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AWSMachineLearningModel.h
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AWSMachineLearningModel.h
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//
// 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 <Foundation/Foundation.h>
#import <AWSCore/AWSNetworking.h>
#import <AWSCore/AWSModel.h>
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
/**
<p>The ID of the ML object to tag. For example, <code>exampleModelId</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable resourceId;
/**
<p>The type of the ML object to tag. </p>
*/
@property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType;
/**
<p>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.</p>
*/
@property (nonatomic, strong) NSArray<AWSMachineLearningTag *> * _Nullable tags;
@end
/**
<p>Amazon ML returns the following elements. </p>
*/
@interface AWSMachineLearningAddTagsOutput : AWSModel
/**
<p>The ID of the ML object that was tagged.</p>
*/
@property (nonatomic, strong) NSString * _Nullable resourceId;
/**
<p>The type of the ML object that was tagged.</p>
*/
@property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType;
@end
/**
<p> Represents the output of a <code>GetBatchPrediction</code> operation.</p><p> The content consists of the detailed metadata, the status, and the data file information of a <code>Batch Prediction</code>.</p>
*/
@interface AWSMachineLearningBatchPrediction : AWSModel
/**
<p>The ID of the <code>DataSource</code> that points to the group of observations to predict.</p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionDataSourceId;
/**
<p>The ID assigned to the <code>BatchPrediction</code> at creation. This value should be identical to the value of the <code>BatchPredictionID</code> in the request. </p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionId;
/**
<p>Long integer type that is a 64-bit signed number.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable computeTime;
/**
<p>The time that the <code>BatchPrediction</code> was created. The time is expressed in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable createdAt;
/**
<p>The AWS user account that invoked the <code>BatchPrediction</code>. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
*/
@property (nonatomic, strong) NSString * _Nullable createdByIamUser;
/**
<p>A timestamp represented in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable finishedAt;
/**
<p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
*/
@property (nonatomic, strong) NSString * _Nullable inputDataLocationS3;
/**
<p>Long integer type that is a 64-bit signed number.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable invalidRecordCount;
/**
<p>The time of the most recent edit to the <code>BatchPrediction</code>. The time is expressed in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt;
/**
<p>The ID of the <code>MLModel</code> that generated predictions for the <code>BatchPrediction</code> request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
/**
<p>A description of the most recent details about processing the batch prediction request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable message;
/**
<p>A user-supplied name or description of the <code>BatchPrediction</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable name;
/**
<p>The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the <code>s3 key</code> portion of the <code>outputURI</code> field: ':', '//', '/./', '/../'.</p>
*/
@property (nonatomic, strong) NSString * _Nullable outputUri;
/**
<p>A timestamp represented in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable startedAt;
/**
<p>The status of the <code>BatchPrediction</code>. This element can have one of the following values:</p><ul><li><code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.</li><li><code>INPROGRESS</code> - The process is underway.</li><li><code>FAILED</code> - The request to perform a batch prediction did not run to completion. It is not usable.</li><li><code>COMPLETED</code> - The batch prediction process completed successfully.</li><li><code>DELETED</code> - The <code>BatchPrediction</code> is marked as deleted. It is not usable.</li></ul>
*/
@property (nonatomic, assign) AWSMachineLearningEntityStatus status;
/**
<p>Long integer type that is a 64-bit signed number.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable totalRecordCount;
@end
/**
*/
@interface AWSMachineLearningCreateBatchPredictionInput : AWSRequest
/**
<p>The ID of the <code>DataSource</code> that points to the group of observations to predict.</p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionDataSourceId;
/**
<p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionId;
/**
<p>A user-supplied name or description of the <code>BatchPrediction</code>. <code>BatchPredictionName</code> can only use the UTF-8 character set.</p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionName;
/**
