自动机器学习开发

ruoyi-modules/management-platform/src/main/java/com/ruoyi/platform/controller/autoML/AutoMLController.java

@@ -5,6 +5,7 @@ import com.ruoyi.common.core.web.domain.AjaxResult;
 import com.ruoyi.common.core.web.domain.GenericsAjaxResult;
 import com.ruoyi.platform.domain.AutoMl;
 import com.ruoyi.platform.service.AutoMLService;
+import com.ruoyi.platform.vo.AutoMlVo;
 import io.swagger.annotations.Api;
 import io.swagger.annotations.ApiOperation;
 import org.springframework.data.domain.Page;
@@ -13,6 +14,7 @@ import org.springframework.web.bind.annotation.*;
 import org.springframework.web.multipart.MultipartFile;
 
 import javax.annotation.Resource;
+import java.io.IOException;
 
 @RestController
 @RequestMapping("autoML")
@@ -33,18 +35,18 @@ public class AutoMLController extends BaseController {
 
     @PostMapping
     @ApiOperation("新增自动机器学习")
-    public GenericsAjaxResult<AutoMl> addAutoMl(@RequestBody AutoMl autoMl) throws Exception {
-        return genericsSuccess(this.autoMLService.save(autoMl));
+    public GenericsAjaxResult<AutoMl> addAutoMl(@RequestBody AutoMlVo autoMlVo) throws Exception {
+        return genericsSuccess(this.autoMLService.save(autoMlVo));
     }
 
     @PutMapping
     @ApiOperation("编辑自动机器学习")
-    public GenericsAjaxResult<String> editAutoMl(@RequestBody AutoMl autoMl) throws Exception {
-        return genericsSuccess(this.autoMLService.edit(autoMl));
+    public GenericsAjaxResult<String> editAutoMl(@RequestBody AutoMlVo autoMlVo) throws Exception {
+        return genericsSuccess(this.autoMLService.edit(autoMlVo));
     }
     @GetMapping("/getAutoMlDetail")
     @ApiOperation("获取自动机器学习详细信息")
-    public GenericsAjaxResult<AutoMl> getAutoMlDetail(@RequestParam("id") Long id){
+    public GenericsAjaxResult<AutoMlVo> getAutoMlDetail(@RequestParam("id") Long id) throws IOException {
         return genericsSuccess(this.autoMLService.getAutoMlDetail(id));
     }
 

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

@@ -9,6 +9,7 @@ import io.swagger.annotations.ApiModelProperty;
 import lombok.Data;
 
 import java.util.Date;
+import java.util.Map;
 
 @Data
 @JsonNaming(PropertyNamingStrategy.SnakeCaseStrategy.class)
@@ -25,9 +26,6 @@ public class AutoMl {
     @ApiModelProperty(value = "任务类型：classification或regression")
     private String taskType;
 
-    @ApiModelProperty(value = "数据集名称")
-    private String datasetName;
-
     @ApiModelProperty(value = "搜索合适模型的时间限制（以秒为单位）。通过增加这个值，auto-sklearn有更高的机会找到更好的模型。默认3600，非必传。")
     private Integer timeLeftForThisTask;
 
@@ -128,9 +126,6 @@ public class AutoMl {
     @ApiModelProperty(value = "文件夹存放配置输出和日志文件，默认/tmp/automl")
     private String tmpFolder;
 
-    @ApiModelProperty(value = "数据集csv文件路径")
-    private String dataCsv;
-
     @ApiModelProperty(value = "数据集csv文件中哪几列是预测目标列，逗号分隔")
     private String targetColumns;
 
@@ -182,6 +177,6 @@ public class AutoMl {
 
     private Date updateTime;
 
-    @TableField(exist = false)
-    private VersionVo versionVo;
+    private String dataset;
+
 }

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

@@ -1,22 +1,24 @@
 package com.ruoyi.platform.service;
 
 import com.ruoyi.platform.domain.AutoMl;
+import com.ruoyi.platform.vo.AutoMlVo;
 import org.springframework.data.domain.Page;
 import org.springframework.data.domain.PageRequest;
 import org.springframework.web.multipart.MultipartFile;
 
+import java.io.IOException;
 import java.util.Map;
 
 public interface AutoMLService {
     Page<AutoMl> queryByPage(String mlName, PageRequest pageRequest);
 
