管理数据集

本页面介绍如何删除和获取与数据集相关的信息。

如需了解如何创建数据集并将数据导入其中,请参阅创建数据集并导入数据

准备工作

您必须先按照准备工作所述设置项目,然后才能使用 AutoML Tables。

列出数据集

一个项目可以包含许多数据集。本部分介绍如何检索项目的可用数据集列表。

控制台

要使用 AutoML Tables 界面查看可用数据集列表,请点击左侧导航菜单顶部的数据集链接,然后选择区域

REST 和命令行

要列出数据集,请使用 datasets.list 方法。

在使用任何请求数据之前,请先进行以下替换:

  • endpoint:全球位置为 automl.googleapis.com,欧盟地区为 eu-automl.googleapis.com
  • project-id:您的 Google Cloud 项目 ID。
  • location:资源的位置:全球位置为 us-central1,欧盟位置为 eu

HTTP 方法和网址:

GET https://endpoint/v1beta1/projects/project-id/locations/location/datasets

如需发送请求,请选择以下方式之一:

curl

执行以下命令:

curl -X GET \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
"https://endpoint/v1beta1/projects/project-id/locations/location/datasets"

PowerShell

执行以下命令:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https://endpoint/v1beta1/projects/project-id/locations/location/datasets" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应:

{
      "name": "projects/29434381/locations/us-central1/datasets/TBL75559",
      "displayName": "test_dataset",
      "createTime": "2019-03-21T00:50:20.660378Z",
      "updateTime": "2019-08-23T19:32:52.025469Z",
      "etag": "AB3BwFoV4USmhM3pT8c6Y5AIA6n51dAmSuObc=",
      "exampleCount": 94356,
      "tablesDatasetMetadata": {
        "primaryTableSpecId": "16930321664",
        "targetColumnSpecId": "46579780096",
        "areStatsFresh": true,
        "targetColumnCorrelations": {
          "6788648672679690240": {
            "cramersV": 0.16511808788616378
          },
          "87292427152392192": {
            "cramersV": 0.20327159375043746
          },
          "2393135436366086144": {
            "cramersV": 0.15513206308654948
          },
          "9094491681893384192": {
            "cramersV": 0.021499396246101456
          },
          "7004821454793474048": {
            "cramersV": 0.030097587339321379
          }
        },
        "statsUpdateTime": "2019-08-16T01:43:38.583Z",
        "tablesDatasetType": "BASIC"
      }
    },
...

Java

如果资源位于欧盟区域,您必须明确设置端点。了解详情

import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.Dataset;
import com.google.cloud.automl.v1beta1.ListDatasetsRequest;
import com.google.cloud.automl.v1beta1.LocationName;
import java.io.IOException;

class ListDatasets {

  static void listDatasets() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    listDatasets(projectId);
  }

  // List the datasets
  static void listDatasets(String projectId) throws IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");
      ListDatasetsRequest request =
          ListDatasetsRequest.newBuilder().setParent(projectLocation.toString()).build();

      // List all the datasets available in the region by applying filter.
      System.out.println("List of datasets:");
      for (Dataset dataset : client.listDatasets(request).iterateAll()) {
        // Display the dataset information
        System.out.format("%nDataset name: %s%n", dataset.getName());
        // To get the dataset id, you have to parse it out of the `name` field. As dataset Ids are
        // required for other methods.
        // Name Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
        String[] names = dataset.getName().split("/");
        String retrievedDatasetId = names[names.length - 1];
        System.out.format("Dataset id: %s%n", retrievedDatasetId);
        System.out.format("Dataset display name: %s%n", dataset.getDisplayName());
        System.out.println("Dataset create time:");
        System.out.format("\tseconds: %s%n", dataset.getCreateTime().getSeconds());
        System.out.format("\tnanos: %s%n", dataset.getCreateTime().getNanos());

        System.out.format("Tables dataset metadata: %s%n", dataset.getTablesDatasetMetadata());

      }
    }
  }
}

Node.js

如果资源位于欧盟区域,您必须明确设置端点。了解详情

const automl = require('@google-cloud/automl');
const util = require('util');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to list all datasets.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const filter = '[FILTER_EXPRESSIONS]' e.g., "tablesDatasetMetadata:*";

// A resource that represents Google Cloud Platform location.
const projectLocation = client.locationPath(projectId, computeRegion);

// List all the datasets available in the region by applying filter.
client
  .listDatasets({parent: projectLocation, filter: filter})
  .then(responses => {
    const dataset = responses[0];

    // Display the dataset information.
    console.log('List of datasets:');
    for (let i = 0; i < dataset.length; i++) {
      const tablesDatasetMetadata = dataset[i].tablesDatasetMetadata;

      console.log(`Dataset name: ${dataset[i].name}`);
      console.log(`Dataset Id: ${dataset[i].name.split('/').pop(-1)}`);
      console.log(`Dataset display name: ${dataset[i].displayName}`);
      console.log(`Dataset example count: ${dataset[i].exampleCount}`);
      console.log('Tables dataset metadata:');
      console.log(
        `\tTarget column correlations: ${util.inspect(
          tablesDatasetMetadata.targetColumnCorrelations,
          false,
          null
        )}`
      );
      console.log(
        `\tPrimary table spec Id: ${tablesDatasetMetadata.primaryTableSpecId}`
      );
      console.log(
        `\tTarget column spec Id: ${tablesDatasetMetadata.targetColumnSpecId}`
      );
      console.log(
        `\tWeight column spec Id: ${tablesDatasetMetadata.weightColumnSpecId}`
      );
      console.log(
        `\tMl use column spec Id: ${tablesDatasetMetadata.mlUseColumnSpecId}`
      );
    }
  })
  .catch(err => {
    console.error(err);
  });

