Struct aws_sdk_machinelearning::model::Evaluation
source · [−]#[non_exhaustive]pub struct Evaluation {Show 14 fields
pub evaluation_id: Option<String>,
pub ml_model_id: Option<String>,
pub evaluation_data_source_id: Option<String>,
pub input_data_location_s3: 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 performance_metrics: Option<PerformanceMetrics>,
pub message: Option<String>,
pub compute_time: Option<i64>,
pub finished_at: Option<DateTime>,
pub started_at: Option<DateTime>,
}
Expand description
Represents the output of GetEvaluation
operation.
The content consists of the detailed metadata and data file information and the current status of the Evaluation
.
Fields (Non-exhaustive)
This struct is marked as non-exhaustive
Struct { .. }
syntax; cannot be matched against without a wildcard ..
; and struct update syntax will not work.evaluation_id: Option<String>
The ID that is assigned to the Evaluation
at creation.
ml_model_id: Option<String>
The ID of the MLModel
that is the focus of the evaluation.
evaluation_data_source_id: Option<String>
The ID of the DataSource
that is used to evaluate the MLModel
.
input_data_location_s3: Option<String>
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
created_by_iam_user: Option<String>
The AWS user account that invoked the evaluation. 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 Evaluation
was created. The time is expressed in epoch time.
last_updated_at: Option<DateTime>
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
name: Option<String>
A user-supplied name or description of the Evaluation
.
status: Option<EntityStatus>
The status of the evaluation. This element can have one of the following values:
-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to evaluate anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
performance_metrics: Option<PerformanceMetrics>
Measurements of how well the MLModel
performed, using observations referenced by the DataSource
. One of the following metrics is returned, based on the type of the MLModel
:
-
BinaryAUC: A binary
MLModel
uses the Area Under the Curve (AUC) technique to measure performance. -
RegressionRMSE: A regression
MLModel
uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. -
MulticlassAvgFScore: A multiclass
MLModel
uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
message: Option<String>
A description of the most recent details about evaluating the MLModel
.
compute_time: Option<i64>
Long integer type that is a 64-bit signed number.
finished_at: Option<DateTime>
A timestamp represented in epoch time.
started_at: Option<DateTime>
A timestamp represented in epoch time.
Implementations
sourceimpl Evaluation
impl Evaluation
sourcepub fn evaluation_id(&self) -> Option<&str>
pub fn evaluation_id(&self) -> Option<&str>
The ID that is assigned to the Evaluation
at creation.
sourcepub fn ml_model_id(&self) -> Option<&str>
pub fn ml_model_id(&self) -> Option<&str>
The ID of the MLModel
that is the focus of the evaluation.
sourcepub fn evaluation_data_source_id(&self) -> Option<&str>
pub fn evaluation_data_source_id(&self) -> Option<&str>
The ID of the DataSource
that is used to evaluate the MLModel
.
sourcepub fn input_data_location_s3(&self) -> Option<&str>
pub fn input_data_location_s3(&self) -> Option<&str>
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
sourcepub fn created_by_iam_user(&self) -> Option<&str>
pub fn created_by_iam_user(&self) -> Option<&str>
The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
sourcepub fn created_at(&self) -> Option<&DateTime>
pub fn created_at(&self) -> Option<&DateTime>
The time that the Evaluation
was created. The time is expressed in epoch time.
sourcepub fn last_updated_at(&self) -> Option<&DateTime>
pub fn last_updated_at(&self) -> Option<&DateTime>
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
sourcepub fn status(&self) -> Option<&EntityStatus>
pub fn status(&self) -> Option<&EntityStatus>
The status of the evaluation. This element can have one of the following values:
-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to evaluate anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
sourcepub fn performance_metrics(&self) -> Option<&PerformanceMetrics>
pub fn performance_metrics(&self) -> Option<&PerformanceMetrics>
Measurements of how well the MLModel
performed, using observations referenced by the DataSource
. One of the following metrics is returned, based on the type of the MLModel
:
-
BinaryAUC: A binary
MLModel
uses the Area Under the Curve (AUC) technique to measure performance. -
RegressionRMSE: A regression
MLModel
uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. -
MulticlassAvgFScore: A multiclass
MLModel
uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
sourcepub fn message(&self) -> Option<&str>
pub fn message(&self) -> Option<&str>
A description of the most recent details about evaluating the MLModel
.
sourcepub fn compute_time(&self) -> Option<i64>
pub fn compute_time(&self) -> Option<i64>
Long integer type that is a 64-bit signed number.
sourcepub fn finished_at(&self) -> Option<&DateTime>
pub fn finished_at(&self) -> Option<&DateTime>
A timestamp represented in epoch time.
sourcepub fn started_at(&self) -> Option<&DateTime>
pub fn started_at(&self) -> Option<&DateTime>
A timestamp represented in epoch time.
sourceimpl Evaluation
impl Evaluation
sourcepub fn builder() -> Builder
pub fn builder() -> Builder
Creates a new builder-style object to manufacture Evaluation
Trait Implementations
sourceimpl Clone for Evaluation
impl Clone for Evaluation
sourcefn clone(&self) -> Evaluation
fn clone(&self) -> Evaluation
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for Evaluation
impl Debug for Evaluation
sourceimpl PartialEq<Evaluation> for Evaluation
impl PartialEq<Evaluation> for Evaluation
sourcefn eq(&self, other: &Evaluation) -> bool
fn eq(&self, other: &Evaluation) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &Evaluation) -> bool
fn ne(&self, other: &Evaluation) -> bool
This method tests for !=
.
impl StructuralPartialEq for Evaluation
Auto Trait Implementations
impl RefUnwindSafe for Evaluation
impl Send for Evaluation
impl Sync for Evaluation
impl Unpin for Evaluation
impl UnwindSafe for Evaluation
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
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.
sourcefn clone_into(&self, target: &mut T)
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