/** * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * SPDX-License-Identifier: Apache-2.0. */ #pragma once #include #include #include #include #include namespace Aws { namespace Utils { namespace Json { class JsonValue; class JsonView; } // namespace Json } // namespace Utils namespace MachineLearning { namespace Model { /** *

Describes the data specification of an Amazon Redshift * DataSource.

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline const Aws::String& GetDataRearrangement() const{ return m_dataRearrangement; } /** *

There are multiple parameters that control what * data is used to create a datasource:

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline bool DataRearrangementHasBeenSet() const { return m_dataRearrangementHasBeenSet; } /** *

There are multiple parameters that control what * data is used to create a datasource:

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline void SetDataRearrangement(const Aws::String& value) { m_dataRearrangementHasBeenSet = true; m_dataRearrangement = value; } /** *

There are multiple parameters that control what * data is used to create a datasource:

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline void SetDataRearrangement(Aws::String&& value) { m_dataRearrangementHasBeenSet = true; m_dataRearrangement = std::move(value); } /** *

There are multiple parameters that control what * data is used to create a datasource:

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline void SetDataRearrangement(const char* value) { m_dataRearrangementHasBeenSet = true; m_dataRearrangement.assign(value); } /** *

There are multiple parameters that control what * data is used to create a datasource:

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline RedshiftDataSpec& WithDataRearrangement(const Aws::String& value) { SetDataRearrangement(value); return *this;} /** *

There are multiple parameters that control what * data is used to create a datasource:

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline RedshiftDataSpec& WithDataRearrangement(Aws::String&& value) { SetDataRearrangement(std::move(value)); return *this;} /** *

There are multiple parameters that control what * data is used to create a datasource:

percentBegin

Use percentBegin to * indicate the beginning of the range of the data used to create the Datasource. * If you do not include percentBegin and percentEnd, * Amazon ML includes all of the data when creating the datasource.
percentEnd

Use percentEnd to * indicate the end of the range of the data used to create the Datasource. If you * do not include percentBegin and percentEnd, Amazon ML * includes all of the data when creating the datasource.
complement

The complement * parameter instructs Amazon ML to use the data that is not included in the range * of percentBegin to percentEnd to create a datasource. * The complement parameter is useful if you need to create * complementary datasources for training and evaluation. To create a complementary * datasource, use the same values for percentBegin and * percentEnd, along with the complement parameter.
*
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.

Datasource for * evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
*
Datasource for training: {"splitting":{"percentBegin":0, * "percentEnd":25, "complement":"true"}}
strategy

To change how Amazon ML splits the * data for a datasource, use the strategy parameter.

The * default value for the strategy parameter is * sequential, meaning that Amazon ML takes all of the data records * between the percentBegin and percentEnd parameters for * the datasource, in the order that the records appear in the input data.
*
The following two DataRearrangement lines are examples of * sequentially ordered training and evaluation datasources:

Datasource for * evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential"}}

Datasource for training: * {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"sequential", "complement":"true"}}

To randomly split * the input data into the proportions indicated by the percentBegin and percentEnd * parameters, set the strategy parameter to random 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 percentBegin and percentEnd. * 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.

The following two * DataRearrangement lines are examples of non-sequentially ordered * training and evaluation datasources:

Datasource for evaluation: * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for * training: {"splitting":{"percentBegin":70, "percentEnd":100, * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", * "complement":"true"}}

*/ inline RedshiftDataSpec& WithDataRearrangement(const char* value) { SetDataRearrangement(value); return *this;} /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

Define your DataSchema * as a series of key-value pairs. attributes and * excludedVariableNames have an array of key-value pairs for their * value. Use the following format to define your DataSchema.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

{ "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" } ],

* "excludedVariableNames": [ "F6" ] }

*/ inline const Aws::String& GetDataSchema() const{ return m_dataSchema; } /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

* "excludedVariableNames": [ "F6" ] }

*/ inline bool DataSchemaHasBeenSet() const { return m_dataSchemaHasBeenSet; } /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

* "excludedVariableNames": [ "F6" ] }

*/ inline void SetDataSchema(const Aws::String& value) { m_dataSchemaHasBeenSet = true; m_dataSchema = value; } /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

* "excludedVariableNames": [ "F6" ] }

*/ inline void SetDataSchema(Aws::String&& value) { m_dataSchemaHasBeenSet = true; m_dataSchema = std::move(value); } /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

* "excludedVariableNames": [ "F6" ] }

*/ inline void SetDataSchema(const char* value) { m_dataSchemaHasBeenSet = true; m_dataSchema.assign(value); } /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

* "excludedVariableNames": [ "F6" ] }

*/ inline RedshiftDataSpec& WithDataSchema(const Aws::String& value) { SetDataSchema(value); return *this;} /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

* "excludedVariableNames": [ "F6" ] }

*/ inline RedshiftDataSpec& WithDataSchema(Aws::String&& value) { SetDataSchema(std::move(value)); return *this;} /** *

A JSON string that represents the schema for an Amazon Redshift * DataSource. The DataSchema defines the structure of * the observation data in the data file(s) referenced in the * DataSource.

A DataSchema is not required if you * specify a DataSchemaUri.

{ * "version": "1.0",

"recordAnnotationFieldName": "F1",

* "recordWeightFieldName": "F2",

"targetFieldName": "F3",

* "dataFormat": "CSV",

"dataFileContainsHeader": true,

* "attributes": [

* "excludedVariableNames": [ "F6" ] }

*/ inline RedshiftDataSpec& WithDataSchema(const char* value) { SetDataSchema(value); return *this;} /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline const Aws::String& GetDataSchemaUri() const{ return m_dataSchemaUri; } /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline bool DataSchemaUriHasBeenSet() const { return m_dataSchemaUriHasBeenSet; } /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline void SetDataSchemaUri(const Aws::String& value) { m_dataSchemaUriHasBeenSet = true; m_dataSchemaUri = value; } /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline void SetDataSchemaUri(Aws::String&& value) { m_dataSchemaUriHasBeenSet = true; m_dataSchemaUri = std::move(value); } /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline void SetDataSchemaUri(const char* value) { m_dataSchemaUriHasBeenSet = true; m_dataSchemaUri.assign(value); } /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline RedshiftDataSpec& WithDataSchemaUri(const Aws::String& value) { SetDataSchemaUri(value); return *this;} /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline RedshiftDataSpec& WithDataSchemaUri(Aws::String&& value) { SetDataSchemaUri(std::move(value)); return *this;} /** *

Describes the schema location for an Amazon Redshift * DataSource.

*/ inline RedshiftDataSpec& WithDataSchemaUri(const char* value) { SetDataSchemaUri(value); return *this;} private: RedshiftDatabase m_databaseInformation; bool m_databaseInformationHasBeenSet; Aws::String m_selectSqlQuery; bool m_selectSqlQueryHasBeenSet; RedshiftDatabaseCredentials m_databaseCredentials; bool m_databaseCredentialsHasBeenSet; Aws::String m_s3StagingLocation; bool m_s3StagingLocationHasBeenSet; Aws::String m_dataRearrangement; bool m_dataRearrangementHasBeenSet; Aws::String m_dataSchema; bool m_dataSchemaHasBeenSet; Aws::String m_dataSchemaUri; bool m_dataSchemaUriHasBeenSet; }; } // namespace Model } // namespace MachineLearning } // namespace Aws

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