Struct rusoto_machinelearning::GetMLModelOutput
source · [−]pub struct GetMLModelOutput {Show 21 fields
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>>,
}
Expand description
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.
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.
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 aMLModel
. -
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
- TheMLModel
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
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 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
toMAXDOUBLE
. 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
toMAXDOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
Trait Implementations
sourceimpl Clone for GetMLModelOutput
impl Clone for GetMLModelOutput
sourcefn clone(&self) -> GetMLModelOutput
fn clone(&self) -> GetMLModelOutput
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 GetMLModelOutput
impl Debug for GetMLModelOutput
sourceimpl Default for GetMLModelOutput
impl Default for GetMLModelOutput
sourcefn default() -> GetMLModelOutput
fn default() -> GetMLModelOutput
Returns the “default value” for a type. Read more
sourceimpl<'de> Deserialize<'de> for GetMLModelOutput
impl<'de> Deserialize<'de> for GetMLModelOutput
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
sourceimpl PartialEq<GetMLModelOutput> for GetMLModelOutput
impl PartialEq<GetMLModelOutput> for GetMLModelOutput
sourcefn eq(&self, other: &GetMLModelOutput) -> bool
fn eq(&self, other: &GetMLModelOutput) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &GetMLModelOutput) -> bool
fn ne(&self, other: &GetMLModelOutput) -> bool
This method tests for !=
.
impl StructuralPartialEq for GetMLModelOutput
Auto Trait Implementations
impl RefUnwindSafe for GetMLModelOutput
impl Send for GetMLModelOutput
impl Sync for GetMLModelOutput
impl Unpin for GetMLModelOutput
impl UnwindSafe for GetMLModelOutput
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