Copyright | (c) 2013-2023 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | Safe-Inferred |
Language | Haskell2010 |
Returns an MLModel
that includes detailed metadata, data source
information, and the current status of the MLModel
.
GetMLModel
provides results in normal or verbose format.
Synopsis
- data GetMLModel = GetMLModel' {}
- newGetMLModel :: Text -> GetMLModel
- getMLModel_verbose :: Lens' GetMLModel (Maybe Bool)
- getMLModel_mLModelId :: Lens' GetMLModel Text
- data GetMLModelResponse = GetMLModelResponse' {
- computeTime :: Maybe Integer
- createdAt :: Maybe POSIX
- createdByIamUser :: Maybe Text
- endpointInfo :: Maybe RealtimeEndpointInfo
- finishedAt :: Maybe POSIX
- inputDataLocationS3 :: Maybe Text
- lastUpdatedAt :: Maybe POSIX
- logUri :: Maybe Text
- mLModelId :: Maybe Text
- mLModelType :: Maybe MLModelType
- message :: Maybe Text
- name :: Maybe Text
- recipe :: Maybe Text
- schema :: Maybe Text
- scoreThreshold :: Maybe Double
- scoreThresholdLastUpdatedAt :: Maybe POSIX
- sizeInBytes :: Maybe Integer
- startedAt :: Maybe POSIX
- status :: Maybe EntityStatus
- trainingDataSourceId :: Maybe Text
- trainingParameters :: Maybe (HashMap Text Text)
- httpStatus :: Int
- newGetMLModelResponse :: Int -> GetMLModelResponse
- getMLModelResponse_computeTime :: Lens' GetMLModelResponse (Maybe Integer)
- getMLModelResponse_createdAt :: Lens' GetMLModelResponse (Maybe UTCTime)
- getMLModelResponse_createdByIamUser :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_endpointInfo :: Lens' GetMLModelResponse (Maybe RealtimeEndpointInfo)
- getMLModelResponse_finishedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
- getMLModelResponse_inputDataLocationS3 :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_lastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
- getMLModelResponse_logUri :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_mLModelId :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_mLModelType :: Lens' GetMLModelResponse (Maybe MLModelType)
- getMLModelResponse_message :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_name :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_recipe :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_schema :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_scoreThreshold :: Lens' GetMLModelResponse (Maybe Double)
- getMLModelResponse_scoreThresholdLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
- getMLModelResponse_sizeInBytes :: Lens' GetMLModelResponse (Maybe Integer)
- getMLModelResponse_startedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
- getMLModelResponse_status :: Lens' GetMLModelResponse (Maybe EntityStatus)
- getMLModelResponse_trainingDataSourceId :: Lens' GetMLModelResponse (Maybe Text)
- getMLModelResponse_trainingParameters :: Lens' GetMLModelResponse (Maybe (HashMap Text Text))
- getMLModelResponse_httpStatus :: Lens' GetMLModelResponse Int
Creating a Request
data GetMLModel Source #
See: newGetMLModel
smart constructor.
Instances
:: Text | |
-> GetMLModel |
Create a value of GetMLModel
with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:verbose:GetMLModel'
, getMLModel_verbose
- Specifies whether the GetMLModel
operation should return Recipe
.
If true, Recipe
is returned.
If false, Recipe
is not returned.
GetMLModel
, getMLModel_mLModelId
- The ID assigned to the MLModel
at creation.
Request Lenses
getMLModel_verbose :: Lens' GetMLModel (Maybe Bool) Source #
Specifies whether the GetMLModel
operation should return Recipe
.
If true, Recipe
is returned.
If false, Recipe
is not returned.
getMLModel_mLModelId :: Lens' GetMLModel Text Source #
The ID assigned to the MLModel
at creation.
Destructuring the Response
data GetMLModelResponse Source #
Represents the output of a GetMLModel
operation, and provides detailed
information about a MLModel
.
See: newGetMLModelResponse
smart constructor.
GetMLModelResponse' | |
|
Instances
newGetMLModelResponse Source #
Create a value of GetMLModelResponse
with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
GetMLModelResponse
, getMLModelResponse_computeTime
- 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.
GetMLModelResponse
, getMLModelResponse_createdAt
- The time that the MLModel
was created. The time is expressed in epoch
time.
GetMLModelResponse
, getMLModelResponse_createdByIamUser
- 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.
GetMLModelResponse
, getMLModelResponse_endpointInfo
- The current endpoint of the MLModel
GetMLModelResponse
, getMLModelResponse_finishedAt
- 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.
GetMLModelResponse
, getMLModelResponse_inputDataLocationS3
- The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
GetMLModelResponse
, getMLModelResponse_lastUpdatedAt
- The time of the most recent edit to the MLModel
. The time is expressed
in epoch time.
$sel:logUri:GetMLModelResponse'
, getMLModelResponse_logUri
- A link to the file that contains logs of the CreateMLModel
operation.
