Struct BatchTransformInput

#[non_exhaustive]
pub struct BatchTransformInput {
    pub data_captured_destination_s3_uri: Option<String>,
    pub dataset_format: Option<MonitoringDatasetFormat>,
    pub local_path: Option<String>,
    pub s3_input_mode: Option<ProcessingS3InputMode>,
    pub s3_data_distribution_type: Option<ProcessingS3DataDistributionType>,
    pub features_attribute: Option<String>,
    pub inference_attribute: Option<String>,
    pub probability_attribute: Option<String>,
    pub probability_threshold_attribute: Option<f64>,
    pub start_time_offset: Option<String>,
    pub end_time_offset: Option<String>,
    pub exclude_features_attribute: Option<String>,
}

Input object for the batch transform job.

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_captured_destination_s3_uri: Option<String>

The Amazon S3 location being used to capture the data.

dataset_format: Option<MonitoringDatasetFormat>

The dataset format for your batch transform job.

local_path: Option<String>

Path to the filesystem where the batch transform data is available to the container.

s3_input_mode: Option<ProcessingS3InputMode>

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

s3_data_distribution_type: Option<ProcessingS3DataDistributionType>

Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

features_attribute: Option<String>

The attributes of the input data that are the input features.

inference_attribute: Option<String>

The attribute of the input data that represents the ground truth label.

probability_attribute: Option<String>

In a classification problem, the attribute that represents the class probability.

probability_threshold_attribute: Option<f64>

The threshold for the class probability to be evaluated as a positive result.

start_time_offset: Option<String>

If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

end_time_offset: Option<String>

If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

exclude_features_attribute: Option<String>

The attributes of the input data to exclude from the analysis.

Implementations

impl BatchTransformInput

pub fn data_captured_destination_s3_uri(&self) -> Option<&str>

The Amazon S3 location being used to capture the data.

pub fn dataset_format(&self) -> Option<&MonitoringDatasetFormat>

The dataset format for your batch transform job.

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

Path to the filesystem where the batch transform data is available to the container.

pub fn s3_input_mode(&self) -> Option<&ProcessingS3InputMode>

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

pub fn s3_data_distribution_type( &self, ) -> Option<&ProcessingS3DataDistributionType>

Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

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

The attributes of the input data that are the input features.

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

The attribute of the input data that represents the ground truth label.

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

In a classification problem, the attribute that represents the class probability.

pub fn probability_threshold_attribute(&self) -> Option<f64>

The threshold for the class probability to be evaluated as a positive result.

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

If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

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

If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

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

The attributes of the input data to exclude from the analysis.

impl BatchTransformInput

pub fn builder() -> BatchTransformInputBuilder

Creates a new builder-style object to manufacture BatchTransformInput.

Trait Implementations

impl Clone for BatchTransformInput

fn clone(&self) -> BatchTransformInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BatchTransformInput

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

Formats the value using the given formatter.

impl PartialEq for BatchTransformInput

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for BatchTransformInput