Struct rusoto_machinelearning::GetMLModelOutput

pub struct GetMLModelOutput {
    pub compute_time: Option<i64>,
    pub created_at: Option<f64>,
    pub created_by_iam_user: Option<String>,
    pub endpoint_info: Option<RealtimeEndpointInfo>,
    pub finished_at: Option<f64>,
    pub input_data_location_s3: Option<String>,
    pub last_updated_at: Option<f64>,
    pub log_uri: Option<String>,
    pub ml_model_id: Option<String>,
    pub ml_model_type: Option<String>,
    pub message: Option<String>,
    pub name: Option<String>,
    pub recipe: Option<String>,
    pub schema: Option<String>,
    pub score_threshold: Option<f32>,
    pub score_threshold_last_updated_at: Option<f64>,
    pub size_in_bytes: Option<i64>,
    pub started_at: Option<f64>,
    pub status: Option<String>,
    pub training_data_source_id: Option<String>,
    pub training_parameters: Option<HashMap<String, String>>,
}

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

Fields

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.

created_at: Option<f64>

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

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.

endpoint_info: Option<RealtimeEndpointInfo>

The current endpoint of the MLModel

finished_at: Option<f64>

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.

input_data_location_s3: Option<String>

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

last_updated_at: Option<f64>

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

log_uri: Option<String>

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

ml_model_id: Option<String>

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

ml_model_type: Option<String>

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?"

message: Option<String>

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

name: Option<String>

A user-supplied name or description of the MLModel.

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.

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.

This parameter is provided as part of the verbose format.

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<f64>

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

size_in_bytes: Option<i64>

started_at: Option<f64>

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

status: Option<String>

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.

training_data_source_id: Option<String>

The ID of the training DataSource.

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 MAXDOUBLE. 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 MAXDOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

Trait Implementations

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 Default for GetMLModelOutput

fn default() -> GetMLModelOutput

Returns the "default value" for a type.

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<'de> Deserialize<'de> for GetMLModelOutput

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.