建立批次推論工作 (AWS SDK)

完成之後準備批次建議的輸入資料，您就可以使用該作業建立批次推論工CreateBatchInferenceJob作。

建立批次推論工作

您可以使用下列程式碼建立批次推論工作。指定工作名稱、解決方案版本的 Amazon 資源名稱 (ARN)，以及您在設定期間為 Amazon Personalize 建立的 IAM 服務角色的 ARN。此角色必須具有對您的輸入和輸出 Amazon S3 儲存貯體的讀取和寫入存取權。

我們建議您為輸出資料使用不同的位置 (資料夾或不同的 Amazon S3 儲存貯體)。輸入和輸出位置使用下列語法：s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json和s3://<name of your S3 bucket>/<output folder name>/。

針對numResults，指定您希望 Amazon Personalize 針對每一行輸入資料預測的項目數。選擇性地提供篩選器 ARN 以篩選建議。如果您的篩選器使用預留位置參數，請確定參數的值包含在輸入 JSON 中。如需更多資訊，請參閱 篩選批次建議和使用者區段 (自訂資源)。

SDK for Python (Boto3)

此範例包含選用的使用者個人化配方特定itemExplorationConfig超參數：explorationWeight和。explorationItemAgeCutOff可選擇包括explorationWeight和explorationItemAgeCutOff值以配置探索。如需詳細資訊，請參閱 用戶個性化配方。

import boto3

personalize_rec = boto3.client(service_name='personalize')

personalize_rec.create_batch_inference_job (
    solutionVersionArn = "Solution version ARN",
    jobName = "Batch job name",
    roleArn = "IAM service role ARN",
    filterArn = "Filter ARN",
    batchInferenceJobConfig = {
        # optional USER_PERSONALIZATION recipe hyperparameters
        "itemExplorationConfig": {      
            "explorationWeight": "0.3",
            "explorationItemAgeCutOff": "30"
        }
    },
    jobInput = 
       {"s3DataSource": {"path": "s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json"}},
    jobOutput = 
       {"s3DataDestination": {"path": "s3://<name of your S3 bucket>/<output folder name>/"}}
)

SDK for Java 2.x

此範例包含選用的使用者個人化配方特定itemExplorationConfig欄位：explorationWeight和explorationItemAgeCutOff。可選擇包括explorationWeight和explorationItemAgeCutOff值以配置探索。如需詳細資訊，請參閱 用戶個性化配方。

public static String createPersonalizeBatchInferenceJob(PersonalizeClient personalizeClient,
                                                        String solutionVersionArn,
                                                        String jobName,
                                                        String filterArn,
                                                        String s3InputDataSourcePath,
                                                        String s3DataDestinationPath,
                                                        String roleArn,
                                                        String explorationWeight,
                                                        String explorationItemAgeCutOff) {

  long waitInMilliseconds = 60 * 1000;
  String status;
  String batchInferenceJobArn;

  try {
      // Set up data input and output parameters.
      S3DataConfig inputSource = S3DataConfig.builder()
              .path(s3InputDataSourcePath)
              .build();
      S3DataConfig outputDestination = S3DataConfig.builder()
              .path(s3DataDestinationPath)
              .build();

      BatchInferenceJobInput jobInput = BatchInferenceJobInput.builder()
              .s3DataSource(inputSource)
              .build();
      BatchInferenceJobOutput jobOutputLocation = BatchInferenceJobOutput.builder()
              .s3DataDestination(outputDestination)
              .build();

      // Optional code to build the User-Personalization specific item exploration config.
      HashMap<String, String> explorationConfig = new HashMap<>();

      explorationConfig.put("explorationWeight", explorationWeight);
      explorationConfig.put("explorationItemAgeCutOff", explorationItemAgeCutOff);

      BatchInferenceJobConfig jobConfig = BatchInferenceJobConfig.builder()
              .itemExplorationConfig(explorationConfig)
              .build();
      // End optional User-Personalization recipe specific code.

