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/*
* Copyright 2018-2023 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.comprehend.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* Describes the result metrics for the test data associated with an documentation classifier.
*
*
* @see AWS API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class ClassifierEvaluationMetrics implements Serializable, Cloneable, StructuredPojo {
/**
*
* The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the
* test documents that were correctly recognized by the total number of labels in the test documents.
*
*/
private Double accuracy;
/**
*
* A measure of the usefulness of the classifier results in the test data. High precision means that the classifier
* returned substantially more relevant results than irrelevant ones.
*
*/
private Double precision;
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results.
*
*/
private Double recall;
/**
*
* A measure of how accurate the classifier results are for the test data. It is derived from the
* Precision and Recall values. The F1Score is the harmonic average of the
* two scores. The highest score is 1, and the worst score is 0.
*
*/
private Double f1Score;
/**
*
* A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer
* returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from
* averaging the precision of all available labels, this is based on the overall score of all precision scores added
* together.
*
*/
private Double microPrecision;
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results. Specifically, this indicates how many of the correct categories in the
* text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of
* averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all
* recall scores added together.
*
*/
private Double microRecall;
/**
*
* A measure of how accurate the classifier results are for the test data. It is a combination of the
* Micro Precision and Micro Recall values. The Micro F1Score is the harmonic
* mean of the two scores. The highest score is 1, and the worst score is 0.
*
*/
private Double microF1Score;
/**
*
* Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels
* compared to the total number of labels. Scores closer to zero are better.
*
*/
private Double hammingLoss;
/**
*
* The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the
* test documents that were correctly recognized by the total number of labels in the test documents.
*
*
* @param accuracy
* The fraction of the labels that were correct recognized. It is computed by dividing the number of labels
* in the test documents that were correctly recognized by the total number of labels in the test documents.
*/
public void setAccuracy(Double accuracy) {
this.accuracy = accuracy;
}
/**
*
* The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the
* test documents that were correctly recognized by the total number of labels in the test documents.
*
*
* @return The fraction of the labels that were correct recognized. It is computed by dividing the number of labels
* in the test documents that were correctly recognized by the total number of labels in the test documents.
*/
public Double getAccuracy() {
return this.accuracy;
}
/**
*
* The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the
* test documents that were correctly recognized by the total number of labels in the test documents.
*
*
* @param accuracy
* The fraction of the labels that were correct recognized. It is computed by dividing the number of labels
* in the test documents that were correctly recognized by the total number of labels in the test documents.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withAccuracy(Double accuracy) {
setAccuracy(accuracy);
return this;
}
/**
*
* A measure of the usefulness of the classifier results in the test data. High precision means that the classifier
* returned substantially more relevant results than irrelevant ones.
*
*
* @param precision
* A measure of the usefulness of the classifier results in the test data. High precision means that the
* classifier returned substantially more relevant results than irrelevant ones.
*/
public void setPrecision(Double precision) {
this.precision = precision;
}
/**
*
* A measure of the usefulness of the classifier results in the test data. High precision means that the classifier
* returned substantially more relevant results than irrelevant ones.
*
*
* @return A measure of the usefulness of the classifier results in the test data. High precision means that the
* classifier returned substantially more relevant results than irrelevant ones.
*/
public Double getPrecision() {
return this.precision;
}
/**
*
* A measure of the usefulness of the classifier results in the test data. High precision means that the classifier
* returned substantially more relevant results than irrelevant ones.
*
*
* @param precision
* A measure of the usefulness of the classifier results in the test data. High precision means that the
* classifier returned substantially more relevant results than irrelevant ones.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withPrecision(Double precision) {
setPrecision(precision);
return this;
}
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results.
*
*
* @param recall
* A measure of how complete the classifier results are for the test data. High recall means that the
* classifier returned most of the relevant results.
*/
public void setRecall(Double recall) {
this.recall = recall;
}
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results.
*
*
* @return A measure of how complete the classifier results are for the test data. High recall means that the
* classifier returned most of the relevant results.
*/
public Double getRecall() {
return this.recall;
}
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results.
*
*
* @param recall
* A measure of how complete the classifier results are for the test data. High recall means that the
* classifier returned most of the relevant results.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withRecall(Double recall) {
setRecall(recall);
return this;
}
/**
*
* A measure of how accurate the classifier results are for the test data. It is derived from the
* Precision and Recall values. The F1Score is the harmonic average of the
* two scores. The highest score is 1, and the worst score is 0.
*
*
* @param f1Score
* A measure of how accurate the classifier results are for the test data. It is derived from the
* Precision and Recall values. The F1Score is the harmonic average of
* the two scores. The highest score is 1, and the worst score is 0.
*/
public void setF1Score(Double f1Score) {
this.f1Score = f1Score;
}
/**
*
* A measure of how accurate the classifier results are for the test data. It is derived from the
* Precision and Recall values. The F1Score is the harmonic average of the
* two scores. The highest score is 1, and the worst score is 0.
