// // Copyright 2010-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"). // You may not use this file except in compliance with the License. // A copy of the License is located at // // http://aws.amazon.com/apache2.0 // // or in the "license" file accompanying this file. This file is distributed // on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either // express or implied. See the License for the specific language governing // permissions and limitations under the License. // #import #import #import NS_ASSUME_NONNULL_BEGIN FOUNDATION_EXPORT NSString *const AWSMachineLearningErrorDomain; typedef NS_ENUM(NSInteger, AWSMachineLearningErrorType) { AWSMachineLearningErrorUnknown, AWSMachineLearningErrorIdempotentParameterMismatch, AWSMachineLearningErrorInternalServer, AWSMachineLearningErrorInvalidInput, AWSMachineLearningErrorInvalidTag, AWSMachineLearningErrorLimitExceeded, AWSMachineLearningErrorPredictorNotMounted, AWSMachineLearningErrorResourceNotFound, AWSMachineLearningErrorTagLimitExceeded, }; typedef NS_ENUM(NSInteger, AWSMachineLearningAlgorithm) { AWSMachineLearningAlgorithmUnknown, AWSMachineLearningAlgorithmSgd, }; typedef NS_ENUM(NSInteger, AWSMachineLearningBatchPredictionFilterVariable) { AWSMachineLearningBatchPredictionFilterVariableUnknown, AWSMachineLearningBatchPredictionFilterVariableCreatedAt, AWSMachineLearningBatchPredictionFilterVariableLastUpdatedAt, AWSMachineLearningBatchPredictionFilterVariableStatus, AWSMachineLearningBatchPredictionFilterVariableName, AWSMachineLearningBatchPredictionFilterVariableIAMUser, AWSMachineLearningBatchPredictionFilterVariableMLModelId, AWSMachineLearningBatchPredictionFilterVariableDataSourceId, AWSMachineLearningBatchPredictionFilterVariableDataURI, }; typedef NS_ENUM(NSInteger, AWSMachineLearningDataSourceFilterVariable) { AWSMachineLearningDataSourceFilterVariableUnknown, AWSMachineLearningDataSourceFilterVariableCreatedAt, AWSMachineLearningDataSourceFilterVariableLastUpdatedAt, AWSMachineLearningDataSourceFilterVariableStatus, AWSMachineLearningDataSourceFilterVariableName, AWSMachineLearningDataSourceFilterVariableDataLocationS3, AWSMachineLearningDataSourceFilterVariableIAMUser, }; typedef NS_ENUM(NSInteger, AWSMachineLearningDetailsAttributes) { AWSMachineLearningDetailsAttributesUnknown, AWSMachineLearningDetailsAttributesPredictiveModelType, AWSMachineLearningDetailsAttributesAlgorithm, }; typedef NS_ENUM(NSInteger, AWSMachineLearningEntityStatus) { AWSMachineLearningEntityStatusUnknown, AWSMachineLearningEntityStatusPending, AWSMachineLearningEntityStatusInprogress, AWSMachineLearningEntityStatusFailed, AWSMachineLearningEntityStatusCompleted, AWSMachineLearningEntityStatusDeleted, }; typedef NS_ENUM(NSInteger, AWSMachineLearningEvaluationFilterVariable) { AWSMachineLearningEvaluationFilterVariableUnknown, AWSMachineLearningEvaluationFilterVariableCreatedAt, AWSMachineLearningEvaluationFilterVariableLastUpdatedAt, AWSMachineLearningEvaluationFilterVariableStatus, AWSMachineLearningEvaluationFilterVariableName, AWSMachineLearningEvaluationFilterVariableIAMUser, AWSMachineLearningEvaluationFilterVariableMLModelId, AWSMachineLearningEvaluationFilterVariableDataSourceId, AWSMachineLearningEvaluationFilterVariableDataURI, }; typedef NS_ENUM(NSInteger, AWSMachineLearningMLModelFilterVariable) { AWSMachineLearningMLModelFilterVariableUnknown, AWSMachineLearningMLModelFilterVariableCreatedAt, AWSMachineLearningMLModelFilterVariableLastUpdatedAt, AWSMachineLearningMLModelFilterVariableStatus, AWSMachineLearningMLModelFilterVariableName, AWSMachineLearningMLModelFilterVariableIAMUser, AWSMachineLearningMLModelFilterVariableTrainingDataSourceId, AWSMachineLearningMLModelFilterVariableRealtimeEndpointStatus, AWSMachineLearningMLModelFilterVariableMLModelType, AWSMachineLearningMLModelFilterVariableAlgorithm, AWSMachineLearningMLModelFilterVariableTrainingDataURI, }; typedef NS_ENUM(NSInteger, AWSMachineLearningMLModelType) { AWSMachineLearningMLModelTypeUnknown, AWSMachineLearningMLModelTypeRegression, AWSMachineLearningMLModelTypeBinary, AWSMachineLearningMLModelTypeMulticlass, }; typedef NS_ENUM(NSInteger, AWSMachineLearningRealtimeEndpointStatus) { AWSMachineLearningRealtimeEndpointStatusUnknown, AWSMachineLearningRealtimeEndpointStatusNone, AWSMachineLearningRealtimeEndpointStatusReady, AWSMachineLearningRealtimeEndpointStatusUpdating, AWSMachineLearningRealtimeEndpointStatusFailed, }; typedef NS_ENUM(NSInteger, AWSMachineLearningSortOrder) { AWSMachineLearningSortOrderUnknown, AWSMachineLearningSortOrderAsc, AWSMachineLearningSortOrderDsc, }; typedef NS_ENUM(NSInteger, AWSMachineLearningTaggableResourceType) { AWSMachineLearningTaggableResourceTypeUnknown, AWSMachineLearningTaggableResourceTypeBatchPrediction, AWSMachineLearningTaggableResourceTypeDataSource, AWSMachineLearningTaggableResourceTypeEvaluation, AWSMachineLearningTaggableResourceTypeMLModel, }; @class AWSMachineLearningAddTagsInput; @class AWSMachineLearningAddTagsOutput; @class AWSMachineLearningBatchPrediction; @class AWSMachineLearningCreateBatchPredictionInput; @class AWSMachineLearningCreateBatchPredictionOutput; @class AWSMachineLearningCreateDataSourceFromRDSInput; @class AWSMachineLearningCreateDataSourceFromRDSOutput; @class AWSMachineLearningCreateDataSourceFromRedshiftInput; @class AWSMachineLearningCreateDataSourceFromRedshiftOutput; @class AWSMachineLearningCreateDataSourceFromS3Input; @class AWSMachineLearningCreateDataSourceFromS3Output; @class AWSMachineLearningCreateEvaluationInput; @class AWSMachineLearningCreateEvaluationOutput; @class AWSMachineLearningCreateMLModelInput; @class AWSMachineLearningCreateMLModelOutput; @class AWSMachineLearningCreateRealtimeEndpointInput; @class AWSMachineLearningCreateRealtimeEndpointOutput; @class AWSMachineLearningDataSource; @class AWSMachineLearningDeleteBatchPredictionInput; @class AWSMachineLearningDeleteBatchPredictionOutput; @class AWSMachineLearningDeleteDataSourceInput; @class AWSMachineLearningDeleteDataSourceOutput; @class AWSMachineLearningDeleteEvaluationInput; @class AWSMachineLearningDeleteEvaluationOutput; @class AWSMachineLearningDeleteMLModelInput; @class AWSMachineLearningDeleteMLModelOutput; @class AWSMachineLearningDeleteRealtimeEndpointInput; @class AWSMachineLearningDeleteRealtimeEndpointOutput; @class AWSMachineLearningDeleteTagsInput; @class AWSMachineLearningDeleteTagsOutput; @class AWSMachineLearningDescribeBatchPredictionsInput; @class AWSMachineLearningDescribeBatchPredictionsOutput; @class AWSMachineLearningDescribeDataSourcesInput; @class AWSMachineLearningDescribeDataSourcesOutput; @class AWSMachineLearningDescribeEvaluationsInput; @class AWSMachineLearningDescribeEvaluationsOutput; @class AWSMachineLearningDescribeMLModelsInput; @class AWSMachineLearningDescribeMLModelsOutput; @class AWSMachineLearningDescribeTagsInput; @class AWSMachineLearningDescribeTagsOutput; @class AWSMachineLearningEvaluation; @class AWSMachineLearningGetBatchPredictionInput; @class AWSMachineLearningGetBatchPredictionOutput; @class AWSMachineLearningGetDataSourceInput; @class AWSMachineLearningGetDataSourceOutput; @class AWSMachineLearningGetEvaluationInput; @class AWSMachineLearningGetEvaluationOutput; @class AWSMachineLearningGetMLModelInput; @class AWSMachineLearningGetMLModelOutput; @class AWSMachineLearningMLModel; @class AWSMachineLearningPerformanceMetrics; @class AWSMachineLearningPredictInput; @class AWSMachineLearningPredictOutput; @class AWSMachineLearningPrediction; @class AWSMachineLearningRDSDataSpec; @class AWSMachineLearningRDSDatabase; @class AWSMachineLearningRDSDatabaseCredentials; @class AWSMachineLearningRDSMetadata; @class AWSMachineLearningRealtimeEndpointInfo; @class AWSMachineLearningRedshiftDataSpec; @class AWSMachineLearningRedshiftDatabase; @class AWSMachineLearningRedshiftDatabaseCredentials; @class AWSMachineLearningRedshiftMetadata; @class AWSMachineLearningS3DataSpec; @class AWSMachineLearningTag; @class AWSMachineLearningUpdateBatchPredictionInput; @class AWSMachineLearningUpdateBatchPredictionOutput; @class AWSMachineLearningUpdateDataSourceInput; @class AWSMachineLearningUpdateDataSourceOutput; @class AWSMachineLearningUpdateEvaluationInput; @class AWSMachineLearningUpdateEvaluationOutput; @class AWSMachineLearningUpdateMLModelInput; @class AWSMachineLearningUpdateMLModelOutput; /** */ @interface AWSMachineLearningAddTagsInput : AWSRequest /**

