❤
npm
Sign UpSign In

@datafire/amazonaws_machinelearning

5.0.0 • Public • Published

@datafire/amazonaws_machinelearning

Client library for Amazon Machine Learning

Installation and Usage

npm install --save @datafire/amazonaws_machinelearning
let amazonaws_machinelearning = require('@datafire/amazonaws_machinelearning').create({
  accessKeyId: "",
  secretAccessKey: "",
  region: ""
});

amazonaws_machinelearning.AddTags({
  "Tags": [],
  "ResourceId": "",
  "ResourceType": ""
}).then(data => {
  console.log(data);
});

Description

Definition of the public APIs exposed by Amazon Machine Learning

Actions

AddTags

amazonaws_machinelearning.AddTags({
  "Tags": [],
  "ResourceId": "",
  "ResourceType": ""
}, context)

Input

Output

CreateBatchPrediction

amazonaws_machinelearning.CreateBatchPrediction({
  "BatchPredictionId": "",
  "MLModelId": "",
  "BatchPredictionDataSourceId": "",
  "OutputUri": ""
}, context)

Input

Output

CreateDataSourceFromRDS

amazonaws_machinelearning.CreateDataSourceFromRDS({
  "DataSourceId": "",
  "RDSData": {
    "DatabaseInformation": {
      "InstanceIdentifier": "",
      "DatabaseName": ""
    },
    "SelectSqlQuery": "",
    "DatabaseCredentials": {
      "Username": "",
      "Password": ""
    },
    "S3StagingLocation": "",
    "ResourceRole": "",
    "ServiceRole": "",
    "SubnetId": "",
    "SecurityGroupIds": []
  },
  "RoleARN": ""
}, context)

Input

Output

CreateDataSourceFromRedshift

amazonaws_machinelearning.CreateDataSourceFromRedshift({
  "DataSourceId": "",
  "DataSpec": {
    "DatabaseInformation": {
      "DatabaseName": "",
      "ClusterIdentifier": ""
    },
    "SelectSqlQuery": "",
    "DatabaseCredentials": {
      "Username": "",
      "Password": ""
    },
    "S3StagingLocation": ""
  },
  "RoleARN": ""
}, context)

Input

Output

CreateDataSourceFromS3

amazonaws_machinelearning.CreateDataSourceFromS3({
  "DataSourceId": "",
  "DataSpec": {
    "DataLocationS3": ""
  }
}, context)

Input

Output

CreateEvaluation

amazonaws_machinelearning.CreateEvaluation({
  "EvaluationId": "",
  "MLModelId": "",
  "EvaluationDataSourceId": ""
}, context)

Input

Output

CreateMLModel

amazonaws_machinelearning.CreateMLModel({
  "MLModelId": "",
  "MLModelType": "",
  "TrainingDataSourceId": ""
}, context)

Input

Output

CreateRealtimeEndpoint

amazonaws_machinelearning.CreateRealtimeEndpoint({
  "MLModelId": ""
}, context)

Input

  • input object

Output

DeleteBatchPrediction

amazonaws_machinelearning.DeleteBatchPrediction({
  "BatchPredictionId": ""
}, context)

Input

  • input object

Output

DeleteDataSource

amazonaws_machinelearning.DeleteDataSource({
  "DataSourceId": ""
}, context)

Input

  • input object

Output

DeleteEvaluation

amazonaws_machinelearning.DeleteEvaluation({
  "EvaluationId": ""
}, context)

Input

  • input object

Output

DeleteMLModel

amazonaws_machinelearning.DeleteMLModel({
  "MLModelId": ""
}, context)

Input

  • input object

Output

DeleteRealtimeEndpoint

amazonaws_machinelearning.DeleteRealtimeEndpoint({
  "MLModelId": ""
}, context)

Input

  • input object

Output

DeleteTags

amazonaws_machinelearning.DeleteTags({
  "TagKeys": [],
  "ResourceId": "",
  "ResourceType": ""
}, context)

