Copyright | (c) 2013-2016 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay <brendan.g.hay@gmail.com> |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | None |
Language | Haskell2010 |
- Service Configuration
- Errors
- Waiters
- Operations
- UpdateDataSource
- DeleteDataSource
- DescribeTags
- CreateDataSourceFromRedshift
- CreateDataSourceFromS3
- CreateMLModel
- DeleteTags
- DeleteBatchPrediction
- UpdateBatchPrediction
- GetMLModel
- GetDataSource
- UpdateEvaluation
- DeleteEvaluation
- DeleteMLModel
- UpdateMLModel
- GetBatchPrediction
- DescribeBatchPredictions (Paginated)
- CreateDataSourceFromRDS
- CreateEvaluation
- Predict
- DeleteRealtimeEndpoint
- CreateBatchPrediction
- GetEvaluation
- DescribeEvaluations (Paginated)
- CreateRealtimeEndpoint
- AddTags
- DescribeMLModels (Paginated)
- DescribeDataSources (Paginated)
- Types
- Algorithm
- BatchPredictionFilterVariable
- DataSourceFilterVariable
- DetailsAttributes
- EntityStatus
- EvaluationFilterVariable
- MLModelFilterVariable
- MLModelType
- RealtimeEndpointStatus
- SortOrder
- TaggableResourceType
- BatchPrediction
- DataSource
- Evaluation
- MLModel
- PerformanceMetrics
- Prediction
- RDSDataSpec
- RDSDatabase
- RDSDatabaseCredentials
- RDSMetadata
- RealtimeEndpointInfo
- RedshiftDataSpec
- RedshiftDatabase
- RedshiftDatabaseCredentials
- RedshiftMetadata
- S3DataSpec
- Tag
Definition of the public APIs exposed by Amazon Machine Learning
- machineLearning :: Service
- _InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError
- _InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError
- _InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError
- _IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError
- _TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError
- _PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError
- _ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError
- _LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError
- mLModelAvailable :: Wait DescribeMLModels
- batchPredictionAvailable :: Wait DescribeBatchPredictions
- dataSourceAvailable :: Wait DescribeDataSources
- evaluationAvailable :: Wait DescribeEvaluations
- module Network.AWS.MachineLearning.UpdateDataSource
- module Network.AWS.MachineLearning.DeleteDataSource
- module Network.AWS.MachineLearning.DescribeTags
- module Network.AWS.MachineLearning.CreateDataSourceFromRedshift
- module Network.AWS.MachineLearning.CreateDataSourceFromS3
- module Network.AWS.MachineLearning.CreateMLModel
- module Network.AWS.MachineLearning.DeleteTags
- module Network.AWS.MachineLearning.DeleteBatchPrediction
- module Network.AWS.MachineLearning.UpdateBatchPrediction
- module Network.AWS.MachineLearning.GetMLModel
- module Network.AWS.MachineLearning.GetDataSource
- module Network.AWS.MachineLearning.UpdateEvaluation
- module Network.AWS.MachineLearning.DeleteEvaluation
- module Network.AWS.MachineLearning.DeleteMLModel
- module Network.AWS.MachineLearning.UpdateMLModel
- module Network.AWS.MachineLearning.GetBatchPrediction
- module Network.AWS.MachineLearning.DescribeBatchPredictions
- module Network.AWS.MachineLearning.CreateDataSourceFromRDS
- module Network.AWS.MachineLearning.CreateEvaluation
- module Network.AWS.MachineLearning.Predict
- module Network.AWS.MachineLearning.DeleteRealtimeEndpoint
- module Network.AWS.MachineLearning.CreateBatchPrediction
- module Network.AWS.MachineLearning.GetEvaluation
- module Network.AWS.MachineLearning.DescribeEvaluations
- module Network.AWS.MachineLearning.CreateRealtimeEndpoint
- module Network.AWS.MachineLearning.AddTags
- module Network.AWS.MachineLearning.DescribeMLModels
- module Network.AWS.MachineLearning.DescribeDataSources
- data Algorithm = SGD
- data BatchPredictionFilterVariable
- data DataSourceFilterVariable
- data DetailsAttributes
- data EntityStatus
- data EvaluationFilterVariable
- data MLModelFilterVariable
- data MLModelType
- data RealtimeEndpointStatus
- data SortOrder
- data TaggableResourceType
- data BatchPrediction
- batchPrediction :: BatchPrediction
- bpStatus :: Lens' BatchPrediction (Maybe EntityStatus)
- bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)
- bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime)
- bpComputeTime :: Lens' BatchPrediction (Maybe Integer)
- bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)
- bpMLModelId :: Lens' BatchPrediction (Maybe Text)
- bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text)
- bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer)
- bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime)
- bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text)
- bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime)
- bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer)
- bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text)
- bpName :: Lens' BatchPrediction (Maybe Text)
- bpMessage :: Lens' BatchPrediction (Maybe Text)
- bpOutputURI :: Lens' BatchPrediction (Maybe Text)
