训练和测试旨在检测洗钱活动的模型

在本指南中，您将了解如何训练和测试用于检测资金的模型 洗钱您将完成一些基本步骤来准备您的环境， 创建 AML AI 实例然后，您提供合成 交易数据（以 BigQuery 表）作为 AML AI 的输入。此输入用于训练和回测模型。

注册预测方后，该 API 会进行模型预测。通过 结果会用于分析 结构化资金。

准备工作

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. Install the Google Cloud CLI.

  3. To initialize the gcloud CLI, run the following command: 

    gcloud init

  4. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  5. Make sure that billing is enabled for your Google Cloud project.

  6. Enable the required APIs: 

    gcloud services enable financialservices.googleapis.com bigquery.googleapis.com cloudkms.googleapis.com bigquerydatatransfer.googleapis.com

  7. If you're using a local shell, then create local authentication credentials for your user account: 

    gcloud auth application-default login

    You don't need to do this if you're using Cloud Shell.

  8. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/financialservices.admin, roles/cloudkms.admin, roles/bigquery.admin 

    gcloud projects add-iam-policy-binding PROJECT_ID --member="USER_IDENTIFIER" --role=ROLE
    • Replace PROJECT_ID with your project ID.

    • Replace USER_IDENTIFIER with the identifier for your user account. For example, user:myemail@example.com.

    • Replace ROLE with each individual role.
    9.
  9. Install the Google Cloud CLI.

  10. To initialize the gcloud CLI, run the following command: 

    gcloud init

  11. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  12. Make sure that billing is enabled for your Google Cloud project.

  13. Enable the required APIs: 

    gcloud services enable financialservices.googleapis.com bigquery.googleapis.com cloudkms.googleapis.com bigquerydatatransfer.googleapis.com

  14. If you're using a local shell, then create local authentication credentials for your user account: 

    gcloud auth application-default login

    You don't need to do this if you're using Cloud Shell.

  15. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/financialservices.admin, roles/cloudkms.admin, roles/bigquery.admin 

    gcloud projects add-iam-policy-binding PROJECT_ID --member="USER_IDENTIFIER" --role=ROLE
    • Replace PROJECT_ID with your project ID.

    • Replace USER_IDENTIFIER with the identifier for your user account. For example, user:myemail@example.com.

    • Replace ROLE with each individual role.
    16.      这些角色具备以下必要权限：

    所需权限

    完成本快速入门需要以下权限：

    权限 说明
    cloudkms.keyRings.create创建 Cloud KMS 密钥环
    cloudkms.cryptoKeys.create创建 Cloud KMS 密钥
    financialservices.v1instances.create创建 AML AI 实例
    financialservices.operations.get获取 AML AI 操作
    cloudkms.cryptoKeys.getIamPolicy获取 Cloud KMS 密钥的 IAM 政策
    cloudkms.cryptoKeys.setIamPolicy为 Cloud KMS 密钥设置 IAM 政策
    bigquery.datasets.create创建 BigQuery 数据集
    bigquery.datasets.get获取 BigQuery 数据集
    bigquery.transfers.get获取 BigQuery Data Transfer Service 转移作业
    bigquery.transfers.update创建或删除 BigQuery Data Transfer Service 转移作业
    bigquery.datasets.setIamPolicy为 BigQuery 数据集设置 IAM 政策
    bigquery.datasets.update更新 BigQuery 数据集
    financialservices.v1datasets.create创建 AML AI 数据集
    financialservices.v1engineconfigs.create创建 AML AI 引擎配置
    financialservices.v1models.create创建 AML AI 模型
    financialservices.v1backtests.create创建 AML AI 回测结果
    financialservices.v1backtests.exportMetadata从 AML AI 回测结果中导出元数据
    financialservices.v1instances.importRegisteredParties将注册方导入 AML AI 实例
    financialservices.v1predictions.create创建 AML AI 预测结果
    bigquery.jobs.create创建 BigQuery 作业
    bigquery.tables.getData从 BigQuery 表中获取数据
    financialservices.v1predictions.delete删除 AML AI 预测结果
    financialservices.v1backtests.delete删除 AML AI 回测结果
    financialservices.v1models.delete删除 AML AI 模型
    financialservices.v1engineconfigs.delete删除 AML AI 引擎配置
    financialservices.v1datasets.delete删除 AML AI 数据集
    financialservices.v1instances.delete删除 AML AI 实例
    bigquery.datasets.delete删除 BigQuery 数据集