Input

Output

DescribeBatchPredictions

amazonaws_machinelearning.DescribeBatchPredictions({}, context)

Input

Output

DescribeDataSources

amazonaws_machinelearning.DescribeDataSources({}, context)

Input

Output

DescribeEvaluations

amazonaws_machinelearning.DescribeEvaluations({}, context)

Input

Output

DescribeMLModels

amazonaws_machinelearning.DescribeMLModels({}, context)

Input

Output

DescribeTags

amazonaws_machinelearning.DescribeTags({
  "ResourceId": "",
  "ResourceType": ""
}, context)

Input

Output

GetBatchPrediction

amazonaws_machinelearning.GetBatchPrediction({
  "BatchPredictionId": ""
}, context)

Input

  • input object

Output

GetDataSource

amazonaws_machinelearning.GetDataSource({
  "DataSourceId": ""
}, context)

Input

Output

GetEvaluation

amazonaws_machinelearning.GetEvaluation({
  "EvaluationId": ""
}, context)

Input

  • input object

Output

GetMLModel

amazonaws_machinelearning.GetMLModel({
  "MLModelId": ""
}, context)

Input

Output

Predict

amazonaws_machinelearning.Predict({
  "MLModelId": "",
  "Record": [],
  "PredictEndpoint": ""
}, context)

Input

Output

UpdateBatchPrediction

amazonaws_machinelearning.UpdateBatchPrediction({
  "BatchPredictionId": "",
  "BatchPredictionName": ""
}, context)

Input

  • input object

Output

UpdateDataSource

amazonaws_machinelearning.UpdateDataSource({
  "DataSourceId": "",
  "DataSourceName": ""
}, context)

Input

Output

UpdateEvaluation

amazonaws_machinelearning.UpdateEvaluation({
  "EvaluationId": "",
  "EvaluationName": ""
}, context)

Input

Output

UpdateMLModel

amazonaws_machinelearning.UpdateMLModel({
  "MLModelId": ""
}, context)

Input

Output

Definitions

AddTagsInput

AddTagsOutput

Algorithm

  • Algorithm string (values: sgd):

    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.

AwsUserArn

  • AwsUserArn string: An Amazon Web Service (AWS) user account identifier. The account identifier can be an AWS root account or an AWS Identity and Access Management (IAM) user.

BatchPrediction

BatchPredictionFilterVariable

  • BatchPredictionFilterVariable string (values: CreatedAt, LastUpdatedAt, Status, Name, IAMUser, MLModelId, DataSourceId, DataURI):

    A list of the variables to use in searching or filtering BatchPrediction.

    • CreatedAt - Sets the search criteria to BatchPrediction creation date.
    • Status - Sets the search criteria to BatchPrediction status.
    • Name - Sets the search criteria to the contents of BatchPrediction Name.
    • IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation.
    • MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.
    • DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction.
    • DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

BatchPredictions

ComparatorValue

  • ComparatorValue string: The value specified in a filtering condition. The ComparatorValue becomes the reference value when matching or evaluating data values in filtering and searching functions.

ComputeStatistics

  • ComputeStatistics boolean

CreateBatchPredictionInput

  • CreateBatchPredictionInput object

CreateBatchPredictionOutput

  • CreateBatchPredictionOutput object:

    Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.

    The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.

CreateDataSourceFromRDSInput

CreateDataSourceFromRDSOutput

  • CreateDataSourceFromRDSOutput object:

    Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.

    The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.

CreateDataSourceFromRedshiftInput

CreateDataSourceFromRedshiftOutput

  • CreateDataSourceFromRedshiftOutput object:

    Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.

    The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

CreateDataSourceFromS3Input

CreateDataSourceFromS3Output

  • CreateDataSourceFromS3Output object:

    Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.

    The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

CreateEvaluationInput

CreateEvaluationOutput

  • CreateEvaluationOutput object:

    Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.

    CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.

CreateMLModelInput

CreateMLModelOutput

  • CreateMLModelOutput object:

    Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.

    The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.

CreateRealtimeEndpointInput

  • CreateRealtimeEndpointInput object

CreateRealtimeEndpointOutput

  • CreateRealtimeEndpointOutput object:

    Represents the output of an CreateRealtimeEndpoint operation.

    The result contains the MLModelId and the endpoint information for the MLModel.

    The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.

DataRearrangement

  • DataRearrangement string

DataSchema

  • DataSchema string:

    The schema of a DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. The DataSource schema is expressed in JSON format.

    DataSchema is not required if you specify a DataSchemaUri

    { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "variables": [ { "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" ] }

DataSource

DataSourceFilterVariable

  • DataSourceFilterVariable string (values: CreatedAt, LastUpdatedAt, Status, Name, DataLocationS3, IAMUser):

    A list of the variables to use in searching or filtering DataSource.

    • CreatedAt - Sets the search criteria to DataSource creation date.
    • Status - Sets the search criteria to DataSource status.
    • Name - Sets the search criteria to the contents of DataSource Name.
    • DataUri - Sets the search criteria to the URI of data files used to create the DataSource. 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 the DataSource creation.
    <title>Note</title>

    The variable names should match the variable names in the DataSource.

DataSources

DeleteBatchPredictionInput

  • DeleteBatchPredictionInput object

DeleteBatchPredictionOutput

  • DeleteBatchPredictionOutput object:

    Represents the output of a DeleteBatchPrediction operation.

    You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.

DeleteDataSourceInput

  • DeleteDataSourceInput object

DeleteDataSourceOutput

  • DeleteDataSourceOutput object: Represents the output of a DeleteDataSource operation.

DeleteEvaluationInput

  • DeleteEvaluationInput object

DeleteEvaluationOutput

  • DeleteEvaluationOutput object:

    Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.

    You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.

DeleteMLModelInput

  • DeleteMLModelInput object

DeleteMLModelOutput

  • DeleteMLModelOutput object:

    Represents the output of a DeleteMLModel operation.

    You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.

DeleteRealtimeEndpointInput

  • DeleteRealtimeEndpointInput object

DeleteRealtimeEndpointOutput

  • DeleteRealtimeEndpointOutput object:

    Represents the output of an DeleteRealtimeEndpoint operation.

    The result contains the MLModelId and the endpoint information for the MLModel.

DeleteTagsInput

DeleteTagsOutput

DescribeBatchPredictionsInput

DescribeBatchPredictionsOutput

  • DescribeBatchPredictionsOutput object: Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.

DescribeDataSourcesInput

DescribeDataSourcesOutput

  • DescribeDataSourcesOutput object: Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.

DescribeEvaluationsInput

DescribeEvaluationsOutput

  • DescribeEvaluationsOutput object: Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.

DescribeMLModelsInput

DescribeMLModelsOutput

  • DescribeMLModelsOutput object: Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.

DescribeTagsInput

DescribeTagsOutput

DetailsAttributes

  • DetailsAttributes string (values: PredictiveModelType, Algorithm): Contains the key values of DetailsMap: PredictiveModelType - Indicates the type of the MLModel. Algorithm - Indicates the algorithm that was used for the MLModel.

DetailsMap

DetailsValue

  • DetailsValue string

EDPPipelineId

  • EDPPipelineId string

EDPResourceRole

  • EDPResourceRole string

EDPSecurityGroupId

  • EDPSecurityGroupId string

EDPSecurityGroupIds

EDPServiceRole

  • EDPServiceRole string

EDPSubnetId

  • EDPSubnetId string

EntityId

  • EntityId string

EntityName

  • EntityName string: A user-supplied name or description of the Amazon ML resource.

EntityStatus

  • EntityStatus string (values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED):

    Object status with the following possible values:

    • PENDING
    • INPROGRESS
    • FAILED
    • COMPLETED
    • DELETED

EpochTime

  • EpochTime string: A timestamp represented in epoch time.