Python

AutoML Tables 的客户端库包含其他 Python 方法,这些方法使用 AutoML Tables API 进行简化。这些方法按名称而不是 ID 来引用数据集和模型。您的数据集和模型的名称必须是唯一的。如需了解详情,请参阅客户端参考

如果资源位于欧盟区域,您必须明确设置端点。了解详情

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# filter = 'filter expression here'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# List all the datasets available in the region by applying filter.
response = client.list_datasets(filter=filter)

print("List of datasets:")
for dataset in response:
    # Display the dataset information.
    print("Dataset name: {}".format(dataset.name))
    print("Dataset id: {}".format(dataset.name.split("/")[-1]))
    print("Dataset display name: {}".format(dataset.display_name))
    metadata = dataset.tables_dataset_metadata
    print(
        "Dataset primary table spec id: {}".format(
            metadata.primary_table_spec_id
        )
    )
    print(
        "Dataset target column spec id: {}".format(
            metadata.target_column_spec_id
        )
    )
    print(
        "Dataset target column spec id: {}".format(
            metadata.target_column_spec_id
        )
    )
    print(
        "Dataset weight column spec id: {}".format(
            metadata.weight_column_spec_id
        )
    )
    print(
        "Dataset ml use column spec id: {}".format(
            metadata.ml_use_column_spec_id
        )
    )
    print("Dataset example count: {}".format(dataset.example_count))
    print("Dataset create time: {}".format(dataset.create_time))
    print("\n")

删除数据集

删除数据集会将数据集从您的项目中永久移除。此操作不会删除根据该数据集创建的任何模型。如果想要删除模型,则必须明确删除它们。

控制台

  1. AutoML Tables 界面中,点击左侧导航菜单顶部的数据集链接,然后选择区域以显示可用数据集的列表。

  2. 点击要删除的行最右侧的“更多操作”菜单,然后选择删除数据集

    AutoML Tables 架构页面

  3. 在确认对话框中点击确认

REST 和命令行

要删除数据集,请使用 datasets.delete 方法。

在使用任何请求数据之前,请先进行以下替换:

  • endpoint:全球位置为 automl.googleapis.com,欧盟地区为 eu-automl.googleapis.com
  • project-id:您的 Google Cloud 项目 ID。
  • location:资源的位置:全球位置为 us-central1,欧盟位置为 eu
  • dataset-id:要删除的数据集的 ID。例如,TBL6543

HTTP 方法和网址:

DELETE https://endpoint/v1beta1/projects/project-id/locations/location/datasets/dataset-id

如需发送请求,请选择以下方式之一:

curl

执行以下命令:

curl -X DELETE \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
"https://endpoint/v1beta1/projects/project-id/locations/location/datasets/dataset-id"

PowerShell

执行以下命令:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method DELETE `
-Headers $headers `
-Uri "https://endpoint/v1beta1/projects/project-id/locations/location/datasets/dataset-id" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/29452381/locations/us-central1/operations/TBL6543",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1beta1.OperationMetadata",
    "createTime": "2019-12-26T17:19:50.684850Z",
    "updateTime": "2019-12-26T17:19:50.684850Z",
    "deleteDetails": {},
    "worksOn": [
      "projects/29452381/locations/us-central1/datasets/TBL6543"
    ],
    "state": "DONE"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

删除数据集是一项长时间运行的操作。您可以轮询操作状态或等待操作返回。了解详情

Java

如果资源位于欧盟区域,您必须明确设置端点。了解详情

import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.DatasetName;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class DeleteDataset {

  static void deleteDataset() throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    deleteDataset(projectId, datasetId);
  }

  // Delete a dataset
  static void deleteDataset(String projectId, String datasetId)
      throws IOException, ExecutionException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the dataset.
      DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
      Empty response = client.deleteDatasetAsync(datasetFullId).get();
      System.out.format("Dataset deleted. %s%n", response);
    }
  }
}

Node.js

如果资源位于欧盟区域,您必须明确设置端点。了解详情

const automl = require('@google-cloud/automl');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to delete a dataset.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const datasetId = '[DATASET_ID]' e.g., "TBL2246891593778855936";

// Get the full path of the dataset.
const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);

// Delete a dataset.
client
  .deleteDataset({name: datasetFullId})
  .then(responses => {
    const operation = responses[0];
    return operation.promise();
  })
  .then(responses => {
    // The final result of the operation.
    const operationDetails = responses[2];

    // Get the dataset delete details.
    console.log('Dataset delete details:');
    console.log('\tOperation details:');
    console.log(`\t\tName: ${operationDetails.name}`);
    console.log(`\t\tDone: ${operationDetails.done}`);
  })
  .catch(err => {
    console.error(err);
  });

Python

AutoML Tables 的客户端库包含其他 Python 方法,这些方法使用 AutoML Tables API 进行简化。这些方法按名称而不是 ID 来引用数据集和模型。您的数据集和模型的名称必须是唯一的。如需了解详情,请参阅客户端参考

如果资源位于欧盟区域,您必须明确设置端点。了解详情

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# dataset_display_name = 'DATASET_DISPLAY_NAME_HERE

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# Delete a dataset.
response = client.delete_dataset(dataset_display_name=dataset_display_name)

# synchronous check of operation status.
print("Dataset deleted. {}".format(response.result()))

后续步骤