  16. 本指南中的 API 请求使用相同的 Google Cloud 项目、位置和硬编码资源 ID，以便您更轻松地完成本指南。资源 ID 遵循 my-resource-type 格式（例如 my-key-ring 和 my-model）。

    请确保为本指南定义了以下替换内容：

    • PROJECT_ID：您的 Google Cloud 项目 ID IAM 设置
    • PROJECT_NUMBER: 与 PROJECT_ID。您 您可在 IAM 设置页面。
    • LOCATION：API 资源的位置；请使用某个受支持的地区
      显示位置
      • us-central1
      • us-east1
      • asia-south1
      • europe-west1
      • europe-west2
      • europe-west4
      • northamerica-northeast1
      • southamerica-east1

创建实例

本部分介绍如何创建实例。AML AI 实例位于所有其他 AML AI 资源的根目录下。每个 实例需要单个关联的客户管理的加密密钥 (CMEK) 用于对由 AML AI 创建的任何数据进行加密。

创建密钥环

如需创建密钥环，请使用 projects.locations.keyRings.create 方法。

REST

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d "" \
     "https://cloudkms.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/keyRings?key_ring_id=my-key-ring"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -Uri "https://cloudkms.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/keyRings?key_ring_id=my-key-ring" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring",
  "createTime": CREATE_TIME
}

gcloud

执行以下命令：

Linux、macOS 或 Cloud Shell

gcloud kms keyrings create my-key-ring \
  --location LOCATION

Windows (PowerShell)

gcloud kms keyrings create my-key-ring `
  --location LOCATION

Windows (cmd.exe)

gcloud kms keyrings create my-key-ring ^
  --location LOCATION
 您应该会收到空响应： 
$

创建密钥

如需创建密钥，请使用 projects.locations.keyRings.cryptoKeys 方法。

REST

请求 JSON 正文： 

{
  "purpose": "ENCRYPT_DECRYPT"
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
  "purpose": "ENCRYPT_DECRYPT"
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://cloudkms.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys?crypto_key_id=my-key"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
  "purpose": "ENCRYPT_DECRYPT"
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://cloudkms.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys?crypto_key_id=my-key" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys/my-key",
  "primary": {
    "name": "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys/my-key/cryptoKeyVersions/1",
    "state": "ENABLED",
    "createTime": CREATE_TIME,
    "protectionLevel": "SOFTWARE",
    "algorithm": "GOOGLE_SYMMETRIC_ENCRYPTION",
    "generateTime": GENERATE_TIME
  },
  "purpose": "ENCRYPT_DECRYPT",
  "createTime": CREATE_TIME,
  "versionTemplate": {
    "protectionLevel": "SOFTWARE",
    "algorithm": "GOOGLE_SYMMETRIC_ENCRYPTION"
  },
  "destroyScheduledDuration": "86400s"
}

gcloud

在使用下面的命令数据之前，请先进行以下替换：

  • LOCATION：密钥环的位置；请使用某个受支持的地区
    显示位置
    • us-central1
    • us-east1
    • asia-south1
    • europe-west1
    • europe-west2
    • europe-west4
    • northamerica-northeast1
    • southamerica-east1

执行以下命令：

Linux、macOS 或 Cloud Shell

gcloud kms keys create my-key \
  --keyring my-key-ring \
  --location LOCATION \
  --purpose "encryption"

Windows (PowerShell)

gcloud kms keys create my-key `
  --keyring my-key-ring `
  --location LOCATION `
  --purpose "encryption"

Windows (cmd.exe)

gcloud kms keys create my-key ^
  --keyring my-key-ring ^
  --location LOCATION ^
  --purpose "encryption"
 您应该会收到一个空响应： 
$

