/*
* 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.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
* <p>
* A list of continuous hyperparameters to tune.
* </p>
*
* @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ContinuousParameterRange" target="_top">AWS
* API Documentation</a>
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class ContinuousParameterRange implements Serializable, Cloneable, StructuredPojo {
/**
* <p>
* The name of the continuous hyperparameter to tune.
* </p>
*/
private String name;
/**
* <p>
* The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
* <code>MaxValue</code>for tuning.
* </p>
*/
private String minValue;
/**
* <p>
* The maximum value for the hyperparameter. The tuning job uses floating-point values between <code>MinValue</code>
* value and this value for tuning.
* </p>
*/
private String maxValue;
/**
* <p>
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a
* hyperparameter scale, see <a
* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:
* </p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* </dl>
*/
private String scalingType;
/**
* <p>
* The name of the continuous hyperparameter to tune.
* </p>
*
* @param name
* The name of the continuous hyperparameter to tune.
*/
public void setName(String name) {
this.name = name;
}
/**
* <p>
* The name of the continuous hyperparameter to tune.
* </p>
*
* @return The name of the continuous hyperparameter to tune.
*/
public String getName() {
return this.name;
}
/**
* <p>
* The name of the continuous hyperparameter to tune.
* </p>
*
* @param name
* The name of the continuous hyperparameter to tune.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContinuousParameterRange withName(String name) {
setName(name);
return this;
}
/**
* <p>
* The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
* <code>MaxValue</code>for tuning.
* </p>
*
* @param minValue
* The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
* <code>MaxValue</code>for tuning.
*/
public void setMinValue(String minValue) {
this.minValue = minValue;
}
/**
* <p>
* The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
* <code>MaxValue</code>for tuning.
* </p>
*
* @return The minimum value for the hyperparameter. The tuning job uses floating-point values between this value
* and <code>MaxValue</code>for tuning.
*/
public String getMinValue() {
return this.minValue;
}
/**
* <p>
* The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
* <code>MaxValue</code>for tuning.
* </p>
*
* @param minValue
* The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
* <code>MaxValue</code>for tuning.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContinuousParameterRange withMinValue(String minValue) {
setMinValue(minValue);
return this;
}
/**
* <p>
* The maximum value for the hyperparameter. The tuning job uses floating-point values between <code>MinValue</code>
* value and this value for tuning.
* </p>
*
* @param maxValue
* The maximum value for the hyperparameter. The tuning job uses floating-point values between
* <code>MinValue</code> value and this value for tuning.
*/
public void setMaxValue(String maxValue) {
this.maxValue = maxValue;
}
/**
* <p>
* The maximum value for the hyperparameter. The tuning job uses floating-point values between <code>MinValue</code>
* value and this value for tuning.
* </p>
*
* @return The maximum value for the hyperparameter. The tuning job uses floating-point values between
* <code>MinValue</code> value and this value for tuning.
*/
public String getMaxValue() {
return this.maxValue;
}
/**
* <p>
* The maximum value for the hyperparameter. The tuning job uses floating-point values between <code>MinValue</code>
* value and this value for tuning.
* </p>
*
* @param maxValue
* The maximum value for the hyperparameter. The tuning job uses floating-point values between
* <code>MinValue</code> value and this value for tuning.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContinuousParameterRange withMaxValue(String maxValue) {
setMaxValue(maxValue);
return this;
}
/**
* <p>
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a
* hyperparameter scale, see <a
* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:
* </p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* </dl>
*
* @param scalingType
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about
* choosing a hyperparameter scale, see <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:</p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic
* scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* @see HyperParameterScalingType
*/
public void setScalingType(String scalingType) {
this.scalingType = scalingType;
}
/**
* <p>
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a
* hyperparameter scale, see <a
* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:
* </p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* </dl>
*
* @return The scale that hyperparameter tuning uses to search the hyperparameter range. For information about
* choosing a hyperparameter scale, see <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:</p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic
* scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* @see HyperParameterScalingType
*/
public String getScalingType() {
return this.scalingType;
}
/**
* <p>
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a
* hyperparameter scale, see <a
* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:
* </p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* </dl>
*
* @param scalingType
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about
* choosing a hyperparameter scale, see <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:</p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic
* scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* @return Returns a reference to this object so that method calls can be chained together.
* @see HyperParameterScalingType
*/
public ContinuousParameterRange withScalingType(String scalingType) {
setScalingType(scalingType);
return this;
}
/**
* <p>
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a
* hyperparameter scale, see <a
* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:
* </p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* </dl>
*
* @param scalingType
* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about
* choosing a hyperparameter scale, see <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type"
* >Hyperparameter Scaling</a>. One of the following values:</p>
* <dl>
* <dt>Auto</dt>
* <dd>
* <p>
* SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
* </p>
* </dd>
* <dt>Linear</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
* </p>
* </dd>
* <dt>Logarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
* </p>
* <p>
* Logarithmic scaling works only for ranges that have only values greater than 0.
* </p>
* </dd>
* <dt>ReverseLogarithmic</dt>
* <dd>
* <p>
* Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic
* scale.
* </p>
* <p>
* Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
* </p>
* </dd>
* @return Returns a reference to this object so that method calls can be chained together.
* @see HyperParameterScalingType
*/
public ContinuousParameterRange withScalingType(HyperParameterScalingType scalingType) {
this.scalingType = scalingType.toString();
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 (getName() != null)
sb.append("Name: ").append(getName()).append(",");
if (getMinValue() != null)
sb.append("MinValue: ").append(getMinValue()).append(",");
if (getMaxValue() != null)
sb.append("MaxValue: ").append(getMaxValue()).append(",");
if (getScalingType() != null)
sb.append("ScalingType: ").append(getScalingType());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof ContinuousParameterRange == false)
return false;
ContinuousParameterRange other = (ContinuousParameterRange) obj;
if (other.getName() == null ^ this.getName() == null)
return false;
if (other.getName() != null && other.getName().equals(this.getName()) == false)
return false;
if (other.getMinValue() == null ^ this.getMinValue() == null)
return false;
if (other.getMinValue() != null && other.getMinValue().equals(this.getMinValue()) == false)
return false;
if (other.getMaxValue() == null ^ this.getMaxValue() == null)
return false;
if (other.getMaxValue() != null && other.getMaxValue().equals(this.getMaxValue()) == false)
return false;
if (other.getScalingType() == null ^ this.getScalingType() == null)
return false;
if (other.getScalingType() != null && other.getScalingType().equals(this.getScalingType()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getName() == null) ? 0 : getName().hashCode());
hashCode = prime * hashCode + ((getMinValue() == null) ? 0 : getMinValue().hashCode());
hashCode = prime * hashCode + ((getMaxValue() == null) ? 0 : getMaxValue().hashCode());
hashCode = prime * hashCode + ((getScalingType() == null) ? 0 : getScalingType().hashCode());
return hashCode;
}
@Override
public ContinuousParameterRange clone() {
try {
return (ContinuousParameterRange) 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.sagemaker.model.transform.ContinuousParameterRangeMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}