Class LabelSchema

java.lang.Object
software.amazon.awssdk.services.frauddetector.model.LabelSchema
All Implemented Interfaces:
Serializable, SdkPojo, ToCopyableBuilder<LabelSchema.Builder,LabelSchema>
@Generated("software.amazon.awssdk:codegen") public final class LabelSchema extends Object implements SdkPojo, Serializable, ToCopyableBuilder<LabelSchema.Builder,LabelSchema>

The label schema.

See Also:

  • Method Details

    • hasLabelMapper

      public final boolean hasLabelMapper()
      For responses, this returns true if the service returned a value for the LabelMapper property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • labelMapper

      public final Map<String,List<String>> labelMapper()

      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.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasLabelMapper() method.

      Returns:
      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.

    • unlabeledEventsTreatment

      public final UnlabeledEventsTreatment 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.

      If the service returns an enum value that is not available in the current SDK version, unlabeledEventsTreatment will return UnlabeledEventsTreatment.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from unlabeledEventsTreatmentAsString().

      Returns:
      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.

      See Also:

    • unlabeledEventsTreatmentAsString

      public final String unlabeledEventsTreatmentAsString()

      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.

      If the service returns an enum value that is not available in the current SDK version, unlabeledEventsTreatment will return UnlabeledEventsTreatment.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from unlabeledEventsTreatmentAsString().

      Returns:
      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.

      See Also:

    • toBuilder

      public LabelSchema.Builder toBuilder()
      Description copied from interface: ToCopyableBuilder
      Take this object and create a builder that contains all of the current property values of this object.
      Specified by:
      toBuilder in interface ToCopyableBuilder<LabelSchema.Builder,LabelSchema>
      Returns:
      a builder for type T

    • builder

      public static LabelSchema.Builder builder()

    • serializableBuilderClass

      public static Class<? extends LabelSchema.Builder> serializableBuilderClass()

    • hashCode

      public final int hashCode()
      Overrides:
      hashCode in class Object

    • equals

      public final boolean equals(Object obj)
      Overrides:
      equals in class Object

    • equalsBySdkFields

      public final boolean equalsBySdkFields(Object obj)
      Description copied from interface: SdkPojo
      Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in an SdkPojo class, and is generated based on a service model.

      If an SdkPojo class does not have any inherited fields, equalsBySdkFields and equals are essentially the same.

      Specified by:
      equalsBySdkFields in interface SdkPojo
      Parameters:
      obj - the object to be compared with
      Returns:
      true if the other object equals to this object by sdk fields, false otherwise.

    • toString

      public final String toString()
      Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
      Overrides:
      toString in class Object

    • getValueForField

      public final <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)

    • sdkFields

      public final List<SdkField<?>> sdkFields()
      Specified by:
      sdkFields in interface SdkPojo
      Returns:
      List of SdkField in this POJO. May be empty list but should never be null.