使用 API 创建实例

如需创建实例，请使用 projects.locations.instances.create 方法。

请求 JSON 正文： 

{
  "kmsKey": "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys/my-key"
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
  "kmsKey": "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys/my-key"
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances?instance_id=my-instance"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
  "kmsKey": "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys/my-key"
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances?instance_id=my-instance" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance",
    "verb": "create",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

如果成功，响应正文将包含一个长时间运行的操作，其中包含一个 ID，该 ID 可用于检索异步操作的持续状态。复制返回的 OPERATION_ID（在接下来的）中使用 部分。

检查结果

使用 projects.locations.operations.get 方法检查实例是否已创建。如果响应包含 "done": false，请重复该命令，直到响应包含 "done": true。

本指南中的操作可能需要几分钟到几小时才能完成。 您必须等到操作完成，然后才能继续阅读本指南，因为该 API 会将某些方法的输出用作其他方法的输入。

在使用任何请求数据之前，请先进行以下替换：

  • OPERATION_ID：操作的标识符

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 print-access-token)" \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "endTime": END_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance",
    "verb": "create",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.Instance",
    "name": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance",
    "createTime": CREATE_TIME,
    "updateTime": UPDATE_TIME,
    "kmsKey": "projects/KMS_PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys/my-key",
    "state": "ACTIVE"
  }
}

授予对 CMEK 密钥的访问权限

该 API 会自动在您的项目中创建一个服务账号。服务账号需要访问 CMEK 密钥，才能使用该密钥加密和解密底层数据。授予对密钥的访问权限。

gcloud kms keys add-iam-policy-binding "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring/cryptoKeys/my-key" \
  --keyring "projects/PROJECT_ID/locations/LOCATION/keyRings/my-key-ring" \
  --location "LOCATION" \
  --member "serviceAccount:service-PROJECT_NUMBER@gcp-sa-financialservices.iam.gserviceaccount.com" \
  --role="roles/cloudkms.cryptoKeyEncrypterDecrypter" \
  --project="PROJECT_ID"

创建 BigQuery 数据集

本部分介绍了如何创建输入和输出 BigQuery 数据集，然后将银行数据示例复制到输入数据集。

创建输出数据集

创建数据集，用于将 AML 流水线输出发送到该数据集。

Bash

bq mk \
  --location=LOCATION \
  --project_id=PROJECT_ID \
  my_bq_output_dataset

PowerShell

bq mk `
  --location=LOCATION `
  --project_id=PROJECT_ID `
  my_bq_output_dataset

创建输入数据集

创建一个数据集，以将示例银行表复制到其中。

Bash

bq mk \
  --location=LOCATION \
  --project_id=PROJECT_ID \
  my_bq_input_dataset

PowerShell

bq mk `
  --location=LOCATION `
  --project_id=PROJECT_ID `
  my_bq_input_dataset

复制示例数据集

银行数据示例以 BigQuery 数据集的形式提供在 Google 的共享数据集项目中。您必须拥有访问权限 AML AI API 以使此数据集可供访问。此数据集的主要特点包括：

  • 10 万个相关方
  • 核心时间范围为 2020 年 1 月 1 日至 2023 年 1 月 1 日； 额外 24 个月的回溯期数据
  • 每月 300 个负风险案例和 20 个正风险案例
  • 具有以下属性的风险案例：
    • 一半的正风险案例都与设计活动有关， 发生在“AML_PROCESS_START”事件之前的两个月内
    • 另一半则涵盖在 AML_PROCESS_START 事件发生前两个月内收到金额最多的相关方
    • 负例是随机生成的
    • 另一方面，生成风险案例的可能性为 0.1% （例如，某个为正数的随机方，或 结构化活动或收入最高，并且报告为负）
  • AML 架构在 AML 输入数据模型中定义。