The ID of the ML object to tag. For example, exampleModelId.

*/ @property (nonatomic, strong) NSString * _Nullable resourceId; /**

The type of the ML object to tag.

*/ @property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType; /**

The key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.

*/ @property (nonatomic, strong) NSArray * _Nullable tags; @end /**

Amazon ML returns the following elements.

*/ @interface AWSMachineLearningAddTagsOutput : AWSModel /**

The ID of the ML object that was tagged.

*/ @property (nonatomic, strong) NSString * _Nullable resourceId; /**

The type of the ML object that was tagged.

*/ @property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType; @end /**

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.

*/ @interface AWSMachineLearningBatchPrediction : AWSModel /**

The ID of the DataSource that points to the group of observations to predict.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionDataSourceId; /**

The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time that the BatchPrediction was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

*/ @property (nonatomic, strong) NSString * _Nullable inputDataLocationS3; /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable invalidRecordCount; /**

The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

The ID of the MLModel that generated predictions for the BatchPrediction request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

A description of the most recent details about processing the batch prediction request.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the BatchPrediction.

*/ @property (nonatomic, strong) NSString * _Nullable name; /**

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: ':', '//', '/./', '/../'.

*/ @property (nonatomic, strong) NSString * _Nullable outputUri; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

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 - The BatchPrediction is marked as deleted. It is not usable.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable totalRecordCount; @end /** */ @interface AWSMachineLearningCreateBatchPredictionInput : AWSRequest /**

The ID of the DataSource that points to the group of observations to predict.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionDataSourceId; /**

A user-supplied ID that uniquely identifies the BatchPrediction.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; /**

A user-supplied name or description of the BatchPrediction. BatchPredictionName can only use the UTF-8 character set.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionName; /**

The ID of the MLModel that will generate predictions for the group of observations.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'.

Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the Amazon Machine Learning Developer Guide.

*/ @property (nonatomic, strong) NSString * _Nullable outputUri; @end /**

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.

*/ @interface AWSMachineLearningCreateBatchPredictionOutput : AWSModel /**

A user-supplied ID that uniquely identifies the BatchPrediction. This value is identical to the value of the BatchPredictionId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; @end /** */ @interface AWSMachineLearningCreateDataSourceFromRDSInput : AWSRequest /**

The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeStatistics; /**

A user-supplied ID that uniquely identifies the DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; /**

A user-supplied name or description of the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceName; /**

The data specification of an Amazon RDS DataSource:

  • DatabaseInformation -

    • DatabaseName - The name of the Amazon RDS database.
    • InstanceIdentifier - A unique identifier for the Amazon RDS database instance.

  • DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.

  • ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see Role templates for data pipelines.

  • ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.

  • SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds] pair for a VPC-based RDS DB instance.

  • SelectSqlQuery - A query that is used to retrieve the observation data for the Datasource.

  • S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

  • DataSchemaUri - The Amazon S3 location of the DataSchema.

  • DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.

  • DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.


    Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

*/ @property (nonatomic, strong) AWSMachineLearningRDSDataSpec * _Nullable RDSData; /**

The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery query from Amazon RDS to Amazon S3.

*/ @property (nonatomic, strong) NSString * _Nullable roleARN; @end /**

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.

*/ @interface AWSMachineLearningCreateDataSourceFromRDSOutput : AWSModel /**

A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; @end /** */ @interface AWSMachineLearningCreateDataSourceFromRedshiftInput : AWSRequest /**

The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeStatistics; /**

A user-supplied ID that uniquely identifies the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; /**

A user-supplied name or description of the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceName; /**

The data specification of an Amazon Redshift DataSource:

  • DatabaseInformation -

    • DatabaseName - The name of the Amazon Redshift database.
    • ClusterIdentifier - The unique ID for the Amazon Redshift cluster.

  • DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.

  • SelectSqlQuery - The query that is used to retrieve the observation data for the Datasource.

  • S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery query is stored in this location.

  • DataSchemaUri - The Amazon S3 location of the DataSchema.

  • DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.

  • DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the DataSource.

    Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

*/ @property (nonatomic, strong) AWSMachineLearningRedshiftDataSpec * _Nullable dataSpec; /**

A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:

  • A security group to allow Amazon ML to execute the SelectSqlQuery query on an Amazon Redshift cluster

  • An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation

*/ @property (nonatomic, strong) NSString * _Nullable roleARN; @end /**

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.

*/ @interface AWSMachineLearningCreateDataSourceFromRedshiftOutput : AWSModel /**

A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; @end /** */ @interface AWSMachineLearningCreateDataSourceFromS3Input : AWSRequest /**

The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeStatistics; /**

A user-supplied identifier that uniquely identifies the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; /**

A user-supplied name or description of the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceName; /**

The data specification of a DataSource:

  • DataLocationS3 - The Amazon S3 location of the observation data.

  • DataSchemaLocationS3 - The Amazon S3 location of the DataSchema.

  • DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.

  • DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.

    Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

*/ @property (nonatomic, strong) AWSMachineLearningS3DataSpec * _Nullable dataSpec; @end /**

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.

*/ @interface AWSMachineLearningCreateDataSourceFromS3Output : AWSModel /**

A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; @end /** */ @interface AWSMachineLearningCreateEvaluationInput : AWSRequest /**

The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationDataSourceId; /**

A user-supplied ID that uniquely identifies the Evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; /**

A user-supplied name or description of the Evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationName; /**

The ID of the MLModel to evaluate.

The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; @end /**

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.

*/ @interface AWSMachineLearningCreateEvaluationOutput : AWSModel /**

The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; @end /** */ @interface AWSMachineLearningCreateMLModelInput : AWSRequest /**

A user-supplied ID that uniquely identifies the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

A user-supplied name or description of the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelName; /**

The category of supervised learning that this MLModel will address. Choose from the following types:

  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

*/ @property (nonatomic, assign) AWSMachineLearningMLModelType MLModelType; /**

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 and none. The default value is none. 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 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 when L2 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 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 when L1 is specified. Use this parameter sparingly.

*/ @property (nonatomic, strong) NSDictionary * _Nullable parameters; /**

The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

*/ @property (nonatomic, strong) NSString * _Nullable recipe; /**

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

*/ @property (nonatomic, strong) NSString * _Nullable recipeUri; /**

The DataSource that points to the training data.

*/ @property (nonatomic, strong) NSString * _Nullable trainingDataSourceId; @end /**

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.

*/ @interface AWSMachineLearningCreateMLModelOutput : AWSModel /**

A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; @end /** */ @interface AWSMachineLearningCreateRealtimeEndpointInput : AWSRequest /**

The ID assigned to the MLModel during creation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; @end /**

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.

 */ @interface AWSMachineLearningCreateRealtimeEndpointOutput : AWSModel /**

A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

The endpoint information of the MLModel

*/ @property (nonatomic, strong) AWSMachineLearningRealtimeEndpointInfo * _Nullable realtimeEndpointInfo; @end /**

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.

*/ @interface AWSMachineLearningDataSource : AWSModel /**

The parameter is true if statistics need to be generated from the observation data.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeStatistics; /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time that the DataSource was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataLocationS3; /**

A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

*/ @property (nonatomic, strong) NSString * _Nullable dataRearrangement; /**

The total number of observations contained in the data files that the DataSource references.

*/ @property (nonatomic, strong) NSNumber * _Nullable dataSizeInBytes; /**

The ID that is assigned to the DataSource during creation.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

A description of the most recent details about creating the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable name; /**

The number of data files referenced by the DataSource.

*/ @property (nonatomic, strong) NSNumber * _Nullable numberOfFiles; /**

The datasource details that are specific to Amazon RDS.

*/ @property (nonatomic, strong) AWSMachineLearningRDSMetadata * _Nullable RDSMetadata; /**

Describes the DataSource details specific to Amazon Redshift.

*/ @property (nonatomic, strong) AWSMachineLearningRedshiftMetadata * _Nullable redshiftMetadata; /**

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

*/ @property (nonatomic, strong) NSString * _Nullable roleARN; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

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.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; @end /** */ @interface AWSMachineLearningDeleteBatchPredictionInput : AWSRequest /**

A user-supplied ID that uniquely identifies the BatchPrediction.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; @end /**

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.

*/ @interface AWSMachineLearningDeleteBatchPredictionOutput : AWSModel /**

A user-supplied ID that uniquely identifies the BatchPrediction. This value should be identical to the value of the BatchPredictionID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; @end /** */ @interface AWSMachineLearningDeleteDataSourceInput : AWSRequest /**

A user-supplied ID that uniquely identifies the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; @end /**

Represents the output of a DeleteDataSource operation.

*/ @interface AWSMachineLearningDeleteDataSourceOutput : AWSModel /**

A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; @end /** */ @interface AWSMachineLearningDeleteEvaluationInput : AWSRequest /**

A user-supplied ID that uniquely identifies the Evaluation to delete.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; @end /**

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.

