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GetModelFeature - 獲取模型特征詳細信息

獲取模型特征詳細信息。

調試

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授權信息

下表是API對應的授權信息,可以在RAM權限策略語句的Action元素中使用,用來給RAM用戶或RAM角色授予調用此API的權限。具體說明如下:

  • 操作:是指具體的權限點。
  • 訪問級別:是指每個操作的訪問級別,取值為寫入(Write)、讀取(Read)或列出(List)。
  • 資源類型:是指操作中支持授權的資源類型。具體說明如下:
    • 對于必選的資源類型,用背景高亮的方式表示。
    • 對于不支持資源級授權的操作,用全部資源表示。
  • 條件關鍵字:是指云產品自身定義的條件關鍵字。
  • 關聯操作:是指成功執行操作所需要的其他權限。操作者必須同時具備關聯操作的權限,操作才能成功。
操作訪問級別資源類型條件關鍵字關聯操作
featurestore:GetModelFeatureget
  • 全部資源
    *

請求語法

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

請求參數

名稱類型必填描述示例值
InstanceIdstring

實例 ID,可從接口 ListInstances 獲取。

fs-cn-********
ModelFeatureIdstring

模型特征 ID,可從接口 ListModelFeatures 獲取。

3

返回參數

名稱類型描述示例值
object

Schema of Response

RequestIdstring

請求 ID。

0C89F5E1-7F24-5EEC-9F05-508A39278CC8
ProjectIdstring

項目 ID。

5
ProjectNamestring

項目名稱。

project1
Namestring

模型特征名稱。

model_feature1
Ownerstring

創建人的阿里云賬號 ID。

1231243253****
GmtCreateTimestring

創建時間。

2023-07-04T14:46:22.227+08:00
GmtModifiedTimestring

更新時間。

2023-07-04T14:46:22.227+08:00
LabelTableIdstring

Label 表 ID。

3
LabelTableNamestring

Label 表名稱。

label_table1
TrainingSetTablestring

導出訓練集表的名稱。

table1
TrainingSetFGTablestring

導出訓練集 FG 表的名稱。

table2
Featuresarray<object>

特征列表。

object

特征。

FeatureViewIdstring

特征視圖 ID。

3
FeatureViewNamestring

特征視圖名稱。

feature_view_1
Namestring

特征名稱。

feature1
Typestring

特征類型。

● INT32

● INT64

● FLOAT

● DOUBLE

● STRING

● BOOLEAN

● TIMESTAMP

INT32
AliasNamestring

特征別名。

feature2
Relationsobject

特征關系。

Domainsarray<object>

Domain 列表。

object
Idstring

Domain ID。

3
Namestring

Domain 名稱。

feature_entity_1
DomainTypestring

Domain 類型。

● FeatureEntity-特征實體

● FeatureView-特征視圖

● ModelFeature-模型特征

FeatureEntity
object
Fromstring

連接頭 ID。

model_feature_2
Tostring

連接尾 ID。

feature_entity_3
ExportTrainingSetTableScriptstring

導出訓練樣本表腳本。

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
LabelPriorityLevellong

Label 表優先級,默認值為 0,設置為 1 表示 Label 表優先,設置為 2 表示 特征視圖優先。

0

示例

正常返回示例

JSON格式

{
  "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
}

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