- data DataSource
- dataSource :: DataSource
- dsStatus :: Lens' DataSource (Maybe EntityStatus)
- dsNumberOfFiles :: Lens' DataSource (Maybe Integer)
- dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime)
- dsCreatedAt :: Lens' DataSource (Maybe UTCTime)
- dsComputeTime :: Lens' DataSource (Maybe Integer)
- dsDataSourceId :: Lens' DataSource (Maybe Text)
- dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata)
- dsDataSizeInBytes :: Lens' DataSource (Maybe Integer)
- dsStartedAt :: Lens' DataSource (Maybe UTCTime)
- dsFinishedAt :: Lens' DataSource (Maybe UTCTime)
- dsCreatedByIAMUser :: Lens' DataSource (Maybe Text)
- dsName :: Lens' DataSource (Maybe Text)
- dsDataLocationS3 :: Lens' DataSource (Maybe Text)
- dsComputeStatistics :: Lens' DataSource (Maybe Bool)
- dsMessage :: Lens' DataSource (Maybe Text)
- dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)
- dsDataRearrangement :: Lens' DataSource (Maybe Text)
- dsRoleARN :: Lens' DataSource (Maybe Text)
- data Evaluation
- evaluation :: Evaluation
- eStatus :: Lens' Evaluation (Maybe EntityStatus)
- ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)
- eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)
- eCreatedAt :: Lens' Evaluation (Maybe UTCTime)
- eComputeTime :: Lens' Evaluation (Maybe Integer)
- eInputDataLocationS3 :: Lens' Evaluation (Maybe Text)
- eMLModelId :: Lens' Evaluation (Maybe Text)
- eStartedAt :: Lens' Evaluation (Maybe UTCTime)
- eFinishedAt :: Lens' Evaluation (Maybe UTCTime)
- eCreatedByIAMUser :: Lens' Evaluation (Maybe Text)
- eName :: Lens' Evaluation (Maybe Text)
- eEvaluationId :: Lens' Evaluation (Maybe Text)
- eMessage :: Lens' Evaluation (Maybe Text)
- eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text)
- data MLModel
- mLModel :: MLModel
- mlmStatus :: Lens' MLModel (Maybe EntityStatus)
- mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
- mlmTrainingParameters :: Lens' MLModel (HashMap Text Text)
- mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
- mlmCreatedAt :: Lens' MLModel (Maybe UTCTime)
- mlmComputeTime :: Lens' MLModel (Maybe Integer)
- mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text)
- mlmMLModelId :: Lens' MLModel (Maybe Text)
- mlmSizeInBytes :: Lens' MLModel (Maybe Integer)
- mlmStartedAt :: Lens' MLModel (Maybe UTCTime)
- mlmScoreThreshold :: Lens' MLModel (Maybe Double)
- mlmFinishedAt :: Lens' MLModel (Maybe UTCTime)
- mlmAlgorithm :: Lens' MLModel (Maybe Algorithm)
- mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text)
- mlmName :: Lens' MLModel (Maybe Text)
- mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)
- mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text)
- mlmMessage :: Lens' MLModel (Maybe Text)
- mlmMLModelType :: Lens' MLModel (Maybe MLModelType)
- data PerformanceMetrics
- performanceMetrics :: PerformanceMetrics
- pmProperties :: Lens' PerformanceMetrics (HashMap Text Text)
- data Prediction
- prediction :: Prediction
- pPredictedValue :: Lens' Prediction (Maybe Double)
- pPredictedLabel :: Lens' Prediction (Maybe Text)
- pPredictedScores :: Lens' Prediction (HashMap Text Double)
- pDetails :: Lens' Prediction (HashMap DetailsAttributes Text)
- data RDSDataSpec
- rdsDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec
- rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text)
- rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text)
- rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text)
- rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase
- rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text
- rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials
- rdsdsS3StagingLocation :: Lens' RDSDataSpec Text
- rdsdsResourceRole :: Lens' RDSDataSpec Text
- rdsdsServiceRole :: Lens' RDSDataSpec Text
- rdsdsSubnetId :: Lens' RDSDataSpec Text
- rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text]
- data RDSDatabase
- rdsDatabase :: Text -> Text -> RDSDatabase
- rdsdInstanceIdentifier :: Lens' RDSDatabase Text
- rdsdDatabaseName :: Lens' RDSDatabase Text
- data RDSDatabaseCredentials
- rdsDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials
- rdsdcUsername :: Lens' RDSDatabaseCredentials Text
- rdsdcPassword :: Lens' RDSDatabaseCredentials Text
- data RDSMetadata
- rdsMetadata :: RDSMetadata
- rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text)
- rmDataPipelineId :: Lens' RDSMetadata (Maybe Text)
- rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase)
- rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text)
- rmResourceRole :: Lens' RDSMetadata (Maybe Text)
- rmServiceRole :: Lens' RDSMetadata (Maybe Text)
- data RealtimeEndpointInfo
- realtimeEndpointInfo :: RealtimeEndpointInfo
- reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)
- reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text)
- reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)
- reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)