-    AutoMl save(AutoMl autoMl) throws Exception;
+    AutoMl save(AutoMlVo autoMlVo) throws Exception;
 
-    String edit(AutoMl autoMl) throws Exception;
+    String edit(AutoMlVo autoMlVo) throws Exception;
 
     String delete(Long id);
 
-    AutoMl getAutoMlDetail(Long id);
+    AutoMlVo getAutoMlDetail(Long id) throws IOException;
 
     Map<String, String> upload(MultipartFile file, String uuid) throws Exception;
 

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

@@ -5,14 +5,14 @@ import com.ruoyi.platform.constant.Constant;
 import com.ruoyi.platform.domain.AutoMl;
 import com.ruoyi.platform.mapper.AutoMLDao;
 import com.ruoyi.platform.service.AutoMLService;
-import com.ruoyi.platform.utils.DVCUtils;
-import com.ruoyi.platform.utils.FileUtil;
-import com.ruoyi.platform.utils.K8sClientUtil;
+import com.ruoyi.platform.utils.*;
+import com.ruoyi.platform.vo.AutoMlVo;
 import io.kubernetes.client.openapi.models.V1Pod;
 import org.apache.commons.io.FileUtils;
 import org.apache.commons.lang3.StringUtils;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.springframework.beans.BeanUtils;
 import org.springframework.beans.factory.annotation.Value;
 import org.springframework.data.domain.Page;
 import org.springframework.data.domain.PageImpl;
@@ -22,6 +22,7 @@ import org.springframework.web.multipart.MultipartFile;
 
 import javax.annotation.Resource;
 import java.io.File;
+import java.io.IOException;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
@@ -59,39 +60,35 @@ public class AutoMLServiceImpl implements AutoMLService {
     }
 
     @Override
-    public AutoMl save(AutoMl autoMl) throws Exception {
-        AutoMl autoMlByName = autoMLDao.getAutoMlByName(autoMl.getMlName());
+    public AutoMl save(AutoMlVo autoMlVo) throws Exception {
+        AutoMl autoMlByName = autoMLDao.getAutoMlByName(autoMlVo.getMlName());
         if (autoMlByName != null) {
             throw new RuntimeException("实验名称已存在");
         }
-
+        AutoMl autoMl = new AutoMl();
+        BeanUtils.copyProperties(autoMlVo, autoMl);
         String username = SecurityUtils.getLoginUser().getUsername();
         autoMl.setCreateBy(username);
         autoMl.setUpdateBy(username);
-
-        String sourcePath = autoMl.getVersionVo().getUrl();
-        String rootPath = localPath + username + "/automl/" + autoMl.getMlName() + "/" + autoMl.getDatasetName();
-        dvcUtils.moveFiles(sourcePath, rootPath);
-        autoMl.setDataCsv(rootPath + "/" + autoMl.getVersionVo().getFileName());
+        String datasetJson = JacksonUtil.toJSONString(autoMlVo.getDataset());
+        autoMl.setDataset(datasetJson);
         autoMLDao.save(autoMl);
         return autoMl;
     }
 
     @Override
-    public String edit(AutoMl autoMl) throws Exception {
-        AutoMl oldAutoMl = autoMLDao.getAutoMlByName(autoMl.getMlName());
-        if (oldAutoMl != null && !oldAutoMl.getId().equals(autoMl.getId())) {
+    public String edit(AutoMlVo autoMlVo) throws Exception {
+        AutoMl oldAutoMl = autoMLDao.getAutoMlByName(autoMlVo.getMlName());
+        if (oldAutoMl != null && !oldAutoMl.getId().equals(autoMlVo.getId())) {
             throw new RuntimeException("实验名称已存在");
         }
-
-        String username = SecurityUtils.getLoginUser().getUsername();
+        AutoMl autoMl = new AutoMl();
+        BeanUtils.copyProperties(autoMlVo, autoMl);
+//        String username = SecurityUtils.getLoginUser().getUsername();
+        String username = "admin";
         autoMl.setUpdateBy(username);
-        if (autoMl.getVersionVo() != null && StringUtils.isNotEmpty(autoMl.getVersionVo().getUrl())) {
-            String sourcePath = autoMl.getVersionVo().getUrl();
-            String rootPath = localPath + username + "/automl/" + autoMl.getMlName() + "/" + autoMl.getDatasetName();
-            dvcUtils.moveFiles(sourcePath, rootPath);
-            autoMl.setDataCsv(rootPath + "/" + autoMl.getVersionVo().getFileName());
-        }
+        String datasetJson = JacksonUtil.toJSONString(autoMlVo.getDataset());
+        autoMl.setDataset(datasetJson);
 
         autoMLDao.edit(autoMl);
         return "修改成功";
@@ -104,8 +101,7 @@ public class AutoMLServiceImpl implements AutoMLService {
             throw new RuntimeException("服务不存在");
         }
 
