/*
* Copyright 2012-2017 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.machinelearning.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.AmazonWebServiceRequest;
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class UpdateMLModelRequest extends com.amazonaws.AmazonWebServiceRequest implements Serializable, Cloneable {
/**
* <p>
* The ID assigned to the <code>MLModel</code> during creation.
* </p>
*/
private String mLModelId;
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*/
private String mLModelName;
/**
* <p>
* The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary
* between a positive prediction and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the
* <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive
* a negative response from the <code>MLModel</code>, such as <code>false</code>.
* </p>
*/
private Float scoreThreshold;
/**
* <p>
* The ID assigned to the <code>MLModel</code> during creation.
* </p>
*
* @param mLModelId
* The ID assigned to the <code>MLModel</code> during creation.
*/
public void setMLModelId(String mLModelId) {
this.mLModelId = mLModelId;
}
/**
* <p>
* The ID assigned to the <code>MLModel</code> during creation.
* </p>
*
* @return The ID assigned to the <code>MLModel</code> during creation.
*/
public String getMLModelId() {
return this.mLModelId;
}
/**
* <p>
* The ID assigned to the <code>MLModel</code> during creation.
* </p>
*
* @param mLModelId
* The ID assigned to the <code>MLModel</code> during creation.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public UpdateMLModelRequest withMLModelId(String mLModelId) {
setMLModelId(mLModelId);
return this;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*
* @param mLModelName
* A user-supplied name or description of the <code>MLModel</code>.
*/
public void setMLModelName(String mLModelName) {
this.mLModelName = mLModelName;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*
* @return A user-supplied name or description of the <code>MLModel</code>.
*/
public String getMLModelName() {
return this.mLModelName;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*
* @param mLModelName
* A user-supplied name or description of the <code>MLModel</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public UpdateMLModelRequest withMLModelName(String mLModelName) {
setMLModelName(mLModelName);
return this;
}
/**
* <p>
* The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary
* between a positive prediction and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the
* <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive
* a negative response from the <code>MLModel</code>, such as <code>false</code>.
* </p>
*
* @param scoreThreshold
* The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary
* between a positive prediction and a negative prediction.</p>
* <p>
* Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the
* <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code>
* receive a negative response from the <code>MLModel</code>, such as <code>false</code>.
*/
public void setScoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
}
/**
* <p>
* The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary
* between a positive prediction and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the
* <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive
* a negative response from the <code>MLModel</code>, such as <code>false</code>.
* </p>
*
* @return The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the
* boundary between a positive prediction and a negative prediction.</p>
* <p>
* Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the
* <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code>
* receive a negative response from the <code>MLModel</code>, such as <code>false</code>.
*/
public Float getScoreThreshold() {
return this.scoreThreshold;
}
/**
* <p>
* The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary
* between a positive prediction and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the
* <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive
* a negative response from the <code>MLModel</code>, such as <code>false</code>.
* </p>
*
* @param scoreThreshold
* The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary
* between a positive prediction and a negative prediction.</p>
* <p>
* Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the
* <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code>
* receive a negative response from the <code>MLModel</code>, such as <code>false</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public UpdateMLModelRequest withScoreThreshold(Float scoreThreshold) {
setScoreThreshold(scoreThreshold);
return this;
}
/**
* Returns a string representation of this object; useful for testing and debugging.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getMLModelId() != null)
sb.append("MLModelId: ").append(getMLModelId()).append(",");
if (getMLModelName() != null)
sb.append("MLModelName: ").append(getMLModelName()).append(",");
if (getScoreThreshold() != null)
sb.append("ScoreThreshold: ").append(getScoreThreshold());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof UpdateMLModelRequest == false)
return false;
UpdateMLModelRequest other = (UpdateMLModelRequest) obj;
if (other.getMLModelId() == null ^ this.getMLModelId() == null)
return false;
if (other.getMLModelId() != null && other.getMLModelId().equals(this.getMLModelId()) == false)
return false;
if (other.getMLModelName() == null ^ this.getMLModelName() == null)
return false;
if (other.getMLModelName() != null && other.getMLModelName().equals(this.getMLModelName()) == false)
return false;
if (other.getScoreThreshold() == null ^ this.getScoreThreshold() == null)
return false;
if (other.getScoreThreshold() != null && other.getScoreThreshold().equals(this.getScoreThreshold()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getMLModelId() == null) ? 0 : getMLModelId().hashCode());
hashCode = prime * hashCode + ((getMLModelName() == null) ? 0 : getMLModelName().hashCode());
hashCode = prime * hashCode + ((getScoreThreshold() == null) ? 0 : getScoreThreshold().hashCode());
return hashCode;
}
@Override
public UpdateMLModelRequest clone() {
return (UpdateMLModelRequest) super.clone();
}
}