- data RedshiftDataSpec
- redshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
- rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text)
- rDataSchema :: Lens' RedshiftDataSpec (Maybe Text)
- rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text)
- rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase
- rSelectSqlQuery :: Lens' RedshiftDataSpec Text
- rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials
- rS3StagingLocation :: Lens' RedshiftDataSpec Text
- data RedshiftDatabase
- redshiftDatabase :: Text -> Text -> RedshiftDatabase
- rdDatabaseName :: Lens' RedshiftDatabase Text
- rdClusterIdentifier :: Lens' RedshiftDatabase Text
- data RedshiftDatabaseCredentials
- redshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials
- rdcUsername :: Lens' RedshiftDatabaseCredentials Text
- rdcPassword :: Lens' RedshiftDatabaseCredentials Text
- data RedshiftMetadata
- redshiftMetadata :: RedshiftMetadata
- redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text)
- redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)
- redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text)
- data S3DataSpec
- s3DataSpec :: Text -> S3DataSpec
- sdsDataSchema :: Lens' S3DataSpec (Maybe Text)
- sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text)
- sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text)
- sdsDataLocationS3 :: Lens' S3DataSpec Text
- data Tag
- tag :: Tag
- tagValue :: Lens' Tag (Maybe Text)
- tagKey :: Lens' Tag (Maybe Text)
Service Configuration
machineLearning :: Service Source #
API version '2014-12-12' of the Amazon Machine Learning SDK configuration.
Errors
Error matchers are designed for use with the functions provided by
Control.Exception.Lens.
This allows catching (and rethrowing) service specific errors returned
by MachineLearning
.
InvalidTagException
_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError Source #
Prism for InvalidTagException' errors.
InternalServerException
_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError Source #
An error on the server occurred when trying to process a request.
InvalidInputException
_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError Source #
An error on the client occurred. Typically, the cause is an invalid input value.
IdempotentParameterMismatchException
_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError Source #
A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
TagLimitExceededException
_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError Source #
Prism for TagLimitExceededException' errors.
PredictorNotMountedException
_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError Source #
The exception is thrown when a predict request is made to an unmounted MLModel
.
ResourceNotFoundException
_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError Source #
A specified resource cannot be located.
LimitExceededException
_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError Source #
The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource
.
Waiters
Waiters poll by repeatedly sending a request until some remote success condition
configured by the Wait
specification is fulfilled. The Wait
specification
determines how many attempts should be made, in addition to delay and retry strategies.
MLModelAvailable
mLModelAvailable :: Wait DescribeMLModels Source #
Polls DescribeMLModels
every 30 seconds until a
successful state is reached. An error is returned after 60 failed checks.
BatchPredictionAvailable
batchPredictionAvailable :: Wait DescribeBatchPredictions Source #
Polls DescribeBatchPredictions
every 30 seconds until a
successful state is reached. An error is returned after 60 failed checks.
DataSourceAvailable
dataSourceAvailable :: Wait DescribeDataSources Source #
Polls DescribeDataSources
every 30 seconds until a
successful state is reached. An error is returned after 60 failed checks.
EvaluationAvailable
evaluationAvailable :: Wait DescribeEvaluations Source #
Polls DescribeEvaluations
every 30 seconds until a
successful state is reached. An error is returned after 60 failed checks.
Operations
Some AWS operations return results that are incomplete and require subsequent
requests in order to obtain the entire result set. The process of sending
subsequent requests to continue where a previous request left off is called
pagination. For example, the ListObjects
operation of Amazon S3 returns up to
1000 objects at a time, and you must send subsequent requests with the
appropriate Marker in order to retrieve the next page of results.
Operations that have an AWSPager
instance can transparently perform subsequent
requests, correctly setting Markers and other request facets to iterate through
the entire result set of a truncated API operation. Operations which support
this have an additional note in the documentation.
Many operations have the ability to filter results on the server side. See the individual operation parameters for details.