-//        String username = SecurityUtils.getLoginUser().getUsername();
-        String username = "admin";
+        String username = SecurityUtils.getLoginUser().getUsername();
         String createBy = autoMl.getCreateBy();
         if (!(StringUtils.equals(username, "admin") || StringUtils.equals(username, createBy))) {
             throw new RuntimeException("无权限删除该服务");
@@ -116,8 +112,14 @@ public class AutoMLServiceImpl implements AutoMLService {
     }
 
     @Override
-    public AutoMl getAutoMlDetail(Long id) {
-        return autoMLDao.getAutoMlById(id);
+    public AutoMlVo getAutoMlDetail(Long id) throws IOException {
+        AutoMl autoMl = autoMLDao.getAutoMlById(id);
+        AutoMlVo autoMlVo = new AutoMlVo();
+        BeanUtils.copyProperties(autoMl, autoMlVo);
+        if (StringUtils.isNotEmpty(autoMl.getDataset())) {
+            autoMlVo.setDataset(JsonUtils.jsonToMap(autoMl.getDataset()));
+        }
+        return autoMlVo;
     }
 
     @Override
@@ -151,21 +153,11 @@ public class AutoMLServiceImpl implements AutoMLService {
 
         StringBuffer command = new StringBuffer();
         command.append("nohup python /opt/automl.py --task_type " + autoMl.getTaskType());
-        if (StringUtils.isNotEmpty(autoMl.getDataCsv())) {
-            command.append(" --data_csv " + autoMl.getDataCsv());
-        } else {
-            throw new Exception("训练数据为空");
-        }
         if (StringUtils.isNotEmpty(autoMl.getTargetColumns())) {
             command.append(" --target_columns " + autoMl.getTargetColumns());
         } else {
             throw new Exception("目标列为空");
         }
-        if (StringUtils.isNotEmpty(autoMl.getDatasetName())) {
-            command.append(" --dataset_name " + autoMl.getDatasetName());
-        } else {
-            throw new Exception("数据集名称为空");
-        }
 
 //        String username = SecurityUtils.getLoginUser().getUsername().toLowerCase();
         String username = "admin";