<p>The ID of the <code>MLModel</code> that will generate predictions for the group of observations. </p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
/**
<p>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 <code>s3 key</code> portion of the <code>outputURI</code> field: ':', '//', '/./', '/../'.</p><p>Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the <a href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable outputUri;
@end
/**
<p> Represents the output of a <code>CreateBatchPrediction</code> operation, and is an acknowledgement that Amazon ML received the request.</p><p>The <code>CreateBatchPrediction</code> operation is asynchronous. You can poll for status updates by using the <code>>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter of the result. </p>
*/
@interface AWSMachineLearningCreateBatchPredictionOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value is identical to the value of the <code>BatchPredictionId</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionId;
@end
/**
*/
@interface AWSMachineLearningCreateDataSourceFromRDSInput : AWSRequest
/**
<p>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code></code>DataSource<code></code> needs to be used for <code>MLModel</code> training. </p>
*/
@property (nonatomic, strong) NSNumber * _Nullable computeStatistics;
/**
<p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. Typically, an Amazon Resource Number (ARN) becomes the ID for a <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
/**
<p>A user-supplied name or description of the <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceName;
/**
<p>The data specification of an Amazon RDS <code>DataSource</code>:</p><ul><li><p>DatabaseInformation - <ul><li><code>DatabaseName</code> - The name of the Amazon RDS database.</li><li><code>InstanceIdentifier </code> - A unique identifier for the Amazon RDS database instance.</li></ul></p></li><li><p>DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.</p></li><li><p>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 <a href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for data pipelines.</p></li><li><p>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 <a href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for data pipelines.</p></li><li><p>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 [<code>SubnetId</code>, <code>SecurityGroupIds</code>] pair for a VPC-based RDS DB instance.</p></li><li><p>SelectSqlQuery - A query that is used to retrieve the observation data for the <code>Datasource</code>.</p></li><li><p>S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using <code>SelectSqlQuery</code> is stored in this location.</p></li><li><p>DataSchemaUri - The Amazon S3 location of the <code>DataSchema</code>.</p></li><li><p>DataSchema - A JSON string representing the schema. This is not required if <code>DataSchemaUri</code> is specified. </p></li><li><p>DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the <code>Datasource</code>. </p><br><p> Sample - <code> "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"</code></p></li></ul>
*/
@property (nonatomic, strong) AWSMachineLearningRDSDataSpec * _Nullable RDSData;
/**
<p>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 <code>SelectSqlQuery</code> query from Amazon RDS to Amazon S3.</p><p></p>
*/
@property (nonatomic, strong) NSString * _Nullable roleARN;
@end
/**
<p> Represents the output of a <code>CreateDataSourceFromRDS</code> operation, and is an acknowledgement that Amazon ML received the request.</p><p>The <code>CreateDataSourceFromRDS</code>> operation is asynchronous. You can poll for updates by using the <code>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter. You can inspect the <code>Message</code> when <code>Status</code> shows up as <code>FAILED</code>. You can also check the progress of the copy operation by going to the <code>DataPipeline</code> console and looking up the pipeline using the <code>pipelineId </code> from the describe call.</p>
*/
@interface AWSMachineLearningCreateDataSourceFromRDSOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
@end
/**
*/
@interface AWSMachineLearningCreateDataSourceFromRedshiftInput : AWSRequest
/**
<p>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code>DataSource</code> needs to be used for <code>MLModel</code> training.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable computeStatistics;
/**
<p>A user-supplied ID that uniquely identifies the <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
/**
<p>A user-supplied name or description of the <code>DataSource</code>. </p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceName;
/**
