Struct aws_sdk_machinelearning::output::get_ml_model_output::Builder

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

A builder for GetMlModelOutput

Implementations

impl Builder

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

The MLModel ID, which is same as the MLModelId in the request.

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

The MLModel ID, which is same as the MLModelId in the request.

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

The ID of the training DataSource.

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

The ID of the training DataSource.

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

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

pub fn created_at(self, input: DateTime) -> Self

The time that the MLModel was created. The time is expressed in epoch time.

pub fn set_created_at(self, input: Option<DateTime>) -> Self

The time that the MLModel was created. The time is expressed in epoch time.

pub fn last_updated_at(self, input: DateTime) -> Self

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

pub fn set_last_updated_at(self, input: Option<DateTime>) -> Self

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

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

A user-supplied name or description of the MLModel.

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

A user-supplied name or description of the MLModel.

pub fn status(self, input: EntityStatus) -> Self

The current status of the MLModel. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.

• INPROGRESS - The request is processing.

• FAILED - The request did not run to completion. The ML model isn't usable.

• COMPLETED - The request completed successfully.

• DELETED - The MLModel is marked as deleted. It isn't usable.

pub fn set_status(self, input: Option<EntityStatus>) -> Self

The current status of the MLModel. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.

• INPROGRESS - The request is processing.

• FAILED - The request did not run to completion. The ML model isn't usable.

• COMPLETED - The request completed successfully.

• DELETED - The MLModel is marked as deleted. It isn't usable.

pub fn size_in_bytes(self, input: i64) -> Self

Long integer type that is a 64-bit signed number.

pub fn set_size_in_bytes(self, input: Option<i64>) -> Self

Long integer type that is a 64-bit signed number.

pub fn endpoint_info(self, input: RealtimeEndpointInfo) -> Self

The current endpoint of the MLModel

pub fn set_endpoint_info(self, input: Option<RealtimeEndpointInfo>) -> Self

The current endpoint of the MLModel

pub fn training_parameters(
    self,
    k: impl Into<String>,
    v: impl Into<String>
) -> Self

Adds a key-value pair to training_parameters.

To override the contents of this collection use set_training_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 to 2147483648. The default value is 33554432.

• sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. 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 as 1.0E-08.

  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 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 as 1.0E-08.

  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

pub fn set_training_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 to 2147483648. The default value is 33554432.

• sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. 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 as 1.0E-08.

  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 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 as 1.0E-08.

  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

pub fn input_data_location_s3(self, input: impl Into<String>) -> Self

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

pub fn set_input_data_location_s3(self, input: Option<String>) -> Self

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

pub fn ml_model_type(self, input: MlModelType) -> Self

Identifies the MLModel category. The following are the available types:

• REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"

• BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"

• MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"

pub fn set_ml_model_type(self, input: Option<MlModelType>) -> Self

Identifies the MLModel category. The following are the available types:

• REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"

• BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"

• MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"

pub fn score_threshold(self, input: f32) -> Self

The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

pub fn set_score_threshold(self, input: Option<f32>) -> Self

The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

pub fn score_threshold_last_updated_at(self, input: DateTime) -> Self

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

pub fn set_score_threshold_last_updated_at(
    self,
    input: Option<DateTime>
) -> Self

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

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

A link to the file that contains logs of the CreateMLModel operation.

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

A link to the file that contains logs of the CreateMLModel operation.

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

A description of the most recent details about accessing the MLModel.

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

A description of the most recent details about accessing the MLModel.

pub fn compute_time(self, input: i64) -> Self

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

pub fn set_compute_time(self, input: Option<i64>) -> Self

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

pub fn finished_at(self, input: DateTime) -> Self

The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

pub fn set_finished_at(self, input: Option<DateTime>) -> Self

The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

pub fn started_at(self, input: DateTime) -> Self

The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

pub fn set_started_at(self, input: Option<DateTime>) -> Self

The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

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

The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

Note: This parameter is provided as part of the verbose format.

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

The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

Note: This parameter is provided as part of the verbose format.

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

The schema used by all of the data files referenced by the DataSource.

Note: This parameter is provided as part of the verbose format.

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

The schema used by all of the data files referenced by the DataSource.

Note: This parameter is provided as part of the verbose format.

pub fn build(self) -> GetMlModelOutput

Consumes the builder and constructs a GetMlModelOutput

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=.

impl StructuralPartialEq for Builder