*
*
* @return A measure of how accurate the classifier results are for the test data. It is derived from the
* Precision and Recall values. The F1Score is the harmonic average
* of the two scores. The highest score is 1, and the worst score is 0.
*/
public Double getF1Score() {
return this.f1Score;
}
/**
*
* A measure of how accurate the classifier results are for the test data. It is derived from the
* Precision and Recall values. The F1Score is the harmonic average of the
* two scores. The highest score is 1, and the worst score is 0.
*
*
* @param f1Score
* A measure of how accurate the classifier results are for the test data. It is derived from the
* Precision and Recall values. The F1Score is the harmonic average of
* the two scores. The highest score is 1, and the worst score is 0.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withF1Score(Double f1Score) {
setF1Score(f1Score);
return this;
}
/**
*
* A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer
* returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from
* averaging the precision of all available labels, this is based on the overall score of all precision scores added
* together.
*
*
* @param microPrecision
* A measure of the usefulness of the recognizer results in the test data. High precision means that the
* recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric
* which comes from averaging the precision of all available labels, this is based on the overall score of
* all precision scores added together.
*/
public void setMicroPrecision(Double microPrecision) {
this.microPrecision = microPrecision;
}
/**
*
* A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer
* returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from
* averaging the precision of all available labels, this is based on the overall score of all precision scores added
* together.
*
*
* @return A measure of the usefulness of the recognizer results in the test data. High precision means that the
* recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric
* which comes from averaging the precision of all available labels, this is based on the overall score of
* all precision scores added together.
*/
public Double getMicroPrecision() {
return this.microPrecision;
}
/**
*
* A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer
* returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from
* averaging the precision of all available labels, this is based on the overall score of all precision scores added
* together.
*
*
* @param microPrecision
* A measure of the usefulness of the recognizer results in the test data. High precision means that the
* recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric
* which comes from averaging the precision of all available labels, this is based on the overall score of
* all precision scores added together.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withMicroPrecision(Double microPrecision) {
setMicroPrecision(microPrecision);
return this;
}
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results. Specifically, this indicates how many of the correct categories in the
* text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of
* averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all
* recall scores added together.
*
*
* @param microRecall
* A measure of how complete the classifier results are for the test data. High recall means that the
* classifier returned most of the relevant results. Specifically, this indicates how many of the correct
* categories in the text that the model can predict. It is a percentage of correct categories in the text
* that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is
* based on the overall score of all recall scores added together.
*/
public void setMicroRecall(Double microRecall) {
this.microRecall = microRecall;
}
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results. Specifically, this indicates how many of the correct categories in the
* text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of
* averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all
* recall scores added together.
*
*
* @return A measure of how complete the classifier results are for the test data. High recall means that the
* classifier returned most of the relevant results. Specifically, this indicates how many of the correct
* categories in the text that the model can predict. It is a percentage of correct categories in the text
* that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is
* based on the overall score of all recall scores added together.
*/
public Double getMicroRecall() {
return this.microRecall;
}
/**
*
* A measure of how complete the classifier results are for the test data. High recall means that the classifier
* returned most of the relevant results. Specifically, this indicates how many of the correct categories in the
* text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of
* averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all
* recall scores added together.
*
*
* @param microRecall
* A measure of how complete the classifier results are for the test data. High recall means that the
* classifier returned most of the relevant results. Specifically, this indicates how many of the correct
* categories in the text that the model can predict. It is a percentage of correct categories in the text
* that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is
* based on the overall score of all recall scores added together.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withMicroRecall(Double microRecall) {
setMicroRecall(microRecall);
return this;
}
/**
*
* A measure of how accurate the classifier results are for the test data. It is a combination of the
* Micro Precision and Micro Recall values. The Micro F1Score is the harmonic
* mean of the two scores. The highest score is 1, and the worst score is 0.
*
*
* @param microF1Score
* A measure of how accurate the classifier results are for the test data. It is a combination of the
* Micro Precision and Micro Recall values. The Micro F1Score is the
* harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
*/
public void setMicroF1Score(Double microF1Score) {
this.microF1Score = microF1Score;
}
/**
*
* A measure of how accurate the classifier results are for the test data. It is a combination of the
* Micro Precision and Micro Recall values. The Micro F1Score is the harmonic
* mean of the two scores. The highest score is 1, and the worst score is 0.
*
*
* @return A measure of how accurate the classifier results are for the test data. It is a combination of the
* Micro Precision and Micro Recall values. The Micro F1Score is the
* harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
*/
public Double getMicroF1Score() {
return this.microF1Score;
}
/**
*
* A measure of how accurate the classifier results are for the test data. It is a combination of the
* Micro Precision and Micro Recall values. The Micro F1Score is the harmonic
* mean of the two scores. The highest score is 1, and the worst score is 0.