GetMLModel
, getMLModelResponse_mLModelId
- The MLModel ID, which is same as the MLModelId
in the request.
GetMLModelResponse
, getMLModelResponse_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?"
GetMLModelResponse
, getMLModelResponse_message
- A description of the most recent details about accessing the MLModel
.
GetMLModelResponse
, getMLModelResponse_name
- A user-supplied name or description of the MLModel
.
$sel:recipe:GetMLModelResponse'
, getMLModelResponse_recipe
- 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.
$sel:schema:GetMLModelResponse'
, getMLModelResponse_schema
- The schema used by all of the data files referenced by the DataSource
.
Note: This parameter is provided as part of the verbose format.
GetMLModelResponse
, getMLModelResponse_scoreThreshold
- 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
.
GetMLModelResponse
, getMLModelResponse_scoreThresholdLastUpdatedAt
- The time of the most recent edit to the ScoreThreshold
. The time is
expressed in epoch time.
GetMLModelResponse
, getMLModelResponse_sizeInBytes
- Undocumented member.
GetMLModelResponse
, getMLModelResponse_startedAt
- The epoch time when Amazon Machine Learning marked the MLModel
as
INPROGRESS
. StartedAt
isn't available if the MLModel
is in the
PENDING
state.
GetMLModelResponse
, getMLModelResponse_status
- 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.
GetMLModelResponse
, getMLModelResponse_trainingDataSourceId
- The ID of the training DataSource
.
GetMLModelResponse
, getMLModelResponse_trainingParameters
- 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
toMAX_DOUBLE
. 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
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
$sel:httpStatus:GetMLModelResponse'
, getMLModelResponse_httpStatus
- The response's http status code.
Response Lenses
getMLModelResponse_computeTime :: Lens' GetMLModelResponse (Maybe Integer) Source #
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.
getMLModelResponse_createdAt :: Lens' GetMLModelResponse (Maybe UTCTime) Source #
The time that the MLModel
was created. The time is expressed in epoch
time.
getMLModelResponse_createdByIamUser :: Lens' GetMLModelResponse (Maybe Text) Source #
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.
getMLModelResponse_endpointInfo :: Lens' GetMLModelResponse (Maybe RealtimeEndpointInfo) Source #
The current endpoint of the MLModel
getMLModelResponse_finishedAt :: Lens' GetMLModelResponse (Maybe UTCTime) Source #
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.
getMLModelResponse_inputDataLocationS3 :: Lens' GetMLModelResponse (Maybe Text) Source #
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
getMLModelResponse_lastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime) Source #
The time of the most recent edit to the MLModel
. The time is expressed
in epoch time.
getMLModelResponse_logUri :: Lens' GetMLModelResponse (Maybe Text) Source #
A link to the file that contains logs of the CreateMLModel
operation.
getMLModelResponse_mLModelId :: Lens' GetMLModelResponse (Maybe Text) Source #
The MLModel ID, which is same as the MLModelId
in the request.
getMLModelResponse_mLModelType :: Lens' GetMLModelResponse (Maybe MLModelType) Source #
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?"
getMLModelResponse_message :: Lens' GetMLModelResponse (Maybe Text) Source #
A description of the most recent details about accessing the MLModel
.
getMLModelResponse_name :: Lens' GetMLModelResponse (Maybe Text) Source #
A user-supplied name or description of the MLModel
.
getMLModelResponse_recipe :: Lens' GetMLModelResponse (Maybe Text) Source #
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.
getMLModelResponse_schema :: Lens' GetMLModelResponse (Maybe Text) Source #
The schema used by all of the data files referenced by the DataSource
.
Note: This parameter is provided as part of the verbose format.
getMLModelResponse_scoreThreshold :: Lens' GetMLModelResponse (Maybe Double) Source #
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
.
getMLModelResponse_scoreThresholdLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime) Source #
The time of the most recent edit to the ScoreThreshold
. The time is
expressed in epoch time.
getMLModelResponse_sizeInBytes :: Lens' GetMLModelResponse (Maybe Integer) Source #
Undocumented member.
getMLModelResponse_startedAt :: Lens' GetMLModelResponse (Maybe UTCTime) Source #
The epoch time when Amazon Machine Learning marked the MLModel
as
INPROGRESS
. StartedAt
isn't available if the MLModel
is in the
PENDING
state.
getMLModelResponse_status :: Lens' GetMLModelResponse (Maybe EntityStatus) Source #
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.
getMLModelResponse_trainingDataSourceId :: Lens' GetMLModelResponse (Maybe Text) Source #
The ID of the training DataSource
.
getMLModelResponse_trainingParameters :: Lens' GetMLModelResponse (Maybe (HashMap Text Text)) Source #
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
toMAX_DOUBLE
. 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
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
getMLModelResponse_httpStatus :: Lens' GetMLModelResponse Int Source #
The response's http status code.