#[non_exhaustive]pub struct Builder { /* private fields */ }
Expand description
A builder for CreateMlModelInput
Implementations
sourceimpl Builder
impl Builder
sourcepub fn ml_model_id(self, input: impl Into<String>) -> Self
pub fn ml_model_id(self, input: impl Into<String>) -> Self
A user-supplied ID that uniquely identifies the MLModel
.
sourcepub fn set_ml_model_id(self, input: Option<String>) -> Self
pub fn set_ml_model_id(self, input: Option<String>) -> Self
A user-supplied ID that uniquely identifies the MLModel
.
sourcepub fn ml_model_name(self, input: impl Into<String>) -> Self
pub fn ml_model_name(self, input: impl Into<String>) -> Self
A user-supplied name or description of the MLModel
.
sourcepub fn set_ml_model_name(self, input: Option<String>) -> Self
pub fn set_ml_model_name(self, input: Option<String>) -> Self
A user-supplied name or description of the MLModel
.
sourcepub fn ml_model_type(self, input: MlModelType) -> Self
pub fn ml_model_type(self, input: MlModelType) -> Self
The category of supervised learning that this MLModel
will address. Choose from the following types:
-
Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. -
Choose
BINARY
if theMLModel
result has two possible values. -
Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
sourcepub fn set_ml_model_type(self, input: Option<MlModelType>) -> Self
pub fn set_ml_model_type(self, input: Option<MlModelType>) -> Self
The category of supervised learning that this MLModel
will address. Choose from the following types:
-
Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. -
Choose
BINARY
if theMLModel
result has two possible values. -
Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
sourcepub fn parameters(self, k: impl Into<String>, v: impl Into<String>) -> Self
pub fn parameters(self, k: impl Into<String>, v: impl Into<String>) -> Self
Adds a key-value pair to parameters
.
To override the contents of this collection use set_parameters
.
A list of the training parameters in the MLModel
. The list is implemented as a map of key-value pairs.
The following is the current set of training parameters:
-
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from
100000
to2147483648
. The default value is33554432
. -
sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. -
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values areauto
andnone
. The default value isnone
. We strongly recommend that you shuffle your data. -
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
sourcepub fn set_parameters(self, input: Option<HashMap<String, String>>) -> Self
pub fn set_parameters(self, input: Option<HashMap<String, String>>) -> Self
A list of the training parameters in the MLModel
. The list is implemented as a map of key-value pairs.
The following is the current set of training parameters:
-
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from
100000
to2147483648
. The default value is33554432
. -
sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. -
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values areauto
andnone
. The default value isnone
. We strongly recommend that you shuffle your data. -
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
sourcepub fn training_data_source_id(self, input: impl Into<String>) -> Self
pub fn training_data_source_id(self, input: impl Into<String>) -> Self
The DataSource
that points to the training data.
sourcepub fn set_training_data_source_id(self, input: Option<String>) -> Self
pub fn set_training_data_source_id(self, input: Option<String>) -> Self
The DataSource
that points to the training data.
sourcepub fn recipe(self, input: impl Into<String>) -> Self
pub fn recipe(self, input: impl Into<String>) -> Self
The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
sourcepub fn set_recipe(self, input: Option<String>) -> Self
pub fn set_recipe(self, input: Option<String>) -> Self
The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
sourcepub fn recipe_uri(self, input: impl Into<String>) -> Self
pub fn recipe_uri(self, input: impl Into<String>) -> Self
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
sourcepub fn set_recipe_uri(self, input: Option<String>) -> Self
pub fn set_recipe_uri(self, input: Option<String>) -> Self
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
sourcepub fn build(self) -> Result<CreateMlModelInput, BuildError>
pub fn build(self) -> Result<CreateMlModelInput, BuildError>
Consumes the builder and constructs a CreateMlModelInput
Trait Implementations
impl StructuralPartialEq for Builder
Auto Trait Implementations
impl RefUnwindSafe for Builder
impl Send for Builder
impl Sync for Builder
impl Unpin for Builder
impl UnwindSafe for Builder
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcepub fn borrow_mut(&mut self) -> &mut T
pub fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcepub fn to_owned(&self) -> T
pub fn to_owned(&self) -> T
Creates owned data from borrowed data, usually by cloning. Read more
sourcepub fn clone_into(&self, target: &mut T)
pub fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more