com.amazonaws.services.sagemaker.model.AutoMLAlgorithmConfig 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;
/**
*
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
*
*
* @see AWS
* API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLAlgorithmConfig implements Serializable, Cloneable, StructuredPojo {
/**
*
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
*
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1
* algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
*
*/
private java.util.List autoMLAlgorithms;
/**
*
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
*
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1
* algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
*
*
* @return The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot
* job.
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a
* minimum of 1 algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
* @see AutoMLAlgorithm
*/
public java.util.List getAutoMLAlgorithms() {
return autoMLAlgorithms;
}
/**
*
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
*
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1
* algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
*
*
* @param autoMLAlgorithms
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot
* job.
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a
* minimum of 1 algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
* @see AutoMLAlgorithm
*/
public void setAutoMLAlgorithms(java.util.Collection autoMLAlgorithms) {
if (autoMLAlgorithms == null) {
this.autoMLAlgorithms = null;
return;
}
this.autoMLAlgorithms = new java.util.ArrayList(autoMLAlgorithms);
}
/**
*
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
*
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1
* algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setAutoMLAlgorithms(java.util.Collection)} or {@link #withAutoMLAlgorithms(java.util.Collection)} if you
* want to override the existing values.
*
*
* @param autoMLAlgorithms
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot
* job.
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a
* minimum of 1 algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLAlgorithm
*/
public AutoMLAlgorithmConfig withAutoMLAlgorithms(String... autoMLAlgorithms) {
if (this.autoMLAlgorithms == null) {
setAutoMLAlgorithms(new java.util.ArrayList(autoMLAlgorithms.length));
}
for (String ele : autoMLAlgorithms) {
this.autoMLAlgorithms.add(ele);
}
return this;
}
/**
*
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
*
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1
* algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
*
*
* @param autoMLAlgorithms
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot
* job.
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a
* minimum of 1 algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLAlgorithm
*/
public AutoMLAlgorithmConfig withAutoMLAlgorithms(java.util.Collection autoMLAlgorithms) {
setAutoMLAlgorithms(autoMLAlgorithms);
return this;
}
/**
*
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
*
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1
* algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
*
*
* @param autoMLAlgorithms
* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot
* job.
*
* -
*
* For the tabular problem type TabularJobConfig:
*
*
*
* Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a
* minimum of 1 algorithm.
*
*
*
* -
*
* In ENSEMBLING mode:
*
*
* -
*
* "catboost"
*
*
* -
*
* "extra-trees"
*
*
* -
*
* "fastai"
*
*
* -
*
* "lightgbm"
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "nn-torch"
*
*
* -
*
* "randomforest"
*
*
* -
*
* "xgboost"
*
*
*
*
* -
*
* In HYPERPARAMETER_TUNING mode:
*
*
* -
*
* "linear-learner"
*
*
* -
*
* "mlp"
*
*
* -
*
* "xgboost"
*
*
*
*
*
*
* -
*
* For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
*
*
* -
*
* Choose your algorithms from this list.
*
*
* -
*
* "cnn-qr"
*
*
* -
*
* "deepar"
*
*
* -
*
* "prophet"
*
*
* -
*
* "arima"
*
*
* -
*
* "npts"
*
*
* -
*
* "ets"
*
*
*
*
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLAlgorithm
*/
public AutoMLAlgorithmConfig withAutoMLAlgorithms(AutoMLAlgorithm... autoMLAlgorithms) {
java.util.ArrayList autoMLAlgorithmsCopy = new java.util.ArrayList(autoMLAlgorithms.length);
for (AutoMLAlgorithm value : autoMLAlgorithms) {
autoMLAlgorithmsCopy.add(value.toString());
}
if (getAutoMLAlgorithms() == null) {
setAutoMLAlgorithms(autoMLAlgorithmsCopy);
} else {
getAutoMLAlgorithms().addAll(autoMLAlgorithmsCopy);
}
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 (getAutoMLAlgorithms() != null)
sb.append("AutoMLAlgorithms: ").append(getAutoMLAlgorithms());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof AutoMLAlgorithmConfig == false)
return false;
AutoMLAlgorithmConfig other = (AutoMLAlgorithmConfig) obj;
if (other.getAutoMLAlgorithms() == null ^ this.getAutoMLAlgorithms() == null)
return false;
if (other.getAutoMLAlgorithms() != null && other.getAutoMLAlgorithms().equals(this.getAutoMLAlgorithms()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getAutoMLAlgorithms() == null) ? 0 : getAutoMLAlgorithms().hashCode());
return hashCode;
}
@Override
public AutoMLAlgorithmConfig clone() {
try {
return (AutoMLAlgorithmConfig) 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.AutoMLAlgorithmConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}