使用pmml跨平台部署机器学习模型Demo——屋价预测
基于屋价数据，在python中训练得到一个线性回归的模型，在JavaWeb中加载模型完成屋价预测的功能。
一、 训练、保存模型
工具：PyCharm-2017、Python-39、sklearn2pmml-0.76.1。
1.训练数据house_price.csv
| No | square_feet | price |
|---|---|---|
| 1 | 150 | 6450 |
| 2 | 200 | 7450 |
| 3 | 250 | 8450 |
| 4 | 300 | 9450 |
| 5 | 350 | 11450 |
| 6 | 400 | 15450 |
| 7 | 600 | 18450 |
2.训练、保存模型
import sklearn2pmml as pmml
from sklearn2pmml import PMMLPipeline
from sklearn import linear_model as lm
import os
import pandas as pd
def save_model(data, model_path):
pipeline = PMMLPipeline([("regression", lm.LinearRegression())])
pipeline.fit(data[["square_feet"]], data["price"])
pmml.sklearn2pmml(pipeline, model_path, with_repr=True)
if __name__ == "__main__":
data = pd.read_csv("house_price.csv")
model_path = model_path = os.path.dirname(os.path.abspath(__file__)) + "/my_liner_model.pmml"
save_model(data, model_path)
print("模型保存完成。")
二、JavaWeb应用开发
工具：IntelliJ IDEA-2018、jdk-14.0.2、Tomcat-9.0.37。
创建maven项目，加入依赖项
<dependencies>
<dependency>
<groupId>org.jpmml</groupId>
<artifactId>pmml-evaluator</artifactId>
<version>1.4.15</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-core</artifactId>
<version>2.2.11</version>
</dependency>
<dependency>
<groupId>javax.xml</groupId>
<artifactId>jaxb-api</artifactId>
<version>2.1</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-impl</artifactId>
<version>2.2.11</version>
</dependency>
<dependency>
<groupId>javax.servlet</groupId>
<artifactId>javax.servlet-api</artifactId>
<version>3.0.1</version>
</dependency>
</dependencies>
项目结构为
界面——index.jsp
<%@ page contentType="text/html;charset=UTF-8" language="java" %>
<html>
<head>
<title>使用pmml跨平台部署机器学习模型Demo</title>
</head>
<body>
<h2>使用pmml跨平台部署机器学习模型Demo——屋价预测</h2>
<form name="form" method="post" action="/PredictServlet">
<label>房子英尺数（整数）：</label>
<input type="text" name="feet" required>
<button type="submit">预测屋价</button>
</form>
<div>
<label>预测价格为：</label>
${price}
</div>
</body>
</html>
Servlet类——PredictServlet.java
package servlet;
import service.PredictService;
import service.imp.PredictServiceImp;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
import java.io.IOException;
@WebServlet("/PredictServlet")
public class PredictServlet extends HttpServlet {
protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
PredictService predictService = new PredictServiceImp();
String feet_str = request.getParameter("feet"); //获取前端传来的值
int feet = Integer.parseInt(feet_str);
double price = predictService.getPredictedPrice(feet); //预测
//请求转发，返回结果
request.setAttribute("price", price);
request.getRequestDispatcher("/index.jsp").forward(request, response);
}
protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
this.doPost(request, response);
}
}
Service接口——PredictService.java
package service;
public interface PredictService {
public double getPredictedPrice(int feet);
}
Service实现类——PredictServiceImp.java
package service.imp;
import org.dmg.pmml.FieldName;
import org.dmg.pmml.PMML;
import org.jpmml.evaluator.*;
import service.PredictService;
import java.io.FileInputStream;
import java.io.InputStream;
import java.util.HashMap;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
public class PredictServiceImp implements PredictService {
public double getPredictedPrice(int feet) {
String model_path = "D:\\my_liner_model.pmml"; //pmml模型文件存放路径
Evaluator model = loadModel(model_path); //加载模型
Object r = predict(model, feet); //预测
double result = Double.parseDouble(String.format("%.2f", r)); //格式化
return result;
}
private static Evaluator loadModel(String model_path){
PMML pmml = new PMML(); //定义PMML对象
InputStream inputStream; //定义输入流
try {
inputStream = new FileInputStream(model_path); //输入流接到磁盘上的模型文件
pmml = org.jpmml.model.PMMLUtil.unmarshal(inputStream); //将输入流解析为PMML对象
}catch (Exception e){
e.printStackTrace();
}
ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory.newInstance(); //实例化一个模型构造工厂
Evaluator evaluator = modelEvaluatorFactory.newModelEvaluator(pmml); //将PMML对象构造为Evaluator模型对象
return evaluator;
}
private static Object predict(Evaluator evaluator, int feet){
Map<String, Integer> data = new HashMap<String, Integer>(); //定义测试数据Map，存入各元自变量
data.put("square_feet", feet); //键"square_feet"为自变量的名称，应与训练数据中的自变量名称一致
List<InputField> inputFieldList = evaluator.getInputFields(); //得到模型各元自变量的属性列表
Map<FieldName, FieldValue> arguments = new LinkedHashMap<FieldName, FieldValue>();
for (InputField inputField : inputFieldList) { //遍历各元自变量的属性列表
FieldName inputFieldName = inputField.getName();
Object rawValue = data.get(inputFieldName.getValue()); //取出该元变量的值
FieldValue inputFieldValue = inputField.prepare(rawValue); //将值加入该元自变量属性中
arguments.put(inputFieldName, inputFieldValue); //变量名和变量值的对加入LinkedHashMap
}
Map<FieldName, ?> result = evaluator.evaluate(arguments); //进行预测
List<TargetField> targetFieldList = evaluator.getTargetFields(); //得到模型各元因变量的属性列表
FieldName targetFieldName = targetFieldList.get(0).getName(); //第一元因变量名称
Object targetFieldValue = result.get(targetFieldName); //由因变量名称得到值
return targetFieldValue;
}
}
三、运行测试
将python中训练得到的pmml模型文件置于D盘根目录下，将文件中的xmlns=”…/PMML-4_4″修改为xmlns=”…/PMML-4_3″。
启动运行，浏览器访问//localhost/，进入页面
输入房子英尺数，点击‘预测屋价’按钮，展示出预测价格
打包下载：
//download.csdn.net/download/Albert201605/45648664
End.
