/* * 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; /** *

* The collection of algorithms run on a dataset for training the model candidates of 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 run on a dataset to train the model candidates of an Autopilot job. *

* *

* 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" *
  *
*

*/ private java.util.List autoMLAlgorithms; /** *

* The selection of algorithms run on a dataset to train the model candidates of an Autopilot job. *

* *

* 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" *
  *
*

* * @return The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

* *

* 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" *
  *
*

getAutoMLAlgorithms() { return autoMLAlgorithms; } /** *

* The selection of algorithms run on a dataset to train the model candidates of an Autopilot job. *

* *

* 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" *
  *
*

* * @param autoMLAlgorithms * The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

* *

* 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" *
  *
*

autoMLAlgorithms) { if (autoMLAlgorithms == null) { this.autoMLAlgorithms = null; return; } this.autoMLAlgorithms = new java.util.ArrayList(autoMLAlgorithms); } /** *

* The selection of algorithms run on a dataset to train the model candidates of an Autopilot job. *

* *

* 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" *
  *
*

* 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 run on a dataset to train the model candidates of an Autopilot job.

* *

* 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" *
  *
*

(autoMLAlgorithms.length)); } for (String ele : autoMLAlgorithms) { this.autoMLAlgorithms.add(ele); } return this; } /** *

* The selection of algorithms run on a dataset to train the model candidates of an Autopilot job. *

* *

* 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" *
  *
*

* * @param autoMLAlgorithms * The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

* *

* 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" *
  *
*

autoMLAlgorithms) { setAutoMLAlgorithms(autoMLAlgorithms); return this; } /** *

* The selection of algorithms run on a dataset to train the model candidates of an Autopilot job. *

* *

* 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" *
  *
*

* * @param autoMLAlgorithms * The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

* *

* 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" *
  *
*

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); } }