Struct aws_sdk_glue::types::builders::FindMatchesParametersBuilder

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

A builder for FindMatchesParameters.

Implementations

impl FindMatchesParametersBuilder

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

The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.

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

The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.

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

The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.

pub fn precision_recall_tradeoff(self, input: f64) -> Self

The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.

The precision metric indicates how often your model is correct when it predicts a match.

The recall metric indicates that for an actual match, how often your model predicts the match.

pub fn set_precision_recall_tradeoff(self, input: Option<f64>) -> Self

The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.

The precision metric indicates how often your model is correct when it predicts a match.

The recall metric indicates that for an actual match, how often your model predicts the match.

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

The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.

The precision metric indicates how often your model is correct when it predicts a match.

The recall metric indicates that for an actual match, how often your model predicts the match.

pub fn accuracy_cost_tradeoff(self, input: f64) -> Self

The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.

Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.

Cost measures how many compute resources, and thus money, are consumed to run the transform.

pub fn set_accuracy_cost_tradeoff(self, input: Option<f64>) -> Self

The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.

Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.

Cost measures how many compute resources, and thus money, are consumed to run the transform.

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

The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.

Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.

Cost measures how many compute resources, and thus money, are consumed to run the transform.

pub fn enforce_provided_labels(self, input: bool) -> Self

The value to switch on or off to force the output to match the provided labels from users. If the value is True, the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False, the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.

Note that setting this value to true may increase the conflation execution time.

pub fn set_enforce_provided_labels(self, input: Option<bool>) -> Self

The value to switch on or off to force the output to match the provided labels from users. If the value is True, the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False, the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.

Note that setting this value to true may increase the conflation execution time.

pub fn get_enforce_provided_labels(&self) -> &Option<bool>

The value to switch on or off to force the output to match the provided labels from users. If the value is True, the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False, the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.

Note that setting this value to true may increase the conflation execution time.

pub fn build(self) -> FindMatchesParameters

Consumes the builder and constructs a FindMatchesParameters.

Trait Implementations

impl Clone for FindMatchesParametersBuilder

fn clone(&self) -> FindMatchesParametersBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for FindMatchesParametersBuilder

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

Formats the value using the given formatter.

impl Default for FindMatchesParametersBuilder

fn default() -> FindMatchesParametersBuilder

Returns the “default value” for a type.

impl PartialEq for FindMatchesParametersBuilder

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