<p>The data specification of an Amazon Redshift <code>DataSource</code>:</p><ul><li><p>DatabaseInformation - <ul><li><code>DatabaseName</code> - The name of the Amazon Redshift database. </li><li><code> ClusterIdentifier</code> - The unique ID for the Amazon Redshift cluster.</li></ul></p></li><li><p>DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.</p></li><li><p>SelectSqlQuery - The query that is used to retrieve the observation data for the <code>Datasource</code>.</p></li><li><p>S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the <code>SelectSqlQuery</code> query is stored in this location.</p></li><li><p>DataSchemaUri - The Amazon S3 location of the <code>DataSchema</code>.</p></li><li><p>DataSchema - A JSON string representing the schema. This is not required if <code>DataSchemaUri</code> is specified. </p></li><li><p>DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the <code>DataSource</code>.</p><p> Sample - <code> "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"</code></p></li></ul>
*/
@property (nonatomic, strong) AWSMachineLearningRedshiftDataSpec * _Nullable dataSpec;
/**
<p>A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following: </p><p><ul><li><p>A security group to allow Amazon ML to execute the <code>SelectSqlQuery</code> query on an Amazon Redshift cluster</p></li><li><p>An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the <code>S3StagingLocation</code></p></li></ul></p>
*/
@property (nonatomic, strong) NSString * _Nullable roleARN;
@end
/**
<p> Represents the output of a <code>CreateDataSourceFromRedshift</code> operation, and is an acknowledgement that Amazon ML received the request.</p><p>The <code>CreateDataSourceFromRedshift</code> operation is asynchronous. You can poll for updates by using the <code>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter. </p>
*/
@interface AWSMachineLearningCreateDataSourceFromRedshiftOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
@end
/**
*/
@interface AWSMachineLearningCreateDataSourceFromS3Input : AWSRequest
/**
<p>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code></code>DataSource<code></code> needs to be used for <code>MLModel</code> training.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable computeStatistics;
/**
<p>A user-supplied identifier that uniquely identifies the <code>DataSource</code>. </p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
/**
<p>A user-supplied name or description of the <code>DataSource</code>. </p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceName;
/**
<p>The data specification of a <code>DataSource</code>:</p><ul><li><p>DataLocationS3 - The Amazon S3 location of the observation data.</p></li><li><p>DataSchemaLocationS3 - The Amazon S3 location of the <code>DataSchema</code>.</p></li><li><p>DataSchema - A JSON string representing the schema. This is not required if <code>DataSchemaUri</code> is specified. </p></li><li><p>DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the <code>Datasource</code>. </p><p> Sample - <code> "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"</code></p></li></ul>
*/
@property (nonatomic, strong) AWSMachineLearningS3DataSpec * _Nullable dataSpec;
@end
/**
<p> Represents the output of a <code>CreateDataSourceFromS3</code> operation, and is an acknowledgement that Amazon ML received the request.</p><p>The <code>CreateDataSourceFromS3</code> operation is asynchronous. You can poll for updates by using the <code>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter. </p>
*/
@interface AWSMachineLearningCreateDataSourceFromS3Output : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
@end
/**
*/
@interface AWSMachineLearningCreateEvaluationInput : AWSRequest
/**
<p>The ID of the <code>DataSource</code> for the evaluation. The schema of the <code>DataSource</code> must match the schema used to create the <code>MLModel</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable evaluationDataSourceId;
/**
<p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable evaluationId;
/**
<p>A user-supplied name or description of the <code>Evaluation</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable evaluationName;
/**
<p>The ID of the <code>MLModel</code> to evaluate.</p><p>The schema used in creating the <code>MLModel</code> must match the schema of the <code>DataSource</code> used in the <code>Evaluation</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
@end
/**
<p> Represents the output of a <code>CreateEvaluation</code> operation, and is an acknowledgement that Amazon ML received the request.</p><p><code>CreateEvaluation</code> operation is asynchronous. You can poll for status updates by using the <code>GetEvcaluation</code> operation and checking the <code>Status</code> parameter. </p>
*/
@interface AWSMachineLearningCreateEvaluationOutput : AWSModel
/**
<p>The user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable evaluationId;
@end
/**
*/
@interface AWSMachineLearningCreateMLModelInput : AWSRequest
/**
<p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
/**
<p>A user-supplied name or description of the <code>MLModel</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelName;
/**
