/*
* 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 collection of settings used for an AutoML job.
* </p>
*
* @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/AutoMLJobConfig" target="_top">AWS API
* Documentation</a>
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLJobConfig implements Serializable, Cloneable, StructuredPojo {
/**
* <p>
* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
* </p>
*/
private AutoMLJobCompletionCriteria completionCriteria;
/**
* <p>
* The security configuration for traffic encryption or Amazon VPC settings.
* </p>
*/
private AutoMLSecurityConfig securityConfig;
/**
* <p>
* The configuration for splitting the input training dataset.
* </p>
* <p>
* Type: AutoMLDataSplitConfig
* </p>
*/
private AutoMLDataSplitConfig dataSplitConfig;
/**
* <p>
* The configuration for generating a candidate for an AutoML job (optional).
* </p>
*/
private AutoMLCandidateGenerationConfig candidateGenerationConfig;
/**
* <p>
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code> mode, Autopilot
* chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and <code>HYPERPARAMETER_TUNING</code> for
* larger ones.
* </p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code> mode.
* </p>
*/
private String mode;
/**
* <p>
* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
* </p>
*
* @param completionCriteria
* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
*/
public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) {
this.completionCriteria = completionCriteria;
}
/**
* <p>
* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
* </p>
*
* @return How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
*/
public AutoMLJobCompletionCriteria getCompletionCriteria() {
return this.completionCriteria;
}
/**
* <p>
* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
* </p>
*
* @param completionCriteria
* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AutoMLJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) {
setCompletionCriteria(completionCriteria);
return this;
}
/**
* <p>
* The security configuration for traffic encryption or Amazon VPC settings.
* </p>
*
* @param securityConfig
* The security configuration for traffic encryption or Amazon VPC settings.
*/
public void setSecurityConfig(AutoMLSecurityConfig securityConfig) {
this.securityConfig = securityConfig;
}
/**
* <p>
* The security configuration for traffic encryption or Amazon VPC settings.
* </p>
*
* @return The security configuration for traffic encryption or Amazon VPC settings.
*/
public AutoMLSecurityConfig getSecurityConfig() {
return this.securityConfig;
}
/**
* <p>
* The security configuration for traffic encryption or Amazon VPC settings.
* </p>
*
* @param securityConfig
* The security configuration for traffic encryption or Amazon VPC settings.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AutoMLJobConfig withSecurityConfig(AutoMLSecurityConfig securityConfig) {
setSecurityConfig(securityConfig);
return this;
}
/**
* <p>
* The configuration for splitting the input training dataset.
* </p>
* <p>
* Type: AutoMLDataSplitConfig
* </p>
*
* @param dataSplitConfig
* The configuration for splitting the input training dataset.</p>
* <p>
* Type: AutoMLDataSplitConfig
*/
public void setDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) {
this.dataSplitConfig = dataSplitConfig;
}
/**
* <p>
* The configuration for splitting the input training dataset.
* </p>
* <p>
* Type: AutoMLDataSplitConfig
* </p>
*
* @return The configuration for splitting the input training dataset.</p>
* <p>
* Type: AutoMLDataSplitConfig
*/
public AutoMLDataSplitConfig getDataSplitConfig() {
return this.dataSplitConfig;
}
/**
* <p>
* The configuration for splitting the input training dataset.
* </p>
* <p>
* Type: AutoMLDataSplitConfig
* </p>
*
* @param dataSplitConfig
* The configuration for splitting the input training dataset.</p>
* <p>
* Type: AutoMLDataSplitConfig
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AutoMLJobConfig withDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) {
setDataSplitConfig(dataSplitConfig);
return this;
}
/**
* <p>
* The configuration for generating a candidate for an AutoML job (optional).
* </p>
*
* @param candidateGenerationConfig
* The configuration for generating a candidate for an AutoML job (optional).
*/
public void setCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig) {
this.candidateGenerationConfig = candidateGenerationConfig;
}
/**
* <p>
* The configuration for generating a candidate for an AutoML job (optional).
* </p>
*
* @return The configuration for generating a candidate for an AutoML job (optional).
*/
public AutoMLCandidateGenerationConfig getCandidateGenerationConfig() {
return this.candidateGenerationConfig;
}
/**
* <p>
* The configuration for generating a candidate for an AutoML job (optional).
* </p>
*
* @param candidateGenerationConfig
* The configuration for generating a candidate for an AutoML job (optional).
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AutoMLJobConfig withCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig) {
setCandidateGenerationConfig(candidateGenerationConfig);
return this;
}
/**
* <p>
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code> mode, Autopilot
* chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and <code>HYPERPARAMETER_TUNING</code> for
* larger ones.
