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 | 106 +++++++++++++++++++++++++++++++++++++++--------------
1 files changed, 78 insertions(+), 28 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 7b47923..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
@@ -1,28 +1,33 @@
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.IAILMDK;
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.MmModelResultstrEntity;
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.mcs.pre.service.MmModelResultstrService;
import com.iailab.module.model.mdk.common.enums.TypeA;
import com.iailab.module.model.mdk.common.exceptions.ModelInvokeException;
import com.iailab.module.model.mdk.predict.PredictModelHandler;
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.ArrayList;
import java.util.Date;
import java.util.HashMap;
import java.util.List;
+import java.util.Map;
/**
* @author PanZhibao
@@ -37,28 +42,40 @@
private MmModelArithSettingsService mmModelArithSettingsService;
@Autowired
- private MmModelResultstrService mmModelResultstrService;
+ private MmItemOutputService mmItemOutputService;
@Autowired
private SampleConstructor sampleConstructor;
+ /**
+ * 根据模型预测,返回预测结果
+ *
+ * @param predictTime
+ * @param predictModel
+ * @return
+ * @throws ModelInvokeException
+ */
@Override
- public 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;
@@ -66,37 +83,70 @@
int portLength = sampleDataList.size();
Object[] param2Values = new Object[portLength + 2];
for (int i = 0; i < portLength; i++) {
- param2Values[i]=sampleDataList.get(i).getMatrix();
+ param2Values[i] = sampleDataList.get(i).getMatrix();
}
param2Values[portLength] = newModelBean.getDataMap().get("models");
- param2Values[portLength+1] = settings;
+ 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 = IAILMDK.run(newModelBean, param2Values);
+ 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", result);
+ jsonObjResult.put("result", modelResult);
log.info(String.valueOf(jsonObjResult));
- MmModelResultstrEntity modelResultstr = mmModelResultstrService.getInfo(predictModel.getResultstrid());
- log.info("模型计算完成：modelId=" + modelId + result);
- double[][] temp = (double[][]) modelResult.get(modelResultstr.getResultstr());
- result.setPredictMatrix(temp);
+ 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(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("IAILModel对象构造失败，modelId=" + modelId);
- log.error(ex.getMessage());
- log.error("调用发生异常，异常信息为:{}" , ex);
+ log.error("调用发生异常，异常信息为:{}", ex);
ex.printStackTrace();
-
+ throw new ModelInvokeException(ex.getMessage());
}
return result;
}
@@ -124,7 +174,7 @@
newModelBean.setParamsArray(paramsArray);
HashMap<String, Object> dataMap = new HashMap<>();
HashMap<String, String> models = new HashMap<>(1);
- models.put("paramFile", predictModel.getModelpath());
+ models.put("model_path", predictModel.getModelpath());
dataMap.put("models", models);
newModelBean.setDataMap(dataMap);
return newModelBean;
--
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