Struct aws_sdk_sagemaker::types::AutoMlChannel

#[non_exhaustive]
pub struct AutoMlChannel {
    pub data_source: Option<AutoMlDataSource>,
    pub compression_type: Option<CompressionType>,
    pub target_attribute_name: Option<String>,
    pub content_type: Option<String>,
    pub channel_type: Option<AutoMlChannelType>,
    pub sample_weight_attribute_name: Option<String>,
}

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.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

data_source: Option<AutoMlDataSource>

The data source for an AutoML channel.

compression_type: Option<CompressionType>

You can use Gzip or None. The default value is None.

target_attribute_name: Option<String>

The name of the target variable in supervised learning, usually represented by 'y'.

content_type: Option<String>

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.

channel_type: Option<AutoMlChannelType>

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.

sample_weight_attribute_name: Option<String>

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.

Implementations

impl AutoMlChannel

pub fn data_source(&self) -> Option<&AutoMlDataSource>

The data source for an AutoML channel.

pub fn compression_type(&self) -> Option<&CompressionType>

You can use Gzip or None. The default value is None.

pub fn target_attribute_name(&self) -> Option<&str>

The name of the target variable in supervised learning, usually represented by 'y'.

pub fn content_type(&self) -> Option<&str>

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.

pub fn channel_type(&self) -> Option<&AutoMlChannelType>

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.

pub fn sample_weight_attribute_name(&self) -> Option<&str>

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.

impl AutoMlChannel

pub fn builder() -> AutoMlChannelBuilder

Creates a new builder-style object to manufacture AutoMlChannel.

Trait Implementations

impl Clone for AutoMlChannel

fn clone(&self) -> AutoMlChannel

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AutoMlChannel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for AutoMlChannel

fn eq(&self, other: &AutoMlChannel) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for AutoMlChannel