/*
* Copyright 2012-2017 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.machinelearning.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
* <p>
* The data specification of an Amazon Relational Database Service (Amazon RDS) <code>DataSource</code>.
* </p>
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class RDSDataSpec implements Serializable, Cloneable, StructuredPojo {
/**
* <p>
* Describes the <code>DatabaseName</code> and <code>InstanceIdentifier</code> of an Amazon RDS database.
* </p>
*/
private RDSDatabase databaseInformation;
/**
* <p>
* The query that is used to retrieve the observation data for the <code>DataSource</code>.
* </p>
*/
private String selectSqlQuery;
/**
* <p>
* The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
* </p>
*/
private RDSDatabaseCredentials databaseCredentials;
/**
* <p>
* The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
* <code>SelectSqlQuery</code> is stored in this location.
* </p>
*/
private String s3StagingLocation;
/**
* <p>
* A JSON string that represents the splitting and rearrangement processing to be applied to a
* <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input data
* is used to create the <code>Datasource</code>.
* </p>
* <p>
* There are multiple parameters that control what data is used to create a datasource:
* </p>
* <ul>
* <li>
* <p>
* <b><code>percentBegin</code></b>
* </p>
* <p>
* Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the Datasource.
* If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data
* when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>percentEnd</code></b>
* </p>
* <p>
* Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If you do
* not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when
* creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>complement</code></b>
* </p>
* <p>
* The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the range of
* <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The <code>complement</code>
* parameter is useful if you need to create complementary datasources for training and evaluation. To create a
* complementary datasource, use the same values for <code>percentBegin</code> and <code>percentEnd</code>, along
* with the <code>complement</code> parameter.
* </p>
* <p>
* For example, the following two datasources do not share any data, and can be used to train and evaluate a model.
* The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
* </p>
* <p>
* Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code>
* </p>
* <p>
* Datasource for training: <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code>
* </p>
* </li>
* <li>
* <p>
* <b><code>strategy</code></b>
* </p>
* <p>
* To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.
* </p>
* <p>
* The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon ML
* takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code> parameters for
* the datasource, in the order that the records appear in the input data.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code>
* </p>
* <p>
* To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters,
* set the <code>strategy</code> parameter to <code>random</code> and provide a string that is used as the seed
* value for the random data splitting (for example, you can use the S3 path to your data as the random seed
* string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number
* between 0 and 100, and then selects the rows that have an assigned number between <code>percentBegin</code> and
* <code>percentEnd</code>. Pseudo-random numbers are assigned using both the input seed string value and the byte
* offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The
* random splitting strategy ensures that variables in the training and evaluation data are distributed similarly.
* It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in
* training and evaluation datasources containing non-similar data records.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code>
* </p>
* </li>
* </ul>
*/
private String dataRearrangement;
/**
* <p>
* A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The <code>DataSchema</code>
* defines the structure of the observation data in the data file(s) referenced in the <code>DataSource</code>.
* </p>
* <p>
* A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>
* </p>
* <p>
* Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and
* <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following format to
* define your <code>DataSchema</code>.
* </p>
* <p>
* { "version": "1.0",
* </p>
* <p>
* "recordAnnotationFieldName": "F1",
* </p>
* <p>
* "recordWeightFieldName": "F2",
* </p>
* <p>
* "targetFieldName": "F3",
* </p>
* <p>
* "dataFormat": "CSV",
* </p>
* <p>
* "dataFileContainsHeader": true,
* </p>
* <p>
* "attributes": [
* </p>
* <p>
* { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3",
* "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType":
* "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
* "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
* </p>
* <p>
* "excludedVariableNames": [ "F6" ] }
* </p>
* <?oxy_insert_end>
*/
private String dataSchema;
/**
* <p>
* The Amazon S3 location of the <code>DataSchema</code>.
* </p>
*/
private String dataSchemaUri;
/**
* <p>
* The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to
* carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*/
private String resourceRole;
/**
* <p>
* The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task
* from Amazon RDS to Amazon S3. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*/
private String serviceRole;
/**
* <p>
* The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry
* out the copy task from Amazon RDS to Amazon S3.
* </p>
*/
private String subnetId;
/**
* <p>
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out
* the copy operation from Amazon RDS to an Amazon S3 task.
* </p>
*/
private com.amazonaws.internal.SdkInternalList<String> securityGroupIds;
/**
* <p>
* Describes the <code>DatabaseName</code> and <code>InstanceIdentifier</code> of an Amazon RDS database.
* </p>
*
* @param databaseInformation
* Describes the <code>DatabaseName</code> and <code>InstanceIdentifier</code> of an Amazon RDS database.
