#[non_exhaustive]pub struct CandidateGenerationConfigBuilder { /* private fields */ }
Expand description
A builder for CandidateGenerationConfig
.
Implementations§
Source§impl CandidateGenerationConfigBuilder
impl CandidateGenerationConfigBuilder
Sourcepub fn algorithms_config(self, input: AutoMlAlgorithmConfig) -> Self
pub fn algorithms_config(self, input: AutoMlAlgorithmConfig) -> Self
Appends an item to algorithms_config
.
To override the contents of this collection use set_algorithms_config
.
Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig
stores the customized selection of algorithms to train on your data.
-
For the tabular problem type
TabularJobConfig
, the list of available algorithms to choose from depends on the training mode set inAutoMLJobConfig.Mode
.-
AlgorithmsConfig
should not be set when the training modeAutoMLJobConfig.Mode
is set toAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
, choose your algorithms from the list provided in AlgorithmConfig.For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
-
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting.
-
Sourcepub fn set_algorithms_config(
self,
input: Option<Vec<AutoMlAlgorithmConfig>>,
) -> Self
pub fn set_algorithms_config( self, input: Option<Vec<AutoMlAlgorithmConfig>>, ) -> Self
Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig
stores the customized selection of algorithms to train on your data.
-
For the tabular problem type
TabularJobConfig
, the list of available algorithms to choose from depends on the training mode set inAutoMLJobConfig.Mode
.-
AlgorithmsConfig
should not be set when the training modeAutoMLJobConfig.Mode
is set toAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
, choose your algorithms from the list provided in AlgorithmConfig.For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
-
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting.
-
Sourcepub fn get_algorithms_config(&self) -> &Option<Vec<AutoMlAlgorithmConfig>>
pub fn get_algorithms_config(&self) -> &Option<Vec<AutoMlAlgorithmConfig>>
Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig
stores the customized selection of algorithms to train on your data.
-
For the tabular problem type
TabularJobConfig
, the list of available algorithms to choose from depends on the training mode set inAutoMLJobConfig.Mode
.-
AlgorithmsConfig
should not be set when the training modeAutoMLJobConfig.Mode
is set toAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
, choose your algorithms from the list provided in AlgorithmConfig.For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
-
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting.
-
Sourcepub fn build(self) -> CandidateGenerationConfig
pub fn build(self) -> CandidateGenerationConfig
Consumes the builder and constructs a CandidateGenerationConfig
.
Trait Implementations§
Source§impl Clone for CandidateGenerationConfigBuilder
impl Clone for CandidateGenerationConfigBuilder
Source§fn clone(&self) -> CandidateGenerationConfigBuilder
fn clone(&self) -> CandidateGenerationConfigBuilder
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moreSource§impl Default for CandidateGenerationConfigBuilder
impl Default for CandidateGenerationConfigBuilder
Source§fn default() -> CandidateGenerationConfigBuilder
fn default() -> CandidateGenerationConfigBuilder
Source§impl PartialEq for CandidateGenerationConfigBuilder
impl PartialEq for CandidateGenerationConfigBuilder
Source§fn eq(&self, other: &CandidateGenerationConfigBuilder) -> bool
fn eq(&self, other: &CandidateGenerationConfigBuilder) -> bool
self
and other
values to be equal, and is used by ==
.impl StructuralPartialEq for CandidateGenerationConfigBuilder
Auto Trait Implementations§
impl Freeze for CandidateGenerationConfigBuilder
impl RefUnwindSafe for CandidateGenerationConfigBuilder
impl Send for CandidateGenerationConfigBuilder
impl Sync for CandidateGenerationConfigBuilder
impl Unpin for CandidateGenerationConfigBuilder
impl UnwindSafe for CandidateGenerationConfigBuilder
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