Client

frauddetector

R Documentation

Amazon Fraud Detector¶

Description¶

This is the Amazon Fraud Detector API Reference. This guide is for developers who need detailed information about Amazon Fraud Detector API actions, data types, and errors. For more information about Amazon Fraud Detector features, see the Amazon Fraud Detector User Guide.

We provide the Query API as well as AWS software development kits (SDK) for Amazon Fraud Detector in Java and Python programming languages.

The Amazon Fraud Detector Query API provides HTTPS requests that use the HTTP verb GET or POST and a Query parameter Action. AWS SDK provides libraries, sample code, tutorials, and other resources for software developers who prefer to build applications using language-specific APIs instead of submitting a request over HTTP or HTTPS. These libraries provide basic functions that automatically take care of tasks such as cryptographically signing your requests, retrying requests, and handling error responses, so that it is easier for you to get started. For more information about the AWS SDKs, go to Tools to build on AWS page, scroll down to the SDK section, and choose plus (+) sign to expand the section.

Usage¶

frauddetector(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments¶

config

Optional configuration of credentials, endpoint, and/or region.

credentials:
- creds:
  - access_key_id: AWS access key ID
  - secret_access_key: AWS secret access key
  - session_token: AWS temporary session token
- profile: The name of a profile to use. If not given, then the default profile is used.
- anonymous: Set anonymous credentials.
endpoint: The complete URL to use for the constructed client.
region: The AWS Region used in instantiating the client.
close_connection: Immediately close all HTTP connections.
timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.
s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.
sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

creds:
- access_key_id: AWS access key ID
- secret_access_key: AWS secret access key
- session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value¶

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax¶

svc <- frauddetector(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations¶

batch_create_variable: Creates a batch of variables
batch_get_variable: Gets a batch of variables
cancel_batch_import_job: Cancels an in-progress batch import job
cancel_batch_prediction_job: Cancels the specified batch prediction job
create_batch_import_job: Creates a batch import job
create_batch_prediction_job: Creates a batch prediction job
create_detector_version: Creates a detector version
create_list: Creates a list
create_model: Creates a model using the specified model type
create_model_version: Creates a version of the model using the specified model type and model id
create_rule: Creates a rule for use with the specified detector
create_variable: Creates a variable
delete_batch_import_job: Deletes the specified batch import job ID record
delete_batch_prediction_job: Deletes a batch prediction job
delete_detector: Deletes the detector
delete_detector_version: Deletes the detector version
delete_entity_type: Deletes an entity type
delete_event: Deletes the specified event
delete_events_by_event_type: Deletes all events of a particular event type
delete_event_type: Deletes an event type
delete_external_model: Removes a SageMaker model from Amazon Fraud Detector
delete_label: Deletes a label
delete_list: Deletes the list, provided it is not used in a rule
delete_model: Deletes a model
delete_model_version: Deletes a model version
delete_outcome: Deletes an outcome
delete_rule: Deletes the rule
delete_variable: Deletes a variable
describe_detector: Gets all versions for a specified detector
describe_model_versions: Gets all of the model versions for the specified model type or for the specified model type and model ID
get_batch_import_jobs: Gets all batch import jobs or a specific job of the specified ID
get_batch_prediction_jobs: Gets all batch prediction jobs or a specific job if you specify a job ID; Retrieves the status of a DeleteEventsByEventType action
get_detectors: Gets all detectors or a single detector if a detectorId is specified
get_detector_version: Gets a particular detector version
get_entity_types: Gets all entity types or a specific entity type if a name is specified
get_event: Retrieves details of events stored with Amazon Fraud Detector
get_event_prediction: Evaluates an event against a detector version
get_event_prediction_metadata: Gets details of the past fraud predictions for the specified event ID, event type, detector ID, and detector version ID that was generated in the specified time period
get_event_types: Gets all event types or a specific event type if name is provided
get_external_models: Gets the details for one or more Amazon SageMaker models that have been imported into the service
get_kms_encryption_key: Gets the encryption key if a KMS key has been specified to be used to encrypt content in Amazon Fraud Detector
get_labels: Gets all labels or a specific label if name is provided
get_list_elements: Gets all the elements in the specified list
get_lists_metadata: Gets the metadata of either all the lists under the account or the specified list
get_models: Gets one or more models
get_model_version: Gets the details of the specified model version
get_outcomes: Gets one or more outcomes
get_rules: Get all rules for a detector (paginated) if ruleId and ruleVersion are not specified
get_variables: Gets all of the variables or the specific variable
list_event_predictions: Gets a list of past predictions
list_tags_for_resource: Lists all tags associated with the resource
put_detector: Creates or updates a detector
put_entity_type: Creates or updates an entity type
put_event_type: Creates or updates an event type
put_external_model: Creates or updates an Amazon SageMaker model endpoint
put_kms_encryption_key: Specifies the KMS key to be used to encrypt content in Amazon Fraud Detector
put_label: Creates or updates label
put_outcome: Creates or updates an outcome
send_event: Stores events in Amazon Fraud Detector without generating fraud predictions for those events
tag_resource: Assigns tags to a resource
untag_resource: Removes tags from a resource
update_detector_version: Updates a detector version
update_detector_version_metadata: Updates the detector version's description
update_detector_version_status: Updates the detector version’s status
update_event_label: Updates the specified event with a new label
update_list: Updates a list
update_model: Updates model description
update_model_version: Updates a model version
update_model_version_status: Updates the status of a model version
update_rule_metadata: Updates a rule's metadata
update_rule_version: Updates a rule version resulting in a new rule version
update_variable: Updates a variable

Examples¶

## Not run: 
svc <- frauddetector()
svc$batch_create_variable(
  Foo = 123
)

## End(Not run)