frauddetector: Amazon Fraud Detector

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

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
get_delete_events_by_event_type_status	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)

paws.machine.learning documentation built on Sept. 12, 2024, 6:23 a.m.