package com.iailab.module.model.mdk.predict.impl;
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import com.alibaba.fastjson.JSON;
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import com.alibaba.fastjson.JSONArray;
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import com.alibaba.fastjson.JSONObject;
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import com.iail.model.IAILModel;
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import com.iailab.module.model.common.enums.CommonConstant;
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import com.iailab.module.model.common.enums.OutResultType;
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import com.iailab.module.model.common.exception.ModelResultErrorException;
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import com.iailab.module.model.mcs.pre.entity.MmItemOutputEntity;
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import com.iailab.module.model.mcs.pre.entity.MmModelArithSettingsEntity;
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import com.iailab.module.model.mcs.pre.entity.MmPredictModelEntity;
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import com.iailab.module.model.mcs.pre.enums.ItemRunStatusEnum;
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import com.iailab.module.model.mcs.pre.service.MmItemOutputService;
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import com.iailab.module.model.mcs.pre.service.MmModelArithSettingsService;
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import com.iailab.module.model.mdk.common.enums.TypeA;
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import com.iailab.module.model.mdk.common.exceptions.ModelInvokeException;
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import com.iailab.module.model.mdk.predict.PredictModelHandler;
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import com.iailab.module.model.mdk.sample.SampleConstructor;
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import com.iailab.module.model.mdk.sample.dto.SampleData;
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import com.iailab.module.model.mdk.vo.PredictResultVO;
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import com.iailab.module.model.mpk.common.MdkConstant;
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import com.iailab.module.model.mpk.common.utils.DllUtils;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Component;
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import java.util.Date;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* @author PanZhibao
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* @Description
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* @createTime 2024年09月01日
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*/
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@Slf4j
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@Component
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public class PredictModelHandlerImpl implements PredictModelHandler {
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@Autowired
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private MmModelArithSettingsService mmModelArithSettingsService;
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@Autowired
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private MmItemOutputService mmItemOutputService;
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@Autowired
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private SampleConstructor sampleConstructor;
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/**
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* 根据模型预测,返回预测结果
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*
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* @param predictTime
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* @param predictModel
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* @return
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* @throws ModelInvokeException
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*/
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@Override
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public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel,String itemName) throws ModelInvokeException {
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PredictResultVO result = new PredictResultVO();
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if (predictModel == null) {
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throw new ModelInvokeException("modelEntity is null");
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}
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String modelId = predictModel.getId();
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try {
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List<SampleData> sampleDataList = sampleConstructor.constructSample(TypeA.Predict.name(), modelId, predictTime);
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String modelPath = predictModel.getModelpath();
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if (modelPath == null) {
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log.info("模型路径不存在，modelId=" + modelId);
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return null;
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}
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IAILModel newModelBean = composeNewModelBean(predictModel);
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HashMap<String, Object> settings = getPredictSettingsByModelId(modelId);
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// 校验setting必须有pyFile，否则可能导致程序崩溃
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if (!settings.containsKey(MdkConstant.PY_FILE_KEY)) {
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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！");
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}
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if (settings == null) {
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log.error("模型setting不存在，modelId=" + modelId);
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return null;
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}
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int portLength = sampleDataList.size();
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Object[] param2Values = new Object[portLength + 2];
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for (int i = 0; i < portLength; i++) {
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param2Values[i] = sampleDataList.get(i).getMatrix();
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}
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param2Values[portLength] = newModelBean.getDataMap().get("models");
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param2Values[portLength + 1] = settings;
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log.info("####################### 预测模型 "+ "【itemId:" + predictModel.getItemid() + ",itemName" + itemName + "】 ##########################");
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// JSONObject jsonObjNewModelBean = new JSONObject();
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// jsonObjNewModelBean.put("newModelBean", newModelBean);
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// log.info(String.valueOf(jsonObjNewModelBean));
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// JSONObject jsonObjParam2Values = new JSONObject();
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// jsonObjParam2Values.put("param2Values", param2Values);
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log.info("参数： " + JSON.toJSONString(param2Values));
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//IAILMDK.run
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HashMap<String, Object> modelResult = DllUtils.run(newModelBean, param2Values, predictModel.getMpkprojectid());
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if (!modelResult.containsKey(CommonConstant.MDK_STATUS_CODE) || !modelResult.containsKey(CommonConstant.MDK_RESULT) ||
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!modelResult.get(CommonConstant.MDK_STATUS_CODE).toString().equals(CommonConstant.MDK_STATUS_100)) {
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throw new ModelResultErrorException("模型结果异常：" + modelResult);
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}
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modelResult = (HashMap<String, Object>) modelResult.get(CommonConstant.MDK_RESULT);
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//打印结果
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log.info("预测模型计算完成：modelId=" + modelId + ",modelName" + predictModel.getMethodname());
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JSONObject jsonObjResult = new JSONObject();
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jsonObjResult.put("result", modelResult);