*/
public void setDatabaseInformation(RDSDatabase databaseInformation) {
this.databaseInformation = databaseInformation;
}
/**
* <p>
* Describes the <code>DatabaseName</code> and <code>InstanceIdentifier</code> of an Amazon RDS database.
* </p>
*
* @return Describes the <code>DatabaseName</code> and <code>InstanceIdentifier</code> of an Amazon RDS database.
*/
public RDSDatabase getDatabaseInformation() {
return this.databaseInformation;
}
/**
* <p>
* Describes the <code>DatabaseName</code> and <code>InstanceIdentifier</code> of an Amazon RDS database.
* </p>
*
* @param databaseInformation
* Describes the <code>DatabaseName</code> and <code>InstanceIdentifier</code> of an Amazon RDS database.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withDatabaseInformation(RDSDatabase databaseInformation) {
setDatabaseInformation(databaseInformation);
return this;
}
/**
* <p>
* The query that is used to retrieve the observation data for the <code>DataSource</code>.
* </p>
*
* @param selectSqlQuery
* The query that is used to retrieve the observation data for the <code>DataSource</code>.
*/
public void setSelectSqlQuery(String selectSqlQuery) {
this.selectSqlQuery = selectSqlQuery;
}
/**
* <p>
* The query that is used to retrieve the observation data for the <code>DataSource</code>.
* </p>
*
* @return The query that is used to retrieve the observation data for the <code>DataSource</code>.
*/
public String getSelectSqlQuery() {
return this.selectSqlQuery;
}
/**
* <p>
* The query that is used to retrieve the observation data for the <code>DataSource</code>.
* </p>
*
* @param selectSqlQuery
* The query that is used to retrieve the observation data for the <code>DataSource</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withSelectSqlQuery(String selectSqlQuery) {
setSelectSqlQuery(selectSqlQuery);
return this;
}
/**
* <p>
* The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
* </p>
*
* @param databaseCredentials
* The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
*/
public void setDatabaseCredentials(RDSDatabaseCredentials databaseCredentials) {
this.databaseCredentials = databaseCredentials;
}
/**
* <p>
* The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
* </p>
*
* @return The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS
* database.
*/
public RDSDatabaseCredentials getDatabaseCredentials() {
return this.databaseCredentials;
}
/**
* <p>
* The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
* </p>
*
* @param databaseCredentials
* The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withDatabaseCredentials(RDSDatabaseCredentials databaseCredentials) {
setDatabaseCredentials(databaseCredentials);
return this;
}
/**
* <p>
* The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
* <code>SelectSqlQuery</code> is stored in this location.
* </p>
*
* @param s3StagingLocation
* The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
* <code>SelectSqlQuery</code> is stored in this location.
*/
public void setS3StagingLocation(String s3StagingLocation) {
this.s3StagingLocation = s3StagingLocation;
}
/**
* <p>
* The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
* <code>SelectSqlQuery</code> is stored in this location.
* </p>
*
* @return The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
* <code>SelectSqlQuery</code> is stored in this location.
*/
public String getS3StagingLocation() {
return this.s3StagingLocation;
}
/**
* <p>
* The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
* <code>SelectSqlQuery</code> is stored in this location.
* </p>
*
* @param s3StagingLocation
* The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
* <code>SelectSqlQuery</code> is stored in this location.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withS3StagingLocation(String s3StagingLocation) {
setS3StagingLocation(s3StagingLocation);
return this;
}
/**
* <p>
* A JSON string that represents the splitting and rearrangement processing to be applied to a
* <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input data
* is used to create the <code>Datasource</code>.
* </p>
* <p>
* There are multiple parameters that control what data is used to create a datasource:
* </p>
* <ul>
* <li>
* <p>
* <b><code>percentBegin</code></b>
* </p>
* <p>
* Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the Datasource.
* If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data
* when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>percentEnd</code></b>
* </p>
* <p>
* Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If you do
* not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when
* creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>complement</code></b>
* </p>
* <p>
* The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the range of
* <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The <code>complement</code>
* parameter is useful if you need to create complementary datasources for training and evaluation. To create a
* complementary datasource, use the same values for <code>percentBegin</code> and <code>percentEnd</code>, along
* with the <code>complement</code> parameter.
* </p>
* <p>
* For example, the following two datasources do not share any data, and can be used to train and evaluate a model.
* The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
* </p>
* <p>
* Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code>
* </p>
* <p>
* Datasource for training: <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code>
* </p>
* </li>
* <li>
* <p>
* <b><code>strategy</code></b>
* </p>
* <p>
* To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.