  1. 将银行示例数据复制到您创建的输入数据集中。

    Bash

    bq mk --transfer_config \
  --project_id=PROJECT_ID \
  --data_source=cross_region_copy \
  --target_dataset="my_bq_input_dataset" \
  --display_name="Copy the AML sample dataset." \
  --schedule=None \
  --params='{
    "source_project_id":"bigquery-public-data",
    "source_dataset_id":"aml_ai_input_dataset",
    "overwrite_destination_table":"true"
  }'

    PowerShell

    bq mk --transfer_config `
--project_id=PROJECT_ID `
--data_source=cross_region_copy `
--target_dataset="my_bq_input_dataset" `
--display_name="Copy the AML sample dataset." `
--schedule=None `
--params='{\"source_project_id\":\"bigquery-public-data\",\"source_dataset_id\":\"aml_ai_input_dataset\",\"overwrite_destination_table\":\"true\"}'

  2. 监控数据传输作业。

    Bash

    bq ls --transfer_config \
--transfer_location=LOCATION \
--project_id=PROJECT_ID \
--filter="dataSourceIds:cross_region_copy"

    PowerShell

    bq ls --transfer_config `
--transfer_location=LOCATION `
--project_id=PROJECT_ID `
--filter="dataSourceIds:cross_region_copy"

    转移完成后，系统会创建一个显示名称为 Copy the AML sample dataset 的数据传输作业。

    您还可以查看 转移状态 使用 Google Cloud 控制台

    您应该会看到类似以下输出的内容。

                         name                           displayName         dataSourceId       state
-------------------------------------------  -----------------------  -----------------  ---------
projects/294024168771/locations/us-central1  Copy AML sample dataset  cross_region_copy  SUCCEEDED

授予对 BigQuery 数据集的访问权限

该 API 会自动在您的项目中创建一个服务账号。服务 账号需要访问 BigQuery 输入和输出数据集。

  1. 授予对输入数据集及其表的读取权限。

    Bash

    bq query --project_id=PROJECT_ID --use_legacy_sql=false \
  'GRANT `roles/bigquery.dataViewer` ON SCHEMA `PROJECT_ID.my_bq_input_dataset` TO "serviceAccount:service-PROJECT_NUMBER@gcp-sa-financialservices.iam.gserviceaccount.com"'

    PowerShell

    bq query --project_id=PROJECT_ID --use_legacy_sql=false "GRANT ``roles/bigquery.dataViewer`` ON SCHEMA ``PROJECT_ID.my_bq_input_dataset`` TO 'serviceAccount:service-PROJECT_NUMBER@gcp-sa-financialservices.iam.gserviceaccount.com'"

  2. 授予对输出数据集的写入权限。

    Bash

    bq query --project_id=PROJECT_ID --use_legacy_sql=false \
  'GRANT `roles/bigquery.dataEditor` ON SCHEMA `PROJECT_ID.my_bq_output_dataset` TO "serviceAccount:service-PROJECT_NUMBER@gcp-sa-financialservices.iam.gserviceaccount.com"'

    PowerShell

    bq query --project_id=PROJECT_ID --use_legacy_sql=false "GRANT ``roles/bigquery.dataEditor`` ON SCHEMA ``PROJECT_ID.my_bq_output_dataset`` TO 'serviceAccount:service-PROJECT_NUMBER@gcp-sa-financialservices.iam.gserviceaccount.com'"

