# frozen_string_literal: true # WARNING ABOUT GENERATED CODE # # This file is generated. See the contributing guide for more information: # https://github.com/aws/aws-sdk-ruby/blob/version-3/CONTRIBUTING.md # # WARNING ABOUT GENERATED CODE module Aws::MachineLearning module Types # @!attribute [rw] tags # 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. # @return [Array] # # @!attribute [rw] resource_id # The ID of the ML object to tag. For example, `exampleModelId`. # @return [String] # # @!attribute [rw] resource_type # The type of the ML object to tag. # @return [String] # class AddTagsInput < Struct.new( :tags, :resource_id, :resource_type) SENSITIVE = [] include Aws::Structure end # Amazon ML returns the following elements. # # @!attribute [rw] resource_id # The ID of the ML object that was tagged. # @return [String] # # @!attribute [rw] resource_type # The type of the ML object that was tagged. # @return [String] # class AddTagsOutput < Struct.new( :resource_id, :resource_type) SENSITIVE = [] include Aws::Structure 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`. # # @!attribute [rw] batch_prediction_id # The ID assigned to the `BatchPrediction` at creation. This value # should be identical to the value of the `BatchPredictionID` in the # request. # @return [String] # # @!attribute [rw] ml_model_id # The ID of the `MLModel` that generated predictions for the # `BatchPrediction` request. # @return [String] # # @!attribute [rw] batch_prediction_data_source_id # The ID of the `DataSource` that points to the group of observations # to predict. # @return [String] # # @!attribute [rw] input_data_location_s3 # The location of the data file or directory in Amazon Simple Storage # Service (Amazon S3). # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time that the `BatchPrediction` was created. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to the `BatchPrediction`. The time # is expressed in epoch time. # @return [Time] # # @!attribute [rw] name # A user-supplied name or description of the `BatchPrediction`. # @return [String] # # @!attribute [rw] status # 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. # @return [String] # # @!attribute [rw] output_uri # 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: ':', '//', '/./', # '/../'. # @return [String] # # @!attribute [rw] message # A description of the most recent details about processing the batch # prediction request. # @return [String] # # @!attribute [rw] compute_time # Long integer type that is a 64-bit signed number. # @return [Integer] # # @!attribute [rw] finished_at # A timestamp represented in epoch time. # @return [Time] # # @!attribute [rw] started_at # A timestamp represented in epoch time. # @return [Time] # # @!attribute [rw] total_record_count # Long integer type that is a 64-bit signed number. # @return [Integer] # # @!attribute [rw] invalid_record_count # Long integer type that is a 64-bit signed number. # @return [Integer] # class BatchPrediction < Struct.new( :batch_prediction_id, :ml_model_id, :batch_prediction_data_source_id, :input_data_location_s3, :created_by_iam_user, :created_at, :last_updated_at, :name, :status, :output_uri, :message, :compute_time, :finished_at, :started_at, :total_record_count, :invalid_record_count) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] batch_prediction_id # A user-supplied ID that uniquely identifies the `BatchPrediction`. # @return [String] # # @!attribute [rw] batch_prediction_name # A user-supplied name or description of the `BatchPrediction`. # `BatchPredictionName` can only use the UTF-8 character set. # @return [String] # # @!attribute [rw] ml_model_id # The ID of the `MLModel` that will generate predictions for the group # of observations. # @return [String] # # @!attribute [rw] batch_prediction_data_source_id # The ID of the `DataSource` that points to the group of observations # to predict. # @return [String] # # @!attribute [rw] output_uri # 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][1]. # # # # [1]: https://docs.aws.amazon.com/machine-learning/latest/dg # @return [String] # class CreateBatchPredictionInput < Struct.new( :batch_prediction_id, :batch_prediction_name, :ml_model_id, :batch_prediction_data_source_id, :output_uri) SENSITIVE = [] include Aws::Structure 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. # # @!attribute [rw] batch_prediction_id # A user-supplied ID that uniquely identifies the `BatchPrediction`. # This value is identical to the value of the `BatchPredictionId` in # the request. # @return [String] # class CreateBatchPredictionOutput < Struct.new( :batch_prediction_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. # Typically, an Amazon Resource Number (ARN) becomes the ID for a # `DataSource`. # @return [String] # # @!attribute [rw] data_source_name # A user-supplied name or description of the `DataSource`. # @return [String] # # @!attribute [rw] rds_data # 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][1] 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][1] 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\}\}"` # # # # [1]: https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html # @return [Types::RDSDataSpec] # # @!attribute [rw] role_arn # 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. # @return [String] # # @!attribute [rw] compute_statistics # 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.

# ` # @return [Boolean] # class CreateDataSourceFromRDSInput < Struct.new( :data_source_id, :data_source_name, :rds_data, :role_arn, :compute_statistics) SENSITIVE = [] include Aws::Structure 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. # # @!attribute [rw] data_source_id # A user-supplied ID that uniquely identifies the datasource. This # value should be identical to the value of the `DataSourceID` in the # request. # @return [String] # class CreateDataSourceFromRDSOutput < Struct.new( :data_source_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. # @return [String] # # @!attribute [rw] data_source_name # A user-supplied name or description of the `DataSource`. # @return [String] # # @!attribute [rw] data_spec # 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\}\}"` # @return [Types::RedshiftDataSpec] # # @!attribute [rw] role_arn # 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` # @return [String] # # @!attribute [rw] compute_statistics # 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. # @return [Boolean] # class CreateDataSourceFromRedshiftInput < Struct.new( :data_source_id, :data_source_name, :data_spec, :role_arn, :compute_statistics) SENSITIVE = [] include Aws::Structure 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. # # @!attribute [rw] data_source_id # A user-supplied ID that uniquely identifies the datasource. This # value should be identical to the value of the `DataSourceID` in the # request. # @return [String] # class CreateDataSourceFromRedshiftOutput < Struct.new( :data_source_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] data_source_id # A user-supplied identifier that uniquely identifies the # `DataSource`. # @return [String] # # @!attribute [rw] data_source_name # A user-supplied name or description of the `DataSource`. # @return [String] # # @!attribute [rw] data_spec # 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\}\}"` # @return [Types::S3DataSpec] # # @!attribute [rw] compute_statistics # 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.