* </p>
* <p>
* The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon ML
* takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code> parameters for
* the datasource, in the order that the records appear in the input data.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code>
* </p>
* <p>
* To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters,
* set the <code>strategy</code> parameter to <code>random</code> and provide a string that is used as the seed
* value for the random data splitting (for example, you can use the S3 path to your data as the random seed
* string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number
* between 0 and 100, and then selects the rows that have an assigned number between <code>percentBegin</code> and
* <code>percentEnd</code>. Pseudo-random numbers are assigned using both the input seed string value and the byte
* offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The
* random splitting strategy ensures that variables in the training and evaluation data are distributed similarly.
* It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in
* training and evaluation datasources containing non-similar data records.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code>
* </p>
* </li>
* </ul>
*
* @param dataRearrangement
* A JSON string that represents the splitting and rearrangement processing to be applied to a
* <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input
* data is used to create the <code>Datasource</code>.</p>
* <p>
* There are multiple parameters that control what data is used to create a datasource:
* </p>
* <ul>
* <li>
* <p>
* <b><code>percentBegin</code></b>
* </p>
* <p>
* Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the
* Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML
* includes all of the data when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>percentEnd</code></b>
* </p>
* <p>
* Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If
* you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the
* data when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>complement</code></b>
* </p>
* <p>
* The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the
* range of <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The
* <code>complement</code> parameter is useful if you need to create complementary datasources for training
* and evaluation. To create a complementary datasource, use the same values for <code>percentBegin</code>
* and <code>percentEnd</code>, along with the <code>complement</code> parameter.
* </p>
* <p>
* For example, the following two datasources do not share any data, and can be used to train and evaluate a
* model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
* </p>
* <p>
* Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code>
* </p>
* </li>
* <li>
* <p>
* <b><code>strategy</code></b>
* </p>
* <p>
* To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.
* </p>
* <p>
* The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon
* ML takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code>
* parameters for the datasource, in the order that the records appear in the input data.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code>
* </p>
* <p>
* To randomly split the input data into the proportions indicated by the percentBegin and percentEnd
* parameters, set the <code>strategy</code> parameter to <code>random</code> and provide a string that is
* used as the seed value for the random data splitting (for example, you can use the S3 path to your data as
* the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a
* pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between
* <code>percentBegin</code> and <code>percentEnd</code>. Pseudo-random numbers are assigned using both the
* input seed string value and the byte offset as a seed, so changing the data results in a different split.
* Any existing ordering is preserved. The random splitting strategy ensures that variables in the training
* and evaluation data are distributed similarly. It is useful in the cases where the input data may have an
* implicit sort order, which would otherwise result in training and evaluation datasources containing
* non-similar data records.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training
* and evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code>
* </p>
* </li>
*/
public void setDataRearrangement(String dataRearrangement) {
this.dataRearrangement = dataRearrangement;
}
/**
* <p>
* A JSON string that represents the splitting and rearrangement processing to be applied to a
* <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input data
* is used to create the <code>Datasource</code>.
* </p>
* <p>
* There are multiple parameters that control what data is used to create a datasource:
* </p>
* <ul>
* <li>
* <p>
* <b><code>percentBegin</code></b>
* </p>
* <p>
* Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the Datasource.
* If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data
* when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>percentEnd</code></b>
* </p>
* <p>
* Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If you do
* not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when
* creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>complement</code></b>
* </p>
* <p>
* The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the range of
* <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The <code>complement</code>
* parameter is useful if you need to create complementary datasources for training and evaluation. To create a
* complementary datasource, use the same values for <code>percentBegin</code> and <code>percentEnd</code>, along
* with the <code>complement</code> parameter.
* </p>
* <p>
* For example, the following two datasources do not share any data, and can be used to train and evaluate a model.
* The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
* </p>
* <p>
* Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code>
* </p>
* <p>
* Datasource for training: <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code>
* </p>
* </li>
* <li>
* <p>
* <b><code>strategy</code></b>
* </p>
* <p>
* To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.
* </p>
* <p>
* The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon ML
* takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code> parameters for
* the datasource, in the order that the records appear in the input data.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code>
* </p>
* <p>
* To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters,
* set the <code>strategy</code> parameter to <code>random</code> and provide a string that is used as the seed
* value for the random data splitting (for example, you can use the S3 path to your data as the random seed
* string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number
* between 0 and 100, and then selects the rows that have an assigned number between <code>percentBegin</code> and
* <code>percentEnd</code>. Pseudo-random numbers are assigned using both the input seed string value and the byte
* offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The
* random splitting strategy ensures that variables in the training and evaluation data are distributed similarly.