创建 AML AI 数据集

创建 AML AI 数据集以指定输入 要使用的 BigQuery 数据集表和时间范围。

如需创建数据集，请使用 projects.locations.instances.datasets.create 方法。

请求 JSON 正文： 

{
  "tableSpecs": {
    "party": "bq://PROJECT_ID.my_bq_input_dataset.party",
    "account_party_link": "bq://PROJECT_ID.my_bq_input_dataset.account_party_link",
    "transaction": "bq://PROJECT_ID.my_bq_input_dataset.transaction",
    "risk_case_event": "bq://PROJECT_ID.my_bq_input_dataset.risk_case_event",
    "party_supplementary_data": "bq://PROJECT_ID.my_bq_input_dataset.party_supplementary_data"
  },
  "dateRange": {
    "startTime": "2020-01-01T00:00:0.00Z",
    "endTime": "2023-01-01T00:00:0.00Z"
  },
  "timeZone": {
    "id": "UTC"
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
  "tableSpecs": {
    "party": "bq://PROJECT_ID.my_bq_input_dataset.party",
    "account_party_link": "bq://PROJECT_ID.my_bq_input_dataset.account_party_link",
    "transaction": "bq://PROJECT_ID.my_bq_input_dataset.transaction",
    "risk_case_event": "bq://PROJECT_ID.my_bq_input_dataset.risk_case_event",
    "party_supplementary_data": "bq://PROJECT_ID.my_bq_input_dataset.party_supplementary_data"
  },
  "dateRange": {
    "startTime": "2020-01-01T00:00:0.00Z",
    "endTime": "2023-01-01T00:00:0.00Z"
  },
  "timeZone": {
    "id": "UTC"
  }
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets?dataset_id=my-dataset"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
  "tableSpecs": {
    "party": "bq://PROJECT_ID.my_bq_input_dataset.party",
    "account_party_link": "bq://PROJECT_ID.my_bq_input_dataset.account_party_link",
    "transaction": "bq://PROJECT_ID.my_bq_input_dataset.transaction",
    "risk_case_event": "bq://PROJECT_ID.my_bq_input_dataset.risk_case_event",
    "party_supplementary_data": "bq://PROJECT_ID.my_bq_input_dataset.party_supplementary_data"
  },
  "dateRange": {
    "startTime": "2020-01-01T00:00:0.00Z",
    "endTime": "2023-01-01T00:00:0.00Z"
  },
  "timeZone": {
    "id": "UTC"
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets?dataset_id=my-dataset" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "verb": "create",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

您可以使用新的操作 ID 检查操作结果。（您可以对本指南中使用的其余 API 请求执行此操作。）

创建引擎配置

创建 AML AI 引擎配置以进行自动调整 根据给定引擎版本和所提供的数据调整超参数。 引擎版本会定期发布，并对应于不同的模型逻辑（例如，定位到零售业务领域，而不是商业业务领域）。

如需创建引擎配置，请使用 projects.locations.instances.engineConfigs.create 方法。

此阶段涉及超参数调优，可能需要一些时间才能处理完毕。如果您的数据未发生重大变化，此步骤可用于 创建和测试许多模型。

请求 JSON 正文： 

{
  "engineVersion": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineVersions/aml-commercial.default.v004.000.202312-000",
  "tuning": {
    "primaryDataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2021-07-01T00:00:00Z"
  },
  "performanceTarget": {
    "partyInvestigationsPerPeriodHint": "30"
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
  "engineVersion": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineVersions/aml-commercial.default.v004.000.202312-000",
  "tuning": {
    "primaryDataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2021-07-01T00:00:00Z"
  },
  "performanceTarget": {
    "partyInvestigationsPerPeriodHint": "30"
  }
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs?engine_config_id=my-engine-config"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
  "engineVersion": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineVersions/aml-commercial.default.v004.000.202312-000",
  "tuning": {
    "primaryDataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2021-07-01T00:00:00Z"
  },
  "performanceTarget": {
    "partyInvestigationsPerPeriodHint": "30"
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs?engine_config_id=my-engine-config" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs/my-engine-config",
    "verb": "create",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

创建模型

在此步骤中，您将使用 12 个月的 截至 2021 年 7 月 1 日的数据。

如需创建模型，请使用 projects.locations.instances.models.create 方法。

请求 JSON 正文： 

{
    "engineConfig": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs/my-engine-config",
    "primaryDataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2021-07-01T00:00:00Z"
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
    "engineConfig": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs/my-engine-config",
    "primaryDataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2021-07-01T00:00:00Z"
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models?model_id=my-model"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
    "engineConfig": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs/my-engine-config",
    "primaryDataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2021-07-01T00:00:00Z"
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models?model_id=my-model" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "verb": "create",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

