GetModelFeature - 获取模型特征详细信息

GET /api/v1/instances/{InstanceId}/modelfeatures/{ModelFeatureId}

{
  "RequestId": "0C89F5E1-7F24-5EEC-9F05-508A39278CC8",
  "ProjectId": "5",
  "ProjectName": "project1",
  "Name": "model_feature1",
  "Owner": "1231243253****",
  "GmtCreateTime": "2023-07-04T14:46:22.227+08:00",
  "GmtModifiedTime": "2023-07-04T14:46:22.227+08:00",
  "LabelTableId": "3",
  "LabelTableName": "label_table1",
  "TrainingSetTable": "table1",
  "TrainingSetFGTable": "table2",
  "Features": [
    {
      "FeatureViewId": "3",
      "FeatureViewName": "feature_view_1",
      "Name": "feature1",
      "Type": "INT32",
      "AliasName": "feature2"
    }
  ],
  "Relations": {
    "Domains": [
      {
        "Id": "3",
        "Name": "feature_entity_1",
        "DomainType": "FeatureEntity"
      }
    ],
    "Links": [
      {
        "From": "model_feature_2",
        "To": "feature_entity_3",
        "Link": "user_id"
      }
    ]
  },
  "ExportTrainingSetTableScript": "from feature_store_py.fs_client import FeatureStoreClient\\nfrom feature_store_py.fs_project import FeatureStoreProject\\nfrom feature_store_py.fs_datasource import LabelInput, MaxComputeDataSource, TrainingSetOutput\\nfrom feature_store_py.fs_features import FeatureSelector\\nfrom feature_store_py.fs_config import LabelInputConfig, PartitionConfig, FeatureViewConfig\\nfrom feature_store_py.fs_config import TrainSetOutputConfig, EASDeployConfig\\nimport datetime\\nimport sys\\n\\ncur_day = args['dt']\\nprint('cur_day = ', cur_day)\\noffset = datetime.timedelta(days=-1)\\npre_day = (datetime.datetime.strptime(cur_day, '%Y%m%d') + offset).strftime('%Y%m%d')\\nprint('pre_day = ', pre_day)\\n\\n\\naccess_key_id = o.account.access_id\\naccess_key_secret = o.account.secret_access_key\\nfs = FeatureStoreClient(access_key_id=access_key_id, access_key_secret=access_key_secret, region='cn-beijing')\\ncur_project_name = 'p1'\\nproject = fs.get_project(cur_project_name)\\n\\nlabel_partitions = PartitionConfig(name = 'ds', value = cur_day)\\nlabel_input_config = LabelInputConfig(partition_config=label_partitions)\\n\\nfeature_view_1_partitions = PartitionConfig(name = 'ds', value = pre_day)\\nfeature_view_1_config = FeatureViewConfig(name = 'user_fea',\\npartition_config=feature_view_1_partitions)\\n\\nfeature_view_2_partitions = PartitionConfig(name = 'ds', value = pre_day)\\nfeature_view_2_config = FeatureViewConfig(name = 'seq_fea',\\npartition_config=feature_view_2_partitions)\\n\\nfeature_view_3_partitions = PartitionConfig(name = 'ds', value = pre_day)\\nfeature_view_3_config = FeatureViewConfig(name = 'item_fea',\\npartition_config=feature_view_3_partitions)\\n\\nfeature_view_config_list = [feature_view_1_config,feature_view_2_config,feature_view_3_config]\\ntrain_set_partitions = PartitionConfig(name = 'ds', value = cur_day)\\ntrain_set_output_config = TrainSetOutputConfig(partition_config=train_set_partitions)\\n\\n\\nmodel_name = 'rank_v1'\\ncur_model = project.get_model(model_name)\\ntask = cur_model.export_train_set(label_input_config, feature_view_config_list, train_set_output_config)\\ntask.wait()\\nprint('task_summary = ', task.task_summary)\\n",
  "LabelPriorityLevel": 0
}