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

@@ -0,0 +1,183 @@
+package com.ruoyi.platform.vo;
+
+import com.baomidou.mybatisplus.annotation.TableField;
+import com.fasterxml.jackson.databind.PropertyNamingStrategy;
+import com.fasterxml.jackson.databind.annotation.JsonNaming;
+import io.swagger.annotations.ApiModel;
+import io.swagger.annotations.ApiModelProperty;
+import lombok.Data;
+
+import java.util.Date;
+import java.util.Map;
+
+@Data
+@JsonNaming(PropertyNamingStrategy.SnakeCaseStrategy.class)
+@ApiModel(description = "自动机器学习")
+public class AutoMlVo {
+    private Long id;
+
+    @ApiModelProperty(value = "实验名称")
+    private String mlName;
+
+    @ApiModelProperty(value = "实验描述")
+    private String mlDescription;
+
+    @ApiModelProperty(value = "任务类型：classification或regression")
+    private String taskType;
+
+    @ApiModelProperty(value = "搜索合适模型的时间限制（以秒为单位）。通过增加这个值，auto-sklearn有更高的机会找到更好的模型。默认3600，非必传。")
+    private Integer timeLeftForThisTask;
+
+    @ApiModelProperty(value = "单次调用机器学习模型的时间限制（以秒为单位）。如果机器学习算法运行超过时间限制，将终止模型拟合。将这个值设置得足够高，这样典型的机器学习算法就可以适用于训练数据。默认600，非必传。")
+    private Integer perRunTimeLimit;
+
+    @ApiModelProperty(value = "集成模型数量，如果设置为0，则没有集成。默认50，非必传。")
+    private Integer ensembleSize;
+
+    @ApiModelProperty(value = "设置为None将禁用集成构建，设置为SingleBest仅使用单个最佳模型而不是集成，设置为default，它将对单目标问题使用EnsembleSelection，对多目标问题使用MultiObjectiveDummyEnsemble。默认default，非必传。")
+    private String ensembleClass;
+
+    @ApiModelProperty(value = "在构建集成时只考虑ensemble_nbest模型。这是受到了“最大限度地利用集成选择”中引入的库修剪概念的启发。这是独立于ensemble_class参数的，并且这个修剪步骤是在构造集成之前完成的。默认50，非必传。")
+    private Integer ensembleNbest;
+
+    @ApiModelProperty(value = "定义在磁盘中保存的模型的最大数量。额外的模型数量将被永久删除。由于这个变量的性质，它设置了一个集成可以使用多少个模型的上限。必须是大于等于1的整数。如果设置为None，则所有模型都保留在磁盘上。默认50，非必传。")
+    private Integer maxModelsOnDisc;
+
+    @ApiModelProperty(value = "随机种子，将决定输出文件名。默认1，非必传。")
+    private Integer seed;
+
+    @ApiModelProperty(value = "机器学习算法的内存限制（MB）。如果auto-sklearn试图分配超过memory_limit MB，它将停止拟合机器学习算法。默认3072，非必传。")
+    private Integer memoryLimit;
+
+    @ApiModelProperty(value = "如果为None，则使用所有可能的分类算法。否则，指定搜索中包含的步骤和组件。有关可用组件，请参见/pipeline/components/<step>/*。与参数exclude不兼容。多选，逗号分隔。包含：adaboost\n" +
+            "bernoulli_nb\n" +
+            "decision_tree\n" +
+            "extra_trees\n" +
+            "gaussian_nb\n" +
+            "gradient_boosting\n" +
+            "k_nearest_neighbors\n" +
+            "lda\n" +
+            "liblinear_svc\n" +
+            "libsvm_svc\n" +
+            "mlp\n" +
+            "multinomial_nb\n" +
+            "passive_aggressive\n" +
+            "qda\n" +
+            "random_forest\n" +
+            "sgd")
+    private String includeClassifier;
+
+    @ApiModelProperty(value = "如果为None，则使用所有可能的特征预处理算法。否则，指定搜索中包含的步骤和组件。有关可用组件，请参见/pipeline/components/<step>/*。与参数exclude不兼容。多选，逗号分隔。包含：densifier\n" +
+            "extra_trees_preproc_for_classification\n" +
+            "extra_trees_preproc_for_regression\n" +
+            "fast_ica\n" +
+            "feature_agglomeration\n" +
+            "kernel_pca\n" +
+            "kitchen_sinks\n" +
+            "liblinear_svc_preprocessor\n" +
+            "no_preprocessing\n" +
+            "nystroem_sampler\n" +
+            "pca\n" +
+            "polynomial\n" +
+            "random_trees_embedding\n" +
+            "select_percentile_classification\n" +
+            "select_percentile_regression\n" +
+            "select_rates_classification\n" +
+            "select_rates_regression\n" +
+            "truncatedSVD")
+    private String includeFeaturePreprocessor;
+
+    @ApiModelProperty(value = "如果为None，则使用所有可能的回归算法。否则，指定搜索中包含的步骤和组件。有关可用组件，请参见/pipeline/components/<step>/*。与参数exclude不兼容。多选，逗号分隔。包含：adaboost,\n" +
+            "ard_regression,\n" +
+            "decision_tree,\n" +
+            "extra_trees,\n" +
+            "gaussian_process,\n" +
+            "gradient_boosting,\n" +
+            "k_nearest_neighbors,\n" +
+            "liblinear_svr,\n" +
+            "libsvm_svr,\n" +
+            "mlp,\n" +
+            "random_forest,\n" +
+            "sgd")
+    private String includeRegressor;
+
+    private String excludeClassifier;
+
+    private String excludeRegressor;
+
+    private String excludeFeaturePreprocessor;
+
+    @ApiModelProperty(value = "测试集的比率，0到1之间")
+    private Float testSize;
+
+    @ApiModelProperty(value = "如何处理过拟合，如果使用基于“cv”的方法或Splitter对象，可能需要使用resampling_strategy_arguments。holdout或crossValid")
+    private String resamplingStrategy;
+
+    @ApiModelProperty(value = "重采样划分训练集和验证集，训练集的比率，0到1之间")
+    private Float trainSize;
+
+    @ApiModelProperty(value = "拆分数据前是否进行shuffle")
+    private Boolean shuffle;
+
+    @ApiModelProperty(value = "交叉验证的折数，当resamplingStrategy为crossValid时，此项必填，为整数")
+    private Integer folds;
+
+    @ApiModelProperty(value = "文件夹存放配置输出和日志文件，默认/tmp/automl")
+    private String tmpFolder;
+
+    @ApiModelProperty(value = "数据集csv文件中哪几列是预测目标列，逗号分隔")
+    private String targetColumns;
+
+    @ApiModelProperty(value = "自定义指标名称")
+    private String metricName;
+
+    @ApiModelProperty(value = "模型优化目标指标及权重，json格式。分类的指标包含：accuracy\n" +
+            "balanced_accuracy\n" +
+            "roc_auc\n" +
+            "average_precision\n" +
+            "log_loss\n" +
+            "precision_macro\n" +
+            "precision_micro\n" +
+            "precision_samples\n" +
+            "precision_weighted\n" +
+            "recall_macro\n" +
+            "recall_micro\n" +
+            "recall_samples\n" +
+            "recall_weighted\n" +
+            "f1_macro\n" +
+            "f1_micro\n" +
+            "f1_samples\n" +
+            "f1_weighted\n" +
+            "回归的指标包含：mean_absolute_error\n" +
+            "mean_squared_error\n" +
+            "root_mean_squared_error\n" +
+            "mean_squared_log_error\n" +
+            "median_absolute_error\n" +
+            "r2")
+    private String metrics;
+
+    @ApiModelProperty(value = "指标优化方向，是越大越好还是越小越好")
+    private Boolean greaterIsBetter;
+
+    @ApiModelProperty(value = "模型计算并打印指标")
+    private String scoringFunctions;
+
+    private Integer state;
+
+    private String  runState;
+
+    private Double progress;
+
+    private String createBy;
+
+    private Date createTime;
+
+    private String updateBy;
+
+    private Date updateTime;
+
+    /**
+     * 对应数据集
+     */
+    private Map<String,Object> dataset;
+}

