Update Experiment

cloudwatchevidently_update_experiment

R Documentation

Updates an Evidently experiment

Description

Updates an Evidently experiment.

Don't use this operation to update an experiment's tag. Instead, use tag_resource.

Usage

cloudwatchevidently_update_experiment(description, experiment,
  metricGoals, onlineAbConfig, project, randomizationSalt, removeSegment,
  samplingRate, segment, treatments)

Arguments

description

An optional description of the experiment.

experiment

[required] The name of the experiment to update.

metricGoals

An array of structures that defines the metrics used for the experiment, and whether a higher or lower value for each metric is the goal.

onlineAbConfig

A structure that contains the configuration of which variation o use as the "control" version. The "control" version is used for comparison with other variations. This structure also specifies how much experiment traffic is allocated to each variation.

project

[required] The name or ARN of the project that contains the experiment that you want to update.

randomizationSalt

When Evidently assigns a particular user session to an experiment, it must use a randomization ID to determine which variation the user session is served. This randomization ID is a combination of the entity ID and randomizationSalt. If you omit randomizationSalt, Evidently uses the experiment name as the randomizationSalt.

removeSegment

Removes a segment from being used in an experiment. You can't use this parameter if the experiment is currently running.

samplingRate

The portion of the available audience that you want to allocate to this experiment, in thousandths of a percent. The available audience is the total audience minus the audience that you have allocated to overrides or current launches of this feature.

This is represented in thousandths of a percent. For example, specify 20,000 to allocate 20% of the available audience.

segment

Adds an audience segment to an experiment. When a segment is used in an experiment, only user sessions that match the segment pattern are used in the experiment. You can't use this parameter if the experiment is currently running.

treatments

An array of structures that define the variations being tested in the experiment.

Value

A list with the following syntax:

list(
  experiment = list(
    arn = "string",
    createdTime = as.POSIXct(
      "2015-01-01"
    ),
    description = "string",
    execution = list(
      endedTime = as.POSIXct(
        "2015-01-01"
      ),
      startedTime = as.POSIXct(
        "2015-01-01"
      )
    ),
    lastUpdatedTime = as.POSIXct(
      "2015-01-01"
    ),
    metricGoals = list(
      list(
        desiredChange = "INCREASE"|"DECREASE",
        metricDefinition = list(
          entityIdKey = "string",
          eventPattern = "string",
          name = "string",
          unitLabel = "string",
          valueKey = "string"
        )
      )
    ),
    name = "string",
    onlineAbDefinition = list(
      controlTreatmentName = "string",
      treatmentWeights = list(
        123
      )
    ),
    project = "string",
    randomizationSalt = "string",
    samplingRate = 123,
    schedule = list(
      analysisCompleteTime = as.POSIXct(
        "2015-01-01"
      )
    ),
    segment = "string",
    status = "CREATED"|"UPDATING"|"RUNNING"|"COMPLETED"|"CANCELLED",
    statusReason = "string",
    tags = list(
      "string"
    ),
    treatments = list(
      list(
        description = "string",
        featureVariations = list(
          "string"
        ),
        name = "string"
      )
    ),
    type = "aws.evidently.onlineab"
  )
)

Request syntax

svc$update_experiment(
  description = "string",
  experiment = "string",
  metricGoals = list(
    list(
      desiredChange = "INCREASE"|"DECREASE",
      metricDefinition = list(
        entityIdKey = "string",
        eventPattern = "string",
        name = "string",
        unitLabel = "string",
        valueKey = "string"
      )
    )
  ),
  onlineAbConfig = list(
    controlTreatmentName = "string",
    treatmentWeights = list(
      123
    )
  ),
  project = "string",
  randomizationSalt = "string",
  removeSegment = TRUE|FALSE,
  samplingRate = 123,
  segment = "string",
  treatments = list(
    list(
      description = "string",
      feature = "string",
      name = "string",
      variation = "string"
    )
  )
)