*/ @interface AWSMachineLearningDeleteEvaluationOutput : AWSModel /**

A user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; @end /** */ @interface AWSMachineLearningDeleteMLModelInput : AWSRequest /**

A user-supplied ID that uniquely identifies the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; @end /**

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.

*/ @interface AWSMachineLearningDeleteMLModelOutput : AWSModel /**

A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; @end /** */ @interface AWSMachineLearningDeleteRealtimeEndpointInput : AWSRequest /**

The ID assigned to the MLModel during creation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; @end /**

Represents the output of an DeleteRealtimeEndpoint operation.

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

*/ @interface AWSMachineLearningDeleteRealtimeEndpointOutput : AWSModel /**

A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

The endpoint information of the MLModel

*/ @property (nonatomic, strong) AWSMachineLearningRealtimeEndpointInfo * _Nullable realtimeEndpointInfo; @end /** */ @interface AWSMachineLearningDeleteTagsInput : AWSRequest /**

The ID of the tagged ML object. For example, exampleModelId.

*/ @property (nonatomic, strong) NSString * _Nullable resourceId; /**

The type of the tagged ML object.

*/ @property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType; /**

One or more tags to delete.

*/ @property (nonatomic, strong) NSArray * _Nullable tagKeys; @end /**

Amazon ML returns the following elements.

*/ @interface AWSMachineLearningDeleteTagsOutput : AWSModel /**

The ID of the ML object from which tags were deleted.

*/ @property (nonatomic, strong) NSString * _Nullable resourceId; /**

The type of the ML object from which tags were deleted.

*/ @property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType; @end /** */ @interface AWSMachineLearningDescribeBatchPredictionsInput : AWSRequest /**

The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ.

*/ @property (nonatomic, strong) NSString * _Nullable EQ; /**

Use one of the following variables to filter a list of BatchPrediction:

  • CreatedAt - Sets the search criteria to the BatchPrediction creation date.
  • Status - Sets the search criteria to the BatchPrediction status.
  • Name - Sets the search criteria to the contents of the BatchPredictionName.
  • 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 Solution (Amazon S3) bucket or directory.
*/ @property (nonatomic, assign) AWSMachineLearningBatchPredictionFilterVariable filterVariable; /**

The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE.

*/ @property (nonatomic, strong) NSString * _Nullable GE; /**

The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT.

*/ @property (nonatomic, strong) NSString * _Nullable GT; /**

The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE.

*/ @property (nonatomic, strong) NSString * _Nullable LE; /**

The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT.

*/ @property (nonatomic, strong) NSString * _Nullable LT; /**

The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.

*/ @property (nonatomic, strong) NSNumber * _Nullable limit; /**

The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE.

*/ @property (nonatomic, strong) NSString * _Nullable NE; /**

An ID of the page in the paginated results.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A string that is found at the beginning of a variable, such as Name or Id.

For example, a Batch Prediction operation could have the Name2014-09-09-HolidayGiftMailer. To search for this BatchPrediction, select Name for the FilterVariable and any of the following strings for the Prefix:

  • 2014-09

  • 2014-09-09

  • 2014-09-09-Holiday

*/ @property (nonatomic, strong) NSString * _Nullable prefix; /**

A two-value parameter that determines the sequence of the resulting list of MLModels.

  • asc - Arranges the list in ascending order (A-Z, 0-9).
  • dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

*/ @property (nonatomic, assign) AWSMachineLearningSortOrder sortOrder; @end /**

Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.

*/ @interface AWSMachineLearningDescribeBatchPredictionsOutput : AWSModel /**

The ID of the next page in the paginated results that indicates at least one more page follows.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A list of BatchPrediction objects that meet the search criteria.

*/ @property (nonatomic, strong) NSArray * _Nullable results; @end /** */ @interface AWSMachineLearningDescribeDataSourcesInput : AWSRequest /**

The equal to operator. The DataSource results will have FilterVariable values that exactly match the value specified with EQ.

*/ @property (nonatomic, strong) NSString * _Nullable EQ; /**

Use one of the following variables to filter a list of DataSource:

  • CreatedAt - Sets the search criteria to DataSource creation dates.
  • Status - Sets the search criteria to DataSource statuses.
  • Name - Sets the search criteria to the contents of DataSourceName.
  • 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.
*/ @property (nonatomic, assign) AWSMachineLearningDataSourceFilterVariable filterVariable; /**

The greater than or equal to operator. The DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE.

*/ @property (nonatomic, strong) NSString * _Nullable GE; /**

The greater than operator. The DataSource results will have FilterVariable values that are greater than the value specified with GT.

*/ @property (nonatomic, strong) NSString * _Nullable GT; /**

The less than or equal to operator. The DataSource results will have FilterVariable values that are less than or equal to the value specified with LE.

*/ @property (nonatomic, strong) NSString * _Nullable LE; /**

The less than operator. The DataSource results will have FilterVariable values that are less than the value specified with LT.

*/ @property (nonatomic, strong) NSString * _Nullable LT; /**

The maximum number of DataSource to include in the result.

*/ @property (nonatomic, strong) NSNumber * _Nullable limit; /**

The not equal to operator. The DataSource results will have FilterVariable values not equal to the value specified with NE.

*/ @property (nonatomic, strong) NSString * _Nullable NE; /**

The ID of the page in the paginated results.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A string that is found at the beginning of a variable, such as Name or Id.

For example, a DataSource could have the Name2014-09-09-HolidayGiftMailer. To search for this DataSource, select Name for the FilterVariable and any of the following strings for the Prefix:

  • 2014-09

  • 2014-09-09

  • 2014-09-09-Holiday

*/ @property (nonatomic, strong) NSString * _Nullable prefix; /**

A two-value parameter that determines the sequence of the resulting list of DataSource.

  • asc - Arranges the list in ascending order (A-Z, 0-9).
  • dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

*/ @property (nonatomic, assign) AWSMachineLearningSortOrder sortOrder; @end /**

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

*/ @interface AWSMachineLearningDescribeDataSourcesOutput : AWSModel /**

An ID of the next page in the paginated results that indicates at least one more page follows.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A list of DataSource that meet the search criteria.

*/ @property (nonatomic, strong) NSArray * _Nullable results; @end /** */ @interface AWSMachineLearningDescribeEvaluationsInput : AWSRequest /**

The equal to operator. The Evaluation results will have FilterVariable values that exactly match the value specified with EQ.

