com.amazonaws.services.sagemaker.model.AutoMLDataSplitConfig 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.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* This structure specifies how to split the data into train and validation datasets.
*
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
* @see AWS
* API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLDataSplitConfig implements Serializable, Cloneable, StructuredPojo {
/**
*
* The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for
* validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting
* this value to be less than 0.5.
*
*/
private Float validationFraction;
/**
*
* The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for
* validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting
* this value to be less than 0.5.
*
*
* @param validationFraction
* The validation fraction (optional) is a float that specifies the portion of the training dataset to be
* used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We
* recommend setting this value to be less than 0.5.
*/
public void setValidationFraction(Float validationFraction) {
this.validationFraction = validationFraction;
}
/**
*
* The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for
* validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting
* this value to be less than 0.5.
*
*
* @return The validation fraction (optional) is a float that specifies the portion of the training dataset to be
* used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We
* recommend setting this value to be less than 0.5.
*/
public Float getValidationFraction() {
return this.validationFraction;
}
/**
*
* The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for
* validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting
* this value to be less than 0.5.
*
*
* @param validationFraction
* The validation fraction (optional) is a float that specifies the portion of the training dataset to be
* used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We
* recommend setting this value to be less than 0.5.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AutoMLDataSplitConfig withValidationFraction(Float validationFraction) {
setValidationFraction(validationFraction);
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 (getValidationFraction() != null)
sb.append("ValidationFraction: ").append(getValidationFraction());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof AutoMLDataSplitConfig == false)
return false;
AutoMLDataSplitConfig other = (AutoMLDataSplitConfig) obj;
if (other.getValidationFraction() == null ^ this.getValidationFraction() == null)
return false;
if (other.getValidationFraction() != null && other.getValidationFraction().equals(this.getValidationFraction()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getValidationFraction() == null) ? 0 : getValidationFraction().hashCode());
return hashCode;
}
@Override
public AutoMLDataSplitConfig clone() {
try {
return (AutoMLDataSplitConfig) 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.AutoMLDataSplitConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}