优化主动学习

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/domain/ActiveLearn.java

@@ -69,13 +69,13 @@ public class ActiveLearn {
     private Integer trainSize;
 
     @ApiModelProperty(value = "初始训练数据量")
-    private Integer nInitial;
+    private Integer initialNum;
 
     @ApiModelProperty(value = "查询次数")
-    private Integer nQueries;
+    private Integer queriesNum;
 
     @ApiModelProperty(value = "每次查询数据量")
-    private Integer nInstances;
+    private Integer instancesNum;
 
     @ApiModelProperty(value = "查询策略:uncertainty_sampling, uncertainty_batch_sampling, max_std_sampling, expected_improvement, upper_confidence_bound")
     private String queryStrategy;
@@ -87,7 +87,7 @@ public class ActiveLearn {
     private String lossClassName;
 
     @ApiModelProperty(value = "多少轮查询保存一次模型参数")
-    private Integer nCheckpoint;
+    private Integer checkpointNum;
 
     @ApiModelProperty(value = "batch_size")
     private Integer batchSize;

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/mapper/ResourceOccupyDao.java

@@ -14,7 +14,7 @@ public interface ResourceOccupyDao {
 
     int edit(@Param("resourceOccupy") ResourceOccupy resourceOccupy);
 
-    List<ResourceOccupy> getResourceOccupyByTask(@Param("taskType") String taskType, @Param("taskId") Long taskId, @Param("taskInsId") Long taskInsId,  @Param("nodeId") String nodeId);
+    List<ResourceOccupy> getResourceOccupyByTask(@Param("taskType") String taskType, @Param("taskId") Long taskId, @Param("taskInsId") Long taskInsId, @Param("nodeId") String nodeId);
 
     int deduceCredit(@Param("credit") Double credit, @Param("userId") Long userId);
 
@@ -30,5 +30,5 @@ public interface ResourceOccupyDao {
 
     Double getDeduceCredit(@Param("userId") Long userId);
 
-    int deleteTaskState(String taskType, Long taskId, Long taskInsId);
+    int deleteTaskState(@Param("taskType") String taskType, @Param("taskId") Long taskId, @Param("taskInsId") Long taskInsId);
 }

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/service/impl/ActiveLearnServiceImpl.java

@@ -146,8 +146,8 @@ public class ActiveLearnServiceImpl implements ActiveLearnService {
             activeLearnParamVo.setDataset(JsonUtils.jsonToMap(activeLearn.getDataset()));
             activeLearnParamVo.setModel(JsonUtils.jsonToMap(activeLearn.getModel()));
             activeLearnParamVo.setImage(JsonUtils.jsonToMap(activeLearn.getImage()));
-            String param = JsonUtils.objectToJson(activeLearnParamVo);
 
+            String param = JsonUtils.getConvertParam(activeLearnParamVo);
             // 调argo转换接口
             try {
                 String convertRes = HttpUtils.sendPost(argoUrl + convertActiveLearn, param);

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/service/impl/AutoMlServiceImpl.java

@@ -157,7 +157,7 @@ public class AutoMlServiceImpl implements AutoMlService {
         AutoMlParamVo autoMlParam = new AutoMlParamVo();
         BeanUtils.copyProperties(autoMl, autoMlParam);
         autoMlParam.setDataset(JsonUtils.jsonToMap(autoMl.getDataset()));
-        String param = JsonUtils.objectToJson(autoMlParam);
+        String param = JsonUtils.getConvertParam(autoMlParam);
         // 调argo转换接口
         try {
             String convertRes = HttpUtils.sendPost(argoUrl + convertAutoML, param);

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/service/impl/RayServiceImpl.java