UpdateDataSource
DeleteDataSource
DescribeTags
CreateDataSourceFromRedshift
CreateDataSourceFromS3
CreateMLModel
DeleteTags
DeleteBatchPrediction
UpdateBatchPrediction
GetMLModel
GetDataSource
UpdateEvaluation
DeleteEvaluation
DeleteMLModel
UpdateMLModel
GetBatchPrediction
DescribeBatchPredictions (Paginated)
CreateDataSourceFromRDS
CreateEvaluation
Predict
DeleteRealtimeEndpoint
CreateBatchPrediction
GetEvaluation
DescribeEvaluations (Paginated)
CreateRealtimeEndpoint
AddTags
DescribeMLModels (Paginated)
DescribeDataSources (Paginated)
Types
Algorithm
The function used to train an MLModel
. Training choices supported by Amazon ML include the following:
SGD
- Stochastic Gradient Descent.RandomForest
- Random forest of decision trees.
BatchPredictionFilterVariable
data BatchPredictionFilterVariable Source #
A list of the variables to use in searching or filtering BatchPrediction
.
CreatedAt
- Sets the search criteria toBatchPrediction
creation date.Status
- Sets the search criteria toBatchPrediction
status.Name
- Sets the search criteria to the contents ofBatchPrediction
____Name
.IAMUser
- Sets the search criteria to the user account that invoked theBatchPrediction
creation.MLModelId
- Sets the search criteria to theMLModel
used in theBatchPrediction
.DataSourceId
- Sets the search criteria to theDataSource
used in theBatchPrediction
.DataURI
- Sets the search criteria to the data file(s) used in theBatchPrediction
. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
BatchCreatedAt | |
BatchDataSourceId | |
BatchDataURI | |
BatchIAMUser | |
BatchLastUpdatedAt | |
BatchMLModelId | |
BatchName | |
BatchStatus |
DataSourceFilterVariable
data DataSourceFilterVariable Source #
A list of the variables to use in searching or filtering DataSource
.
CreatedAt
- Sets the search criteria toDataSource
creation date.Status
- Sets the search criteria toDataSource
status.Name
- Sets the search criteria to the contents ofDataSource
____Name
.DataUri
- Sets the search criteria to the URI of data files used to create theDataSource
. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.IAMUser
- Sets the search criteria to the user account that invoked theDataSource
creation.
Note
The variable names should match the variable names in the DataSource
.
DetailsAttributes
data DetailsAttributes Source #
Contains the key values of DetailsMap
: PredictiveModelType
- Indicates the type of the MLModel
. Algorithm
- Indicates the algorithm that was used for the MLModel
.
EntityStatus
data EntityStatus Source #
Object status with the following possible values:
PENDING
INPROGRESS
FAILED
COMPLETED
DELETED
EvaluationFilterVariable
data EvaluationFilterVariable Source #
A list of the variables to use in searching or filtering Evaluation
.
CreatedAt
- Sets the search criteria toEvaluation
creation date.Status
- Sets the search criteria toEvaluation
status.Name
- Sets the search criteria to the contents ofEvaluation
____Name
.IAMUser
- Sets the search criteria to the user account that invoked an evaluation.MLModelId
- Sets the search criteria to thePredictor
that was evaluated.DataSourceId
- Sets the search criteria to theDataSource
used in evaluation.DataUri
- Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
EvalCreatedAt | |
EvalDataSourceId | |
EvalDataURI | |
EvalIAMUser | |
EvalLastUpdatedAt | |
EvalMLModelId | |
EvalName | |
EvalStatus |
MLModelFilterVariable
data MLModelFilterVariable Source #
MLMFVAlgorithm | |
MLMFVCreatedAt | |
MLMFVIAMUser | |
MLMFVLastUpdatedAt | |
MLMFVMLModelType | |
MLMFVName | |
MLMFVRealtimeEndpointStatus | |
MLMFVStatus | |
MLMFVTrainingDataSourceId | |
MLMFVTrainingDataURI |
MLModelType
data MLModelType Source #
RealtimeEndpointStatus
data RealtimeEndpointStatus Source #
SortOrder
The sort order specified in a listing condition. Possible values include the following:
asc
- Present the information in ascending order (from A-Z).dsc
- Present the information in descending order (from Z-A).
TaggableResourceType
data TaggableResourceType Source #
BatchPrediction
data BatchPrediction Source #
Represents the output of a GetBatchPrediction
operation.
The content consists of the detailed metadata, the status, and the data file information of a 'Batch Prediction'.
