Struct aws_sdk_sagemaker::types::builders::AutoMlChannelBuilder

#[non_exhaustive]
pub struct AutoMlChannelBuilder { /* private fields */ }

A builder for AutoMlChannel.

Implementations

impl AutoMlChannelBuilder

pub fn data_source(self, input: AutoMlDataSource) -> Self

The data source for an AutoML channel.

pub fn set_data_source(self, input: Option<AutoMlDataSource>) -> Self

The data source for an AutoML channel.

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

The data source for an AutoML channel.

pub fn compression_type(self, input: CompressionType) -> Self

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

pub fn set_compression_type(self, input: Option<CompressionType>) -> Self

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

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

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

pub fn target_attribute_name(self, input: impl Into<String>) -> Self

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

This field is required.

pub fn set_target_attribute_name(self, input: Option<String>) -> Self

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

pub fn get_target_attribute_name(&self) -> &Option<String>

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

pub fn content_type(self, input: impl Into<String>) -> Self

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 set_content_type(self, input: Option<String>) -> Self

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 get_content_type(&self) -> &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.

pub fn channel_type(self, input: AutoMlChannelType) -> Self

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 set_channel_type(self, input: Option<AutoMlChannelType>) -> Self

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 get_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, input: impl Into<String>) -> Self

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.

pub fn set_sample_weight_attribute_name(self, input: Option<String>) -> Self

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.

pub fn get_sample_weight_attribute_name(&self) -> &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.

pub fn build(self) -> AutoMlChannel

Consumes the builder and constructs a AutoMlChannel.

Trait Implementations

impl Clone for AutoMlChannelBuilder

fn clone(&self) -> AutoMlChannelBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlChannelBuilder

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

Formats the value using the given formatter.

impl Default for AutoMlChannelBuilder

fn default() -> AutoMlChannelBuilder

Returns the “default value” for a type.

impl PartialEq for AutoMlChannelBuilder

fn eq(&self, other: &AutoMlChannelBuilder) -> 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 AutoMlChannelBuilder