* It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in
* training and evaluation datasources containing non-similar data records.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code>
* </p>
* </li>
* </ul>
*
* @return A JSON string that represents the splitting and rearrangement processing to be applied to a
* <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the
* input data is used to create the <code>Datasource</code>.</p>
* <p>
* There are multiple parameters that control what data is used to create a datasource:
* </p>
* <ul>
* <li>
* <p>
* <b><code>percentBegin</code></b>
* </p>
* <p>
* Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the
* Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML
* includes all of the data when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>percentEnd</code></b>
* </p>
* <p>
* Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource.
* If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of
* the data when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>complement</code></b>
* </p>
* <p>
* The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the
* range of <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The
* <code>complement</code> parameter is useful if you need to create complementary datasources for training
* and evaluation. To create a complementary datasource, use the same values for <code>percentBegin</code>
* and <code>percentEnd</code>, along with the <code>complement</code> parameter.
* </p>
* <p>
* For example, the following two datasources do not share any data, and can be used to train and evaluate a
* model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
* </p>
* <p>
* Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code>
* </p>
* </li>
* <li>
* <p>
* <b><code>strategy</code></b>
* </p>
* <p>
* To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.
* </p>
* <p>
* The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon
* ML takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code>
* parameters for the datasource, in the order that the records appear in the input data.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code>
* </p>
* <p>
* To randomly split the input data into the proportions indicated by the percentBegin and percentEnd
* parameters, set the <code>strategy</code> parameter to <code>random</code> and provide a string that is
* used as the seed value for the random data splitting (for example, you can use the S3 path to your data
* as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a
* pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between
* <code>percentBegin</code> and <code>percentEnd</code>. Pseudo-random numbers are assigned using both the
* input seed string value and the byte offset as a seed, so changing the data results in a different split.
* Any existing ordering is preserved. The random splitting strategy ensures that variables in the training
* and evaluation data are distributed similarly. It is useful in the cases where the input data may have an
* implicit sort order, which would otherwise result in training and evaluation datasources containing
* non-similar data records.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training
* and evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code>
* </p>
* </li>
*/
public String getDataRearrangement() {
return this.dataRearrangement;
}
/**
* <p>
* A JSON string that represents the splitting and rearrangement processing to be applied to a
* <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input data
* is used to create the <code>Datasource</code>.
* </p>
* <p>
* There are multiple parameters that control what data is used to create a datasource:
* </p>
* <ul>
* <li>
* <p>
* <b><code>percentBegin</code></b>
* </p>
* <p>
* Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the Datasource.
* If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data
* when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>percentEnd</code></b>
* </p>
* <p>
* Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If you do
* not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when
* creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>complement</code></b>
* </p>
* <p>
* The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the range of
* <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The <code>complement</code>
* parameter is useful if you need to create complementary datasources for training and evaluation. To create a
* complementary datasource, use the same values for <code>percentBegin</code> and <code>percentEnd</code>, along
* with the <code>complement</code> parameter.
* </p>
* <p>
* For example, the following two datasources do not share any data, and can be used to train and evaluate a model.
* The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
* </p>
* <p>
* Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code>
* </p>
* <p>
* Datasource for training: <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code>
* </p>
* </li>
* <li>
* <p>
* <b><code>strategy</code></b>
* </p>
* <p>
* To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.
* </p>
* <p>
* The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon ML
* takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code> parameters for
* the datasource, in the order that the records appear in the input data.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code>
* </p>
* <p>
* To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters,
* set the <code>strategy</code> parameter to <code>random</code> and provide a string that is used as the seed
* value for the random data splitting (for example, you can use the S3 path to your data as the random seed
* string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number
* between 0 and 100, and then selects the rows that have an assigned number between <code>percentBegin</code> and
* <code>percentEnd</code>. Pseudo-random numbers are assigned using both the input seed string value and the byte
* offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The
* random splitting strategy ensures that variables in the training and evaluation data are distributed similarly.
* It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in
* training and evaluation datasources containing non-similar data records.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code>
* </p>
* </li>
* </ul>
*
* @param dataRearrangement
* A JSON string that represents the splitting and rearrangement processing to be applied to a
* <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input
* data is used to create the <code>Datasource</code>.</p>
* <p>
* There are multiple parameters that control what data is used to create a datasource:
* </p>
* <ul>
* <li>
* <p>
* <b><code>percentBegin</code></b>
* </p>
* <p>
* Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the
* Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML
* includes all of the data when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>percentEnd</code></b>
* </p>
* <p>
* Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If
* you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the
* data when creating the datasource.