See: batchPrediction
smart constructor.
batchPrediction :: BatchPrediction Source #
Creates a value of BatchPrediction
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
bpStatus :: Lens' BatchPrediction (Maybe EntityStatus) Source #
The status of the BatchPrediction
. This element can have one of the following values:
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.INPROGRESS
- The process is underway.FAILED
- The request to perform a batch prediction did not run to completion. It is not usable.COMPLETED
- The batch prediction process completed successfully.DELETED
- TheBatchPrediction
is marked as deleted. It is not usable.
bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #
The time of the most recent edit to the BatchPrediction
. The time is expressed in epoch time.
bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #
The time that the BatchPrediction
was created. The time is expressed in epoch time.
bpComputeTime :: Lens' BatchPrediction (Maybe Integer) Source #
Undocumented member.
bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text) Source #
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
bpMLModelId :: Lens' BatchPrediction (Maybe Text) Source #
The ID of the MLModel
that generated predictions for the BatchPrediction
request.
bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text) Source #
The ID of the DataSource
that points to the group of observations to predict.
bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer) Source #
Undocumented member.
bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #
Undocumented member.
bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text) Source #
The ID assigned to the BatchPrediction
at creation. This value should be identical to the value of the BatchPredictionID
in the request.
bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #
Undocumented member.
bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer) Source #
Undocumented member.
bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text) Source #
The AWS user account that invoked the BatchPrediction
. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
bpName :: Lens' BatchPrediction (Maybe Text) Source #
A user-supplied name or description of the BatchPrediction
.
bpMessage :: Lens' BatchPrediction (Maybe Text) Source #
A description of the most recent details about processing the batch prediction request.
bpOutputURI :: Lens' BatchPrediction (Maybe Text) Source #
The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the 's3 key' portion of the outputURI
field: ':', '//', '/./', '/../'.
DataSource
data DataSource Source #
Represents the output of the GetDataSource
operation.
The content consists of the detailed metadata and data file information and the current status of the DataSource
.
See: dataSource
smart constructor.
dataSource :: DataSource Source #
Creates a value of DataSource
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
dsStatus :: Lens' DataSource (Maybe EntityStatus) Source #
The current status of the DataSource
. This element can have one of the following values:
- PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a
DataSource
. - INPROGRESS - The creation process is underway.
- FAILED - The request to create a
DataSource
did not run to completion. It is not usable. - COMPLETED - The creation process completed successfully.
- DELETED - The
DataSource
is marked as deleted. It is not usable.
dsNumberOfFiles :: Lens' DataSource (Maybe Integer) Source #
The number of data files referenced by the DataSource
.
dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime) Source #
The time of the most recent edit to the BatchPrediction
. The time is expressed in epoch time.
dsCreatedAt :: Lens' DataSource (Maybe UTCTime) Source #
The time that the DataSource
was created. The time is expressed in epoch time.
dsComputeTime :: Lens' DataSource (Maybe Integer) Source #
Undocumented member.
dsDataSourceId :: Lens' DataSource (Maybe Text) Source #
The ID that is assigned to the DataSource
during creation.
dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata) Source #
Undocumented member.
dsDataSizeInBytes :: Lens' DataSource (Maybe Integer) Source #
The total number of observations contained in the data files that the DataSource
references.
dsStartedAt :: Lens' DataSource (Maybe UTCTime) Source #
Undocumented member.
dsFinishedAt :: Lens' DataSource (Maybe UTCTime) Source #
Undocumented member.
dsCreatedByIAMUser :: Lens' DataSource (Maybe Text) Source #
The AWS user account from which the DataSource
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
dsName :: Lens' DataSource (Maybe Text) Source #
A user-supplied name or description of the DataSource
.
dsDataLocationS3 :: Lens' DataSource (Maybe Text) Source #
The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource
.
dsComputeStatistics :: Lens' DataSource (Maybe Bool) Source #
The parameter is true
if statistics need to be generated from the observation data.
dsMessage :: Lens' DataSource (Maybe Text) Source #
A description of the most recent details about creating the DataSource
.
dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata) Source #
Undocumented member.
dsDataRearrangement :: Lens' DataSource (Maybe Text) Source #
A JSON string that represents the splitting and rearrangement requirement used when this DataSource
was created.
Evaluation
data Evaluation Source #
Represents the output of GetEvaluation
operation.
The content consists of the detailed metadata and data file information and the current status of the Evaluation
.
See: evaluation
smart constructor.
evaluation :: Evaluation Source #
Creates a value of Evaluation
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
eStatus :: Lens' Evaluation (Maybe EntityStatus) Source #
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.
ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics) Source #
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.
eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime) Source #
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
eCreatedAt :: Lens' Evaluation (Maybe UTCTime) Source #
The time that the Evaluation
was created. The time is expressed in epoch time.
eComputeTime :: Lens' Evaluation (Maybe Integer) Source #
Undocumented member.
eInputDataLocationS3 :: Lens' Evaluation (Maybe Text) Source #
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
eMLModelId :: Lens' Evaluation (Maybe Text) Source #
The ID of the MLModel
that is the focus of the evaluation.
eStartedAt :: Lens' Evaluation (Maybe UTCTime) Source #
Undocumented member.
eFinishedAt :: Lens' Evaluation (Maybe UTCTime) Source #
Undocumented member.
eCreatedByIAMUser :: Lens' Evaluation (Maybe Text) Source #
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.
eName :: Lens' Evaluation (Maybe Text) Source #
A user-supplied name or description of the Evaluation
.
eEvaluationId :: Lens' Evaluation (Maybe Text) Source #
The ID that is assigned to the Evaluation
at creation.
eMessage :: Lens' Evaluation (Maybe Text) Source #
A description of the most recent details about evaluating the MLModel
.
eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text) Source #
The ID of the DataSource
that is used to evaluate the MLModel
.