ErrorCode

  • ErrorCode integer

ErrorMessage

  • ErrorMessage string

Evaluation

EvaluationFilterVariable

  • EvaluationFilterVariable string (values: CreatedAt, LastUpdatedAt, Status, Name, IAMUser, MLModelId, DataSourceId, DataURI):

    A list of the variables to use in searching or filtering Evaluation.

    • CreatedAt - Sets the search criteria to Evaluation creation date.
    • Status - Sets the search criteria to Evaluation status.
    • Name - Sets the search criteria to the contents of Evaluation Name.
    • IAMUser - Sets the search criteria to the user account that invoked an evaluation.
    • MLModelId - Sets the search criteria to the Predictor that was evaluated.
    • DataSourceId - Sets the search criteria to the DataSource 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.

Evaluations

GetBatchPredictionInput

  • GetBatchPredictionInput object

GetBatchPredictionOutput

GetDataSourceInput

GetDataSourceOutput

GetEvaluationInput

  • GetEvaluationInput object

GetEvaluationOutput

GetMLModelInput

GetMLModelOutput

IdempotentParameterMismatchException

  • IdempotentParameterMismatchException object: 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.

IntegerType

  • IntegerType integer: Integer type that is a 32-bit signed number.

InternalServerException

  • InternalServerException object: An error on the server occurred when trying to process a request.

InvalidInputException

  • InvalidInputException object: An error on the client occurred. Typically, the cause is an invalid input value.

InvalidTagException

Label

  • Label string

LimitExceededException

  • LimitExceededException object: The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.

LongType

  • LongType integer: Long integer type that is a 64-bit signed number.

MLModel

MLModelFilterVariable

  • MLModelFilterVariable string (values: CreatedAt, LastUpdatedAt, Status, Name, IAMUser, TrainingDataSourceId, RealtimeEndpointStatus, MLModelType, Algorithm, TrainingDataURI)

MLModelName

  • MLModelName string

MLModelType

  • MLModelType string (values: REGRESSION, BINARY, MULTICLASS)

MLModels

Message

  • Message string: Description of the most recent details about an object.

PageLimit

  • PageLimit integer

PerformanceMetrics

  • PerformanceMetrics object:

    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.

PerformanceMetricsProperties

PerformanceMetricsPropertyKey

  • PerformanceMetricsPropertyKey string

PerformanceMetricsPropertyValue

  • PerformanceMetricsPropertyValue string

PredictInput

  • PredictInput object

PredictOutput

Prediction

  • Prediction object:

    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 a BINARY or MULTICLASS MLModel request.

    • PredictedScores - Contains the raw classification score corresponding to each label.

    • PredictedValue - Present for a REGRESSION MLModel request.

PredictorNotMountedException

  • PredictorNotMountedException object: The exception is thrown when a predict request is made to an unmounted MLModel.

PresignedS3Url

  • PresignedS3Url string

RDSDataSpec

RDSDatabase

RDSDatabaseCredentials

RDSDatabaseName

  • RDSDatabaseName string: The name of a database hosted on an RDS DB instance.

RDSDatabasePassword

  • RDSDatabasePassword string: The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the RDSSelectQuery query.

RDSDatabaseUsername

  • RDSDatabaseUsername string: The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.

RDSInstanceIdentifier

  • RDSInstanceIdentifier string: Identifier of RDS DB Instances.

RDSMetadata

RDSSelectSqlQuery

  • RDSSelectSqlQuery string: The SQL query to be executed against the Amazon RDS database. The SQL query should be valid for the Amazon RDS type being used.

RealtimeEndpointInfo

RealtimeEndpointStatus

  • RealtimeEndpointStatus string (values: NONE, READY, UPDATING, FAILED)

Recipe

  • Recipe string

Record

  • Record array: A map of variable name-value pairs that represent an observation.