*/ @property (nonatomic, strong) NSString * _Nullable EQ; /**

Use one of the following variable to filter a list of Evaluation objects:

  • CreatedAt - Sets the search criteria to the Evaluation creation date.
  • Status - Sets the search criteria to the Evaluation status.
  • Name - Sets the search criteria to the contents of EvaluationName.
  • IAMUser - Sets the search criteria to the user account that invoked an Evaluation.
  • MLModelId - Sets the search criteria to the MLModel 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 Solution (Amazon S3) bucket or directory.
*/ @property (nonatomic, assign) AWSMachineLearningEvaluationFilterVariable filterVariable; /**

The greater than or equal to operator. The Evaluation results will have FilterVariable values that are greater than or equal to the value specified with GE.

*/ @property (nonatomic, strong) NSString * _Nullable GE; /**

The greater than operator. The Evaluation results will have FilterVariable values that are greater than the value specified with GT.

*/ @property (nonatomic, strong) NSString * _Nullable GT; /**

The less than or equal to operator. The Evaluation results will have FilterVariable values that are less than or equal to the value specified with LE.

*/ @property (nonatomic, strong) NSString * _Nullable LE; /**

The less than operator. The Evaluation results will have FilterVariable values that are less than the value specified with LT.

*/ @property (nonatomic, strong) NSString * _Nullable LT; /**

The maximum number of Evaluation to include in the result.

*/ @property (nonatomic, strong) NSNumber * _Nullable limit; /**

The not equal to operator. The Evaluation results will have FilterVariable values not equal to the value specified with NE.

*/ @property (nonatomic, strong) NSString * _Nullable NE; /**

The ID of the page in the paginated results.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A string that is found at the beginning of a variable, such as Name or Id.

For example, an Evaluation could have the Name2014-09-09-HolidayGiftMailer. To search for this Evaluation, select Name for the FilterVariable and any of the following strings for the Prefix:

  • 2014-09

  • 2014-09-09

  • 2014-09-09-Holiday

*/ @property (nonatomic, strong) NSString * _Nullable prefix; /**

A two-value parameter that determines the sequence of the resulting list of Evaluation.

  • asc - Arranges the list in ascending order (A-Z, 0-9).
  • dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

*/ @property (nonatomic, assign) AWSMachineLearningSortOrder sortOrder; @end /**

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

*/ @interface AWSMachineLearningDescribeEvaluationsOutput : AWSModel /**

The ID of the next page in the paginated results that indicates at least one more page follows.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A list of Evaluation that meet the search criteria.

*/ @property (nonatomic, strong) NSArray * _Nullable results; @end /** */ @interface AWSMachineLearningDescribeMLModelsInput : AWSRequest /**

The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ.

*/ @property (nonatomic, strong) NSString * _Nullable EQ; /**

Use one of the following variables to filter a list of MLModel:

  • CreatedAt - Sets the search criteria to MLModel creation date.
  • Status - Sets the search criteria to MLModel status.
  • Name - Sets the search criteria to the contents of MLModelName.
  • IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.
  • TrainingDataSourceId - Sets the search criteria to the DataSource used to train one or more MLModel.
  • RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time endpoint status.
  • MLModelType - Sets the search criteria to MLModel type: binary, regression, or multi-class.
  • Algorithm - Sets the search criteria to the algorithm that the MLModel uses.
  • TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
*/ @property (nonatomic, assign) AWSMachineLearningMLModelFilterVariable filterVariable; /**

The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE.

*/ @property (nonatomic, strong) NSString * _Nullable GE; /**

The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT.

*/ @property (nonatomic, strong) NSString * _Nullable GT; /**

The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE.

*/ @property (nonatomic, strong) NSString * _Nullable LE; /**

The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT.

*/ @property (nonatomic, strong) NSString * _Nullable LT; /**

The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.

*/ @property (nonatomic, strong) NSNumber * _Nullable limit; /**

The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE.

*/ @property (nonatomic, strong) NSString * _Nullable NE; /**

The ID of the page in the paginated results.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A string that is found at the beginning of a variable, such as Name or Id.

For example, an MLModel could have the Name2014-09-09-HolidayGiftMailer. To search for this MLModel, select Name for the FilterVariable and any of the following strings for the Prefix:

  • 2014-09

  • 2014-09-09

  • 2014-09-09-Holiday

*/ @property (nonatomic, strong) NSString * _Nullable prefix; /**

A two-value parameter that determines the sequence of the resulting list of MLModel.

  • asc - Arranges the list in ascending order (A-Z, 0-9).
  • dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

*/ @property (nonatomic, assign) AWSMachineLearningSortOrder sortOrder; @end /**

Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.

*/ @interface AWSMachineLearningDescribeMLModelsOutput : AWSModel /**

The ID of the next page in the paginated results that indicates at least one more page follows.

*/ @property (nonatomic, strong) NSString * _Nullable nextToken; /**

A list of MLModel that meet the search criteria.

*/ @property (nonatomic, strong) NSArray * _Nullable results; @end /** */ @interface AWSMachineLearningDescribeTagsInput : AWSRequest /**

The ID of the ML object. For example, exampleModelId.

*/ @property (nonatomic, strong) NSString * _Nullable resourceId; /**

The type of the ML object.

*/ @property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType; @end /**

Amazon ML returns the following elements.

*/ @interface AWSMachineLearningDescribeTagsOutput : AWSModel /**

The ID of the tagged ML object.

*/ @property (nonatomic, strong) NSString * _Nullable resourceId; /**

The type of the tagged ML object.

*/ @property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType; /**

A list of tags associated with the ML object.

*/ @property (nonatomic, strong) NSArray * _Nullable tags; @end /**

Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation.

*/ @interface AWSMachineLearningEvaluation : AWSModel /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time that the Evaluation was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

The ID of the DataSource that is used to evaluate the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationDataSourceId; /**

The ID that is assigned to the Evaluation at creation.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable inputDataLocationS3; /**

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

The ID of the MLModel that is the focus of the evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

A description of the most recent details about evaluating the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the Evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable name; /**

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.

*/ @property (nonatomic, strong) AWSMachineLearningPerformanceMetrics * _Nullable performanceMetrics; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

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 an MLModel.
  • INPROGRESS - The evaluation is underway.
  • FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.
  • COMPLETED - The evaluation process completed successfully.
  • DELETED - The Evaluation is marked as deleted. It is not usable.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; @end /** */ @interface AWSMachineLearningGetBatchPredictionInput : AWSRequest /**

An ID assigned to the BatchPrediction at creation.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; @end /**

Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.

