一种基于pmml和java部署的机器学习预测模型.zip

package com.raoqiang.model.pmml; import org.dmg.pmml.FieldName; import org.dmg.pmml.PMML; import org.jpmml.evaluator.*; import java.io.File; 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 PMMLPrediction { public static void main(String[] args) throws Exception { String pathxml="C:\\Users\\raoqiang\\Downloads\\jpmml-lightgbm-master\\target\\lightdbm0.pmml"; Map map=new HashMap<>(); map.put("EXT_SOURCES_MEAN", "1.1444091796875E-5"); // map.put("AMT_CREDIT", "500000"); predictLrHeart(map, pathxml); } public static void predictLrHeart(Map<String, Double> irismap,String pathxml)throws Exception { PMML pmml; // 模型导入 File file = new File(pathxml); InputStream inputStream = new FileInputStream(file); try (InputStream is = inputStream) { pmml = org.jpmml.model.PMMLUtil.unmarshal(is); ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory .newInstance(); ModelEvaluator<?> modelEvaluator = modelEvaluatorFactory .newModelEvaluator(pmml); Evaluator evaluator = (Evaluator) modelEvaluator; List<InputField> inputFields = evaluator.getInputFields(); // 过模型的原始特征，从画像中获取数据，作为模型输入 Map<FieldName, FieldValue> arguments = new LinkedHashMap<>(); for (InputField inputField : inputFields) { FieldName inputFieldName = inputField.getName(); Object rawValue = irismap .get(inputFieldName.getValue()); FieldValue inputFieldValue = inputField.prepare(rawValue); arguments.put(inputFieldName, inputFieldValue); } Map<FieldName, ?> results = evaluator.evaluate(arguments); List<TargetField> targetFields = evaluator.getTargetFields(); //对于分类问题等有多个输出。 for (TargetField targetField : targetFields) { FieldName targetFieldName = targetField.getName(); Object targetFieldValue = results.get(targetFieldName); System.err.println("target: " + targetFieldName.getValue() + " value: " + targetFieldValue); } } } }