创建回测结果

回测预测使用基于现有历史数据训练好的模型。针对 2023 年 1 月之前的 12 个月数据（未用于训练）创建回测结果。这些月份用于确定，如果我们在 2022 年 1 月至 12 月期间在生产环境中使用了截至 2021 年 7 月训练的模型，可能需要处理多少个案例。

如需创建回测结果，请使用 projects.locations.instances.backtestResults.create 方法。

请求 JSON 正文： 

{
    "model": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "dataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2023-01-01T00:00:00Z",
    "backtestPeriods": 12,
    "performanceTarget": {
      "partyInvestigationsPerPeriodHint": "150"
    }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
    "model": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "dataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2023-01-01T00:00:00Z",
    "backtestPeriods": 12,
    "performanceTarget": {
      "partyInvestigationsPerPeriodHint": "150"
    }
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults?backtest_result_id=my-backtest-results"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
    "model": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "dataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2023-01-01T00:00:00Z",
    "backtestPeriods": 12,
    "performanceTarget": {
      "partyInvestigationsPerPeriodHint": "150"
    }
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults?backtest_result_id=my-backtest-results" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults/my-backtest-results",
    "verb": "create",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

导出回测结果元数据

回测运行完毕后，您需要将其结果导出到 BigQuery 查看它们。从回测中导出元数据 结果，请使用 projects.locations.instances.backtestResults.exportMetadata 方法。

请求 JSON 正文： 

{
  "structuredMetadataDestination": {
    "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_backtest_results_metadata",
    "writeDisposition": "WRITE_TRUNCATE"
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
  "structuredMetadataDestination": {
    "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_backtest_results_metadata",
    "writeDisposition": "WRITE_TRUNCATE"
  }
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults/my-backtest-results:exportMetadata"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
  "structuredMetadataDestination": {
    "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_backtest_results_metadata",
    "writeDisposition": "WRITE_TRUNCATE"
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults/my-backtest-results:exportMetadata" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults/my-backtest-results",
    "verb": "exportMetadata",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

操作完成后，请执行以下操作：

  1. 在 Google Cloud 控制台中打开 BigQuery。

    前往 Google Cloud 控制台

  2. 在探索器窗格中，找到并展开您的项目。

  3. 展开 my_bq_output_dataset 并点击 my_backtest_results_metadata。

  4. 在菜单栏中，点击预览。

  5. 在 name 列中，找到包含 ObservedRecallValues 的行。

    BigQuery 中观察到的召回率值。

  6. 假设您每月的调查容量为 120 项。使用 "partyInvestigationsPerPeriod": "120"` 查找 Recall 值对象。对于以下示例值，如果您将调查范围限制为风险得分高于 0.53 的方，则预计每个月会调查 120 个新方。在回测期（2022 年）内，您将发现之前系统发现的 86% 的案例（以及当前流程未发现的其他案例）。

    {
  "recallValues": [
    ...
    {
      "partyInvestigationsPerPeriod": "105",
      "recallValue": 0.8142077,
      "scoreThreshold": 0.6071321
    },
    {
      "partyInvestigationsPerPeriod": "120",
      "recallValue": 0.863388,
      "scoreThreshold": 0.5339603
    },
    {
      "partyInvestigationsPerPeriod": "135",
      "recallValue": 0.89071035,
      "scoreThreshold": 0.4739899
    },
    ...
  ]
}

如需详细了解其他字段，请参阅 回测结果。

通过更改 partyInvestigationsPerPeriodHint 字段，您可以修改 回测次数。获取得分以便调查、注册方和针对方生成预测。

导入已注册的相关方

在创建预测结果之前，您需要导入已注册的相关方（即数据集中的客户）。

要导入注册方，请使用 projects.locations.instances.importRegisteredParties 方法。

请求 JSON 正文： 

{
  "partyTables": [
     "bq://PROJECT_ID.my_bq_input_dataset.party_registration"
  ],
  "mode": "REPLACE",
  "lineOfBusiness": "COMMERCIAL"
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
  "partyTables": [
     "bq://PROJECT_ID.my_bq_input_dataset.party_registration"
  ],
  "mode": "REPLACE",
  "lineOfBusiness": "COMMERCIAL"
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance:importRegisteredParties"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
  "partyTables": [
     "bq://PROJECT_ID.my_bq_input_dataset.party_registration"
  ],
  "mode": "REPLACE",
  "lineOfBusiness": "COMMERCIAL"
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance:importRegisteredParties" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance",
    "verb": "importRegisteredParties",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

