Struct aws_sdk_machinelearning::output::GetMlModelOutput

#[non_exhaustive]
pub struct GetMlModelOutput {

    pub ml_model_id: Option<String>,
    pub training_data_source_id: Option<String>,
    pub created_by_iam_user: Option<String>,
    pub created_at: Option<DateTime>,
    pub last_updated_at: Option<DateTime>,
    pub name: Option<String>,
    pub status: Option<EntityStatus>,
    pub size_in_bytes: Option<i64>,
    pub endpoint_info: Option<RealtimeEndpointInfo>,
    pub training_parameters: Option<HashMap<String, String>>,
    pub input_data_location_s3: Option<String>,
    pub ml_model_type: Option<MlModelType>,
    pub score_threshold: Option<f32>,
    pub score_threshold_last_updated_at: Option<DateTime>,
    pub log_uri: Option<String>,
    pub message: Option<String>,
    pub compute_time: Option<i64>,
    pub finished_at: Option<DateTime>,
    pub started_at: Option<DateTime>,
    pub recipe: Option<String>,
    pub schema: Option<String>,
}

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

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.

ml_model_id: Option<String>

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

training_data_source_id: Option<String>

The ID of the training DataSource.

created_by_iam_user: Option<String>

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.

created_at: Option<DateTime>

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

last_updated_at: Option<DateTime>

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

name: Option<String>

A user-supplied name or description of the MLModel.

status: Option<EntityStatus>

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.

size_in_bytes: Option<i64>

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

endpoint_info: Option<RealtimeEndpointInfo>

The current endpoint of the MLModel

training_parameters: Option<HashMap<String, String>>

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.

input_data_location_s3: Option<String>

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

ml_model_type: Option<MlModelType>

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?"

score_threshold: Option<f32>

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.

score_threshold_last_updated_at: Option<DateTime>

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

log_uri: Option<String>

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

message: Option<String>

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

compute_time: Option<i64>

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.

finished_at: Option<DateTime>

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.

started_at: Option<DateTime>

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

recipe: Option<String>

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.

schema: Option<String>

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

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

Implementations

impl GetMlModelOutput

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

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

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

The ID of the training DataSource.

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

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) -> Option<&DateTime>

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

pub fn last_updated_at(&self) -> Option<&DateTime>

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

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

A user-supplied name or description of the MLModel.

pub fn status(&self) -> Option<&EntityStatus>

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) -> Option<i64>

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

pub fn endpoint_info(&self) -> Option<&RealtimeEndpointInfo>

The current endpoint of the MLModel

pub fn training_parameters(&self) -> Option<&HashMap<String, String>>

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) -> Option<&str>

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

pub fn ml_model_type(&self) -> Option<&MlModelType>

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) -> Option<f32>

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) -> Option<&DateTime>

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

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

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

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

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

pub fn compute_time(&self) -> Option<i64>

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) -> Option<&DateTime>

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) -> Option<&DateTime>

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) -> Option<&str>

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) -> Option<&str>

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

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

impl GetMlModelOutput

pub fn builder() -> Builder

Creates a new builder-style object to manufacture GetMlModelOutput

Trait Implementations

impl Clone for GetMlModelOutput

fn clone(&self) -> GetMlModelOutput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for GetMlModelOutput

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

Formats the value using the given formatter.

impl PartialEq<GetMlModelOutput> for GetMlModelOutput

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

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

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

This method tests for !=.

impl StructuralPartialEq for GetMlModelOutput