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log.info(String.valueOf(jsonObjResult));
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List<MmItemOutputEntity> itemOutputList = mmItemOutputService.getByItemid(predictModel.getItemid());
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Map<MmItemOutputEntity, double[]> predictMatrixs = new HashMap<>();
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Map<MmItemOutputEntity, Double> predictDoubleValues = new HashMap<>();
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for (MmItemOutputEntity output : itemOutputList) {
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if (!modelResult.containsKey(output.getResultstr())) {
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continue;
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}
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OutResultType outResultType = OutResultType.getEumByCode(output.getResultType());
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switch (outResultType) {
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case D1:
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double[] temp1 = (double[]) modelResult.get(output.getResultstr());
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predictMatrixs.put(output, temp1);
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break;
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case D2:
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double[][] temp2 = (double[][]) modelResult.get(output.getResultstr());
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double[] tempColumn = new double[temp2.length];
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for (int i = 0; i < tempColumn.length; i++) {
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tempColumn[i] = temp2[i][output.getResultIndex()];
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}
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predictMatrixs.put(output, tempColumn);
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break;
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case D:
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Double temp3 = (Double) modelResult.get(output.getResultstr());
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predictDoubleValues.put(output, temp3);
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break;
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default:
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break;
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}
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}
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result.setPredictMatrixs(predictMatrixs);
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result.setPredictDoubleValues(predictDoubleValues);
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result.setModelResult(modelResult);
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result.setPredictTime(predictTime);
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} catch (Exception ex) {
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log.error("调用发生异常，异常信息为:{}", ex);
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ex.printStackTrace();
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throw new ModelInvokeException(ex.getMessage());
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}
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return result;
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}
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/**
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* 构造IAILMDK.run()方法的newModelBean参数
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*
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* @param predictModel
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* @return
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*/
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private IAILModel composeNewModelBean(MmPredictModelEntity predictModel) {
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IAILModel newModelBean = new IAILModel();
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newModelBean.setClassName(predictModel.getClassname().trim());
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newModelBean.setMethodName(predictModel.getMethodname().trim());
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//构造参数类型
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String[] paArStr = predictModel.getModelparamstructure().trim().split(",");
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Class<?>[] paramsArray = new Class[paArStr.length];
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for (int i = 0; i < paArStr.length; i++) {
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if ("[[D".equals(paArStr[i])) {
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paramsArray[i] = double[][].class;
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} else if ("Map".equals(paArStr[i]) || "java.util.HashMap".equals(paArStr[i])) {
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paramsArray[i] = HashMap.class;
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}
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}
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newModelBean.setParamsArray(paramsArray);
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HashMap<String, Object> dataMap = new HashMap<>();
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HashMap<String, String> models = new HashMap<>(1);
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models.put("model_path", predictModel.getModelpath());
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dataMap.put("models", models);
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newModelBean.setDataMap(dataMap);
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return newModelBean;
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}
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/**
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* 根据模型id获取参数map
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*
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* @param modelId
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* @return
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*/
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private HashMap<String, Object> getPredictSettingsByModelId(String modelId) {
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List<MmModelArithSettingsEntity> list = mmModelArithSettingsService.getByModelId(modelId);
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HashMap<String, Object> result = new HashMap<>();
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for (MmModelArithSettingsEntity entry : list) {
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String valueType = entry.getValuetype().trim(); //去除两端空格
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if ("int".equals(valueType)) {
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int value = Integer.parseInt(entry.getValue());
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result.put(entry.getKey(), value);
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} else if ("double".equals(valueType)) {
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double value = Double.parseDouble(entry.getValue());
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result.put(entry.getKey(), value);
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} else if ("string".equals(valueType)) {
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String value = entry.getValue();
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result.put(entry.getKey(), value);
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} else if ("decimalArray".equals(valueType)) {
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JSONArray valueArray = JSONArray.parseArray(entry.getValue());
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double[] value = new double[valueArray.size()];
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for (int i = 0; i < valueArray.size(); i++) {
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value[i] = Double.parseDouble(valueArray.get(i).toString());
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}
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result.put(entry.getKey(), value);
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} else if ("decimal".equals(valueType)) {
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double value = Double.parseDouble(entry.getValue());
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result.put(entry.getKey(), value);
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}
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}
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return result;
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}
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}
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