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

@@ -2,23 +2,26 @@
 <!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" "http://mybatis.org/dtd/mybatis-3-mapper.dtd">
 <mapper namespace="com.ruoyi.platform.mapper.AutoMLDao">
     <insert id="save">
-        insert into auto_ml(ml_name, ml_description, task_type, dataset_name, time_left_for_this_task,
+        insert into auto_ml(ml_name, ml_description, task_type, dataset, time_left_for_this_task,
                             per_run_time_limit, ensemble_size, ensemble_class, ensemble_nbest, max_models_on_disc, seed,
                             memory_limit,
                             include_classifier, include_feature_preprocessor, include_regressor, exclude_classifier,
                             exclude_regressor, exclude_feature_preprocessor, test_size, resampling_strategy, train_size,
-                            shuffle, folds, data_csv, target_columns, metric_name, metrics,greater_is_better,scoring_functions,tmp_folder,
-                            create_by,update_by)
-        values (#{autoMl.mlName}, #{autoMl.mlDescription}, #{autoMl.taskType}, #{autoMl.datasetName},
+                            shuffle, folds, target_columns, metric_name, metrics, greater_is_better, scoring_functions,
+                            tmp_folder,
+                            create_by, update_by)
+        values (#{autoMl.mlName}, #{autoMl.mlDescription}, #{autoMl.taskType}, #{autoMl.dataset},
                 #{autoMl.timeLeftForThisTask}, #{autoMl.perRunTimeLimit},
                 #{autoMl.ensembleSize}, #{autoMl.ensembleClass}, #{autoMl.ensembleNbest},
                 #{autoMl.maxModelsOnDisc}, #{autoMl.seed},
                 #{autoMl.memoryLimit}, #{autoMl.includeClassifier}, #{autoMl.includeFeaturePreprocessor},
                 #{autoMl.includeRegressor}, #{autoMl.excludeClassifier},
-                #{autoMl.excludeRegressor}, #{autoMl.excludeFeaturePreprocessor}, #{autoMl.testSize}, #{autoMl.resamplingStrategy},
+                #{autoMl.excludeRegressor}, #{autoMl.excludeFeaturePreprocessor}, #{autoMl.testSize},
+                #{autoMl.resamplingStrategy},
                 #{autoMl.trainSize}, #{autoMl.shuffle},
-                #{autoMl.folds}, #{autoMl.dataCsv},
-                #{autoMl.targetColumns},  #{autoMl.metricName}, #{autoMl.metrics},#{autoMl.greaterIsBetter},#{autoMl.scoringFunctions},#{autoMl.tmpFolder},
+                #{autoMl.folds},
+                #{autoMl.targetColumns}, #{autoMl.metricName}, #{autoMl.metrics}, #{autoMl.greaterIsBetter},
+                #{autoMl.scoringFunctions}, #{autoMl.tmpFolder},
                 #{autoMl.createBy}, #{autoMl.updateBy})
     </insert>
 