* </p>
* </li>
* <li>
* <p>
* <b><code>complement</code></b>
* </p>
* <p>
* The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the
* range of <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The
* <code>complement</code> parameter is useful if you need to create complementary datasources for training
* and evaluation. To create a complementary datasource, use the same values for <code>percentBegin</code>
* and <code>percentEnd</code>, along with the <code>complement</code> parameter.
* </p>
* <p>
* For example, the following two datasources do not share any data, and can be used to train and evaluate a
* model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
* </p>
* <p>
* Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code>
* </p>
* </li>
* <li>
* <p>
* <b><code>strategy</code></b>
* </p>
* <p>
* To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.
* </p>
* <p>
* The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon
* ML takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code>
* parameters for the datasource, in the order that the records appear in the input data.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and
* evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code>
* </p>
* <p>
* To randomly split the input data into the proportions indicated by the percentBegin and percentEnd
* parameters, set the <code>strategy</code> parameter to <code>random</code> and provide a string that is
* used as the seed value for the random data splitting (for example, you can use the S3 path to your data as
* the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a
* pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between
* <code>percentBegin</code> and <code>percentEnd</code>. Pseudo-random numbers are assigned using both the
* input seed string value and the byte offset as a seed, so changing the data results in a different split.
* Any existing ordering is preserved. The random splitting strategy ensures that variables in the training
* and evaluation data are distributed similarly. It is useful in the cases where the input data may have an
* implicit sort order, which would otherwise result in training and evaluation datasources containing
* non-similar data records.
* </p>
* <p>
* The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training
* and evaluation datasources:
* </p>
* <p>
* Datasource for evaluation:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code>
* </p>
* <p>
* Datasource for training:
* <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code>
* </p>
* </li>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withDataRearrangement(String dataRearrangement) {
setDataRearrangement(dataRearrangement);
return this;
}
/**
* <p>
* A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The <code>DataSchema</code>
* defines the structure of the observation data in the data file(s) referenced in the <code>DataSource</code>.
* </p>
* <p>
* A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>
* </p>
* <p>
* Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and
* <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following format to
* define your <code>DataSchema</code>.
* </p>
* <p>
* { "version": "1.0",
* </p>
* <p>
* "recordAnnotationFieldName": "F1",
* </p>
* <p>
* "recordWeightFieldName": "F2",
* </p>
* <p>
* "targetFieldName": "F3",
* </p>
* <p>
* "dataFormat": "CSV",
* </p>
* <p>
* "dataFileContainsHeader": true,
* </p>
* <p>
* "attributes": [
* </p>
* <p>
* { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3",
* "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType":
* "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
* "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
* </p>
* <p>
* "excludedVariableNames": [ "F6" ] }
* </p>
* <?oxy_insert_end>
*
* @param dataSchema
* A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The
* <code>DataSchema</code> defines the structure of the observation data in the data file(s) referenced in
* the <code>DataSource</code>.</p>
* <p>
* A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>
* </p>
* <p>
* Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and
* <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following
* format to define your <code>DataSchema</code>.
* </p>
* <p>
* { "version": "1.0",
* </p>
* <p>
* "recordAnnotationFieldName": "F1",
* </p>
* <p>
* "recordWeightFieldName": "F2",
* </p>
* <p>
* "targetFieldName": "F3",
* </p>
* <p>
* "dataFormat": "CSV",
* </p>
* <p>
* "dataFileContainsHeader": true,
* </p>
* <p>
* "attributes": [
* </p>
* <p>
* { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName":
* "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5",
* "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7",
* "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
* </p>
* <p>
* "excludedVariableNames": [ "F6" ] }
* </p>
*/
public void setDataSchema(String dataSchema) {
this.dataSchema = dataSchema;
}
/**
* <p>
* A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The <code>DataSchema</code>
* defines the structure of the observation data in the data file(s) referenced in the <code>DataSource</code>.
* </p>
* <p>
* A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>
* </p>
* <p>
* Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and
* <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following format to
* define your <code>DataSchema</code>.
