From 7f0bcd00c556889ba890e5e68c681f1b5d4267e1 Mon Sep 17 00:00:00 2001
From: dengzedong <dengzedong@email>
Date: 星期四, 19 十二月 2024 18:07:20 +0800
Subject: [PATCH] 预测项状态添加模型结果异常
---
iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java | 89 ++++++++++++++++++++++++++------------------
1 files changed, 52 insertions(+), 37 deletions(-)
diff --git a/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java b/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java
index 5a107da..f2c3cef 100644
--- a/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java
+++ b/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java
@@ -1,12 +1,15 @@
package com.iailab.module.model.mdk.predict.impl;
+import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.iail.model.IAILModel;
import com.iailab.module.model.common.enums.CommonConstant;
+import com.iailab.module.model.common.enums.OutResultType;
import com.iailab.module.model.mcs.pre.entity.MmItemOutputEntity;
import com.iailab.module.model.mcs.pre.entity.MmModelArithSettingsEntity;
import com.iailab.module.model.mcs.pre.entity.MmPredictModelEntity;
+import com.iailab.module.model.mcs.pre.enums.ItemRunStatusEnum;
import com.iailab.module.model.mcs.pre.service.MmItemOutputService;
import com.iailab.module.model.mcs.pre.service.MmModelArithSettingsService;
import com.iailab.module.model.mdk.common.enums.TypeA;
@@ -15,12 +18,16 @@
import com.iailab.module.model.mdk.sample.SampleConstructor;
import com.iailab.module.model.mdk.sample.dto.SampleData;
import com.iailab.module.model.mdk.vo.PredictResultVO;
+import com.iailab.module.model.mpk.common.MdkConstant;
import com.iailab.module.model.mpk.common.utils.DllUtils;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
-import java.util.*;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
/**
* @author PanZhibao
@@ -49,22 +56,26 @@
* @throws ModelInvokeException
*/
@Override
- public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel) throws ModelInvokeException {
+ public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel,String itemName, ItemRunStatusEnum itemRunStatusEnum) throws ModelInvokeException {
PredictResultVO result = new PredictResultVO();
if (predictModel == null) {
throw new ModelInvokeException("modelEntity is null");
}
String modelId = predictModel.getId();
-
try {
List<SampleData> sampleDataList = sampleConstructor.constructSample(TypeA.Predict.name(), modelId, predictTime);
String modelPath = predictModel.getModelpath();
if (modelPath == null) {
- System.out.println("模型路径不存在，modelId=" + modelId);
+ log.info("模型路径不存在，modelId=" + modelId);
return null;
}
IAILModel newModelBean = composeNewModelBean(predictModel);
HashMap<String, Object> settings = getPredictSettingsByModelId(modelId);
+ // 校验setting必须有pyFile，否则可能导致程序崩溃
+ if (!settings.containsKey(MdkConstant.PY_FILE_KEY)) {
+ throw new RuntimeException("模型设置参数缺少必要信息【" + MdkConstant.PY_FILE_KEY + "】,请重新上传模型！");
+ }
+
if (settings == null) {
log.error("模型setting不存在，modelId=" + modelId);
return null;
@@ -77,61 +88,65 @@
param2Values[portLength] = newModelBean.getDataMap().get("models");
param2Values[portLength + 1] = settings;
- log.info("#######################预测模型 " + predictModel.getItemid() + " ##########################");
- JSONObject jsonObjNewModelBean = new JSONObject();
- jsonObjNewModelBean.put("newModelBean", newModelBean);
- log.info(String.valueOf(jsonObjNewModelBean));
- JSONObject jsonObjParam2Values = new JSONObject();
- jsonObjParam2Values.put("param2Values", param2Values);
- log.info(String.valueOf(jsonObjParam2Values));
+ log.info("####################### 预测模型 "+ "【itemId:" + predictModel.getItemid() + ",itemName" + itemName + "】 ##########################");