MLModel
Creates a value of MLModel
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
mlmStatus :: Lens' MLModel (Maybe EntityStatus) Source #
The current status of an MLModel
. This element can have one of the following values:
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to create anMLModel
.INPROGRESS
- The creation process is underway.FAILED
- The request to create anMLModel
didn't run to completion. The model isn't usable.COMPLETED
- The creation process completed successfully.DELETED
- TheMLModel
is marked as deleted. It isn't usable.
mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) Source #
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
mlmTrainingParameters :: Lens' MLModel (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' 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 the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are
auto
andnone
. The default value isnone
. 'sgd.l1RegularizationAmount' - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in 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 whenL2
is specified. Use this parameter sparingly.'sgd.l2RegularizationAmount' - The coefficient regularization L2 norm, which 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 whenL1
is specified. Use this parameter sparingly.
mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) Source #
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
mlmCreatedAt :: Lens' MLModel (Maybe UTCTime) Source #
The time that the MLModel
was created. The time is expressed in epoch time.
mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text) Source #
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
mlmCreatedByIAMUser :: Lens' MLModel (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.
mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo) Source #
The current endpoint of the MLModel
.
mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text) Source #
The ID of the training DataSource
. The CreateMLModel
operation uses the TrainingDataSourceId
.
mlmMessage :: Lens' MLModel (Maybe Text) Source #
A description of the most recent details about accessing the MLModel
.
mlmMLModelType :: Lens' MLModel (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 a child-friendly web site?".MULTICLASS
- Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM- risk trade?".
PerformanceMetrics
data PerformanceMetrics Source #
Measurements of how well the MLModel
performed on known observations. One of the following metrics is returned, based on the type of the MLModel
:
- BinaryAUC: The binary
MLModel
uses the Area Under the Curve (AUC) technique to measure performance. - RegressionRMSE: The 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: The multiclass
MLModel
uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
See: performanceMetrics
smart constructor.
performanceMetrics :: PerformanceMetrics Source #
Creates a value of PerformanceMetrics
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
pmProperties :: Lens' PerformanceMetrics (HashMap Text Text) Source #
Undocumented member.
Prediction
data Prediction Source #
The output from a Predict
operation:
Details
- Contains the following attributes: 'DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS' 'DetailsAttributes.ALGORITHM - SGD'PredictedLabel
- Present for either aBINARY
orMULTICLASS
MLModel
request.PredictedScores
- Contains the raw classification score corresponding to each label.PredictedValue
- Present for aREGRESSION
MLModel
request.
See: prediction
smart constructor.
prediction :: Prediction Source #
Creates a value of Prediction
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
pPredictedValue :: Lens' Prediction (Maybe Double) Source #
The prediction value for REGRESSION
MLModel
.
pPredictedLabel :: Lens' Prediction (Maybe Text) Source #
The prediction label for either a BINARY
or MULTICLASS
MLModel
.
pPredictedScores :: Lens' Prediction (HashMap Text Double) Source #
Undocumented member.
pDetails :: Lens' Prediction (HashMap DetailsAttributes Text) Source #
Undocumented member.
RDSDataSpec
data RDSDataSpec Source #
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource
.
See: rdsDataSpec
smart constructor.
:: RDSDatabase | |
-> Text | |
-> RDSDatabaseCredentials | |
-> Text | |
-> Text | |
-> Text | |
-> Text | |
-> RDSDataSpec |
Creates a value of RDSDataSpec
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text) Source #
The Amazon S3 location of the DataSchema
.
rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text) Source #
A JSON string that represents the schema for an Amazon RDS DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
.
A DataSchema
is not required if you specify a DataSchemaUri
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their value. Use the following format to define your DataSchema
.
{ "version": "1.0",
"recordAnnotationFieldName": "F1",
"recordWeightFieldName": "F2",
"targetFieldName": "F3",
"dataFormat": "CSV",
"dataFileContainsHeader": true,
"attributes": [
{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
"excludedVariableNames": [ "F6" ] }
rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text) Source #
A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource
. If the DataRearrangement
parameter is not provided, all of the input data is used to create the Datasource
.