*/ @interface AWSMachineLearningGetBatchPredictionOutput : AWSModel /**

The ID of the DataSource that was used to create the BatchPrediction.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionDataSourceId; /**

An ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; /**

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction, normalized and scaled on computation resources. ComputeTime is only available if the BatchPrediction is in the COMPLETED state.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time when the BatchPrediction was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

The epoch time when Amazon Machine Learning marked the BatchPrediction as COMPLETED or FAILED. FinishedAt is only available when the BatchPrediction is in the COMPLETED or FAILED state.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

*/ @property (nonatomic, strong) NSString * _Nullable inputDataLocationS3; /**

The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction.

*/ @property (nonatomic, strong) NSNumber * _Nullable invalidRecordCount; /**

The time of the most recent edit to BatchPrediction. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

A link to the file that contains logs of the CreateBatchPrediction operation.

*/ @property (nonatomic, strong) NSString * _Nullable logUri; /**

The ID of the MLModel that generated predictions for the BatchPrediction request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

A description of the most recent details about processing the batch prediction request.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the BatchPrediction.

*/ @property (nonatomic, strong) NSString * _Nullable name; /**

The location of an Amazon S3 bucket or directory to receive the operation results.

*/ @property (nonatomic, strong) NSString * _Nullable outputUri; /**

The epoch time when Amazon Machine Learning marked the BatchPrediction as INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING state.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

The status of the BatchPrediction, which can be one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.
  • INPROGRESS - The batch predictions are in progress.
  • 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 - The BatchPrediction is marked as deleted. It is not usable.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; /**

The number of total records that Amazon Machine Learning saw while processing the BatchPrediction.

*/ @property (nonatomic, strong) NSNumber * _Nullable totalRecordCount; @end /** */ @interface AWSMachineLearningGetDataSourceInput : AWSRequest /**

The ID assigned to the DataSource at creation.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; /**

Specifies whether the GetDataSource operation should return DataSourceSchema.

If true, DataSourceSchema is returned.

If false, DataSourceSchema is not returned.

*/ @property (nonatomic, strong) NSNumber * _Nullable verbose; @end /**

Represents the output of a GetDataSource operation and describes a DataSource.

*/ @interface AWSMachineLearningGetDataSourceOutput : AWSModel /**

The parameter is true if statistics need to be generated from the observation data.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeStatistics; /**

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the DataSource, normalized and scaled on computation resources. ComputeTime is only available if the DataSource is in the COMPLETED state and the ComputeStatistics is set to true.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time that the DataSource was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

*/ @property (nonatomic, strong) NSString * _Nullable dataLocationS3; /**

A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

*/ @property (nonatomic, strong) NSString * _Nullable dataRearrangement; /**

The total size of observations in the data files.

*/ @property (nonatomic, strong) NSNumber * _Nullable dataSizeInBytes; /**

The ID assigned to the DataSource at creation. This value should be identical to the value of the DataSourceId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; /**

The schema used by all of the data files of this DataSource.

Note

This parameter is provided as part of the verbose format.

 */ @property (nonatomic, strong) NSString * _Nullable dataSourceSchema; /**

The epoch time when Amazon Machine Learning marked the DataSource as COMPLETED or FAILED. FinishedAt is only available when the DataSource is in the COMPLETED or FAILED state.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The time of the most recent edit to the DataSource. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

A link to the file containing logs of CreateDataSourceFrom* operations.

*/ @property (nonatomic, strong) NSString * _Nullable logUri; /**

The user-supplied description of the most recent details about creating the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable name; /**

The number of data files referenced by the DataSource.

*/ @property (nonatomic, strong) NSNumber * _Nullable numberOfFiles; /**

The datasource details that are specific to Amazon RDS.

*/ @property (nonatomic, strong) AWSMachineLearningRDSMetadata * _Nullable RDSMetadata; /**

Describes the DataSource details specific to Amazon Redshift.

*/ @property (nonatomic, strong) AWSMachineLearningRedshiftMetadata * _Nullable redshiftMetadata; /**

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

*/ @property (nonatomic, strong) NSString * _Nullable roleARN; /**

The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS. StartedAt isn't available if the DataSource is in the PENDING state.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

The current status of the DataSource. This element can have one of the following values:

  • PENDING - 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.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; @end /** */ @interface AWSMachineLearningGetEvaluationInput : AWSRequest /**

The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; @end /**

Represents the output of a GetEvaluation operation and describes an Evaluation.

*/ @interface AWSMachineLearningGetEvaluationOutput : AWSModel /**

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the Evaluation, normalized and scaled on computation resources. ComputeTime is only available if the Evaluation is in the COMPLETED state.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time that the Evaluation was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

The DataSource used for this evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationDataSourceId; /**

The evaluation ID which is same as the EvaluationId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; /**

The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED or FAILED state.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

*/ @property (nonatomic, strong) NSString * _Nullable inputDataLocationS3; /**

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

A link to the file that contains logs of the CreateEvaluation operation.

*/ @property (nonatomic, strong) NSString * _Nullable logUri; /**

The ID of the MLModel that was the focus of the evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

A description of the most recent details about evaluating the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the Evaluation.

*/ @property (nonatomic, strong) NSString * _Nullable name; /**

Measurements of how well the MLModel performed using observations referenced by the DataSource. One of the following metric 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.

*/ @property (nonatomic, strong) AWSMachineLearningPerformanceMetrics * _Nullable performanceMetrics; /**

The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. StartedAt isn't available if the Evaluation is in the PENDING state.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

The status of the evaluation. This element can have one of the following values:

  • PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel.
  • INPROGRESS - The evaluation is underway.
  • FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.
  • COMPLETED - The evaluation process completed successfully.
  • DELETED - The Evaluation is marked as deleted. It is not usable.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; @end /** */ @interface AWSMachineLearningGetMLModelInput : AWSRequest /**

The ID assigned to the MLModel at creation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

Specifies whether the GetMLModel operation should return Recipe.

If true, Recipe is returned.

If false, Recipe is not returned.

*/ @property (nonatomic, strong) NSNumber * _Nullable verbose; @end /**

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

*/ @interface AWSMachineLearningGetMLModelOutput : AWSModel /**

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.