* </p>
* <p>
* { "version": "1.0",
* </p>
* <p>
* "recordAnnotationFieldName": "F1",
* </p>
* <p>
* "recordWeightFieldName": "F2",
* </p>
* <p>
* "targetFieldName": "F3",
* </p>
* <p>
* "dataFormat": "CSV",
* </p>
* <p>
* "dataFileContainsHeader": true,
* </p>
* <p>
* "attributes": [
* </p>
* <p>
* { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3",
* "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType":
* "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
* "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
* </p>
* <p>
* "excludedVariableNames": [ "F6" ] }
* </p>
* <?oxy_insert_end>
*
* @return A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The
* <code>DataSchema</code> defines the structure of the observation data in the data file(s) referenced in
* the <code>DataSource</code>.</p>
* <p>
* A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>
* </p>
* <p>
* Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and
* <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following
* format to define your <code>DataSchema</code>.
* </p>
* <p>
* { "version": "1.0",
* </p>
* <p>
* "recordAnnotationFieldName": "F1",
* </p>
* <p>
* "recordWeightFieldName": "F2",
* </p>
* <p>
* "targetFieldName": "F3",
* </p>
* <p>
* "dataFormat": "CSV",
* </p>
* <p>
* "dataFileContainsHeader": true,
* </p>
* <p>
* "attributes": [
* </p>
* <p>
* { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName":
* "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5",
* "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7",
* "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
* </p>
* <p>
* "excludedVariableNames": [ "F6" ] }
* </p>
*/
public String getDataSchema() {
return this.dataSchema;
}
/**
* <p>
* A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The <code>DataSchema</code>
* defines the structure of the observation data in the data file(s) referenced in the <code>DataSource</code>.
* </p>
* <p>
* A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>
* </p>
* <p>
* Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and
* <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following format to
* define your <code>DataSchema</code>.
* </p>
* <p>
* { "version": "1.0",
* </p>
* <p>
* "recordAnnotationFieldName": "F1",
* </p>
* <p>
* "recordWeightFieldName": "F2",
* </p>
* <p>
* "targetFieldName": "F3",
* </p>
* <p>
* "dataFormat": "CSV",
* </p>
* <p>
* "dataFileContainsHeader": true,
* </p>
* <p>
* "attributes": [
* </p>
* <p>
* { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3",
* "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType":
* "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
* "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
* </p>
* <p>
* "excludedVariableNames": [ "F6" ] }
* </p>
* <?oxy_insert_end>
*
* @param dataSchema
* A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The
* <code>DataSchema</code> defines the structure of the observation data in the data file(s) referenced in
* the <code>DataSource</code>.</p>
* <p>
* A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>
* </p>
* <p>
* Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and
* <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following
* format to define your <code>DataSchema</code>.
* </p>
* <p>
* { "version": "1.0",
* </p>
* <p>
* "recordAnnotationFieldName": "F1",
* </p>
* <p>
* "recordWeightFieldName": "F2",
* </p>
* <p>
* "targetFieldName": "F3",
* </p>
* <p>
* "dataFormat": "CSV",
* </p>
* <p>
* "dataFileContainsHeader": true,
* </p>
* <p>
* "attributes": [
* </p>
* <p>
* { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName":
* "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5",
* "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7",
* "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
* </p>
* <p>
* "excludedVariableNames": [ "F6" ] }
* </p>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withDataSchema(String dataSchema) {
setDataSchema(dataSchema);
return this;
}
/**
* <p>
* The Amazon S3 location of the <code>DataSchema</code>.
* </p>
*
* @param dataSchemaUri
* The Amazon S3 location of the <code>DataSchema</code>.
*/
public void setDataSchemaUri(String dataSchemaUri) {
this.dataSchemaUri = dataSchemaUri;
}
/**
* <p>
* The Amazon S3 location of the <code>DataSchema</code>.
* </p>
*
* @return The Amazon S3 location of the <code>DataSchema</code>.
*/
public String getDataSchemaUri() {
return this.dataSchemaUri;
}
/**
* <p>
* The Amazon S3 location of the <code>DataSchema</code>.
* </p>
*
* @param dataSchemaUri
* The Amazon S3 location of the <code>DataSchema</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withDataSchemaUri(String dataSchemaUri) {
setDataSchemaUri(dataSchemaUri);
return this;
}
/**
* <p>
* The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to
* carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*
* @param resourceRole
* The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2)
* instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see
* <a href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role
* templates</a> for data pipelines.
*/
public void setResourceRole(String resourceRole) {
this.resourceRole = resourceRole;
}
/**
* <p>
* The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to
* carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*
* @return The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2)
* instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see
* <a href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role
* templates</a> for data pipelines.
*/
public String getResourceRole() {
return this.resourceRole;
}
/**
* <p>
* The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to
* carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*
* @param resourceRole
* The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2)
* instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see
* <a href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role
* templates</a> for data pipelines.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withResourceRole(String resourceRole) {
setResourceRole(resourceRole);
return this;
}
/**
* <p>
* The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task
* from Amazon RDS to Amazon S3. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*
* @param serviceRole
* The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the
* copy task from Amazon RDS to Amazon S3. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a>
* for data pipelines.