There are multiple parameters that control what data is used to create a datasource:
percentBegin
Use
percentBegin
to indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.percentEnd
Use
percentEnd
to indicate the end of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.complement
The
complement
parameter instructs Amazon ML to use the data that is not included in the range ofpercentBegin
topercentEnd
to create a datasource. Thecomplement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBegin
andpercentEnd
, along with thecomplement
parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation: '{"splitting":{"percentBegin":0, "percentEnd":25}}'
Datasource for training: '{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}'
strategy
To change how Amazon ML splits the data for a datasource, use the
strategy
parameter.The default value for the
strategy
parameter issequential
, meaning that Amazon ML takes all of the data records between thepercentBegin
andpercentEnd
parameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangement
lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}'
Datasource for training: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}'
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the
strategy
parameter torandom
and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number betweenpercentBegin
andpercentEnd
. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two
DataRearrangement
lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}'
Datasource for training: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}'
rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase Source #
Describes the DatabaseName
and InstanceIdentifier
of an Amazon RDS database.
rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text Source #
The query that is used to retrieve the observation data for the DataSource
.
rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials Source #
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
rdsdsS3StagingLocation :: Lens' RDSDataSpec Text Source #
The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery
is stored in this location.
rdsdsResourceRole :: Lens' RDSDataSpec Text Source #
The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
rdsdsServiceRole :: Lens' RDSDataSpec Text Source #
The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
rdsdsSubnetId :: Lens' RDSDataSpec Text Source #
The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.
rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text] Source #
The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
RDSDatabase
data RDSDatabase Source #
The database details of an Amazon RDS database.
See: rdsDatabase
smart constructor.
Creates a value of RDSDatabase
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rdsdInstanceIdentifier :: Lens' RDSDatabase Text Source #
The ID of an RDS DB instance.
rdsdDatabaseName :: Lens' RDSDatabase Text Source #
Undocumented member.
RDSDatabaseCredentials
data RDSDatabaseCredentials Source #
The database credentials to connect to a database on an RDS DB instance.
See: rdsDatabaseCredentials
smart constructor.
rdsDatabaseCredentials Source #
Creates a value of RDSDatabaseCredentials
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rdsdcUsername :: Lens' RDSDatabaseCredentials Text Source #
Undocumented member.
rdsdcPassword :: Lens' RDSDatabaseCredentials Text Source #
Undocumented member.
RDSMetadata
data RDSMetadata Source #
The datasource details that are specific to Amazon RDS.
See: rdsMetadata
smart constructor.
rdsMetadata :: RDSMetadata Source #
Creates a value of RDSMetadata
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text) Source #
The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose
is true in GetDataSourceInput
.
rmDataPipelineId :: Lens' RDSMetadata (Maybe Text) Source #
The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.
rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase) Source #
The database details required to connect to an Amazon RDS.
rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text) Source #
Undocumented member.
rmResourceRole :: Lens' RDSMetadata (Maybe Text) Source #
The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
rmServiceRole :: Lens' RDSMetadata (Maybe Text) Source #
The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
RealtimeEndpointInfo
data RealtimeEndpointInfo Source #
Describes the real-time endpoint information for an MLModel
.
See: realtimeEndpointInfo
smart constructor.
realtimeEndpointInfo :: RealtimeEndpointInfo Source #
Creates a value of RealtimeEndpointInfo
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime) Source #
The time that the request to create the real-time endpoint for the MLModel
was received. The time is expressed in epoch time.
reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text) Source #
The URI that specifies where to send real-time prediction requests for the MLModel
.
Note
The application must wait until the real-time endpoint is ready before using this URI.
reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus) Source #
The current status of the real-time endpoint for the MLModel
. This element can have one of the following values:
NONE
- Endpoint does not exist or was previously deleted.READY
- Endpoint is ready to be used for real-time predictions.UPDATING
- Updating/creating the endpoint.
reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int) Source #
The maximum processing rate for the real-time endpoint for MLModel
, measured in incoming requests per second.
RedshiftDataSpec
data RedshiftDataSpec Source #
Describes the data specification of an Amazon Redshift DataSource
.
See: redshiftDataSpec
smart constructor.
Creates a value of RedshiftDataSpec
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text) Source #
Describes the schema location for an Amazon Redshift DataSource
.
rDataSchema :: Lens' RedshiftDataSpec (Maybe Text) Source #
A JSON string that represents the schema for an Amazon Redshift DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
.
A DataSchema
is not required if you specify a DataSchemaUri
.
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their value. Use the following format to define your DataSchema
.
{ "version": "1.0",
"recordAnnotationFieldName": "F1",
"recordWeightFieldName": "F2",
"targetFieldName": "F3",
"dataFormat": "CSV",
"dataFileContainsHeader": true,
"attributes": [
{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
"excludedVariableNames": [ "F6" ] }
rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text) Source #
A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource
. If the DataRearrangement
parameter is not provided, all of the input data is used to create the Datasource
.