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time that the MLModel was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

The current endpoint of the MLModel

*/ @property (nonatomic, strong) AWSMachineLearningRealtimeEndpointInfo * _Nullable endpointInfo; /**

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.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

*/ @property (nonatomic, strong) NSString * _Nullable inputDataLocationS3; /**

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

A link to the file that contains logs of the CreateMLModel operation.

*/ @property (nonatomic, strong) NSString * _Nullable logUri; /**

The MLModel ID, which is same as the MLModelId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

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?"
*/ @property (nonatomic, assign) AWSMachineLearningMLModelType MLModelType; /**

A description of the most recent details about accessing the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable name; /**

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.

 */ @property (nonatomic, strong) NSString * _Nullable recipe; /**

The schema used by all of the data files referenced by the DataSource.

Note

This parameter is provided as part of the verbose format.

 */ @property (nonatomic, strong) NSString * _Nullable schema; /**

The scoring threshold is used in binary classification MLModelmodels. 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.

*/ @property (nonatomic, strong) NSNumber * _Nullable scoreThreshold; /**

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable scoreThresholdLastUpdatedAt; /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable sizeInBytes; /**

The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

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 a MLModel.
  • 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 - The MLModel is marked as deleted. It isn't usable.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; /**

The ID of the training DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable trainingDataSourceId; /**

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 data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. 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 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 when L2 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 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 when L1 is specified. Use this parameter sparingly.

*/ @property (nonatomic, strong) NSDictionary * _Nullable trainingParameters; @end /**

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

*/ @interface AWSMachineLearningMLModel : AWSModel /**

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
*/ @property (nonatomic, assign) AWSMachineLearningAlgorithm algorithm; /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable computeTime; /**

The time that the MLModel was created. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable createdByIamUser; /**

The current endpoint of the MLModel.

*/ @property (nonatomic, strong) AWSMachineLearningRealtimeEndpointInfo * _Nullable endpointInfo; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable finishedAt; /**

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

*/ @property (nonatomic, strong) NSString * _Nullable inputDataLocationS3; /**

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt; /**

The ID assigned to the MLModel at creation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

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?".
*/ @property (nonatomic, assign) AWSMachineLearningMLModelType MLModelType; /**

A description of the most recent details about accessing the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable message; /**

A user-supplied name or description of the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable name; /** */ @property (nonatomic, strong) NSNumber * _Nullable scoreThreshold; /**

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable scoreThresholdLastUpdatedAt; /**

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

*/ @property (nonatomic, strong) NSNumber * _Nullable sizeInBytes; /**

A timestamp represented in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable startedAt; /**

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 an MLModel.
  • INPROGRESS - The creation process is underway.
  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It isn't usable.
*/ @property (nonatomic, assign) AWSMachineLearningEntityStatus status; /**

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

*/ @property (nonatomic, strong) NSString * _Nullable trainingDataSourceId; /**

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 and none. The default value is none.

  • 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 when L2 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 when L1 is specified. Use this parameter sparingly.

*/ @property (nonatomic, strong) NSDictionary * _Nullable trainingParameters; @end /**

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.

*/ @interface AWSMachineLearningPerformanceMetrics : AWSModel /** */ @property (nonatomic, strong) NSDictionary * _Nullable properties; @end /** */ @interface AWSMachineLearningPredictInput : AWSRequest /**

A unique identifier of the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /** */ @property (nonatomic, strong) NSString * _Nullable predictEndpoint; /**

A map of variable name-value pairs that represent an observation.

*/ @property (nonatomic, strong) NSDictionary * _Nullable record; @end /** */ @interface AWSMachineLearningPredictOutput : AWSModel /**

The output from a Predict operation:

  • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD

  • PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.

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

  • PredictedValue - Present for a REGRESSIONMLModel request.

*/ @property (nonatomic, strong) AWSMachineLearningPrediction * _Nullable prediction; @end /**

The output from a Predict operation:

  • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD

  • PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.

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

  • PredictedValue - Present for a REGRESSIONMLModel request.

*/ @interface AWSMachineLearningPrediction : AWSModel /** Provides any additional details regarding the prediction. */ @property (nonatomic, strong) NSDictionary * _Nullable details; /**

The prediction label for either a BINARY or MULTICLASSMLModel.

*/ @property (nonatomic, strong) NSString * _Nullable predictedLabel; /** Provides the raw classification score corresponding to each label. */ @property (nonatomic, strong) NSDictionary * _Nullable predictedScores; /** The prediction value for REGRESSIONMLModel. */ @property (nonatomic, strong) NSNumber * _Nullable predictedValue; @end /**

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

Required parameters: [DatabaseInformation, SelectSqlQuery, DatabaseCredentials, S3StagingLocation, ResourceRole, ServiceRole, SubnetId, SecurityGroupIds] */ @interface AWSMachineLearningRDSDataSpec : AWSModel /**

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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement 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 is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd 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 to random 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 between percentBegin and percentEnd. 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"}}

*/ @property (nonatomic, strong) NSString * _Nullable dataRearrangement; /**

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" ] }

*/ @property (nonatomic, strong) NSString * _Nullable dataSchema; /**

The Amazon S3 location of the DataSchema.

*/ @property (nonatomic, strong) NSString * _Nullable dataSchemaUri; /**

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

*/ @property (nonatomic, strong) AWSMachineLearningRDSDatabaseCredentials * _Nullable databaseCredentials; /**

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

*/ @property (nonatomic, strong) AWSMachineLearningRDSDatabase * _Nullable databaseInformation; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable resourceRole; /**

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

*/ @property (nonatomic, strong) NSString * _Nullable s3StagingLocation; /**

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.

*/ @property (nonatomic, strong) NSArray * _Nullable securityGroupIds; /**

The query that is used to retrieve the observation data for the DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable selectSqlQuery; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable serviceRole; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable subnetId; @end /**

The database details of an Amazon RDS database.

Required parameters: [InstanceIdentifier, DatabaseName] */ @interface AWSMachineLearningRDSDatabase : AWSModel /**

The name of a database hosted on an RDS DB instance.

*/ @property (nonatomic, strong) NSString * _Nullable databaseName; /**

The ID of an RDS DB instance.

*/ @property (nonatomic, strong) NSString * _Nullable instanceIdentifier; @end /**

The database credentials to connect to a database on an RDS DB instance.

Required parameters: [Username, Password] */ @interface AWSMachineLearningRDSDatabaseCredentials : AWSModel /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable password; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable username; @end /**

The datasource details that are specific to Amazon RDS.