@@ -163,7 +163,7 @@ public class RayServiceImpl implements RayService {
             rayParamVo.setDataset(JsonUtils.jsonToMap(ray.getDataset()));
             rayParamVo.setModel(JsonUtils.jsonToMap(ray.getModel()));
             rayParamVo.setImage(JsonUtils.jsonToMap(ray.getImage()));
-            String param = JsonUtils.objectToJson(rayParamVo);
+            String param = JsonUtils.getConvertParam(rayParamVo);
 
             // 调argo转换接口
             try {

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/utils/JsonUtils.java

@@ -1,11 +1,12 @@
 package com.ruoyi.platform.utils;
+
+import com.alibaba.fastjson2.JSON;
 import com.fasterxml.jackson.core.JsonProcessingException;
 import com.fasterxml.jackson.databind.ObjectMapper;
-import org.json.JSONObject;
+import com.ruoyi.common.security.utils.SecurityUtils;
 
 import java.io.IOException;
 import java.util.HashMap;
-import java.util.Iterator;
 import java.util.Map;
 
 public class JsonUtils {
@@ -33,9 +34,8 @@ public class JsonUtils {
     }
 
 
-
     // 将JSON字符串转换为扁平化的Map
-    public static Map<String, Object> flattenJson(String prefix,  Map<String, Object> map) {
+    public static Map<String, Object> flattenJson(String prefix, Map<String, Object> map) {
         Map<String, Object> flatMap = new HashMap<>();
 
         for (Map.Entry<String, Object> entry : map.entrySet()) {
@@ -56,4 +56,17 @@ public class JsonUtils {
     public static Map<String, Object> objectToMap(Object object) throws IOException {
         return objectMapper.convertValue(object, Map.class);
     }
+
+    public static String getConvertParam(Object object) throws JsonProcessingException {
+        HashMap<Object, Object> paramMap = new HashMap<>();
+        paramMap.put("data", JSON.parseObject(objectToJson(object), Map.class));
+
+        HashMap<Object, Object> userInfoMap = new HashMap<>();
+        userInfoMap.put("name", SecurityUtils.getLoginUser().getUsername());
+        userInfoMap.put("token", SecurityUtils.getLoginUser().getSysUser().getOriginPassword());
+        HashMap<Object, Object> extraInfoMap = new HashMap<>();
+        extraInfoMap.put("user_info", userInfoMap);
+        paramMap.put("extra_info", extraInfoMap);
+        return objectToJson(paramMap);
+    }
 }

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/vo/ActiveLearnParamVo.java

@@ -43,11 +43,11 @@ public class ActiveLearnParamVo {
 
     private Integer trainSize;
 
-    private Integer nInitial;
+    private Integer initialNum;
 
-    private Integer nQueries;
+    private Integer queriesNum;
 
-    private Integer nInstances;
+    private Integer instancesNum;
 
     private String queryStrategy;
 
@@ -55,7 +55,7 @@ public class ActiveLearnParamVo {
 
     private String lossClassName;
 
-    private Integer nCheckpoint;
+    private Integer checkpointNum;
 
     private Integer batchSize;
 

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/vo/ActiveLearnVo.java

@@ -24,6 +24,9 @@ public class ActiveLearnVo {
     @ApiModelProperty(value = "任务类型：classification或regression")
     private String taskType;
 
+    @ApiModelProperty(value = "框架类型: sklearn, keras, pytorch")
+    private String frameworkType;
+
     @ApiModelProperty(value = "代码")
     private Map<String, Object> codeConfig;
 
@@ -67,13 +70,13 @@ public class ActiveLearnVo {
     private Integer trainSize;
 
     @ApiModelProperty(value = "初始训练数据量")
-    private Integer nInitial;
+    private Integer initialNum;
 
     @ApiModelProperty(value = "查询次数")
-    private Integer nQueries;
+    private Integer queriesNum;
 
     @ApiModelProperty(value = "每次查询数据量")
-    private Integer nInstances;
+    private Integer instancesNum;
 
