Struct aws_sdk_frauddetector::types::builders::LabelSchemaBuilder

#[non_exhaustive]
pub struct LabelSchemaBuilder { /* private fields */ }

A builder for LabelSchema.

Implementations

impl LabelSchemaBuilder

pub fn label_mapper(self, k: impl Into<String>, v: Vec<String>) -> Self

Adds a key-value pair to label_mapper.

To override the contents of this collection use set_label_mapper.

The label mapper maps the Amazon Fraud Detector supported model classification labels (FRAUD, LEGIT) to the appropriate event type labels. For example, if "FRAUD" and "LEGIT" are Amazon Fraud Detector supported labels, this mapper could be: {"FRAUD" => ["0"], "LEGIT" => ["1"]} or {"FRAUD" => ["false"], "LEGIT" => ["true"]} or {"FRAUD" => ["fraud", "abuse"], "LEGIT" => ["legit", "safe"]}. The value part of the mapper is a list, because you may have multiple label variants from your event type for a single Amazon Fraud Detector label.

pub fn set_label_mapper( self, input: Option<HashMap<String, Vec<String>>> ) -> Self

The label mapper maps the Amazon Fraud Detector supported model classification labels (FRAUD, LEGIT) to the appropriate event type labels. For example, if "FRAUD" and "LEGIT" are Amazon Fraud Detector supported labels, this mapper could be: {"FRAUD" => ["0"], "LEGIT" => ["1"]} or {"FRAUD" => ["false"], "LEGIT" => ["true"]} or {"FRAUD" => ["fraud", "abuse"], "LEGIT" => ["legit", "safe"]}. The value part of the mapper is a list, because you may have multiple label variants from your event type for a single Amazon Fraud Detector label.

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

The label mapper maps the Amazon Fraud Detector supported model classification labels (FRAUD, LEGIT) to the appropriate event type labels. For example, if "FRAUD" and "LEGIT" are Amazon Fraud Detector supported labels, this mapper could be: {"FRAUD" => ["0"], "LEGIT" => ["1"]} or {"FRAUD" => ["false"], "LEGIT" => ["true"]} or {"FRAUD" => ["fraud", "abuse"], "LEGIT" => ["legit", "safe"]}. The value part of the mapper is a list, because you may have multiple label variants from your event type for a single Amazon Fraud Detector label.

pub fn unlabeled_events_treatment(self, input: UnlabeledEventsTreatment) -> Self

The action to take for unlabeled events.

• Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.

• Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.

• Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.

• Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.

By default, Amazon Fraud Detector ignores the unlabeled data.

pub fn set_unlabeled_events_treatment( self, input: Option<UnlabeledEventsTreatment> ) -> Self

The action to take for unlabeled events.

• Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.

• Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.

• Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.

• Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.

By default, Amazon Fraud Detector ignores the unlabeled data.

pub fn get_unlabeled_events_treatment( &self ) -> &Option<UnlabeledEventsTreatment>

The action to take for unlabeled events.

• Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.

• Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.

• Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.

• Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.

By default, Amazon Fraud Detector ignores the unlabeled data.

pub fn build(self) -> LabelSchema

Consumes the builder and constructs a LabelSchema.

Trait Implementations

impl Clone for LabelSchemaBuilder

fn clone(&self) -> LabelSchemaBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for LabelSchemaBuilder

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

Formats the value using the given formatter.

impl Default for LabelSchemaBuilder

fn default() -> LabelSchemaBuilder

Returns the “default value” for a type.

impl PartialEq for LabelSchemaBuilder

fn eq(&self, other: &LabelSchemaBuilder) -> 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 LabelSchemaBuilder