/*
* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License").
* You may not use this file except in compliance with the License.
* A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed
* on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
* express or implied. See the License for the specific language governing
* permissions and limitations under the License.
*/
/*
* Do not modify this file. This file is generated from the frauddetector-2019-11-15.normal.json service model.
*/
using System;
using System.Collections.Generic;
using System.Xml.Serialization;
using System.Text;
using System.IO;
using System.Net;
using Amazon.Runtime;
using Amazon.Runtime.Internal;
namespace Amazon.FraudDetector.Model
{
///
/// The label schema.
///
public partial class LabelSchema
{
private Dictionary> _labelMapper = new Dictionary>();
private UnlabeledEventsTreatment _unlabeledEventsTreatment;
///
/// Gets and sets the property 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.
///
///
public Dictionary> LabelMapper
{
get { return this._labelMapper; }
set { this._labelMapper = value; }
}
// Check to see if LabelMapper property is set
internal bool IsSetLabelMapper()
{
return this._labelMapper != null && this._labelMapper.Count > 0;
}
///
/// Gets and sets the property 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.
///
///
public UnlabeledEventsTreatment UnlabeledEventsTreatment
{
get { return this._unlabeledEventsTreatment; }
set { this._unlabeledEventsTreatment = value; }
}
// Check to see if UnlabeledEventsTreatment property is set
internal bool IsSetUnlabeledEventsTreatment()
{
return this._unlabeledEventsTreatment != null;
}
}
}