AutoMLChannel (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.sagemaker.model.AutoMLChannel

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLChannel
extends Object
implements Serializable, Cloneable, StructuredPojo
```
A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel.

A validation dataset must contain the same headers as the training dataset.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

AutoMLChannel()

Constructors
Constructor and Description
`AutoMLChannel()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`AutoMLChannel`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getChannelType()` The channel type (optional) is an `enum` string.
`String`	`getCompressionType()` You can use `Gzip` or `None`.
`String`	`getContentType()` The content type of the data from the input source.
`AutoMLDataSource`	`getDataSource()` The data source for an AutoML channel.
`String`	`getSampleWeightAttributeName()` If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
`String`	`getTargetAttributeName()` The name of the target variable in supervised learning, usually represented by 'y'.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setChannelType(String channelType)` The channel type (optional) is an `enum` string.
`void`	`setCompressionType(String compressionType)` You can use `Gzip` or `None`.
`void`	`setContentType(String contentType)` The content type of the data from the input source.
`void`	`setDataSource(AutoMLDataSource dataSource)` The data source for an AutoML channel.
`void`	`setSampleWeightAttributeName(String sampleWeightAttributeName)` If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
`void`	`setTargetAttributeName(String targetAttributeName)` The name of the target variable in supervised learning, usually represented by 'y'.
`String`	`toString()` Returns a string representation of this object.
`AutoMLChannel`	`withChannelType(AutoMLChannelType channelType)` The channel type (optional) is an `enum` string.
`AutoMLChannel`	`withChannelType(String channelType)` The channel type (optional) is an `enum` string.
`AutoMLChannel`	`withCompressionType(CompressionType compressionType)` You can use `Gzip` or `None`.
`AutoMLChannel`	`withCompressionType(String compressionType)` You can use `Gzip` or `None`.
`AutoMLChannel`	`withContentType(String contentType)` The content type of the data from the input source.
`AutoMLChannel`	`withDataSource(AutoMLDataSource dataSource)` The data source for an AutoML channel.
`AutoMLChannel`	`withSampleWeightAttributeName(String sampleWeightAttributeName)` If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
`AutoMLChannel`	`withTargetAttributeName(String targetAttributeName)` The name of the target variable in supervised learning, usually represented by 'y'.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - AutoMLChannel
```
public AutoMLChannel()
```
- Method Detail
  - setDataSource
```
public void setDataSource(AutoMLDataSource dataSource)
```
    The data source for an AutoML channel.
    
    Parameters:
    
    dataSource - The data source for an AutoML channel.
  - getDataSource
```
public AutoMLDataSource getDataSource()
```
    The data source for an AutoML channel.
    
    Returns:
    
    The data source for an AutoML channel.
  - withDataSource
```
public AutoMLChannel withDataSource(AutoMLDataSource dataSource)
```
    The data source for an AutoML channel.
    
    Parameters:
    
    dataSource - The data source for an AutoML channel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCompressionType
```
public void setCompressionType(String compressionType)
```
    You can use Gzip or None. The default value is None.
    
    Parameters:
    
    compressionType - You can use Gzip or None. The default value is None.
    
    See Also:
    
    CompressionType
  - getCompressionType
```
public String getCompressionType()
```
    You can use Gzip or None. The default value is None.
    
    Returns:
    
    You can use Gzip or None. The default value is None.
    
    See Also:
    
    CompressionType
  - withCompressionType
```
public AutoMLChannel withCompressionType(String compressionType)
```
    You can use Gzip or None. The default value is None.
    
    Parameters:
    
    compressionType - You can use Gzip or None. The default value is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CompressionType
  - withCompressionType
```
public AutoMLChannel withCompressionType(CompressionType compressionType)
```
    You can use Gzip or None. The default value is None.
    
    Parameters:
    
    compressionType - You can use Gzip or None. The default value is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CompressionType
  - setTargetAttributeName
```
public void setTargetAttributeName(String targetAttributeName)
```
    The name of the target variable in supervised learning, usually represented by 'y'.
    
    Parameters:
    
    targetAttributeName - The name of the target variable in supervised learning, usually represented by 'y'.
  - getTargetAttributeName
```
public String getTargetAttributeName()
```
    The name of the target variable in supervised learning, usually represented by 'y'.
    
    Returns:
    
    The name of the target variable in supervised learning, usually represented by 'y'.
  - withTargetAttributeName
```
public AutoMLChannel withTargetAttributeName(String targetAttributeName)
```
    The name of the target variable in supervised learning, usually represented by 'y'.
    
    Parameters:
    
    targetAttributeName - The name of the target variable in supervised learning, usually represented by 'y'.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setContentType
```
public void setContentType(String contentType)
```
    The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
    
    Parameters:
    
    contentType - The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
  - getContentType
```
public String getContentType()
```
    The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
    
    Returns:
    
    The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
  - withContentType
```
public AutoMLChannel withContentType(String contentType)
```
    The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
    
    Parameters:
    
    contentType - The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setChannelType
```
public void setChannelType(String channelType)
```
    The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    Parameters:
    
    channelType - The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    See Also:
    
    AutoMLChannelType
  - getChannelType
```
public String getChannelType()
```
    The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    Returns:
    
    The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    See Also:
    
    AutoMLChannelType
  - withChannelType
```
public AutoMLChannel withChannelType(String channelType)
```
    The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    Parameters:
    
    channelType - The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    AutoMLChannelType
  - withChannelType
```
public AutoMLChannel withChannelType(AutoMLChannelType channelType)
```
    The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    Parameters:
    
    channelType - The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    AutoMLChannelType
  - setSampleWeightAttributeName
```
public void setSampleWeightAttributeName(String sampleWeightAttributeName)
```
    If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
    
    Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
    
    Support for sample weights is available in Ensembling mode only.
    
    Parameters:
    
    sampleWeightAttributeName - If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
    
    Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
    
    Support for sample weights is available in Ensembling mode only.
  - getSampleWeightAttributeName
```
public String getSampleWeightAttributeName()
```
    If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
    
    Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
    
    Support for sample weights is available in Ensembling mode only.
    
    Returns:
    
    If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
    
    Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
    
    Support for sample weights is available in Ensembling mode only.
  - withSampleWeightAttributeName
```
public AutoMLChannel withSampleWeightAttributeName(String sampleWeightAttributeName)
```
    If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
    
    Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
    
    Support for sample weights is available in Ensembling mode only.
    
    Parameters:
    
    sampleWeightAttributeName - If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
    
    Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
    
    Support for sample weights is available in Ensembling mode only.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - toString
```
public String toString()
```
    Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    A string representation of this object.
    
    See Also:
    
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public AutoMLChannel clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

AWS SDK for Java 1.x API Reference - 1.12.559

Class AutoMLChannel

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

AutoMLChannel

Method Detail

setDataSource

getDataSource

withDataSource

setCompressionType

getCompressionType

withCompressionType

withCompressionType

setTargetAttributeName

getTargetAttributeName

withTargetAttributeName

setContentType

getContentType

withContentType

setChannelType

getChannelType

withChannelType

withChannelType

setSampleWeightAttributeName

getSampleWeightAttributeName

withSampleWeightAttributeName

toString

equals

hashCode

clone

marshall