描述
Python 训练算法模型，保存为 PMML 文件。然后 Java 调用 PMML 文件做预测，这样线上就变成纯 JAVA 项目了。
相关项目
- Python 端的项目：https://github.com/jpmml/sklearn2pmml
- Java端 项目：https://github.com/jpmml/jpmml-model
- 对应的库：https://repo1.maven.org/maven2/org/jpmml/
安装
- Python
pip install scikit-learn==0.24.2 pip install sklearn2pmml==0.74.4 - JAVA ```xml
## 使用
python 训练模型
```python
from sklearn import tree
from sklearn.datasets import load_iris
from sklearn2pmml.pipeline import PMMLPipeline
from sklearn2pmml import sklearn2pmml
iris = load_iris()
X, y = iris.data, iris.target
pipeline = PMMLPipeline([("classifier", tree.DecisionTreeClassifier())]) # 用决策树分类
pipeline.fit(X, y)
sklearn2pmml(pipeline, "iris.pmml", with_repr=True)
java 调用模型做预测
package com.alipay.tianqian;
import org.dmg.pmml.FieldName;
import org.jpmml.evaluator.*;
import org.xml.sax.SAXException;
import javax.xml.bind.JAXBException;
import java.io.*;
import java.util.*;
public class TestPmml {
public static void main(String[] args) throws JAXBException, SAXException, IOException {
TestPmml obj = new TestPmml();
Evaluator model = new LoadingModelEvaluatorBuilder()
.load(new File("src/iris.pmml"))
.build();
List<Map<String, Object>> inputs = new ArrayList<>();
inputs.add(obj.getRawMap(5.1, 3.5, 1.4, 0.2));
inputs.add(obj.getRawMap(4.9, 3, 1.5, 4));
for (Map<String, Object> input : inputs) {
Map<String, Object> output = obj.predict(model, input);
System.out.println("X=" + input + " -> y=" + output.get("y"));
}
}
private Map<String, Object> getRawMap(Object a, Object b, Object c, Object d) {
Map<String, Object> data = new HashMap<String, Object>();
data.put("x1", a);
data.put("x2", b);
data.put("x3", c);
data.put("x4", d);
return data;
}
/**
* 运行模型得到结果。
*/
private Map<String, Object> predict(Evaluator evaluator, Map<String, Object> data) {
Map<FieldName, FieldValue> input = getFieldMap(evaluator, data);
Map<String, Object> output = evaluate(evaluator, input);
return output;
}
/**
* 把原始输入转换成PMML格式的输入。
*/
private Map<FieldName, FieldValue> getFieldMap(Evaluator evaluator, Map<String, Object> input) {
List<InputField> inputFields = evaluator.getInputFields();
Map<FieldName, FieldValue> map = new LinkedHashMap<FieldName, FieldValue>();
for (InputField field : inputFields) {
FieldName fieldName = field.getName();
Object rawValue = input.get(fieldName.getValue());
FieldValue value = field.prepare(rawValue);
map.put(fieldName, value);
}
return map;
}
/**
* 运行模型得到结果。
*/
private Map<String, Object> evaluate(Evaluator evaluator, Map<FieldName, FieldValue> input) {
Map<FieldName, ?> results = evaluator.evaluate(input);
List<TargetField> targetFields = evaluator.getTargetFields();
Map<String, Object> output = new LinkedHashMap<String, Object>();
for (TargetField field : targetFields) {
FieldName fieldName = field.getName();
Object value = results.get(fieldName);
if (value instanceof Computable) {
Computable computable = (Computable) value;
value = computable.getResult();
}
output.put(fieldName.getValue(), value);
}
return output;
}
}
旧版本
版本
- PMML 文件：4.3版本
- python 版本
pip install sklearn2pmml==0.56.2 pip install scikit-learn==0.22.2 - java 版本： ```xml
### 旧版本的使用
Python 训练模型，步骤同上
Java调模型，做预测
```java
package com.alipay.tianqian.service;
import org.dmg.pmml.FieldName;
import org.dmg.pmml.PMML;
import org.jpmml.evaluator.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.xml.sax.SAXException;
import javax.xml.bind.JAXBException;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStream;
import java.util.*;
public class TestPmml2 {
public static void main(String args[]) throws FileNotFoundException, JAXBException, SAXException {
String fp = "src/iris.pmml";
TestPmml2 obj = new TestPmml2();
Evaluator model = obj.loadPmml(fp);
List<Map<String, Object>> inputs = new ArrayList<>();
inputs.add(obj.getRawMap(5.1, 3.5, 1.4, 0.2));
inputs.add(obj.getRawMap(4.9, 3, 1.5, 0.9));
for (int i = 0; i < inputs.size(); i++) {
Map<String, Object> output = obj.predict(model, inputs.get(i));
System.out.println("X=" + inputs.get(i) + " -> y=" + output.get("y"));
}
}
private Evaluator loadPmml(String fp) throws FileNotFoundException, JAXBException, SAXException {
InputStream is = new FileInputStream(fp);
PMML pmml = org.jpmml.model.PMMLUtil.unmarshal(is);
try {
is.close();
} catch (IOException e) {
e.printStackTrace();
}
ModelEvaluatorFactory factory = ModelEvaluatorFactory.newInstance();
return factory.newModelEvaluator(pmml);
}
private Map<String, Object> getRawMap(Object a, Object b, Object c, Object d) {
Map<String, Object> data = new HashMap<String, Object>();
data.put("x1", a);
data.put("x2", b);
data.put("x3", c);
data.put("x4", d);
return data;
}
/**
* 运行模型得到结果。
*/
private Map<String, Object> predict(Evaluator evaluator, Map<String, Object> data) {
Map<FieldName, FieldValue> input = getFieldMap(evaluator, data);
Map<String, Object> output = evaluate(evaluator, input);
return output;
}
/**
* 把原始输入转换成PMML格式的输入。
*/
private Map<FieldName, FieldValue> getFieldMap(Evaluator evaluator, Map<String, Object> input) {
List<InputField> inputFields = evaluator.getInputFields();
Map<FieldName, FieldValue> map = new LinkedHashMap<FieldName, FieldValue>();
for (InputField field : inputFields) {
FieldName fieldName = field.getName();
Object rawValue = input.get(fieldName.getValue());
FieldValue value = field.prepare(rawValue);
map.put(fieldName, value);
}
return map;
}
/**
* 运行模型得到结果。
*/
private Map<String, Object> evaluate(Evaluator evaluator, Map<FieldName, FieldValue> input) {
Map<FieldName, ?> results = evaluator.evaluate(input);
List<TargetField> targetFields = evaluator.getTargetFields();
Map<String, Object> output = new LinkedHashMap<String, Object>();
for (int i = 0; i < targetFields.size(); i++) {
TargetField field = targetFields.get(i);
FieldName fieldName = field.getName();
Object value = results.get(fieldName);
if (value instanceof Computable) {
Computable computable = (Computable) value;
value = computable.getResult();
}
output.put(fieldName.getValue(), value);
}
return output;
}
}
