Struct aws_sdk_frauddetector::operation::get_event_prediction::GetEventPredictionInput

#[non_exhaustive]
pub struct GetEventPredictionInput {
    pub detector_id: Option<String>,
    pub detector_version_id: Option<String>,
    pub event_id: Option<String>,
    pub event_type_name: Option<String>,
    pub entities: Option<Vec<Entity>>,
    pub event_timestamp: Option<String>,
    pub event_variables: Option<HashMap<String, String>>,
    pub external_model_endpoint_data_blobs: Option<HashMap<String, ModelEndpointDataBlob>>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

detector_id: Option<String>

The detector ID.

detector_version_id: Option<String>

The detector version ID.

event_id: Option<String>

The unique ID used to identify the event.

event_type_name: Option<String>

The event type associated with the detector specified for the prediction.

entities: Option<Vec<Entity>>

The entity type (associated with the detector's event type) and specific entity ID representing who performed the event. If an entity id is not available, use "UNKNOWN."

event_timestamp: Option<String>

Timestamp that defines when the event under evaluation occurred. The timestamp must be specified using ISO 8601 standard in UTC.

event_variables: Option<HashMap<String, String>>

Names of the event type's variables you defined in Amazon Fraud Detector to represent data elements and their corresponding values for the event you are sending for evaluation.

You must provide at least one eventVariable

To ensure most accurate fraud prediction and to simplify your data preparation, Amazon Fraud Detector will replace all missing variables or values as follows:

For Amazon Fraud Detector trained models:

If a null value is provided explicitly for a variable or if a variable is missing, model will replace the null value or the missing variable (no variable name in the eventVariables map) with calculated default mean/medians for numeric variables and with special values for categorical variables.

For imported SageMaker models:

If a null value is provided explicitly for a variable, the model and rules will use “null” as the value. If a variable is not provided (no variable name in the eventVariables map), model and rules will use the default value that is provided for the variable.

external_model_endpoint_data_blobs: Option<HashMap<String, ModelEndpointDataBlob>>

The Amazon SageMaker model endpoint input data blobs.

Implementations

impl GetEventPredictionInput

pub fn detector_id(&self) -> Option<&str>

The detector ID.

pub fn detector_version_id(&self) -> Option<&str>

The detector version ID.

pub fn event_id(&self) -> Option<&str>

The unique ID used to identify the event.

pub fn event_type_name(&self) -> Option<&str>

The event type associated with the detector specified for the prediction.

pub fn entities(&self) -> &[Entity]

The entity type (associated with the detector's event type) and specific entity ID representing who performed the event. If an entity id is not available, use "UNKNOWN."

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .entities.is_none().

pub fn event_timestamp(&self) -> Option<&str>

Timestamp that defines when the event under evaluation occurred. The timestamp must be specified using ISO 8601 standard in UTC.

pub fn event_variables(&self) -> Option<&HashMap<String, String>>

Names of the event type's variables you defined in Amazon Fraud Detector to represent data elements and their corresponding values for the event you are sending for evaluation.

You must provide at least one eventVariable

To ensure most accurate fraud prediction and to simplify your data preparation, Amazon Fraud Detector will replace all missing variables or values as follows:

For Amazon Fraud Detector trained models:

If a null value is provided explicitly for a variable or if a variable is missing, model will replace the null value or the missing variable (no variable name in the eventVariables map) with calculated default mean/medians for numeric variables and with special values for categorical variables.

For imported SageMaker models:

If a null value is provided explicitly for a variable, the model and rules will use “null” as the value. If a variable is not provided (no variable name in the eventVariables map), model and rules will use the default value that is provided for the variable.

pub fn external_model_endpoint_data_blobs( &self ) -> Option<&HashMap<String, ModelEndpointDataBlob>>

The Amazon SageMaker model endpoint input data blobs.

impl GetEventPredictionInput

pub fn builder() -> GetEventPredictionInputBuilder

Creates a new builder-style object to manufacture GetEventPredictionInput.

Trait Implementations

impl Clone for GetEventPredictionInput

fn clone(&self) -> GetEventPredictionInput

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GetEventPredictionInput

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for GetEventPredictionInput

fn eq(&self, other: &GetEventPredictionInput) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for GetEventPredictionInput