请持续检查该操作的结果，直到该操作完成。完成后，您应该会在 JSON 输出中看到 10,000 个注册的方。

创建预测结果

在数据集中创建过去 12 个月的预测结果；这些月 未使用过的模型。创建预测结果会为 所有预测期的各时段的数据。

要创建预测结果，请使用 projects.locations.instances.predictionResults.create 方法。

请求 JSON 正文： 

{
    "model": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "dataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2023-01-01T00:00:00Z",
    "predictionPeriods": "12",
    "outputs": {
      "predictionDestination": {
        "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_prediction_results",
        "writeDisposition": "WRITE_TRUNCATE"
      },
      "explainabilityDestination": {
        "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_prediction_results_explainability",
        "writeDisposition": "WRITE_TRUNCATE"
      }
    }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

cat > request.json << 'EOF'
{
    "model": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "dataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2023-01-01T00:00:00Z",
    "predictionPeriods": "12",
    "outputs": {
      "predictionDestination": {
        "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_prediction_results",
        "writeDisposition": "WRITE_TRUNCATE"
      },
      "explainabilityDestination": {
        "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_prediction_results_explainability",
        "writeDisposition": "WRITE_TRUNCATE"
      }
    }
}
EOF

然后，执行以下命令以发送 REST 请求：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/predictionResults?prediction_result_id=my-prediction-results"

PowerShell

将请求正文保存在名为 request.json 的文件中。在终端中运行以下命令，在当前目录中创建或覆盖此文件：

@'
{
    "model": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "dataset": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "endTime": "2023-01-01T00:00:00Z",
    "predictionPeriods": "12",
    "outputs": {
      "predictionDestination": {
        "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_prediction_results",
        "writeDisposition": "WRITE_TRUNCATE"
      },
      "explainabilityDestination": {
        "tableUri": "bq://PROJECT_ID.my_bq_output_dataset.my_prediction_results_explainability",
        "writeDisposition": "WRITE_TRUNCATE"
      }
    }
}
'@  | Out-File -FilePath request.json -Encoding utf8

然后，执行以下命令以发送 REST 请求：

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

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/predictionResults?prediction_result_id=my-prediction-results" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/predictionResults/my-prediction-results",
    "verb": "create",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

在 Google Cloud 控制台中分析单个结构设计案例

  1. 在 Google Cloud 控制台中打开 BigQuery。

    开始 Google Cloud 控制台

  2. 在详细信息窗格中，点击 Untitled Query 标签页以查看编辑器。

  3. 将以下 SQL 语句复制到编辑器中，然后点击运行。

    SELECT *
FROM `PROJECT_ID.my_bq_input_dataset.transaction`
WHERE account_id = '1E60OAUNKP84WDKB' AND DATE_TRUNC(book_time, MONTH) = "2022-08-01"
ORDER by book_time

    此语句会检查账号 ID 1E60OAUNKP84WDKB 在 2022 年 8 月的状态。此账号已与相关方 ID EGS4NJD38JZ8NTL8 相关联。您可以使用 AccountPartyLink 表查找给定账号 ID 的相关方 ID。

    交易数据会显示针对 这些交易可能表明存在欺诈行为（即将一笔大额资金交易拆分为金额较小的多笔交易）或结构化交易。

    单个各方的可疑交易数据。

  4. 将以下 SQL 语句复制到编辑器中，然后点击运行。

    SELECT *
FROM `PROJECT_ID.my_bq_input_dataset.risk_case_event`
WHERE party_id = 'EGS4NJD38JZ8NTL8'

