Get Ml Input Channel

cleanroomsml_get_ml_input_channel

R Documentation

Returns information about an ML input channel

Description

Returns information about an ML input channel.

Usage

cleanroomsml_get_ml_input_channel(mlInputChannelArn,
  membershipIdentifier)

Arguments

mlInputChannelArn	[required] The Amazon Resource Name (ARN) of the ML input channel that you want to get.
membershipIdentifier	[required] The membership ID of the membership that contains the ML input channel that you want to get.

Value

A list with the following syntax:

list(
  membershipIdentifier = "string",
  collaborationIdentifier = "string",
  mlInputChannelArn = "string",
  name = "string",
  configuredModelAlgorithmAssociations = list(
    "string"
  ),
  status = "CREATE_PENDING"|"CREATE_IN_PROGRESS"|"CREATE_FAILED"|"ACTIVE"|"DELETE_PENDING"|"DELETE_IN_PROGRESS"|"DELETE_FAILED"|"INACTIVE",
  statusDetails = list(
    statusCode = "string",
    message = "string"
  ),
  retentionInDays = 123,
  numberOfRecords = 123,
  privacyBudgets = list(
    accessBudgets = list(
      list(
        resourceArn = "string",
        details = list(
          list(
            startTime = as.POSIXct(
              "2015-01-01"
            ),
            endTime = as.POSIXct(
              "2015-01-01"
            ),
            remainingBudget = 123,
            budget = 123,
            budgetType = "CALENDAR_DAY"|"CALENDAR_MONTH"|"CALENDAR_WEEK"|"LIFETIME",
            autoRefresh = "ENABLED"|"DISABLED"
          )
        ),
        aggregateRemainingBudget = 123
      )
    )
  ),
  description = "string",
  syntheticDataConfiguration = list(
    syntheticDataParameters = list(
      epsilon = 123.0,
      maxMembershipInferenceAttackScore = 123.0,
      columnClassification = list(
        columnMapping = list(
          list(
            columnName = "string",
            columnType = "CATEGORICAL"|"NUMERICAL",
            isPredictiveValue = TRUE|FALSE
          )
        )
      )
    ),
    syntheticDataEvaluationScores = list(
      dataPrivacyScores = list(
        membershipInferenceAttackScores = list(
          list(
            attackVersion = "DISTANCE_TO_CLOSEST_RECORD_V1",
            score = 123.0
          )
        )
      )
    )
  ),
  payerConfiguration = list(
    computePayerAccountId = "string",
    syntheticDataPayerAccountId = "string"
  ),
  createTime = as.POSIXct(
    "2015-01-01"
  ),
  updateTime = as.POSIXct(
    "2015-01-01"
  ),
  inputChannel = list(
    dataSource = list(
      protectedQueryInputParameters = list(
        sqlParameters = list(
          queryString = "string",
          analysisTemplateArn = "string",
          parameters = list(
            "string"
          )
        ),
        computeConfiguration = list(
          worker = list(
            type = "CR.1X"|"CR.4X",
            number = 123,
            properties = list(
              spark = list(
                "string"
              )
            )
          )
        ),
        resultFormat = "CSV"|"PARQUET"
      )
    ),
    roleArn = "string"
  ),
  protectedQueryIdentifier = "string",
  numberOfFiles = 123.0,
  sizeInGb = 123.0,
  kmsKeyArn = "string",
  tags = list(
    "string"
  )
)

Request syntax

svc$get_ml_input_channel(
  mlInputChannelArn = "string",
  membershipIdentifier = "string"
)