*/ @interface AWSMachineLearningRDSMetadata : AWSModel /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable dataPipelineId; /**

The database details required to connect to an Amazon RDS.

*/ @property (nonatomic, strong) AWSMachineLearningRDSDatabase * _Nullable database; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable databaseUserName; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable resourceRole; /**

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

*/ @property (nonatomic, strong) NSString * _Nullable selectSqlQuery; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable serviceRole; @end /**

Describes the real-time endpoint information for an MLModel.

*/ @interface AWSMachineLearningRealtimeEndpointInfo : AWSModel /**

The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

*/ @property (nonatomic, strong) NSDate * _Nullable createdAt; /**

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.
*/ @property (nonatomic, assign) AWSMachineLearningRealtimeEndpointStatus endpointStatus; /**

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.

 */ @property (nonatomic, strong) NSString * _Nullable endpointUrl; /**

The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

*/ @property (nonatomic, strong) NSNumber * _Nullable peakRequestsPerSecond; @end /**

Describes the data specification of an Amazon Redshift DataSource.

Required parameters: [DatabaseInformation, SelectSqlQuery, DatabaseCredentials, S3StagingLocation] */ @interface AWSMachineLearningRedshiftDataSpec : AWSModel /**

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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement 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 is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd 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 to random 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 between percentBegin and percentEnd. 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"}}

*/ @property (nonatomic, strong) NSString * _Nullable dataRearrangement; /**

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" ] }

*/ @property (nonatomic, strong) NSString * _Nullable dataSchema; /**

Describes the schema location for an Amazon Redshift DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable dataSchemaUri; /**

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

*/ @property (nonatomic, strong) AWSMachineLearningRedshiftDatabaseCredentials * _Nullable databaseCredentials; /**

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

*/ @property (nonatomic, strong) AWSMachineLearningRedshiftDatabase * _Nullable databaseInformation; /**

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

*/ @property (nonatomic, strong) NSString * _Nullable s3StagingLocation; /**

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

*/ @property (nonatomic, strong) NSString * _Nullable selectSqlQuery; @end /**

Describes the database details required to connect to an Amazon Redshift database.

Required parameters: [DatabaseName, ClusterIdentifier] */ @interface AWSMachineLearningRedshiftDatabase : AWSModel /**

The ID of an Amazon Redshift cluster.

*/ @property (nonatomic, strong) NSString * _Nullable clusterIdentifier; /**

The name of a database hosted on an Amazon Redshift cluster.

*/ @property (nonatomic, strong) NSString * _Nullable databaseName; @end /**

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

Required parameters: [Username, Password] */ @interface AWSMachineLearningRedshiftDatabaseCredentials : AWSModel /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable password; /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable username; @end /**

Describes the DataSource details specific to Amazon Redshift.

*/ @interface AWSMachineLearningRedshiftMetadata : AWSModel /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable databaseUserName; /**

Describes the database details required to connect to an Amazon Redshift database.

*/ @property (nonatomic, strong) AWSMachineLearningRedshiftDatabase * _Nullable redshiftDatabase; /**

The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.

*/ @property (nonatomic, strong) NSString * _Nullable selectSqlQuery; @end /**

Describes the data specification of a DataSource.

Required parameters: [DataLocationS3] */ @interface AWSMachineLearningS3DataSpec : AWSModel /**

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.

*/ @property (nonatomic, strong) NSString * _Nullable dataLocationS3; /**

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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement 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 is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd 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 to random 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 between percentBegin and percentEnd. 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"}}

*/ @property (nonatomic, strong) NSString * _Nullable dataRearrangement; /**

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" ] }

*/ @property (nonatomic, strong) NSString * _Nullable dataSchema; /**

Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3.

*/ @property (nonatomic, strong) NSString * _Nullable dataSchemaLocationS3; @end /**

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

*/ @interface AWSMachineLearningTag : AWSModel /**

A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

*/ @property (nonatomic, strong) NSString * _Nullable key; /**

An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

*/ @property (nonatomic, strong) NSString * _Nullable value; @end /** */ @interface AWSMachineLearningUpdateBatchPredictionInput : AWSRequest /**

The ID assigned to the BatchPrediction during creation.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; /**

A new user-supplied name or description of the BatchPrediction.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionName; @end /**

Represents the output of an UpdateBatchPrediction operation.

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

*/ @interface AWSMachineLearningUpdateBatchPredictionOutput : AWSModel /**

The ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request.

*/ @property (nonatomic, strong) NSString * _Nullable batchPredictionId; @end /** */ @interface AWSMachineLearningUpdateDataSourceInput : AWSRequest /**

The ID assigned to the DataSource during creation.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; /**

A new user-supplied name or description of the DataSource that will replace the current description.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceName; @end /**

Represents the output of an UpdateDataSource operation.

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

*/ @interface AWSMachineLearningUpdateDataSourceOutput : AWSModel /**

The ID assigned to the DataSource during creation. This value should be identical to the value of the DataSourceID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable dataSourceId; @end /** */ @interface AWSMachineLearningUpdateEvaluationInput : AWSRequest /**

The ID assigned to the Evaluation during creation.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; /**

A new user-supplied name or description of the Evaluation that will replace the current content.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationName; @end /**

Represents the output of an UpdateEvaluation operation.

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

*/ @interface AWSMachineLearningUpdateEvaluationOutput : AWSModel /**

The ID assigned to the Evaluation during creation. This value should be identical to the value of the Evaluation in the request.

*/ @property (nonatomic, strong) NSString * _Nullable evaluationId; @end /** */ @interface AWSMachineLearningUpdateMLModelInput : AWSRequest /**

The ID assigned to the MLModel during creation.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; /**

A user-supplied name or description of the MLModel.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelName; /**

The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the ScoreThreshold receive a positive result from the MLModel, such as true. Output values less than the ScoreThreshold receive a negative response from the MLModel, such as false.

*/ @property (nonatomic, strong) NSNumber * _Nullable scoreThreshold; @end /**

Represents the output of an UpdateMLModel operation.

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

*/ @interface AWSMachineLearningUpdateMLModelOutput : AWSModel /**

The ID assigned to the MLModel during creation. This value should be identical to the value of the MLModelID in the request.

*/ @property (nonatomic, strong) NSString * _Nullable MLModelId; @end NS_ASSUME_NONNULL_END