*/
public void setServiceRole(String serviceRole) {
this.serviceRole = serviceRole;
}
/**
* <p>
* The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task
* from Amazon RDS to Amazon S3. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*
* @return The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the
* copy task from Amazon RDS to Amazon S3. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a>
* for data pipelines.
*/
public String getServiceRole() {
return this.serviceRole;
}
/**
* <p>
* The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task
* from Amazon RDS to Amazon S3. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for
* data pipelines.
* </p>
*
* @param serviceRole
* The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the
* copy task from Amazon RDS to Amazon S3. For more information, see <a
* href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a>
* for data pipelines.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withServiceRole(String serviceRole) {
setServiceRole(serviceRole);
return this;
}
/**
* <p>
* The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry
* out the copy task from Amazon RDS to Amazon S3.
* </p>
*
* @param subnetId
* The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to
* carry out the copy task from Amazon RDS to Amazon S3.
*/
public void setSubnetId(String subnetId) {
this.subnetId = subnetId;
}
/**
* <p>
* The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry
* out the copy task from Amazon RDS to Amazon S3.
* </p>
*
* @return The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline
* to carry out the copy task from Amazon RDS to Amazon S3.
*/
public String getSubnetId() {
return this.subnetId;
}
/**
* <p>
* The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry
* out the copy task from Amazon RDS to Amazon S3.
* </p>
*
* @param subnetId
* The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to
* carry out the copy task from Amazon RDS to Amazon S3.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withSubnetId(String subnetId) {
setSubnetId(subnetId);
return this;
}
/**
* <p>
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out
* the copy operation from Amazon RDS to an Amazon S3 task.
* </p>
*
* @return The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are
* appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data
* Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
*/
public java.util.List<String> getSecurityGroupIds() {
if (securityGroupIds == null) {
securityGroupIds = new com.amazonaws.internal.SdkInternalList<String>();
}
return securityGroupIds;
}
/**
* <p>
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out
* the copy operation from Amazon RDS to an Amazon S3 task.
* </p>
*
* @param securityGroupIds
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to
* carry out the copy operation from Amazon RDS to an Amazon S3 task.
*/
public void setSecurityGroupIds(java.util.Collection<String> securityGroupIds) {
if (securityGroupIds == null) {
this.securityGroupIds = null;
return;
}
this.securityGroupIds = new com.amazonaws.internal.SdkInternalList<String>(securityGroupIds);
}
/**
* <p>
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out
* the copy operation from Amazon RDS to an Amazon S3 task.
* </p>
* <p>
* <b>NOTE:</b> This method appends the values to the existing list (if any). Use
* {@link #setSecurityGroupIds(java.util.Collection)} or {@link #withSecurityGroupIds(java.util.Collection)} if you
* want to override the existing values.
* </p>
*
* @param securityGroupIds
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to
* carry out the copy operation from Amazon RDS to an Amazon S3 task.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withSecurityGroupIds(String... securityGroupIds) {
if (this.securityGroupIds == null) {
setSecurityGroupIds(new com.amazonaws.internal.SdkInternalList<String>(securityGroupIds.length));
}
for (String ele : securityGroupIds) {
this.securityGroupIds.add(ele);
}
return this;
}
/**
* <p>
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out
* the copy operation from Amazon RDS to an Amazon S3 task.