* </p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code> mode.
* </p>
*
* @param mode
* The method that Autopilot uses to train the data. You can either specify the mode manually or let
* Autopilot choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code>
* mode, Autopilot chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and
* <code>HYPERPARAMETER_TUNING</code> for larger ones.</p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code>
* mode.
* @see AutoMLMode
*/
public void setMode(String mode) {
this.mode = mode;
}
/**
* <p>
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code> mode, Autopilot
* chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and <code>HYPERPARAMETER_TUNING</code> for
* larger ones.
* </p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code> mode.
* </p>
*
* @return The method that Autopilot uses to train the data. You can either specify the mode manually or let
* Autopilot choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code>
* mode, Autopilot chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and
* <code>HYPERPARAMETER_TUNING</code> for larger ones.</p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code>
* mode.
* @see AutoMLMode
*/
public String getMode() {
return this.mode;
}
/**
* <p>
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code> mode, Autopilot
* chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and <code>HYPERPARAMETER_TUNING</code> for
* larger ones.
* </p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code> mode.
* </p>
*
* @param mode
* The method that Autopilot uses to train the data. You can either specify the mode manually or let
* Autopilot choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code>
* mode, Autopilot chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and
* <code>HYPERPARAMETER_TUNING</code> for larger ones.</p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code>
* mode.
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMode
*/
public AutoMLJobConfig withMode(String mode) {
setMode(mode);
return this;
}
/**
* <p>
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code> mode, Autopilot
* chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and <code>HYPERPARAMETER_TUNING</code> for
* larger ones.
* </p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code> mode.
* </p>
*
* @param mode
* The method that Autopilot uses to train the data. You can either specify the mode manually or let
* Autopilot choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code>
* mode, Autopilot chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and
* <code>HYPERPARAMETER_TUNING</code> for larger ones.</p>
* <p>
* The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.
* </p>
* <p>
* The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See <a href=
* "https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support"
* >Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code>
* mode.
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMode
*/
public AutoMLJobConfig withMode(AutoMLMode mode) {
this.mode = mode.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 (getCompletionCriteria() != null)
sb.append("CompletionCriteria: ").append(getCompletionCriteria()).append(",");
if (getSecurityConfig() != null)
sb.append("SecurityConfig: ").append(getSecurityConfig()).append(",");
if (getDataSplitConfig() != null)
sb.append("DataSplitConfig: ").append(getDataSplitConfig()).append(",");
if (getCandidateGenerationConfig() != null)
sb.append("CandidateGenerationConfig: ").append(getCandidateGenerationConfig()).append(",");
if (getMode() != null)
sb.append("Mode: ").append(getMode());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof AutoMLJobConfig == false)
return false;
AutoMLJobConfig other = (AutoMLJobConfig) obj;
if (other.getCompletionCriteria() == null ^ this.getCompletionCriteria() == null)
return false;
if (other.getCompletionCriteria() != null && other.getCompletionCriteria().equals(this.getCompletionCriteria()) == false)
return false;
if (other.getSecurityConfig() == null ^ this.getSecurityConfig() == null)
return false;
if (other.getSecurityConfig() != null && other.getSecurityConfig().equals(this.getSecurityConfig()) == false)
return false;
if (other.getDataSplitConfig() == null ^ this.getDataSplitConfig() == null)
return false;
if (other.getDataSplitConfig() != null && other.getDataSplitConfig().equals(this.getDataSplitConfig()) == false)
return false;
if (other.getCandidateGenerationConfig() == null ^ this.getCandidateGenerationConfig() == null)
return false;
if (other.getCandidateGenerationConfig() != null && other.getCandidateGenerationConfig().equals(this.getCandidateGenerationConfig()) == false)
return false;
if (other.getMode() == null ^ this.getMode() == null)
return false;
if (other.getMode() != null && other.getMode().equals(this.getMode()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getCompletionCriteria() == null) ? 0 : getCompletionCriteria().hashCode());
hashCode = prime * hashCode + ((getSecurityConfig() == null) ? 0 : getSecurityConfig().hashCode());
hashCode = prime * hashCode + ((getDataSplitConfig() == null) ? 0 : getDataSplitConfig().hashCode());
hashCode = prime * hashCode + ((getCandidateGenerationConfig() == null) ? 0 : getCandidateGenerationConfig().hashCode());
hashCode = prime * hashCode + ((getMode() == null) ? 0 : getMode().hashCode());
return hashCode;
}
@Override
public AutoMLJobConfig clone() {
try {
return (AutoMLJobConfig) 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.AutoMLJobConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}