There are multiple parameters that control what data is used to create a datasource:
percentBegin
Use
percentBegin
to indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.percentEnd
Use
percentEnd
to indicate the end of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.complement
The
complement
parameter instructs Amazon ML to use the data that is not included in the range ofpercentBegin
topercentEnd
to create a datasource. Thecomplement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBegin
andpercentEnd
, along with thecomplement
parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation: '{"splitting":{"percentBegin":0, "percentEnd":25}}'
Datasource for training: '{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}'
strategy
To change how Amazon ML splits the data for a datasource, use the
strategy
parameter.The default value for the
strategy
parameter issequential
, meaning that Amazon ML takes all of the data records between thepercentBegin
andpercentEnd
parameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangement
lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}'
Datasource for training: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}'
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the
strategy
parameter torandom
and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number betweenpercentBegin
andpercentEnd
. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two
DataRearrangement
lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}'
Datasource for training: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}'
rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase Source #
Describes the DatabaseName
and ClusterIdentifier
for an Amazon Redshift DataSource
.
rSelectSqlQuery :: Lens' RedshiftDataSpec Text Source #
Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource
.
rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials Source #
Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
rS3StagingLocation :: Lens' RedshiftDataSpec Text Source #
Describes an Amazon S3 location to store the result set of the SelectSqlQuery
query.
RedshiftDatabase
data RedshiftDatabase Source #
Describes the database details required to connect to an Amazon Redshift database.
See: redshiftDatabase
smart constructor.
Creates a value of RedshiftDatabase
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rdDatabaseName :: Lens' RedshiftDatabase Text Source #
Undocumented member.
rdClusterIdentifier :: Lens' RedshiftDatabase Text Source #
Undocumented member.
RedshiftDatabaseCredentials
data RedshiftDatabaseCredentials Source #
Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
See: redshiftDatabaseCredentials
smart constructor.
redshiftDatabaseCredentials Source #
Creates a value of RedshiftDatabaseCredentials
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rdcUsername :: Lens' RedshiftDatabaseCredentials Text Source #
Undocumented member.
rdcPassword :: Lens' RedshiftDatabaseCredentials Text Source #
Undocumented member.
RedshiftMetadata
data RedshiftMetadata Source #
Describes the DataSource
details specific to Amazon Redshift.
See: redshiftMetadata
smart constructor.
redshiftMetadata :: RedshiftMetadata Source #
Creates a value of RedshiftMetadata
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text) Source #
The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose
is true in GetDataSourceInput.
redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase) Source #
Undocumented member.
redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text) Source #
Undocumented member.
S3DataSpec
data S3DataSpec Source #
Describes the data specification of a DataSource
.
See: s3DataSpec
smart constructor.
Creates a value of S3DataSpec
with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
sdsDataSchema :: Lens' S3DataSpec (Maybe Text) Source #
A JSON string that represents the schema for an Amazon S3 DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
.
You must provide either the DataSchema
or the DataSchemaLocationS3
.
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their value. Use the following format to define your DataSchema
.
{ "version": "1.0",
"recordAnnotationFieldName": "F1",
"recordWeightFieldName": "F2",
"targetFieldName": "F3",
"dataFormat": "CSV",
"dataFileContainsHeader": true,
"attributes": [
{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
"excludedVariableNames": [ "F6" ] }
sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text) Source #
Describes the schema location in Amazon S3. You must provide either the DataSchema
or the DataSchemaLocationS3
.
sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text) Source #
A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource
. If the DataRearrangement
parameter is not provided, all of the input data is used to create the Datasource
.
There are multiple parameters that control what data is used to create a datasource:
percentBegin
Use
percentBegin
to indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.percentEnd
Use
percentEnd
to indicate the end of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.complement
The
complement
parameter instructs Amazon ML to use the data that is not included in the range ofpercentBegin
topercentEnd
to create a datasource. Thecomplement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBegin
andpercentEnd
, along with thecomplement
parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation: '{"splitting":{"percentBegin":0, "percentEnd":25}}'
Datasource for training: '{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}'
strategy
To change how Amazon ML splits the data for a datasource, use the
strategy
parameter.The default value for the
strategy
parameter issequential
, meaning that Amazon ML takes all of the data records between thepercentBegin
andpercentEnd
parameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangement
lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}'
Datasource for training: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}'
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the
strategy
parameter torandom
and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number betweenpercentBegin
andpercentEnd
. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two
DataRearrangement
lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}'
Datasource for training: '{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}'
sdsDataLocationS3 :: Lens' S3DataSpec Text Source #
The location of the data file(s) used by a DataSource
. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.
Tag
A custom key-value pair associated with an ML object, such as an ML model.
See: tag
smart constructor.