+// JSONObject jsonObjNewModelBean = new JSONObject();
+// jsonObjNewModelBean.put("newModelBean", newModelBean);
+// log.info(String.valueOf(jsonObjNewModelBean));
+// JSONObject jsonObjParam2Values = new JSONObject();
+// jsonObjParam2Values.put("param2Values", param2Values);
+ log.info("参数： " + JSON.toJSONString(param2Values));
//IAILMDK.run
HashMap<String, Object> modelResult = DllUtils.run(newModelBean, param2Values, predictModel.getMpkprojectid());
if (!modelResult.containsKey(CommonConstant.MDK_STATUS_CODE) || !modelResult.containsKey(CommonConstant.MDK_RESULT) ||
!modelResult.get(CommonConstant.MDK_STATUS_CODE).toString().equals(CommonConstant.MDK_STATUS_100)) {
+ itemRunStatusEnum = ItemRunStatusEnum.MODELRESULTERROR;
throw new RuntimeException("模型结果异常：" + modelResult);
}
modelResult = (HashMap<String, Object>) modelResult.get(CommonConstant.MDK_RESULT);
//打印结果
+ log.info("预测模型计算完成：modelId=" + modelId + ",modelName" + predictModel.getMethodname());
JSONObject jsonObjResult = new JSONObject();
jsonObjResult.put("result", modelResult);
log.info(String.valueOf(jsonObjResult));
- List<MmItemOutputEntity> ItemOutputList = mmItemOutputService.getByItemid(predictModel.getItemid());
- log.info("模型计算完成：modelId=" + modelId + modelResult);
-
- Map<MmItemOutputEntity, double[]> predictMatrixs = new HashMap<>(ItemOutputList.size());
-
- for (MmItemOutputEntity outputEntity : ItemOutputList) {
- String resultStr = outputEntity.getResultstr();
- if (modelResult.containsKey(resultStr)) {
- if (outputEntity.getResultType() == 1) {
- // 一维数组
- Double[] temp = (Double[]) modelResult.get(resultStr);
- double[] temp1 = new double[temp.length];
- for (int i = 0; i < temp.length; i++) {
- temp1[i] = temp[i].doubleValue();
+ List<MmItemOutputEntity> itemOutputList = mmItemOutputService.getByItemid(predictModel.getItemid());
+ Map<MmItemOutputEntity, double[]> predictMatrixs = new HashMap<>();
+ Map<MmItemOutputEntity, Double> predictDoubleValues = new HashMap<>();
+ for (MmItemOutputEntity output : itemOutputList) {
+ if (!modelResult.containsKey(output.getResultstr())) {
+ continue;
+ }
+ OutResultType outResultType = OutResultType.getEumByCode(output.getResultType());
+ switch (outResultType) {
+ case D1:
+ double[] temp1 = (double[]) modelResult.get(output.getResultstr());
+ predictMatrixs.put(output, temp1);
+ break;
+ case D2:
+ double[][] temp2 = (double[][]) modelResult.get(output.getResultstr());
+ double[] tempColumn = new double[temp2.length];
+ for (int i = 0; i < tempColumn.length; i++) {
+ tempColumn[i] = temp2[i][output.getResultIndex()];
}
- predictMatrixs.put(outputEntity, temp1);
- } else if (outputEntity.getResultType() == 2) {
- // 二维数组
- Double[][] temp = (Double[][]) modelResult.get(resultStr);
- Double[] temp2 = temp[outputEntity.getResultIndex()];
- double[] temp1 = new double[temp2.length];
- for (int i = 0; i < temp2.length; i++) {
- temp1[i] = temp2[i].doubleValue();
- }
- predictMatrixs.put(outputEntity, temp1);
- }
+ predictMatrixs.put(output, tempColumn);
+ break;
+ case D:
+ Double temp3 = (Double) modelResult.get(output.getResultstr());
+ predictDoubleValues.put(output, temp3);
+ break;
+ default:
+ break;
}
}
result.setPredictMatrixs(predictMatrixs);
+ result.setPredictDoubleValues(predictDoubleValues);
result.setModelResult(modelResult);
result.setPredictTime(predictTime);
} catch (Exception ex) {
log.error("调用发生异常，异常信息为:{}", ex);
- log.error(ex.getMessage());
ex.printStackTrace();
+ throw new ModelInvokeException(ex.getMessage());
}
return result;
}
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