RedshiftClusterIdentifier

  • RedshiftClusterIdentifier string: The ID of an Amazon Redshift cluster.

RedshiftDataSpec

RedshiftDatabase

RedshiftDatabaseCredentials

RedshiftDatabaseName

  • RedshiftDatabaseName string: The name of a database hosted on an Amazon Redshift cluster.

RedshiftDatabasePassword

  • RedshiftDatabasePassword string: A password to be used by Amazon ML to connect to a database on an Amazon Redshift cluster. The password should have sufficient permissions to execute a RedshiftSelectSqlQuery query. The password should be valid for an Amazon Redshift USER.

RedshiftDatabaseUsername

  • RedshiftDatabaseUsername string: A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery query. The username should be valid for an Amazon Redshift USER.

RedshiftMetadata

RedshiftSelectSqlQuery

  • RedshiftSelectSqlQuery string: Describes the SQL query to execute on the Amazon Redshift database. The SQL query should be valid for an Amazon Redshift SELECT.

ResourceNotFoundException

  • ResourceNotFoundException object: A specified resource cannot be located.

RoleARN

  • RoleARN string: The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename.

S3DataSpec

  • S3DataSpec object: Describes the data specification of a DataSource.

S3Url

  • S3Url string: A reference to a file or bucket on Amazon Simple Storage Service (Amazon S3).

ScoreThreshold

  • ScoreThreshold number

ScoreValue

  • ScoreValue number

ScoreValuePerLabelMap

  • ScoreValuePerLabelMap array: Provides the raw classification score corresponding to each label.

SortOrder

  • SortOrder string (values: asc, dsc):

    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).

StringType

  • StringType string: String type.

Tag

  • Tag object: A custom key-value pair associated with an ML object, such as an ML model.

TagKey

  • TagKey string

TagKeyList

  • TagKeyList array

TagLimitExceededException

TagList

  • TagList array

TagValue

  • TagValue string

TaggableResourceType

  • TaggableResourceType string (values: BatchPrediction, DataSource, Evaluation, MLModel)

TrainingParameters

UpdateBatchPredictionInput

  • UpdateBatchPredictionInput object

UpdateBatchPredictionOutput

  • UpdateBatchPredictionOutput object:

    Represents the output of an UpdateBatchPrediction operation.

    You can see the updated content by using the GetBatchPrediction operation.

UpdateDataSourceInput

  • UpdateDataSourceInput object

UpdateDataSourceOutput

  • UpdateDataSourceOutput object:

    Represents the output of an UpdateDataSource operation.

    You can see the updated content by using the GetBatchPrediction operation.

UpdateEvaluationInput

  • UpdateEvaluationInput object

UpdateEvaluationOutput

  • UpdateEvaluationOutput object:

    Represents the output of an UpdateEvaluation operation.

    You can see the updated content by using the GetEvaluation operation.

UpdateMLModelInput

UpdateMLModelOutput

  • UpdateMLModelOutput object:

    Represents the output of an UpdateMLModel operation.

    You can see the updated content by using the GetMLModel operation.

VariableName

  • VariableName string: The name of a variable. Currently it's used to specify the name of the target value, label, weight, and tags.

VariableValue

  • VariableValue string: The value of a variable. Currently it's used to specify values of the target value, weights, and tag variables and for filtering variable values.

Verbose

  • Verbose boolean: Specifies whether a describe operation should return exhaustive or abbreviated information.

VipURL

  • VipURL string

floatLabel

  • floatLabel number

Readme

Keywords

none

Package Sidebar

Install

npm i @datafire/amazonaws_machinelearning

Repository

github.com/DataFire/integrations

Homepage

github.com/DataFire/integrations#readme

Weekly Downloads

0

Version

5.0.0

License

MIT

Unpacked Size

285 kB

Total Files

5

Last publish

Collaborators

  • datafire
Try on RunKit
Report malware