      CreateBatchInferenceJobRequest createBatchInferenceJobRequest = CreateBatchInferenceJobRequest.builder()
              .solutionVersionArn(solutionVersionArn)
              .jobInput(jobInput)
              .jobOutput(jobOutputLocation)
              .jobName(jobName)
              .filterArn(filterArn)
              .roleArn(roleArn)
              .batchInferenceJobConfig(jobConfig)   // Optional
              .build();

      batchInferenceJobArn = personalizeClient.createBatchInferenceJob(createBatchInferenceJobRequest)
              .batchInferenceJobArn();
      DescribeBatchInferenceJobRequest describeBatchInferenceJobRequest = DescribeBatchInferenceJobRequest.builder()
              .batchInferenceJobArn(batchInferenceJobArn)
              .build();

      long maxTime = Instant.now().getEpochSecond() + 3 * 60 * 60;

      // wait until the batch inference job is complete.
      while (Instant.now().getEpochSecond() < maxTime) {

          BatchInferenceJob batchInferenceJob = personalizeClient
                  .describeBatchInferenceJob(describeBatchInferenceJobRequest)
                  .batchInferenceJob();

          status = batchInferenceJob.status();
          System.out.println("Batch inference job status: " + status);

          if (status.equals("ACTIVE") || status.equals("CREATE FAILED")) {
              break;
          }
          try {
              Thread.sleep(waitInMilliseconds);
          } catch (InterruptedException e) {
              System.out.println(e.getMessage());
          }
      }
      return batchInferenceJobArn;

  } catch (PersonalizeException e) {
      System.out.println(e.awsErrorDetails().errorMessage());
  }
  return "";
}

SDK for JavaScript v3

// Get service clients module and commands using ES6 syntax.
import { CreateBatchInferenceJobCommand } from
  "@aws-sdk/client-personalize";
import { personalizeClient } from "./libs/personalizeClients.js";

// Or, create the client here.
// const personalizeClient = new PersonalizeClient({ region: "REGION"});

// Set the batch inference job's parameters.

export const createBatchInferenceJobParam = {
  jobName: 'JOB_NAME',
  jobInput: {         /* required */
    s3DataSource: {   /* required */
      path: 'INPUT_PATH', /* required */
      // kmsKeyArn: 'INPUT_KMS_KEY_ARN' /* optional */'
    }
  },
  jobOutput: {         /* required */
    s3DataDestination: {   /* required */
      path: 'OUTPUT_PATH', /* required */
      // kmsKeyArn: 'OUTPUT_KMS_KEY_ARN' /* optional */'
    }
  },
  roleArn: 'ROLE_ARN', /* required */
  solutionVersionArn: 'SOLUTION_VERSION_ARN', /* required */
  numResults: 20 /* optional integer*/
};

export const run = async () => {
  try {
    const response = await personalizeClient.send(new CreateBatchInferenceJobCommand(createBatchInferenceJobParam));
    console.log("Success", response);
    return response; // For unit tests.
  } catch (err) {
    console.log("Error", err);
  }
};
run();

處理批次任務可能需要一段時間才能完成。您可以藉由呼叫 DescribeBatchInferenceJob 並傳遞 batchRecommendationsJobArn 作為輸入參數來檢查任務的狀態。您也可以撥打電話ListBatchInferenceJobs列出 AWS 環境中的所有 Amazon Personalize 批次推論任務。

建立可產生主題的批次推論工作

若要為類似項目產生主題，您必須使用類似項目方法，而您的項目資料集必須具有文字欄位和項目名稱資料欄。如需有關包含主題之建議的更多資訊，請參閱包含內容生成器主題的 Batch 推薦。

下列程式碼會建立批次推論工作，產生包含主題的建議。將batchInferenceJobMode設定保留為"THEME_GENERATION"。取代COLUMNN_NAME為儲存物品名稱資料的欄名稱。

import boto3

personalize_rec = boto3.client(service_name='personalize')

personalize_rec.create_batch_inference_job (
    solutionVersionArn = "Solution version ARN",
    jobName = "Batch job name",
    roleArn = "IAM service role ARN",
    filterArn = "Filter ARN",
    batchInferenceJobMode = "THEME_GENERATION",
    themeGenerationConfig = {
      "fieldsForThemeGeneration": {
          "itemName": "COLUMN_NAME"
      }
    },
    jobInput = 
       {"s3DataSource": {"path": "s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json"}},
    jobOutput = 
       {"s3DataDestination": {"path": "s3://<name of your S3 bucket>/<output folder name>/"}}
)