# ` # @return [Boolean] # class CreateDataSourceFromS3Input < Struct.new( :data_source_id, :data_source_name, :data_spec, :compute_statistics) SENSITIVE = [] include Aws::Structure 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. # # @!attribute [rw] data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. This # value should be identical to the value of the `DataSourceID` in the # request. # @return [String] # class CreateDataSourceFromS3Output < Struct.new( :data_source_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] evaluation_id # A user-supplied ID that uniquely identifies the `Evaluation`. # @return [String] # # @!attribute [rw] evaluation_name # A user-supplied name or description of the `Evaluation`. # @return [String] # # @!attribute [rw] ml_model_id # 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`. # @return [String] # # @!attribute [rw] evaluation_data_source_id # The ID of the `DataSource` for the evaluation. The schema of the # `DataSource` must match the schema used to create the `MLModel`. # @return [String] # class CreateEvaluationInput < Struct.new( :evaluation_id, :evaluation_name, :ml_model_id, :evaluation_data_source_id) SENSITIVE = [] include Aws::Structure 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. # # @!attribute [rw] evaluation_id # The user-supplied ID that uniquely identifies the `Evaluation`. This # value should be identical to the value of the `EvaluationId` in the # request. # @return [String] # class CreateEvaluationOutput < Struct.new( :evaluation_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. # @return [String] # # @!attribute [rw] ml_model_name # A user-supplied name or description of the `MLModel`. # @return [String] # # @!attribute [rw] ml_model_type # 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][1]. # # # # [1]: https://docs.aws.amazon.com/machine-learning/latest/dg # @return [String] # # @!attribute [rw] parameters # 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. # @return [Hash] # # @!attribute [rw] training_data_source_id # The `DataSource` that points to the training data. # @return [String] # # @!attribute [rw] recipe # 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. # @return [String] # # @!attribute [rw] recipe_uri # 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. # @return [String] # class CreateMLModelInput < Struct.new( :ml_model_id, :ml_model_name, :ml_model_type, :parameters, :training_data_source_id, :recipe, :recipe_uri) SENSITIVE = [] include Aws::Structure 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. # # @!attribute [rw] ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. This # value should be identical to the value of the `MLModelId` in the # request. # @return [String] # class CreateMLModelOutput < Struct.new( :ml_model_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] ml_model_id # The ID assigned to the `MLModel` during creation. # @return [String] # class CreateRealtimeEndpointInput < Struct.new( :ml_model_id) SENSITIVE = [] include Aws::Structure end # Represents the output of an `CreateRealtimeEndpoint` operation. # # The result contains the `MLModelId` and the endpoint information for # the `MLModel`. # # **Note:** The endpoint information includes the URI of the `MLModel`; # that is, the location to send online prediction requests for the # specified `MLModel`. # # @!attribute [rw] ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. This # value should be identical to the value of the `MLModelId` in the # request. # @return [String] # # @!attribute [rw] realtime_endpoint_info # The endpoint information of the `MLModel` # @return [Types::RealtimeEndpointInfo] # class CreateRealtimeEndpointOutput < Struct.new( :ml_model_id, :realtime_endpoint_info) SENSITIVE = [] include Aws::Structure 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`. # # @!attribute [rw] data_source_id # The ID that is assigned to the `DataSource` during creation. # @return [String] # # @!attribute [rw] data_location_s3 # The location and name of the data in Amazon Simple Storage Service # (Amazon S3) that is used by a `DataSource`. # @return [String] # # @!attribute [rw] data_rearrangement # A JSON string that represents the splitting and rearrangement # requirement used when this `DataSource` was created. # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time that the `DataSource` was created. The time is expressed in # epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to the `BatchPrediction`. The time # is expressed in epoch time. # @return [Time] # # @!attribute [rw] data_size_in_bytes # The total number of observations contained in the data files that # the `DataSource` references. # @return [Integer] # # @!attribute [rw] number_of_files # The number of data files referenced by the `DataSource`. # @return [Integer] # # @!attribute [rw] name # A user-supplied name or description of the `DataSource`. # @return [String] # # @!attribute [rw] status # 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. # @return [String] # # @!attribute [rw] message # A description of the most recent details about creating the # `DataSource`. # @return [String] # # @!attribute [rw] redshift_metadata # Describes the `DataSource` details specific to Amazon Redshift. # @return [Types::RedshiftMetadata] # # @!attribute [rw] rds_metadata # The datasource details that are specific to Amazon RDS. # @return [Types::RDSMetadata] # # @!attribute [rw] role_arn # The Amazon Resource Name (ARN) of an [AWS IAM Role][1], such as the # following: arn:aws:iam::account:role/rolename. # # # # [1]: https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts # @return [String] # # @!attribute [rw] compute_statistics # The parameter is `true` if statistics need to be generated from the # observation data. # @return [Boolean] # # @!attribute [rw] compute_time # Long integer type that is a 64-bit signed number. # @return [Integer] # # @!attribute [rw] finished_at # A timestamp represented in epoch time. # @return [Time] # # @!attribute [rw] started_at # A timestamp represented in epoch time. # @return [Time] # class DataSource < Struct.new( :data_source_id, :data_location_s3, :data_rearrangement, :created_by_iam_user, :created_at, :last_updated_at, :data_size_in_bytes, :number_of_files, :name, :status, :message, :redshift_metadata, :rds_metadata, :role_arn, :compute_statistics, :compute_time, :finished_at, :started_at) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] batch_prediction_id # A user-supplied ID that uniquely identifies the `BatchPrediction`. # @return [String] # class DeleteBatchPredictionInput < Struct.new( :batch_prediction_id) SENSITIVE = [] include Aws::Structure 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`. # # @!attribute [rw] batch_prediction_id # A user-supplied ID that uniquely identifies the `BatchPrediction`. # This value should be identical to the value of the # `BatchPredictionID` in the request. # @return [String] # class DeleteBatchPredictionOutput < Struct.new( :batch_prediction_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. # @return [String] # class DeleteDataSourceInput < Struct.new( :data_source_id) SENSITIVE = [] include Aws::Structure end # Represents the output of a `DeleteDataSource` operation. # # @!attribute [rw] data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. This # value should be identical to the value of the `DataSourceID` in the # request. # @return [String] # class DeleteDataSourceOutput < Struct.new( :data_source_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] evaluation_id # A user-supplied ID that uniquely identifies the `Evaluation` to # delete. # @return [String] # class DeleteEvaluationInput < Struct.new( :evaluation_id) SENSITIVE = [] include Aws::Structure 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`. # # @!attribute [rw] evaluation_id # A user-supplied ID that uniquely identifies the `Evaluation`. This # value should be identical to the value of the `EvaluationId` in the # request. # @return [String] # class DeleteEvaluationOutput < Struct.new( :evaluation_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. # @return [String] # class DeleteMLModelInput < Struct.new( :ml_model_id) SENSITIVE = [] include Aws::Structure 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`. # # @!attribute [rw] ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. This # value should be identical to the value of the `MLModelID` in the # request. # @return [String] # class DeleteMLModelOutput < Struct.new( :ml_model_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] ml_model_id # The ID assigned to the `MLModel` during creation. # @return [String] # class DeleteRealtimeEndpointInput < Struct.new( :ml_model_id) SENSITIVE = [] include Aws::Structure end # Represents the output of an `DeleteRealtimeEndpoint` operation. # # The result contains the `MLModelId` and the endpoint information for # the `MLModel`. # # @!attribute [rw] ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. This # value should be identical to the value of the `MLModelId` in the # request. # @return [String] # # @!attribute [rw] realtime_endpoint_info # The endpoint information of the `MLModel` # @return [Types::RealtimeEndpointInfo] # class DeleteRealtimeEndpointOutput < Struct.new( :ml_model_id, :realtime_endpoint_info) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] tag_keys # One or more tags to delete. # @return [Array] # # @!attribute [rw] resource_id # The ID of the tagged ML object. For example, `exampleModelId`. # @return [String] # # @!attribute [rw] resource_type # The type of the tagged ML object. # @return [String] # class DeleteTagsInput < Struct.new( :tag_keys, :resource_id, :resource_type) SENSITIVE = [] include Aws::Structure end # Amazon ML returns the following elements. # # @!attribute [rw] resource_id # The ID of the ML object from which tags were deleted. # @return [String] # # @!attribute [rw] resource_type # The type of the ML object from which tags were deleted. # @return [String] # class DeleteTagsOutput < Struct.new( :resource_id, :resource_type) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] filter_variable # 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 # `BatchPrediction` `Name`. # # * `IAMUser` - Sets the search criteria to the user account that # invoked the `BatchPrediction` creation. # # * `MLModelId` - Sets the search criteria to the `MLModel` used in # the `BatchPrediction`. # # * `DataSourceId` - Sets the search criteria to the `DataSource` used # in the `BatchPrediction`. # # * `DataURI` - Sets the search criteria to the data file(s) used in # the `BatchPrediction`. The URL can identify either a file or an # Amazon Simple Storage Solution (Amazon S3) bucket or directory. # @return [String] # # @!attribute [rw] eq # The equal to operator. The `BatchPrediction` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # @return [String] # # @!attribute [rw] gt # The greater than operator. The `BatchPrediction` results will have # `FilterVariable` values that are greater than the value specified # with `GT`. # @return [String] # # @!attribute [rw] lt # The less than operator. The `BatchPrediction` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # @return [String] # # @!attribute [rw] ge # 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`. # @return [String] # # @!attribute [rw] le # 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`. # @return [String] # # @!attribute [rw] ne # The not equal to operator. The `BatchPrediction` results will have # `FilterVariable` values not equal to the value specified with `NE`. # @return [String] # # @!attribute [rw] prefix # 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 `Name` # `2014-09-09-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 # @return [String] # # @!attribute [rw] sort_order # A two-value parameter that determines the sequence of the resulting # list of `MLModel`s. # # * `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`. # @return [String] # # @!attribute [rw] next_token # An ID of the page in the paginated results. # @return [String] # # @!attribute [rw] limit # 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`. # @return [Integer] # class DescribeBatchPredictionsInput < Struct.new( :filter_variable, :eq, :gt, :lt, :ge, :le, :ne, :prefix, :sort_order, :next_token, :limit) SENSITIVE = [] include Aws::Structure end # Represents the output of a `DescribeBatchPredictions` operation. The # content is essentially a list of `BatchPrediction`s. # # @!attribute [rw] results # A list of `BatchPrediction` objects that meet the search criteria. # @return [Array] # # @!attribute [rw] next_token # The ID of the next page in the paginated results that indicates at # least one more page follows. # @return [String] # class DescribeBatchPredictionsOutput < Struct.new( :results, :next_token) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] filter_variable # 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 `DataSource` # `Name`. # # * `DataUri` - Sets