     @ApiModelProperty(value = "查询策略:uncertainty_sampling, uncertainty_batch_sampling, max_std_sampling, expected_improvement, upper_confidence_bound")
     private String queryStrategy;
@@ -85,7 +88,7 @@ public class ActiveLearnVo {
     private String lossClassName;
 
     @ApiModelProperty(value = "多少轮查询保存一次模型参数")
-    private Integer nCheckpoint;
+    private Integer checkpointNum;
 
     @ApiModelProperty(value = "batch_size")
     private Integer batchSize;

ruoyi-modules/management-platform/src/main/resources/mapper/managementPlatform/ActiveLearnDaoMapper.xml

@@ -25,8 +25,8 @@
                                  model, model_py, model_class_name,
                                  classifier_alg, regressor_alg, dataset_py, dataset_class_name,
                                  dataset, data_size, image, computing_resource_id, shuffle,
-                                 train_size, n_initial, n_queries, n_instances, query_strategy,
-                                 loss_py, loss_class_name, n_checkpoint, batch_size, epochs,
+                                 train_size, initial_num, queries_num, instances_num, query_strategy,
+                                 loss_py, loss_class_name, checkpoint_num, batch_size, epochs,
                                  lr, create_by, update_by)
         values (#{activeLearn.name}, #{activeLearn.description}, #{activeLearn.taskType}, #{activeLearn.frameworkType}, #{activeLearn.codeConfig},
                 #{activeLearn.model},
@@ -36,9 +36,9 @@
                 #{activeLearn.dataset}, #{activeLearn.dataSize},
                 #{activeLearn.image},
                 #{activeLearn.computingResourceId}, #{activeLearn.shuffle},
-                #{activeLearn.trainSize}, #{activeLearn.nInitial}, #{activeLearn.nQueries}, #{activeLearn.nInstances},
+                #{activeLearn.trainSize}, #{activeLearn.initialNum}, #{activeLearn.queriesNum}, #{activeLearn.instancesNum},
                 #{activeLearn.queryStrategy}, #{activeLearn.lossPy},
-                #{activeLearn.lossClassName}, #{activeLearn.nCheckpoint}, #{activeLearn.batchSize},
+                #{activeLearn.lossClassName}, #{activeLearn.checkpointNum}, #{activeLearn.batchSize},
                 #{activeLearn.epochs}, #{activeLearn.lr}, #{activeLearn.createBy}, #{activeLearn.updateBy})
     </insert>
 
@@ -99,14 +99,14 @@
             <if test="activeLearn.trainSize != null">
                 train_size = #{activeLearn.trainSize},
             </if>
-            <if test="activeLearn.nInitial != null">
-                n_initial = #{activeLearn.nInitial},
+            <if test="activeLearn.initialNum != null">
+                initial_num = #{activeLearn.initialNum},
             </if>
-            <if test="activeLearn.nQueries != null">
-                n_queries = #{activeLearn.nQueries},
+            <if test="activeLearn.queriesNum != null">
+                queries_num = #{activeLearn.queriesNum},
             </if>
-            <if test="activeLearn.nInstances != null">
-                n_instances = #{activeLearn.nInstances},
+            <if test="activeLearn.instancesNum != null">
+                instances_num = #{activeLearn.instancesNum},
             </if>
             <if test="activeLearn.queryStrategy != null and activeLearn.queryStrategy !=''">
                 query_strategy = #{activeLearn.queryStrategy},
@@ -117,8 +117,8 @@
             <if test="activeLearn.lossClassName != null and activeLearn.lossClassName !=''">
                 loss_class_name = #{activeLearn.lossClassName},
             </if>
-            <if test="activeLearn.nCheckpoint != null">
-                n_checkpoint = #{activeLearn.nCheckpoint},
+            <if test="activeLearn.checkpointNum != null">
+                checkpoint_num = #{activeLearn.checkpointNum},
             </if>
             <if test="activeLearn.batchSize != null">
                 batch_size = #{activeLearn.batchSize},