@@ -40,8 +43,8 @@
             <if test="autoMl.taskType != null and autoMl.taskType !=''">
                 task_type = #{autoMl.taskType},
             </if>
-            <if test="autoMl.datasetName != null and autoMl.datasetName !=''">
-                dataset_name = #{autoMl.datasetName},
+            <if test="autoMl.dataset != null and autoMl.dataset !=''">
+                dataset = #{autoMl.dataset},
             </if>
             <if test="autoMl.timeLeftForThisTask != null">
                 time_left_for_this_task = #{autoMl.timeLeftForThisTask},
@@ -67,24 +70,13 @@
             <if test="autoMl.memoryLimit != null">
                 memory_limit = #{autoMl.memoryLimit},
             </if>
-            <if test="autoMl.includeClassifier != null and autoMl.includeClassifier !=''">
-                include_classifier = #{autoMl.includeClassifier},
-            </if>
-            <if test="autoMl.includeFeaturePreprocessor != null and autoMl.includeFeaturePreprocessor !=''">
-                include_feature_preprocessor = #{autoMl.includeFeaturePreprocessor},
-            </if>
-            <if test="autoMl.includeRegressor != null and autoMl.includeRegressor !=''">
-                include_regressor = #{autoMl.includeRegressor},
-            </if>
-            <if test="autoMl.excludeClassifier != null and autoMl.excludeClassifier !=''">
-                exclude_classifier = #{autoMl.excludeClassifier},
-            </if>
-            <if test="autoMl.excludeRegressor != null and autoMl.excludeRegressor !=''">
-                exclude_regressor = #{autoMl.excludeRegressor},
-            </if>
-            <if test="autoMl.excludeFeaturePreprocessor != null and autoMl.excludeFeaturePreprocessor !=''">
-                exclude_feature_preprocessor = #{autoMl.excludeFeaturePreprocessor},
-            </if>
+            include_classifier = #{autoMl.includeClassifier},
+            include_feature_preprocessor = #{autoMl.includeFeaturePreprocessor},
+            include_regressor = #{autoMl.includeRegressor},
+            exclude_classifier = #{autoMl.excludeClassifier},
+            exclude_regressor = #{autoMl.excludeRegressor},
+            exclude_feature_preprocessor = #{autoMl.excludeFeaturePreprocessor},
+            scoring_functions = #{autoMl.scoringFunctions},
             <if test="autoMl.testSize != null and autoMl.testSize !=''">
                 test_size = #{autoMl.testSize},
             </if>
@@ -100,9 +92,6 @@
             <if test="autoMl.folds != null">
                 folds = #{autoMl.folds},
             </if>
-            <if test="autoMl.dataCsv != null and autoMl.dataCsv !=''">
-                data_csv = #{autoMl.dataCsv},
-            </if>
             <if test="autoMl.tmpFolder != null and autoMl.tmpFolder !=''">
                 tmp_folder = #{autoMl.tmpFolder},
             </if>
@@ -112,12 +101,9 @@
             <if test="autoMl.metrics != null and autoMl.metrics !=''">
                 metrics = #{autoMl.metrics},
             </if>
-            <if test="autoMl.greater_is_better != null">
+            <if test="autoMl.greaterIsBetter != null">
                 greater_is_better = #{autoMl.greaterIsBetter},
             </if>
-            <if test="autoMl.scoringFunctions != null and autoMl.scoringFunctions !=''">
-                scoring_functions = #{autoMl.scoringFunctions},
-            </if>
             <if test="autoMl.targetColumns != null and autoMl.targetColumns !=''">
                 target_columns = #{autoMl.targetColumns},
             </if>