<p>The category of supervised learning that this <code>MLModel</code> will address. Choose from the following types:</p><ul><li>Choose <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a numeric value.</li><li>Choose <code>BINARY</code> if the <code>MLModel</code> result has two possible values.</li><li>Choose <code>MULTICLASS</code> if the <code>MLModel</code> result has a limited number of values. </li></ul><p> For more information, see the <a href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</p>
*/
@property (nonatomic, assign) AWSMachineLearningMLModelType MLModelType;
/**
<p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p><p>The following is the current set of training parameters: </p><ul><li><p><code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p><p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p></li><li><p><code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p></li><li><p><code>sgd.shuffleType</code> - 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 <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We <?oxy_insert_start author="laurama" timestamp="20160329T131121-0700">strongly recommend that you shuffle your data.<?oxy_insert_end></p></li><li><p><code>sgd.l1RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p><p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p></li><li><p><code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p><p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p></li></ul>
*/
@property (nonatomic, strong) NSDictionary<NSString *, NSString *> * _Nullable parameters;
/**
<p>The data recipe for creating the <code>MLModel</code>. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</p>
*/
@property (nonatomic, strong) NSString * _Nullable recipe;
/**
<p>The Amazon Simple Storage Service (Amazon S3) location and file name that contains the <code>MLModel</code> 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.</p>
*/
@property (nonatomic, strong) NSString * _Nullable recipeUri;
/**
<p>The <code>DataSource</code> that points to the training data.</p>
*/
@property (nonatomic, strong) NSString * _Nullable trainingDataSourceId;
@end
/**
<p> Represents the output of a <code>CreateMLModel</code> operation, and is an acknowledgement that Amazon ML received the request.</p><p>The <code>CreateMLModel</code> operation is asynchronous. You can poll for status updates by using the <code>GetMLModel</code> operation and checking the <code>Status</code> parameter. </p>
*/
@interface AWSMachineLearningCreateMLModelOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request. </p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
@end
/**
*/
@interface AWSMachineLearningCreateRealtimeEndpointInput : AWSRequest
/**
<p>The ID assigned to the <code>MLModel</code> during creation.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
@end
/**
<p>Represents the output of an <code>CreateRealtimeEndpoint</code> operation.</p><p>The result contains the <code>MLModelId</code> and the endpoint information for the <code>MLModel</code>.</p><note><p>The endpoint information includes the URI of the <code>MLModel</code>; that is, the location to send online prediction requests for the specified <code>MLModel</code>.</p></note>
*/
@interface AWSMachineLearningCreateRealtimeEndpointOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
/**
<p>The endpoint information of the <code>MLModel</code></p>
*/
@property (nonatomic, strong) AWSMachineLearningRealtimeEndpointInfo * _Nullable realtimeEndpointInfo;
@end
/**
<p> Represents the output of the <code>GetDataSource</code> operation. </p><p> The content consists of the detailed metadata and data file information and the current status of the <code>DataSource</code>. </p>
*/
@interface AWSMachineLearningDataSource : AWSModel
/**
<p> The parameter is <code>true</code> if statistics need to be generated from the observation data. </p>
*/
@property (nonatomic, strong) NSNumber * _Nullable computeStatistics;
/**
<p>Long integer type that is a 64-bit signed number.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable computeTime;
/**
<p>The time that the <code>DataSource</code> was created. The time is expressed in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable createdAt;
/**
<p>The AWS user account from which the <code>DataSource</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
*/
@property (nonatomic, strong) NSString * _Nullable createdByIamUser;
/**
<p>The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataLocationS3;
/**
<p>A JSON string that represents the splitting and rearrangement requirement used when this <code>DataSource</code> was created.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataRearrangement;
/**
<p>The total number of observations contained in the data files that the <code>DataSource</code> references.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable dataSizeInBytes;
/**
<p>The ID that is assigned to the <code>DataSource</code> during creation.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
/**