    此声明表明，存在导致此方退出的情况。风险案例在可疑活动两个月后开始。

    单个相关方的风险信号事件。

  5. 将以下 SQL 语句复制到编辑器中，然后点击运行。

    SELECT *
FROM `PROJECT_ID.my_bq_output_dataset.my_prediction_results`
WHERE party_id = 'EGS4NJD38JZ8NTL8'
ORDER BY risk_period_end_time

    查看预测结果后，您会发现该方在出现可疑活动后的几个月内，风险信号从几乎为零（请注意指数值）跃升到较高值。您的结果可能与所示结果不同。

    单个方的风险评分增加。

    风险评分不是概率。风险评分始终应与其他风险评分相对比进行评估。例如，如果其他风险信号较低，一个看似较小的值也可以被视为正例。

  6. 将以下 SQL 语句复制到编辑器中，然后点击运行。

    SELECT *
FROM `PROJECT_ID.my_bq_output_dataset.my_prediction_results_explainability`
WHERE party_id = 'EGS4NJD38JZ8NTL8'
AND risk_period_end_time = '2022-10-01'

    通过查看可解释性结果，您可以看到正确的特征族得分最高。

    预测的可解释性结果。

清理

为避免因本页面中使用的资源导致您的 Google Cloud 账号产生费用，请删除包含这些资源的 Google Cloud 项目。

删除预测结果

如需删除预测结果，请使用 projects.locations.instances.predictionResults.delete 方法。

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 print-access-token)" \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/predictionResults/my-prediction-results"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/predictionResults/my-prediction-results" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/predictionResults/my-prediction-results",
    "verb": "delete",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

删除回测结果

要删除回测结果，请使用 projects.locations.instances.backtestResults.delete 方法。

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 print-access-token)" \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults/my-backtest-results"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults/my-backtest-results" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/backtestResults/my-backtest-results",
    "verb": "delete",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

删除模型

如需删除模型，请使用 projects.locations.instances.models.delete 方法。

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 print-access-token)" \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/models/my-model",
    "verb": "delete",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

删除引擎配置

如需删除引擎配置，请使用 projects.locations.instances.engineConfigs.delete 方法。

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 print-access-token)" \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs/my-engine-config"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs/my-engine-config" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/engineConfigs/my-engine-config",
    "verb": "delete",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

删除数据集

如要删除数据集，请使用 projects.locations.instances.datasets.delete 方法。

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 print-access-token)" \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance/datasets/my-dataset",
    "verb": "delete",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

删除实例

如需删除实例，请使用 projects.locations.instances.delete 方法。

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 print-access-token)" \
     "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://financialservices.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/instances/my-instance" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.financialservices.v1.OperationMetadata",
    "createTime": CREATE_TIME,
    "target": "projects/PROJECT_ID/locations/LOCATION/instances/my-instance",
    "verb": "delete",
    "requestedCancellation": false,
    "apiVersion": "v1"
  },
  "done": false
}

删除 BigQuery 数据集

bq rm -r -f -d PROJECT_ID:my_bq_input_dataset

bq rm -r -f -d PROJECT_ID:my_bq_output_dataset

删除转移作业配置

  1. 列出项目中的转移作业。

    Bash

    bq ls --transfer_config \
  --transfer_location=LOCATION \
  --project_id=PROJECT_ID  \
  --filter="dataSourceIds:cross_region_copy"

    PowerShell

    bq ls --transfer_config `
  --transfer_location=LOCATION `
  --project_id=PROJECT_ID `
  --filter="dataSourceIds:cross_region_copy"

  2. 系统应返回类似如下所示的输出。

    name                                                                                       displayName                    dataSourceId       state
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
projects/PROJECT_NUMBER/locations/LOCATION/transferConfigs/TRANSFER_CONFIG_ID    Copy the AML sample dataset.   cross_region_copy   SUCCEEDED

    复制整个名称，从 projects/ 开始，到 TRANSFER_CONFIG_ID。

  3. 删除转移配置。

    Bash

    bq rm --transfer_config TRANSFER_CONFIG_NAME

    PowerShell

    bq rm --transfer_config TRANSFER_CONFIG_NAME

后续步骤