Package software.amazon.awssdk.services.frauddetector.model

Class LabelSchema

    • Method Detail

      • 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:
        UnlabeledEventsTreatment

      • 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:
        UnlabeledEventsTreatment

      • hashCode

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

      • equals

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

      • 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)