/* * 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; /** *

* Stores the configuration information for how a candidate is generated (optional). *

* * @see AWS API Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class AutoMLCandidateGenerationConfig implements Serializable, Cloneable, StructuredPojo { /** *

* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot * job. You can input FeatureAttributeNames (optional) in JSON format as shown below: *

*

* { "FeatureAttributeNames":["col1", "col2", ...] }. *

*

* You can also specify the data type of the feature (optional) in the format shown below: *

*

* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } } *

* *

* These column keys may not include the target column. *

* *

* In ensembling mode, Autopilot only supports the following data types: numeric, * categorical, text, and datetime. In HPO mode, Autopilot can support * numeric, categorical, text, datetime, and * sequence. *

*

* If only FeatureDataTypes is provided, the column keys (col1, col2,..) * should be a subset of the column names in the input data. *

*

* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys * should be a subset of the column names provided in FeatureAttributeNames. *

*

* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] * are case sensitive and should be a list of strings containing unique values that are a subset of the column names * in the input data. The list of columns provided must not include the target column. *

*/ private String featureSpecificationS3Uri; /** *

* Stores the configuration information for the selection of algorithms used to train the model candidates. *

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. *

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

* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot * job. You can input FeatureAttributeNames (optional) in JSON format as shown below: *

*

* { "FeatureAttributeNames":["col1", "col2", ...] }. *

*

* You can also specify the data type of the feature (optional) in the format shown below: *

*

* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } } *

* *

* These column keys may not include the target column. *

* *

* In ensembling mode, Autopilot only supports the following data types: numeric, * categorical, text, and datetime. In HPO mode, Autopilot can support * numeric, categorical, text, datetime, and * sequence. *

*

* If only FeatureDataTypes is provided, the column keys (col1, col2,..) * should be a subset of the column names in the input data. *

*

* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys * should be a subset of the column names provided in FeatureAttributeNames. *

*

* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] * are case sensitive and should be a list of strings containing unique values that are a subset of the column names * in the input data. The list of columns provided must not include the target column. *

* * @param featureSpecificationS3Uri * A URL to the Amazon S3 data source containing selected features from the input data source to run an * Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below: *

*

* { "FeatureAttributeNames":["col1", "col2", ...] }. *

*

* You can also specify the data type of the feature (optional) in the format shown below: *

*

* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } } *

* *

* These column keys may not include the target column. *

* *

* In ensembling mode, Autopilot only supports the following data types: numeric, * categorical, text, and datetime. In HPO mode, Autopilot can support * numeric, categorical, text, datetime, and * sequence. *

*

* If only FeatureDataTypes is provided, the column keys (col1, col2 * ,..) should be a subset of the column names in the input data. *

*

* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column * keys should be a subset of the column names provided in FeatureAttributeNames. *

*

* The key name FeatureAttributeNames is fixed. The values listed in * ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique * values that are a subset of the column names in the input data. The list of columns provided must not * include the target column. */ public void setFeatureSpecificationS3Uri(String featureSpecificationS3Uri) { this.featureSpecificationS3Uri = featureSpecificationS3Uri; } /** *

* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot * job. You can input FeatureAttributeNames (optional) in JSON format as shown below: *

*

* { "FeatureAttributeNames":["col1", "col2", ...] }. *

*

* You can also specify the data type of the feature (optional) in the format shown below: *

*

* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } } *

* *

* These column keys may not include the target column. *

* *

* In ensembling mode, Autopilot only supports the following data types: numeric, * categorical, text, and datetime. In HPO mode, Autopilot can support * numeric, categorical, text, datetime, and * sequence. *

*

* If only FeatureDataTypes is provided, the column keys (col1, col2,..) * should be a subset of the column names in the input data. *

*

* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys * should be a subset of the column names provided in FeatureAttributeNames. *

*

* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] * are case sensitive and should be a list of strings containing unique values that are a subset of the column names * in the input data. The list of columns provided must not include the target column. *

* * @return A URL to the Amazon S3 data source containing selected features from the input data source to run an * Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below: *

*

* { "FeatureAttributeNames":["col1", "col2", ...] }. *

*

* You can also specify the data type of the feature (optional) in the format shown below: *

*

* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } } *

* *

* These column keys may not include the target column. *

* *

* In ensembling mode, Autopilot only supports the following data types: numeric, * categorical, text, and datetime. In HPO mode, Autopilot can * support numeric, categorical, text, datetime, and * sequence. *

*

* If only FeatureDataTypes is provided, the column keys (col1, col2 * ,..) should be a subset of the column names in the input data. *

*

* If both FeatureDataTypes and FeatureAttributeNames are provided, then the * column keys should be a subset of the column names provided in FeatureAttributeNames. *

*

* The key name FeatureAttributeNames is fixed. The values listed in * ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique * values that are a subset of the column names in the input data. The list of columns provided must not * include the target column. */ public String getFeatureSpecificationS3Uri() { return this.featureSpecificationS3Uri; } /** *

* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot * job. You can input FeatureAttributeNames (optional) in JSON format as shown below: *

*

* { "FeatureAttributeNames":["col1", "col2", ...] }. *

*

* You can also specify the data type of the feature (optional) in the format shown below: *

*

* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } } *

* *

* These column keys may not include the target column. *

* *

* In ensembling mode, Autopilot only supports the following data types: numeric, * categorical, text, and datetime. In HPO mode, Autopilot can support * numeric, categorical, text, datetime, and * sequence. *

*

* If only FeatureDataTypes is provided, the column keys (col1, col2,..) * should be a subset of the column names in the input data. *

*

* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys * should be a subset of the column names provided in FeatureAttributeNames. *

*

* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] * are case sensitive and should be a list of strings containing unique values that are a subset of the column names * in the input data. The list of columns provided must not include the target column. *

* * @param featureSpecificationS3Uri * A URL to the Amazon S3 data source containing selected features from the input data source to run an * Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below: *

*

* { "FeatureAttributeNames":["col1", "col2", ...] }. *

*

* You can also specify the data type of the feature (optional) in the format shown below: *

*

* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } } *

* *

* These column keys may not include the target column. *

* *

* In ensembling mode, Autopilot only supports the following data types: numeric, * categorical, text, and datetime. In HPO mode, Autopilot can support * numeric, categorical, text, datetime, and * sequence. *

*

* If only FeatureDataTypes is provided, the column keys (col1, col2 * ,..) should be a subset of the column names in the input data. *

*

* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column * keys should be a subset of the column names provided in FeatureAttributeNames. *

*

* The key name FeatureAttributeNames is fixed. The values listed in * ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique * values that are a subset of the column names in the input data. The list of columns provided must not * include the target column. * @return Returns a reference to this object so that method calls can be chained together. */ public AutoMLCandidateGenerationConfig withFeatureSpecificationS3Uri(String featureSpecificationS3Uri) { setFeatureSpecificationS3Uri(featureSpecificationS3Uri); return this; } /** *

* Stores the configuration information for the selection of algorithms used to train the model candidates. *

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. *

* * @return Stores the configuration information for the selection of algorithms used to train the model * candidates.

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. */ public java.util.List getAlgorithmsConfig() { return algorithmsConfig; } /** *

* Stores the configuration information for the selection of algorithms used to train the model candidates. *

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. *

* * @param algorithmsConfig * Stores the configuration information for the selection of algorithms used to train the model * candidates.

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. */ public void setAlgorithmsConfig(java.util.Collection algorithmsConfig) { if (algorithmsConfig == null) { this.algorithmsConfig = null; return; } this.algorithmsConfig = new java.util.ArrayList(algorithmsConfig); } /** *

* Stores the configuration information for the selection of algorithms used to train the model candidates. *

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. *

*

* 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 * Stores the configuration information for the selection of algorithms used to train the model * candidates.

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. * @return Returns a reference to this object so that method calls can be chained together. */ public AutoMLCandidateGenerationConfig withAlgorithmsConfig(AutoMLAlgorithmConfig... algorithmsConfig) { if (this.algorithmsConfig == null) { setAlgorithmsConfig(new java.util.ArrayList(algorithmsConfig.length)); } for (AutoMLAlgorithmConfig ele : algorithmsConfig) { this.algorithmsConfig.add(ele); } return this; } /** *

* Stores the configuration information for the selection of algorithms used to train the model candidates. *

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. *

* * @param algorithmsConfig * Stores the configuration information for the selection of algorithms used to train the model * candidates.

*

* The list of available algorithms to choose from depends on the training mode set in * AutoMLJobConfig.Mode . *

* *

* For the list of all algorithms per training mode, see * AutoMLAlgorithmConfig. *

*

* For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. * @return Returns a reference to this object so that method calls can be chained together. */ public AutoMLCandidateGenerationConfig 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 (getFeatureSpecificationS3Uri() != null) sb.append("FeatureSpecificationS3Uri: ").append(getFeatureSpecificationS3Uri()).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 AutoMLCandidateGenerationConfig == false) return false; AutoMLCandidateGenerationConfig other = (AutoMLCandidateGenerationConfig) obj; if (other.getFeatureSpecificationS3Uri() == null ^ this.getFeatureSpecificationS3Uri() == null) return false; if (other.getFeatureSpecificationS3Uri() != null && other.getFeatureSpecificationS3Uri().equals(this.getFeatureSpecificationS3Uri()) == false) return false; 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 + ((getFeatureSpecificationS3Uri() == null) ? 0 : getFeatureSpecificationS3Uri().hashCode()); hashCode = prime * hashCode + ((getAlgorithmsConfig() == null) ? 0 : getAlgorithmsConfig().hashCode()); return hashCode; } @Override public AutoMLCandidateGenerationConfig clone() { try { return (AutoMLCandidateGenerationConfig) 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.AutoMLCandidateGenerationConfigMarshaller.getInstance().marshall(this, protocolMarshaller); } }