<p>A timestamp represented in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable finishedAt;
/**
<p>The time of the most recent edit to the <code>BatchPrediction</code>. The time is expressed in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable lastUpdatedAt;
/**
<p>A description of the most recent details about creating the <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable message;
/**
<p>A user-supplied name or description of the <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable name;
/**
<p>The number of data files referenced by the <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable numberOfFiles;
/**
<p>The datasource details that are specific to Amazon RDS.</p>
*/
@property (nonatomic, strong) AWSMachineLearningRDSMetadata * _Nullable RDSMetadata;
/**
<p>Describes the <code>DataSource</code> details specific to Amazon Redshift.</p>
*/
@property (nonatomic, strong) AWSMachineLearningRedshiftMetadata * _Nullable redshiftMetadata;
/**
<p>The Amazon Resource Name (ARN) of an <a href="http://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts">AWS IAM Role</a>, such as the following: arn:aws:iam::account:role/rolename. </p>
*/
@property (nonatomic, strong) NSString * _Nullable roleARN;
/**
<p>A timestamp represented in epoch time.</p>
*/
@property (nonatomic, strong) NSDate * _Nullable startedAt;
/**
<p>The current status of the <code>DataSource</code>. This element can have one of the following values: </p><ul><li>PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a <code>DataSource</code>.</li><li>INPROGRESS - The creation process is underway.</li><li>FAILED - The request to create a <code>DataSource</code> did not run to completion. It is not usable.</li><li>COMPLETED - The creation process completed successfully.</li><li>DELETED - The <code>DataSource</code> is marked as deleted. It is not usable.</li></ul>
*/
@property (nonatomic, assign) AWSMachineLearningEntityStatus status;
@end
/**
*/
@interface AWSMachineLearningDeleteBatchPredictionInput : AWSRequest
/**
<p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionId;
@end
/**
<p> Represents the output of a <code>DeleteBatchPrediction</code> operation.</p><p>You can use the <code>GetBatchPrediction</code> operation and check the value of the <code>Status</code> parameter to see whether a <code>BatchPrediction</code> is marked as <code>DELETED</code>.</p>
*/
@interface AWSMachineLearningDeleteBatchPredictionOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable batchPredictionId;
@end
/**
*/
@interface AWSMachineLearningDeleteDataSourceInput : AWSRequest
/**
<p>A user-supplied ID that uniquely identifies the <code>DataSource</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
@end
/**
<p> Represents the output of a <code>DeleteDataSource</code> operation.</p>
*/
@interface AWSMachineLearningDeleteDataSourceOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable dataSourceId;
@end
/**
*/
@interface AWSMachineLearningDeleteEvaluationInput : AWSRequest
/**
<p>A user-supplied ID that uniquely identifies the <code>Evaluation</code> to delete.</p>
*/
@property (nonatomic, strong) NSString * _Nullable evaluationId;
@end
/**
<p> Represents the output of a <code>DeleteEvaluation</code> operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.</p><p>You can use the <code>GetEvaluation</code> operation and check the value of the <code>Status</code> parameter to see whether an <code>Evaluation</code> is marked as <code>DELETED</code>.</p>
*/
@interface AWSMachineLearningDeleteEvaluationOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable evaluationId;
@end
/**
*/
@interface AWSMachineLearningDeleteMLModelInput : AWSRequest
/**
<p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
@end
/**
<p>Represents the output of a <code>DeleteMLModel</code> operation.</p><p>You can use the <code>GetMLModel</code> operation and check the value of the <code>Status</code> parameter to see whether an <code>MLModel</code> is marked as <code>DELETED</code>.</p>
*/
@interface AWSMachineLearningDeleteMLModelOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
@end
/**
*/
@interface AWSMachineLearningDeleteRealtimeEndpointInput : AWSRequest
/**
<p>The ID assigned to the <code>MLModel</code> during creation.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
@end
/**
<p>Represents the output of an <code>DeleteRealtimeEndpoint</code> operation.</p><p>The result contains the <code>MLModelId</code> and the endpoint information for the <code>MLModel</code>. </p>
*/
@interface AWSMachineLearningDeleteRealtimeEndpointOutput : AWSModel
/**
<p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
*/
@property (nonatomic, strong) NSString * _Nullable MLModelId;
/**
<p>The endpoint information of the <code>MLModel</code></p>
*/
@property (nonatomic, strong) AWSMachineLearningRealtimeEndpointInfo * _Nullable realtimeEndpointInfo;
@end
/**
*/
@interface AWSMachineLearningDeleteTagsInput : AWSRequest
/**