*
*
* @param microF1Score
* A measure of how accurate the classifier results are for the test data. It is a combination of the
* Micro Precision and Micro Recall values. The Micro F1Score is the
* harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withMicroF1Score(Double microF1Score) {
setMicroF1Score(microF1Score);
return this;
}
/**
*
* Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels
* compared to the total number of labels. Scores closer to zero are better.
*
*
* @param hammingLoss
* Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels
* compared to the total number of labels. Scores closer to zero are better.
*/
public void setHammingLoss(Double hammingLoss) {
this.hammingLoss = hammingLoss;
}
/**
*
* Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels
* compared to the total number of labels. Scores closer to zero are better.
*
*
* @return Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong
* labels compared to the total number of labels. Scores closer to zero are better.
*/
public Double getHammingLoss() {
return this.hammingLoss;
}
/**
*
* Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels
* compared to the total number of labels. Scores closer to zero are better.
*
*
* @param hammingLoss
* Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels
* compared to the total number of labels. Scores closer to zero are better.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClassifierEvaluationMetrics withHammingLoss(Double hammingLoss) {
setHammingLoss(hammingLoss);
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 (getAccuracy() != null)
sb.append("Accuracy: ").append(getAccuracy()).append(",");
if (getPrecision() != null)
sb.append("Precision: ").append(getPrecision()).append(",");
if (getRecall() != null)
sb.append("Recall: ").append(getRecall()).append(",");
if (getF1Score() != null)
sb.append("F1Score: ").append(getF1Score()).append(",");
if (getMicroPrecision() != null)
sb.append("MicroPrecision: ").append(getMicroPrecision()).append(",");
if (getMicroRecall() != null)
sb.append("MicroRecall: ").append(getMicroRecall()).append(",");
if (getMicroF1Score() != null)
sb.append("MicroF1Score: ").append(getMicroF1Score()).append(",");
if (getHammingLoss() != null)
sb.append("HammingLoss: ").append(getHammingLoss());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof ClassifierEvaluationMetrics == false)
return false;
ClassifierEvaluationMetrics other = (ClassifierEvaluationMetrics) obj;
if (other.getAccuracy() == null ^ this.getAccuracy() == null)
return false;
if (other.getAccuracy() != null && other.getAccuracy().equals(this.getAccuracy()) == false)
return false;
if (other.getPrecision() == null ^ this.getPrecision() == null)
return false;
if (other.getPrecision() != null && other.getPrecision().equals(this.getPrecision()) == false)
return false;
if (other.getRecall() == null ^ this.getRecall() == null)
return false;
if (other.getRecall() != null && other.getRecall().equals(this.getRecall()) == false)
return false;
if (other.getF1Score() == null ^ this.getF1Score() == null)
return false;
if (other.getF1Score() != null && other.getF1Score().equals(this.getF1Score()) == false)
return false;
if (other.getMicroPrecision() == null ^ this.getMicroPrecision() == null)
return false;
if (other.getMicroPrecision() != null && other.getMicroPrecision().equals(this.getMicroPrecision()) == false)
return false;
if (other.getMicroRecall() == null ^ this.getMicroRecall() == null)
return false;
if (other.getMicroRecall() != null && other.getMicroRecall().equals(this.getMicroRecall()) == false)
return false;
if (other.getMicroF1Score() == null ^ this.getMicroF1Score() == null)
return false;
if (other.getMicroF1Score() != null && other.getMicroF1Score().equals(this.getMicroF1Score()) == false)
return false;
if (other.getHammingLoss() == null ^ this.getHammingLoss() == null)
return false;
if (other.getHammingLoss() != null && other.getHammingLoss().equals(this.getHammingLoss()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getAccuracy() == null) ? 0 : getAccuracy().hashCode());
hashCode = prime * hashCode + ((getPrecision() == null) ? 0 : getPrecision().hashCode());
hashCode = prime * hashCode + ((getRecall() == null) ? 0 : getRecall().hashCode());
hashCode = prime * hashCode + ((getF1Score() == null) ? 0 : getF1Score().hashCode());
hashCode = prime * hashCode + ((getMicroPrecision() == null) ? 0 : getMicroPrecision().hashCode());
hashCode = prime * hashCode + ((getMicroRecall() == null) ? 0 : getMicroRecall().hashCode());
hashCode = prime * hashCode + ((getMicroF1Score() == null) ? 0 : getMicroF1Score().hashCode());
hashCode = prime * hashCode + ((getHammingLoss() == null) ? 0 : getHammingLoss().hashCode());
return hashCode;
}
@Override
public ClassifierEvaluationMetrics clone() {
try {
return (ClassifierEvaluationMetrics) 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.comprehend.model.transform.ClassifierEvaluationMetricsMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}