.3,62.0,72.0,2019-02-01 02:00:00,15.0,80.0,94.0,26.0,8.0,xxx监测站,-0.9,175.7,0.8,116.97810856433719,36.61655020673796,shik
1011.0,0.3,64.0,76.0,2019-02-01 03:00:00,15.0,80.0,102.0,26.0,8.0,xxx监测站,-1.0,166.9,0.9,116.97810856433719,36.61655020673796,shik
1011.0,0.3,65.0,80.0,2019-02-01 04:00:00,15.0,80.0,110.0,26.0,8.0,xxx监测站,-0.8,191.1,0.9,116.97810856433719,36.61655020673796,shik
1011.0,0.3,66.0,84.0,2019-02-01 05:00:00,15.0,80.0,117.0,26.0,8.0,xxx监测站,-1.1,211.4,1.0,116.97810856433719,36.61655020673796,shik
1011.0,0.3,68.0,85.0,2019-02-01 06:00:00,15.0,80.0,119.0,26.0,8.0,xxx监测站,-1.4,137.3,1.3,116.97810856433719,36.61655020673796,shik
1011.0,0.3,68.0,65.75,2019-02-01 07:00:00,15.0,80.0,130.6,26.0,8.0,xxx监测站,-1.3,147.0,1.5,116.97810856433719,36.61655020673796,shik
1011.0,0.3,58.0,46.5,2019-02-01 08:00:00,15.0,80.0,142.2,26.0,8.0,xxx监测站,0.7,157.0,1.4,116.97810856433719,36.61655020673796,shik