com.amazonaws.services.sagemaker.model.CandidateGenerationConfig Maven / Gradle / Ivy
/*
* 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;
/**
*
* Stores the configuration information for how model candidates are generated using an AutoML job V2.
*
*
* @see AWS API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class CandidateGenerationConfig implements Serializable, Cloneable, StructuredPojo {
/**
*
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can
* customize the algorithm list by selecting a subset of algorithms for your problem type.
*
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to choose
* from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set
* to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your
* algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms support
* for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for time-series forecasting.
*
*
*
*
*
*/
private java.util.List algorithmsConfig;
/**
*
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can
* customize the algorithm list by selecting a subset of algorithms for your problem type.
*
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to choose
* from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set
* to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your
* algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms support
* for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for time-series forecasting.
*
*
*
*
*
*
* @return Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data,
* you can customize the algorithm list by selecting a subset of algorithms for your problem type.
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to
* choose from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode
* is set to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose
* your algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms
* support for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for time-series forecasting.
*
*
*
*
*/
public java.util.List getAlgorithmsConfig() {
return algorithmsConfig;
}
/**
*
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can
* customize the algorithm list by selecting a subset of algorithms for your problem type.
*
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to choose
* from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set
* to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your
* algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms support
* for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for time-series forecasting.
*
*
*
*
*
*
* @param algorithmsConfig
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data,
* you can customize the algorithm list by selecting a subset of algorithms for your problem type.
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to
* choose from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode
* is set to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose
* your algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms
* support for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for time-series forecasting.
*
*
*
*
*/
public void setAlgorithmsConfig(java.util.Collection algorithmsConfig) {
if (algorithmsConfig == null) {
this.algorithmsConfig = null;
return;
}
this.algorithmsConfig = new java.util.ArrayList(algorithmsConfig);
}
/**
*
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can
* customize the algorithm list by selecting a subset of algorithms for your problem type.
*
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to choose
* from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set
* to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your
* algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms support
* for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for time-series forecasting.
*
*
*
*
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setAlgorithmsConfig(java.util.Collection)} or {@link #withAlgorithmsConfig(java.util.Collection)} if you
* want to override the existing values.
*
*
* @param algorithmsConfig
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data,
* you can customize the algorithm list by selecting a subset of algorithms for your problem type.
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to
* choose from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode
* is set to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose
* your algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms
* support for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for time-series forecasting.
*
*
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CandidateGenerationConfig withAlgorithmsConfig(AutoMLAlgorithmConfig... algorithmsConfig) {
if (this.algorithmsConfig == null) {
setAlgorithmsConfig(new java.util.ArrayList(algorithmsConfig.length));
}
for (AutoMLAlgorithmConfig ele : algorithmsConfig) {
this.algorithmsConfig.add(ele);
}
return this;
}
/**
*
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can
* customize the algorithm list by selecting a subset of algorithms for your problem type.
*
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to choose
* from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set
* to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your
* algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms support
* for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one
* only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of
* algorithms for time-series forecasting.
*
*
*
*
*
*
* @param algorithmsConfig
* Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data,
* you can customize the algorithm list by selecting a subset of algorithms for your problem type.
*
* AlgorithmsConfig stores the customized selection of algorithms to train on your data.
*
*
* -
*
* For the tabular problem type TabularJobConfig, the list of available algorithms to
* choose from depends on the training mode set in
* AutoMLJobConfig.Mode .
*
*
* -
*
* AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode
* is set to AUTO.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for the given training mode.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for the given training mode.
*
*
*
*
* For the list of all algorithms per training mode, see
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose
* your algorithms from the list provided in
* AlgorithmConfig.
*
*
* For more information on each algorithm, see the Algorithms
* support for time-series forecasting section in the Autopilot developer guide.
*
*
* -
*
* When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set
* and one only.
*
*
* If the list of algorithms provided as values for AutoMLAlgorithms is empty,
* CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
*
*
* -
*
* When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full
* set of algorithms for time-series forecasting.
*
*
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CandidateGenerationConfig withAlgorithmsConfig(java.util.Collection algorithmsConfig) {
setAlgorithmsConfig(algorithmsConfig);
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 (getAlgorithmsConfig() != null)
sb.append("AlgorithmsConfig: ").append(getAlgorithmsConfig());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof CandidateGenerationConfig == false)
return false;
CandidateGenerationConfig other = (CandidateGenerationConfig) obj;
if (other.getAlgorithmsConfig() == null ^ this.getAlgorithmsConfig() == null)
return false;
if (other.getAlgorithmsConfig() != null && other.getAlgorithmsConfig().equals(this.getAlgorithmsConfig()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getAlgorithmsConfig() == null) ? 0 : getAlgorithmsConfig().hashCode());
return hashCode;
}
@Override
public CandidateGenerationConfig clone() {
try {
return (CandidateGenerationConfig) 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.CandidateGenerationConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}