開始使用網域資料集群組 (SDK適用於 JavaScript v3)

將下列 Avro 結構描述儲存為工作目錄中的JSON檔案。此結構描述與您在完成時建立的CSV檔案中的欄相符建立訓練資料 (網域資料集群組)。

{
  "type": "record",
  "name": "Interactions",
  "namespace": "com.amazonaws.personalize.schema",
  "fields": [
      {
          "name": "USER_ID",
          "type": "string"
      },
      {
          "name": "ITEM_ID",
          "type": "string"
      },
      {
          "name": "EVENT_TYPE",
          "type": "string"
      },
      {
          "name": "TIMESTAMP",
          "type": "long"
      }
  ],
  "version": "1.0"
}

使用以下createDomainSchema.js代碼在 Amazon Personalize 化中創建域架構。請SCHEMA_PATH以您剛建立的結構描述 .json 檔案的路徑取代。更新createSchemaParam以指定結構描述的名稱，並用於domain指定VIDEO_ON_DEMAND。

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

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

import fs from 'fs';

let schemaFilePath = "SCHEMA_PATH";
let mySchema = "";

try {
  mySchema = fs.readFileSync(schemaFilePath).toString();
} catch (err) {
  mySchema = 'TEST' // for unit tests.
}

// Set the domain schema parameters.
export const createDomainSchemaParam = {
  name: 'NAME', /* required */
  schema: mySchema, /* required */
  domain: 'DOMAIN'   /* required for a domain dataset group, specify ECOMMERCE or VIDEO_ON_DEMAND */
};

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

使用下列createDomainDatasetGroup.js程式碼在 Amazon Personalize 中建立網域資料集群組。更新domainDatasetGroupParams以指定資料集群組的名稱，並用於domain指定VIDEO_ON_DEMAND。

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

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

// Set the domain dataset group parameters.
export const domainDatasetGroupParams = {
  name: 'NAME',  /* required */
  domain: 'DOMAIN'   /* required for a domain dsg, specify ECOMMERCE or VIDEO_ON_DEMAND */
}

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

使用以下createDataset.js代碼在 Amazon Personalize 化中創建項目交互數據集。更新createDatasetParam以指定您剛建立的資料集群組和結構描述的 Amazon 資源名稱 (ARN)、為該資料集命名，然後指定Interactions。datasetType

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

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

// Set the dataset's parameters.
export const createDatasetParam = {
  datasetGroupArn: 'DATASET_GROUP_ARN', /* required */
  datasetType: 'DATASET_TYPE', /* required */
  name: 'NAME', /* required */
  schemaArn: 'SCHEMA_ARN' /* required */
}

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

使用以下createDatasetImportJob.js代碼導入數據。更新以指datasetImportJobParam定下列項目：

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

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

// Set the dataset import job parameters.
export const datasetImportJobParam = {
  datasetArn: 'DATASET_ARN', /* required */
  dataSource: {  /* required */
    dataLocation: 'S3_PATH'
  },
  jobName: 'NAME',/* required */
  roleArn: 'ROLE_ARN' /* required */
}

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