* </p>
*
* @param securityGroupIds
* The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate
* ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to
* carry out the copy operation from Amazon RDS to an Amazon S3 task.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public RDSDataSpec withSecurityGroupIds(java.util.Collection<String> securityGroupIds) {
setSecurityGroupIds(securityGroupIds);
return this;
}
/**
* Returns a string representation of this object; useful for testing and debugging.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getDatabaseInformation() != null)
sb.append("DatabaseInformation: ").append(getDatabaseInformation()).append(",");
if (getSelectSqlQuery() != null)
sb.append("SelectSqlQuery: ").append(getSelectSqlQuery()).append(",");
if (getDatabaseCredentials() != null)
sb.append("DatabaseCredentials: ").append(getDatabaseCredentials()).append(",");
if (getS3StagingLocation() != null)
sb.append("S3StagingLocation: ").append(getS3StagingLocation()).append(",");
if (getDataRearrangement() != null)
sb.append("DataRearrangement: ").append(getDataRearrangement()).append(",");
if (getDataSchema() != null)
sb.append("DataSchema: ").append(getDataSchema()).append(",");
if (getDataSchemaUri() != null)
sb.append("DataSchemaUri: ").append(getDataSchemaUri()).append(",");
if (getResourceRole() != null)
sb.append("ResourceRole: ").append(getResourceRole()).append(",");
if (getServiceRole() != null)
sb.append("ServiceRole: ").append(getServiceRole()).append(",");
if (getSubnetId() != null)
sb.append("SubnetId: ").append(getSubnetId()).append(",");
if (getSecurityGroupIds() != null)
sb.append("SecurityGroupIds: ").append(getSecurityGroupIds());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof RDSDataSpec == false)
return false;
RDSDataSpec other = (RDSDataSpec) obj;
if (other.getDatabaseInformation() == null ^ this.getDatabaseInformation() == null)
return false;
if (other.getDatabaseInformation() != null && other.getDatabaseInformation().equals(this.getDatabaseInformation()) == false)
return false;
if (other.getSelectSqlQuery() == null ^ this.getSelectSqlQuery() == null)
return false;
if (other.getSelectSqlQuery() != null && other.getSelectSqlQuery().equals(this.getSelectSqlQuery()) == false)
return false;
if (other.getDatabaseCredentials() == null ^ this.getDatabaseCredentials() == null)
return false;
if (other.getDatabaseCredentials() != null && other.getDatabaseCredentials().equals(this.getDatabaseCredentials()) == false)
return false;
if (other.getS3StagingLocation() == null ^ this.getS3StagingLocation() == null)
return false;
if (other.getS3StagingLocation() != null && other.getS3StagingLocation().equals(this.getS3StagingLocation()) == false)
return false;
if (other.getDataRearrangement() == null ^ this.getDataRearrangement() == null)
return false;
if (other.getDataRearrangement() != null && other.getDataRearrangement().equals(this.getDataRearrangement()) == false)
return false;
if (other.getDataSchema() == null ^ this.getDataSchema() == null)
return false;
if (other.getDataSchema() != null && other.getDataSchema().equals(this.getDataSchema()) == false)
return false;
if (other.getDataSchemaUri() == null ^ this.getDataSchemaUri() == null)
return false;
if (other.getDataSchemaUri() != null && other.getDataSchemaUri().equals(this.getDataSchemaUri()) == false)
return false;
if (other.getResourceRole() == null ^ this.getResourceRole() == null)
return false;
if (other.getResourceRole() != null && other.getResourceRole().equals(this.getResourceRole()) == false)
return false;
if (other.getServiceRole() == null ^ this.getServiceRole() == null)
return false;
if (other.getServiceRole() != null && other.getServiceRole().equals(this.getServiceRole()) == false)
return false;
if (other.getSubnetId() == null ^ this.getSubnetId() == null)
return false;
if (other.getSubnetId() != null && other.getSubnetId().equals(this.getSubnetId()) == false)
return false;
if (other.getSecurityGroupIds() == null ^ this.getSecurityGroupIds() == null)
return false;
if (other.getSecurityGroupIds() != null && other.getSecurityGroupIds().equals(this.getSecurityGroupIds()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getDatabaseInformation() == null) ? 0 : getDatabaseInformation().hashCode());
hashCode = prime * hashCode + ((getSelectSqlQuery() == null) ? 0 : getSelectSqlQuery().hashCode());
hashCode = prime * hashCode + ((getDatabaseCredentials() == null) ? 0 : getDatabaseCredentials().hashCode());
hashCode = prime * hashCode + ((getS3StagingLocation() == null) ? 0 : getS3StagingLocation().hashCode());
hashCode = prime * hashCode + ((getDataRearrangement() == null) ? 0 : getDataRearrangement().hashCode());
hashCode = prime * hashCode + ((getDataSchema() == null) ? 0 : getDataSchema().hashCode());
hashCode = prime * hashCode + ((getDataSchemaUri() == null) ? 0 : getDataSchemaUri().hashCode());
hashCode = prime * hashCode + ((getResourceRole() == null) ? 0 : getResourceRole().hashCode());
hashCode = prime * hashCode + ((getServiceRole() == null) ? 0 : getServiceRole().hashCode());
hashCode = prime * hashCode + ((getSubnetId() == null) ? 0 : getSubnetId().hashCode());
hashCode = prime * hashCode + ((getSecurityGroupIds() == null) ? 0 : getSecurityGroupIds().hashCode());
return hashCode;
}
@Override
public RDSDataSpec clone() {
try {
return (RDSDataSpec) 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.machinelearning.model.transform.RDSDataSpecMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}