com.amazonaws.services.frauddetector.model.TrainingMetrics Maven / Gradle / Ivy
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/*
* Copyright 2019-2024 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.
*/
package com.amazonaws.services.frauddetector.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* The training metric details.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class TrainingMetrics implements Serializable, Cloneable, StructuredPojo {
/**
*
* The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all
* possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has
* a score of 1.0.
*
*/
private Float auc;
/**
*
* The data points details.
*
*/
private java.util.List metricDataPoints;
/**
*
* The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all
* possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has
* a score of 1.0.
*
*
* @param auc
* The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across
* all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect
* model has a score of 1.0.
*/
public void setAuc(Float auc) {
this.auc = auc;
}
/**
*
* The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all
* possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has
* a score of 1.0.
*
*
* @return The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across
* all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a
* perfect model has a score of 1.0.
*/
public Float getAuc() {
return this.auc;
}
/**
*
* The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all
* possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has
* a score of 1.0.
*
*
* @param auc
* The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across
* all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect
* model has a score of 1.0.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingMetrics withAuc(Float auc) {
setAuc(auc);
return this;
}
/**
*
* The data points details.
*
*
* @return The data points details.
*/
public java.util.List getMetricDataPoints() {
return metricDataPoints;
}
/**
*
* The data points details.
*
*
* @param metricDataPoints
* The data points details.
*/
public void setMetricDataPoints(java.util.Collection metricDataPoints) {
if (metricDataPoints == null) {
this.metricDataPoints = null;
return;
}
this.metricDataPoints = new java.util.ArrayList(metricDataPoints);
}
/**
*
* The data points details.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setMetricDataPoints(java.util.Collection)} or {@link #withMetricDataPoints(java.util.Collection)} if you
* want to override the existing values.
*
*
* @param metricDataPoints
* The data points details.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingMetrics withMetricDataPoints(MetricDataPoint... metricDataPoints) {
if (this.metricDataPoints == null) {
setMetricDataPoints(new java.util.ArrayList(metricDataPoints.length));
}
for (MetricDataPoint ele : metricDataPoints) {
this.metricDataPoints.add(ele);
}
return this;
}
/**
*
* The data points details.
*
*
* @param metricDataPoints
* The data points details.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingMetrics withMetricDataPoints(java.util.Collection metricDataPoints) {
setMetricDataPoints(metricDataPoints);
return this;
}
/**
* 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.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getAuc() != null)
sb.append("Auc: ").append(getAuc()).append(",");
if (getMetricDataPoints() != null)
sb.append("MetricDataPoints: ").append(getMetricDataPoints());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof TrainingMetrics == false)
return false;
TrainingMetrics other = (TrainingMetrics) obj;
if (other.getAuc() == null ^ this.getAuc() == null)
return false;
if (other.getAuc() != null && other.getAuc().equals(this.getAuc()) == false)
return false;
if (other.getMetricDataPoints() == null ^ this.getMetricDataPoints() == null)
return false;
if (other.getMetricDataPoints() != null && other.getMetricDataPoints().equals(this.getMetricDataPoints()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getAuc() == null) ? 0 : getAuc().hashCode());
hashCode = prime * hashCode + ((getMetricDataPoints() == null) ? 0 : getMetricDataPoints().hashCode());
return hashCode;
}
@Override
public TrainingMetrics clone() {
try {
return (TrainingMetrics) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.frauddetector.model.transform.TrainingMetricsMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}