<p>The ID of the tagged ML object. For example, <code>exampleModelId</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable resourceId;
/**
<p>The type of the tagged ML object.</p>
*/
@property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType;
/**
<p>One or more tags to delete.</p>
*/
@property (nonatomic, strong) NSArray<NSString *> * _Nullable tagKeys;
@end
/**
<p>Amazon ML returns the following elements. </p>
*/
@interface AWSMachineLearningDeleteTagsOutput : AWSModel
/**
<p>The ID of the ML object from which tags were deleted.</p>
*/
@property (nonatomic, strong) NSString * _Nullable resourceId;
/**
<p>The type of the ML object from which tags were deleted.</p>
*/
@property (nonatomic, assign) AWSMachineLearningTaggableResourceType resourceType;
@end
/**
*/
@interface AWSMachineLearningDescribeBatchPredictionsInput : AWSRequest
/**
<p>The equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that exactly match the value specified with <code>EQ</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable EQ;
/**
<p>Use one of the following variables to filter a list of <code>BatchPrediction</code>:</p><ul><li><code>CreatedAt</code> - Sets the search criteria to the <code>BatchPrediction</code> creation date.</li><li><code>Status</code> - Sets the search criteria to the <code>BatchPrediction</code> status.</li><li><code>Name</code> - Sets the search criteria to the contents of the <code>BatchPrediction</code><b></b><code>Name</code>.</li><li><code>IAMUser</code> - Sets the search criteria to the user account that invoked the <code>BatchPrediction</code> creation.</li><li><code>MLModelId</code> - Sets the search criteria to the <code>MLModel</code> used in the <code>BatchPrediction</code>.</li><li><code>DataSourceId</code> - Sets the search criteria to the <code>DataSource</code> used in the <code>BatchPrediction</code>.</li><li><code>DataURI</code> - Sets the search criteria to the data file(s) used in the <code>BatchPrediction</code>. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.</li></ul>
*/
@property (nonatomic, assign) AWSMachineLearningBatchPredictionFilterVariable filterVariable;
/**
<p>The greater than or equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are greater than or equal to the value specified with <code>GE</code>. </p>
*/
@property (nonatomic, strong) NSString * _Nullable GE;
/**
<p>The greater than operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are greater than the value specified with <code>GT</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable GT;
/**
<p>The less than or equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are less than or equal to the value specified with <code>LE</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable LE;
/**
<p>The less than operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are less than the value specified with <code>LT</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable LT;
/**
<p>The number of pages of information to include in the result. The range of acceptable values is <code>1</code> through <code>100</code>. The default value is <code>100</code>.</p>
*/
@property (nonatomic, strong) NSNumber * _Nullable limit;
/**
<p>The not equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values not equal to the value specified with <code>NE</code>.</p>
*/
@property (nonatomic, strong) NSString * _Nullable NE;
/**
<p>An ID of the page in the paginated results.</p>
*/
@property (nonatomic, strong) NSString * _Nullable nextToken;
/**
<p>A string that is found at the beginning of a variable, such as <code>Name</code> or <code>Id</code>.</p><p>For example, a <code>Batch Prediction</code> operation could have the <code>Name</code><code>2014-09-09-HolidayGiftMailer</code>. To search for this <code>BatchPrediction</code>, select <code>Name</code> for the <code>FilterVariable</code> and any of the following strings for the <code>Prefix</code>: </p><ul><li><p>2014-09</p></li><li><p>2014-09-09</p></li><li><p>2014-09-09-Holiday</p></li></ul>
*/
@property (nonatomic, strong) NSString * _Nullable prefix;
/**
<p>A two-value parameter that determines the sequence of the resulting list of <code>MLModel</code>s.</p><ul><li><code>asc</code> - Arranges the list in ascending order (A-Z, 0-9).</li><li><code>dsc</code> - Arranges the list in descending order (Z-A, 9-0).</li></ul><p>Results are sorted by <code>FilterVariable</code>.</p>
*/
@property (nonatomic, assign) AWSMachineLearningSortOrder sortOrder;
@end
/**
<p>Represents the output of a <code>DescribeBatchPredictions</code> operation. The content is essentially a list of <code>BatchPrediction</code>s.</p>
*/
@interface AWSMachineLearningDescribeBatchPredictionsOutput : AWSModel
/**
<p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
*/
@property (nonatomic, strong) NSString * _Nullable nextToken;
/**
<p>A list of <code>BatchPrediction</code> objects that meet the search criteria. </p>
*/
@property (nonatomic, strong) NSArray<AWSMachineLearningBatchPrediction *> * _Nullable results;
@end
/**
*/