iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java

@@ -1,30 +1,34 @@
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.mcs.pre.controller.admin.MmItemOutputController;
import com.iailab.module.model.common.enums.CommonConstant;
import com.iailab.module.model.common.enums.OutResultType;
import com.iailab.module.model.common.exception.ModelResultErrorException;
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.security.core.parameters.P;
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
@@ -39,30 +43,40 @@
    private MmModelArithSettingsService mmModelArithSettingsService;

    @Autowired
    private MmModelResultstrService mmModelResultstrService;
    @Autowired
    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) 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;
@@ -70,70 +84,69 @@
            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("status_code") || !modelResult.containsKey("result") || Integer.parseInt(modelResult.get("status_code").toString()) != 100) {
                throw new RuntimeException("模型结果异常：" + modelResult);
            if (!modelResult.containsKey(CommonConstant.MDK_STATUS_CODE) || !modelResult.containsKey(CommonConstant.MDK_RESULT) ||
                    !modelResult.get(CommonConstant.MDK_STATUS_CODE).toString().equals(CommonConstant.MDK_STATUS_100)) {
                throw new ModelResultErrorException("模型结果异常：" + modelResult);
            }

            modelResult = (HashMap<String, Object>) modelResult.get("result");
            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("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;
    }