the search criteria to the URI of data files used # to create the `DataSource`. The URI can identify either a file or # an Amazon Simple Storage Service (Amazon S3) bucket or directory. # # * `IAMUser` - Sets the search criteria to the user account that # invoked the `DataSource` creation. # @return [String] # # @!attribute [rw] eq # The equal to operator. The `DataSource` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # @return [String] # # @!attribute [rw] gt # The greater than operator. The `DataSource` results will have # `FilterVariable` values that are greater than the value specified # with `GT`. # @return [String] # # @!attribute [rw] lt # The less than operator. The `DataSource` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # @return [String] # # @!attribute [rw] ge # 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`. # @return [String] # # @!attribute [rw] le # 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`. # @return [String] # # @!attribute [rw] ne # The not equal to operator. The `DataSource` results will have # `FilterVariable` values not equal to the value specified with `NE`. # @return [String] # # @!attribute [rw] prefix # A string that is found at the beginning of a variable, such as # `Name` or `Id`. # # For example, a `DataSource` could have the `Name` # `2014-09-09-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 # @return [String] # # @!attribute [rw] sort_order # 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`. # @return [String] # # @!attribute [rw] next_token # The ID of the page in the paginated results. # @return [String] # # @!attribute [rw] limit # The maximum number of `DataSource` to include in the result. # @return [Integer] # class DescribeDataSourcesInput < Struct.new( :filter_variable, :eq, :gt, :lt, :ge, :le, :ne, :prefix, :sort_order, :next_token, :limit) SENSITIVE = [] include Aws::Structure end # Represents the query results from a DescribeDataSources operation. The # content is essentially a list of `DataSource`. # # @!attribute [rw] results # A list of `DataSource` that meet the search criteria. # @return [Array] # # @!attribute [rw] next_token # An ID of the next page in the paginated results that indicates at # least one more page follows. # @return [String] # class DescribeDataSourcesOutput < Struct.new( :results, :next_token) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] filter_variable # 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 `Evaluation` # `Name`. # # * `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. # @return [String] # # @!attribute [rw] eq # The equal to operator. The `Evaluation` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # @return [String] # # @!attribute [rw] gt # The greater than operator. The `Evaluation` results will have # `FilterVariable` values that are greater than the value specified # with `GT`. # @return [String] # # @!attribute [rw] lt # The less than operator. The `Evaluation` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # @return [String] # # @!attribute [rw] ge # 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`. # @return [String] # # @!attribute [rw] le # 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`. # @return [String] # # @!attribute [rw] ne # The not equal to operator. The `Evaluation` results will have # `FilterVariable` values not equal to the value specified with `NE`. # @return [String] # # @!attribute [rw] prefix # A string that is found at the beginning of a variable, such as # `Name` or `Id`. # # For example, an `Evaluation` could have the `Name` # `2014-09-09-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 # @return [String] # # @!attribute [rw] sort_order # 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`. # @return [String] # # @!attribute [rw] next_token # The ID of the page in the paginated results. # @return [String] # # @!attribute [rw] limit # The maximum number of `Evaluation` to include in the result. # @return [Integer] # class DescribeEvaluationsInput < Struct.new( :filter_variable, :eq, :gt, :lt, :ge, :le, :ne, :prefix, :sort_order, :next_token, :limit) SENSITIVE = [] include Aws::Structure end # Represents the query results from a `DescribeEvaluations` operation. # The content is essentially a list of `Evaluation`. # # @!attribute [rw] results # A list of `Evaluation` that meet the search criteria. # @return [Array] # # @!attribute [rw] next_token # The ID of the next page in the paginated results that indicates at # least one more page follows. # @return [String] # class DescribeEvaluationsOutput < Struct.new( :results, :next_token) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] filter_variable # 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 `MLModel` # `Name`. # # * `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. # @return [String] # # @!attribute [rw] eq # The equal to operator. The `MLModel` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # @return [String] # # @!attribute [rw] gt # The greater than operator. The `MLModel` results will have # `FilterVariable` values that are greater than the value specified # with `GT`. # @return [String] # # @!attribute [rw] lt # The less than operator. The `MLModel` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # @return [String] # # @!attribute [rw] ge # 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`. # @return [String] # # @!attribute [rw] le # 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`. # @return [String] # # @!attribute [rw] ne # The not equal to operator. The `MLModel` results will have # `FilterVariable` values not equal to the value specified with `NE`. # @return [String] # # @!attribute [rw] prefix # A string that is found at the beginning of a variable, such as # `Name` or `Id`. # # For example, an `MLModel` could have the `Name` # `2014-09-09-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 # @return [String] # # @!attribute [rw] sort_order # 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`. # @return [String] # # @!attribute [rw] next_token # The ID of the page in the paginated results. # @return [String] # # @!attribute [rw] limit # 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`. # @return [Integer] # class DescribeMLModelsInput < Struct.new( :filter_variable, :eq, :gt, :lt, :ge, :le, :ne, :prefix, :sort_order, :next_token, :limit) SENSITIVE = [] include Aws::Structure end # Represents the output of a `DescribeMLModels` operation. The content # is essentially a list of `MLModel`. # # @!attribute [rw] results # A list of `MLModel` that meet the search criteria. # @return [Array] # # @!attribute [rw] next_token # The ID of the next page in the paginated results that indicates at # least one more page follows. # @return [String] # class DescribeMLModelsOutput < Struct.new( :results, :next_token) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] resource_id # The ID of the ML object. For example, `exampleModelId`. # @return [String] # # @!attribute [rw] resource_type # The type of the ML object. # @return [String] # class DescribeTagsInput < Struct.new( :resource_id, :resource_type) SENSITIVE = [] include Aws::Structure end # Amazon ML returns the following elements. # # @!attribute [rw] resource_id # The ID of the tagged ML object. # @return [String] # # @!attribute [rw] resource_type # The type of the tagged ML object. # @return [String] # # @!attribute [rw] tags # A list of tags associated with the ML object. # @return [Array] # class DescribeTagsOutput < Struct.new( :resource_id, :resource_type, :tags) SENSITIVE = [] include Aws::Structure 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`. # # @!attribute [rw] evaluation_id # The ID that is assigned to the `Evaluation` at creation. # @return [String] # # @!attribute [rw] ml_model_id # The ID of the `MLModel` that is the focus of the evaluation. # @return [String] # # @!attribute [rw] evaluation_data_source_id # The ID of the `DataSource` that is used to evaluate the `MLModel`. # @return [String] # # @!attribute [rw] input_data_location_s3 # The location and name of the data in Amazon Simple Storage Server # (Amazon S3) that is used in the evaluation. # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time that the `Evaluation` was created. The time is expressed in # epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to the `Evaluation`. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] name # A user-supplied name or description of the `Evaluation`. # @return [String] # # @!attribute [rw] status # 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. # @return [String] # # @!attribute [rw] performance_metrics # 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][1]. # # # # [1]: https://docs.aws.amazon.com/machine-learning/latest/dg # @return [Types::PerformanceMetrics] # # @!attribute [rw] message # A description of the most recent details about evaluating the # `MLModel`. # @return [String] # # @!attribute [rw] compute_time # Long integer type that is a 64-bit signed number. # @return [Integer] # # @!attribute [rw] finished_at # A timestamp represented in epoch time. # @return [Time] # # @!attribute [rw] started_at # A timestamp represented in epoch time. # @return [Time] # class Evaluation < Struct.new( :evaluation_id, :ml_model_id, :evaluation_data_source_id, :input_data_location_s3, :created_by_iam_user, :created_at, :last_updated_at, :name, :status, :performance_metrics, :message, :compute_time, :finished_at, :started_at) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] batch_prediction_id # An ID assigned to the `BatchPrediction` at creation. # @return [String] # class GetBatchPredictionInput < Struct.new( :batch_prediction_id) SENSITIVE = [] include Aws::Structure end # Represents the output of a `GetBatchPrediction` operation and # describes a `BatchPrediction`. # # @!attribute [rw] batch_prediction_id # An ID assigned to the `BatchPrediction` at creation. This value # should be identical to the value of the `BatchPredictionID` in the # request. # @return [String] # # @!attribute [rw] ml_model_id # The ID of the `MLModel` that generated predictions for the # `BatchPrediction` request. # @return [String] # # @!attribute [rw] batch_prediction_data_source_id # The ID of the `DataSource` that was used to create the # `BatchPrediction`. # @return [String] # # @!attribute [rw] input_data_location_s3 # The location of the data file or directory in Amazon Simple Storage # Service (Amazon S3). # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time when the `BatchPrediction` was created. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to `BatchPrediction`. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] name # A user-supplied name or description of the `BatchPrediction`. # @return [String] # # @!attribute [rw] status # 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. # @return [String] # # @!attribute [rw] output_uri # The location of an Amazon S3 bucket or directory to receive the # operation results. # @return [String] # # @!attribute [rw] log_uri # A link to the file that contains logs of the `CreateBatchPrediction` # operation. # @return [String] # # @!attribute [rw] message # A description of the most recent details about processing the batch # prediction request. # @return [String] # # @!attribute [rw] compute_time # 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. # @return [Integer] # # @!attribute [rw] finished_at # 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. # @return [Time] # # @!attribute [rw] started_at # The epoch time when Amazon Machine Learning marked the # `BatchPrediction` as `INPROGRESS`. `StartedAt` isn't available if # the `BatchPrediction` is in the `PENDING` state. # @return [Time] # # @!attribute [rw] total_record_count # The number of total records that Amazon Machine Learning saw while # processing the `BatchPrediction`. # @return [Integer] # # @!attribute [rw] invalid_record_count # The number of invalid records that Amazon Machine Learning saw while # processing the `BatchPrediction`. # @return [Integer] # class GetBatchPredictionOutput < Struct.new( :batch_prediction_id, :ml_model_id, :batch_prediction_data_source_id, :input_data_location_s3, :created_by_iam_user, :created_at, :last_updated_at, :name, :status, :output_uri, :log_uri, :message, :compute_time, :finished_at, :started_at, :total_record_count, :invalid_record_count) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] data_source_id # The ID assigned to the `DataSource` at creation. # @return [String] # # @!attribute [rw] verbose # Specifies whether the `GetDataSource` operation should return # `DataSourceSchema`. # # If true, `DataSourceSchema` is returned. # # If false, `DataSourceSchema` is not returned. # @return [Boolean] # class GetDataSourceInput < Struct.new( :data_source_id, :verbose) SENSITIVE = [] include Aws::Structure end # Represents the output of a `GetDataSource` operation and describes a # `DataSource`. # # @!attribute [rw] data_source_id # The ID assigned to the `DataSource` at creation. This value should # be identical to the value of the `DataSourceId` in the request. # @return [String] # # @!attribute [rw] data_location_s3 # The location of the data file or directory in Amazon Simple Storage # Service (Amazon S3). # @return [String] # # @!attribute [rw] data_rearrangement # A JSON string that represents the splitting and rearrangement # requirement used when this `DataSource` was created. # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time that the `DataSource` was created. The time is expressed in # epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to the `DataSource`. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] data_size_in_bytes # The total size of observations in the data files. # @return [Integer] # # @!attribute [rw] number_of_files # The number of data files referenced by the `DataSource`. # @return [Integer] # # @!attribute [rw] name # A user-supplied name or description of the `DataSource`. # @return [String] # # @!attribute [rw] status # 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. # @return [String] # # @!attribute [rw] log_uri # A link to the file containing logs of `CreateDataSourceFrom*` # operations. # @return [String] # # @!attribute [rw] message # The user-supplied description of the most recent details about # creating the `DataSource`. # @return [String] # # @!attribute [rw] redshift_metadata # Describes the `DataSource` details specific to Amazon Redshift. # @return [Types::RedshiftMetadata] # # @!attribute [rw] rds_metadata # The datasource details that are specific to Amazon RDS. # @return [Types::RDSMetadata] # # @!attribute [rw] role_arn # The Amazon Resource Name (ARN) of an [AWS IAM Role][1], such as the # following: arn:aws:iam::account:role/rolename. # # # # [1]: https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts # @return [String] # # @!attribute [rw] compute_statistics # The parameter is `true` if statistics need to be generated from the # observation data. # @return [Boolean] # # @!attribute [rw] compute_time # 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. # @return [Integer] # # @!attribute [rw] finished_at # 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. # @return [Time] # # @!attribute [rw] started_at # The epoch time when Amazon Machine Learning marked the `DataSource` # as `INPROGRESS`. `StartedAt` isn't available if the `DataSource` is # in the `PENDING` state. # @return [Time] # # @!attribute [rw] data_source_schema # The schema used by all of the data files of this `DataSource`. # # **Note:** This parameter is provided as part of the verbose format. # @return [String] # class GetDataSourceOutput < Struct.new( :data_source_id, :data_location_s3, :data_rearrangement, :created_by_iam_user, :created_at, :last_updated_at, :data_size_in_bytes, :number_of_files, :name, :status, :log_uri, :message, :redshift_metadata, :rds_metadata, :role_arn, :compute_statistics, :compute_time, :finished_at, :started_at, :data_source_schema) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] evaluation_id # 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. # @return [String] # class GetEvaluationInput < Struct.new( :evaluation_id) SENSITIVE = [] include Aws::Structure end # Represents the output of a `GetEvaluation` operation and describes an # `Evaluation`. # # @!attribute [rw] evaluation_id # The evaluation ID which is same as the `EvaluationId` in the # request. # @return [String] # # @!attribute [rw] ml_model_id # The ID of the `MLModel` that was the focus of the evaluation. # @return [String] # # @!attribute [rw] evaluation_data_source_id # The `DataSource` used for this evaluation. # @return [String] # # @!attribute [rw] input_data_location_s3 # The location of the data file or directory in Amazon Simple Storage # Service (Amazon S3). # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time that the `Evaluation` was created. The time is expressed in # epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to the `Evaluation`. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] name # A user-supplied name or description of the `Evaluation`. # @return [String] # # @!attribute [rw] status # 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. # @return [String] # # @!attribute [rw] performance_metrics # 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][1]. # # # # [1]: https://docs.aws.amazon.com/machine-learning/latest/dg # @return [Types::PerformanceMetrics] # # @!attribute [rw] log_uri # A link to the file that contains logs of the `CreateEvaluation` # operation. # @return [String] # # @!attribute [rw] message # A description of the most recent details about evaluating the # `MLModel`. # @return [String] # # @!attribute [rw] compute_time # 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. # @return [Integer] # # @!attribute [rw] finished_at # 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. # @return [Time] # # @!attribute [rw] started_at # The epoch time when Amazon Machine Learning marked the `Evaluation` # as `INPROGRESS`. `StartedAt` isn't available if the `Evaluation` is # in the `PENDING` state. # @return [Time] # class GetEvaluationOutput < Struct.new( :evaluation_id, :ml_model_id, :evaluation_data_source_id, :input_data_location_s3, :created_by_iam_user, :created_at, :last_updated_at, :name, :status, :performance_metrics, :log_uri, :message, :compute_time, :finished_at, :started_at) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] ml_model_id # The ID assigned to the `MLModel` at creation. # @return [String] # # @!attribute [rw] verbose # Specifies whether the `GetMLModel` operation should return `Recipe`. # # If true, `Recipe` is returned. # # If false, `Recipe` is not returned. # @return [Boolean] # class GetMLModelInput < Struct.new( :ml_model_id, :verbose) SENSITIVE = [] include Aws::Structure end # Represents the output of a `GetMLModel` operation, and provides # detailed information about a `MLModel`. # # @!attribute [rw] ml_model_id # The MLModel ID, which is same as the `MLModelId` in the request. # @return [String] # # @!attribute [rw] training_data_source_id # The ID of the training `DataSource`. # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time that the `MLModel` was created. The time is expressed in # epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to the `MLModel`. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] name # A user-supplied name or description of the `MLModel`. # @return [String] # # @!attribute [rw] status # The current status of the `MLModel`. This element can have one of # the following values: # # * `PENDING` - Amazon Machine Learning (Amazon ML) submitted a # request to describe 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. # @return [String] # # @!attribute [rw] size_in_bytes # Long integer type that is a 64-bit signed number. # @return [Integer] # # @!attribute [rw] endpoint_info # The current endpoint of the `MLModel` # @return [Types::RealtimeEndpointInfo] # # @!attribute [rw] training_parameters # 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. # @return [Hash] # # @!attribute [rw] input_data_location_s3 # The location of the data file or directory in Amazon Simple Storage # Service (Amazon S3). # @return [String] # # @!attribute [rw] ml_model_type # 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?" # @return [String] # # @!attribute [rw] score_threshold # The scoring threshold is used in binary classification `MLModel` # models. It marks the boundary between a positive prediction and a # negative prediction. # # Output values greater than or equal to the threshold receive a # positive result from the MLModel, such as `true`. Output values less # than the threshold receive a negative response from the MLModel, # such as `false`. # @return [Float] # # @!attribute [rw] score_threshold_last_updated_at # The time of the most recent edit to the `ScoreThreshold`. The time # is expressed in epoch time. # @return [Time] # # @!attribute [rw] log_uri # A link to the file that contains logs of the `CreateMLModel` # operation. # @return [String] # # @!attribute [rw] message # A description of the most recent details about accessing the # `MLModel`. # @return [String] # # @!attribute [rw] compute_time # 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. # @return [Integer] # # @!attribute [rw] finished_at # 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. # @return [Time] # # @!attribute [rw] started_at # The epoch time when Amazon Machine Learning marked the `MLModel` as # `INPROGRESS`. `StartedAt` isn't available if the `MLModel` is in # the `PENDING` state. # @return [Time] # # @!attribute [rw] recipe # The recipe to use when training the `MLModel`. The `Recipe` provides # detailed information about the observation data to use during # training, and manipulations to perform on the observation data # during training. # # **Note:** This parameter is provided as part of the verbose format. # @return [String] # # @!attribute [rw] schema # The schema used by all of the data files referenced by the # `DataSource`. # # **Note:** This parameter is provided as part of the verbose format. # @return [String] # class GetMLModelOutput < Struct.new( :ml_model_id, :training_data_source_id, :created_by_iam_user, :created_at, :last_updated_at, :name, :status, :size_in_bytes, :endpoint_info, :training_parameters, :input_data_location_s3, :ml_model_type, :score_threshold, :score_threshold_last_updated_at, :log_uri, :message, :compute_time, :finished_at, :started_at, :recipe, :schema) SENSITIVE = [] include Aws::Structure end # A second request to use or change an object was not allowed. This can # result from retrying a request using a parameter that was not present # in the original request. # # @!attribute [rw] message # @return [String] # # @!attribute [rw] code # @return [Integer] # class IdempotentParameterMismatchException < Struct.new( :message, :code) SENSITIVE = [] include Aws::Structure end # An error on the server occurred when trying to process a request. # # @!attribute [rw] message # @return [String] # # @!attribute [rw] code # @return [Integer] # class InternalServerException < Struct.new( :message, :code) SENSITIVE = [] include Aws::Structure end # An error on the client occurred. Typically, the cause is an invalid # input value. # # @!attribute [rw] message # @return [String] # # @!attribute [rw] code # @return [Integer] # class InvalidInputException < Struct.new( :message, :code) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] message # @return [String] # class InvalidTagException < Struct.new( :message) SENSITIVE = [] include Aws::Structure end # The subscriber exceeded the maximum number of operations. This # exception can occur when listing objects such as `DataSource`. # # @!attribute [rw] message # @return [String] # # @!attribute [rw] code # @return [Integer] # class LimitExceededException < Struct.new( :message, :code) SENSITIVE = [] include Aws::Structure end # Represents the output of a `GetMLModel` operation. # # The content consists of the detailed metadata and the current status # of the `MLModel`. # # @!attribute [rw] ml_model_id # The ID assigned to the `MLModel` at creation. # @return [String] # # @!attribute [rw] training_data_source_id # The ID of the training `DataSource`. The `CreateMLModel` operation # uses the `TrainingDataSourceId`. # @return [String] # # @!attribute [rw] created_by_iam_user # 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. # @return [String] # # @!attribute [rw] created_at # The time that the `MLModel` was created. The time is expressed in # epoch time. # @return [Time] # # @!attribute [rw] last_updated_at # The time of the most recent edit to the `MLModel`. The time is # expressed in epoch time. # @return [Time] # # @!attribute [rw] name # A user-supplied name or description of the `MLModel`. # @return [String] # # @!attribute [rw] status # 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. # @return [String] # # @!attribute [rw] size_in_bytes # Long integer type that is a 64-bit signed number. # @return [Integer] # # @!attribute [rw] endpoint_info # The current endpoint of the `MLModel`. # @return [Types::RealtimeEndpointInfo] # # @!attribute [rw] training_parameters # 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. # @return [Hash] # # @!attribute [rw] input_data_location_s3 # The location of the data file or directory in Amazon Simple Storage # Service (Amazon S3). # @return [String] # # @!attribute [rw] algorithm # 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. # # ^ # @return [String] # # @!attribute [rw] ml_model_type # 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?". # @return [String] # # @!attribute [rw] score_threshold # @return [Float] # # @!attribute [rw] score_threshold_last_updated_at # The time of the most recent edit to the `ScoreThreshold`. The time # is expressed in epoch time. # @return [Time] # # @!attribute [rw] message # A description of the most recent details about accessing the # `MLModel`. # @return [String] # # @!attribute [rw] compute_time # Long integer type that is a 64-bit signed number. # @return [Integer] # # @!attribute [rw] finished_at # A timestamp represented in epoch time. # @return [Time] # # @!attribute [rw] started_at # A timestamp represented in epoch time. # @return [Time] # class MLModel < Struct.new( :ml_model_id, :training_data_source_id, :created_by_iam_user, :created_at, :last_updated_at, :name, :status, :size_in_bytes, :endpoint_info, :training_parameters, :input_data_location_s3, :algorithm, :ml_model_type, :score_threshold, :score_threshold_last_updated_at, :message, :compute_time, :finished_at, :started_at) SENSITIVE = [] include Aws::Structure 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][1]. # # # # [1]: https://docs.aws.amazon.com/machine-learning/latest/dg # # @!attribute [rw] properties # @return [Hash] # class PerformanceMetrics < Struct.new( :properties) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] ml_model_id # A unique identifier of the `MLModel`. # @return [String] # # @!attribute [rw] record # A map of variable name-value pairs that represent an observation. # @return [Hash] # # @!attribute [rw] predict_endpoint # @return [String] # class PredictInput < Struct.new( :ml_model_id, :record, :predict_endpoint) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] prediction # The output from a `Predict` operation: # # * `Details` - Contains the following attributes: # `DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | # MULTICLASS` `DetailsAttributes.ALGORITHM - SGD` # # * `PredictedLabel` - Present for either a `BINARY` or `MULTICLASS` # `MLModel` request. # # * `PredictedScores` - Contains the raw classification score # corresponding to each label. # # * `PredictedValue` - Present for a `REGRESSION` `MLModel` request. # @return [Types::Prediction] # class PredictOutput < Struct.new( :prediction) SENSITIVE = [] include Aws::Structure end # The output from a `Predict` operation: # # * `Details` - Contains the following attributes: # `DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | # MULTICLASS` `DetailsAttributes.ALGORITHM - SGD` # # * `PredictedLabel` - Present for either a `BINARY` or `MULTICLASS` # `MLModel` request. # # * `PredictedScores` - Contains the raw classification score # corresponding to each label. # # * `PredictedValue` - Present for a `REGRESSION` `MLModel` request. # # @!attribute [rw] predicted_label # The prediction label for either a `BINARY` or `MULTICLASS` # `MLModel`. # @return [String] # # @!attribute [rw] predicted_value # The prediction value for `REGRESSION` `MLModel`. # @return [Float] # # @!attribute [rw] predicted_scores # Provides the raw classification score corresponding to each label. # @return [Hash] # # @!attribute [rw] details # Provides any additional details regarding the prediction. # @return [Hash] # class Prediction < Struct.new( :predicted_label, :predicted_value, :predicted_scores, :details) SENSITIVE = [] include Aws::Structure end # The exception is thrown when a predict request is made to an unmounted # `MLModel`. # # @!attribute [rw] message # @return [String] # class PredictorNotMountedException < Struct.new( :message) SENSITIVE = [] include Aws::Structure end # The data specification of an Amazon Relational Database Service # (Amazon RDS) `DataSource`. # # @!attribute [rw] database_information # Describes the `DatabaseName` and `InstanceIdentifier` of an Amazon # RDS database. # @return [Types::RDSDatabase] # # @!attribute [rw] select_sql_query # The query that is used to retrieve the observation data for the # `DataSource`. # @return [String] # # @!attribute [rw] database_credentials # The AWS Identity and Access Management (IAM) credentials that are # used connect to the Amazon RDS database. # @return [Types::RDSDatabaseCredentials] # # @!attribute [rw] s3_staging_location # The Amazon S3 location for staging Amazon RDS data. The data # retrieved from Amazon RDS using `SelectSqlQuery` is stored in this # location. # @return [String] # # @!attribute [rw] data_rearrangement # 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"\}\}` # @return [String] # # @!attribute [rw] data_schema # 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": \[ "F6" \] \\} # @return [String] # # @!attribute [rw] data_schema_uri # The Amazon S3 location of the `DataSchema`. # @return [String] # # @!attribute [rw] resource_role # 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][1] for data pipelines. # # # # [1]: https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html # @return [String] # # @!attribute [rw] service_role # 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][1] for data # pipelines. # # # # [1]: https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html # @return [String] # # @!attribute [rw] subnet_id # 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. # @return [String] # # @!attribute [rw] security_group_ids # 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. # @return [Array] # class RDSDataSpec < Struct.new( :database_information, :select_sql_query, :database_credentials, :s3_staging_location, :data_rearrangement, :data_schema, :data_schema_uri, :resource_role, :service_role, :subnet_id, :security_group_ids) SENSITIVE = [] include Aws::Structure end # The database details of an Amazon RDS database. # # @!attribute [rw] instance_identifier # The ID of an RDS DB instance. # @return [String] # # @!attribute [rw] database_name # The name of a database hosted on an RDS DB instance. # @return [String] # class RDSDatabase < Struct.new( :instance_identifier, :database_name) SENSITIVE = [] include Aws::Structure end # The database credentials to connect to a database on an RDS DB # instance. # # @!attribute [rw] username # 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. # @return [String] # # @!attribute [rw] password # 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. # @return [String] # class RDSDatabaseCredentials < Struct.new( :username, :password) SENSITIVE = [:password] include Aws::Structure end # The datasource details that are specific to Amazon RDS. # # @!attribute [rw] database # The database details required to connect to an Amazon RDS. # @return [Types::RDSDatabase] # # @!attribute [rw] database_user_name # 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. # @return [String] # # @!attribute [rw] select_sql_query # The SQL query that is supplied during CreateDataSourceFromRDS. # Returns only if `Verbose` is true in `GetDataSourceInput`. # @return [String] # # @!attribute [rw] resource_role # 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][1] for data pipelines. # # # # [1]: https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html # @return [String] # # @!attribute [rw] service_role # 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][1] for data # pipelines. # # # # [1]: https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html # @return [String] # # @!attribute [rw] data_pipeline_id # 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. # @return [String] # class RDSMetadata < Struct.new( :database, :database_user_name, :select_sql_query, :resource_role, :service_role, :data_pipeline_id) SENSITIVE = [] include Aws::Structure end # Describes the real-time endpoint information for an `MLModel`. # # @!attribute [rw] peak_requests_per_second # The maximum processing rate for the real-time endpoint for # `MLModel`, measured in incoming requests per second. # @return [Integer] # # @!attribute [rw] created_at # The time that the request to create the real-time endpoint for the # `MLModel` was received. The time is expressed in epoch time. # @return [Time] # # @!attribute [rw] endpoint_url # 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. # @return [String] # # @!attribute [rw] endpoint_status # 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. # @return [String] # class RealtimeEndpointInfo < Struct.new( :peak_requests_per_second, :created_at, :endpoint_url, :endpoint_status) SENSITIVE = [] include Aws::Structure end # Describes the data specification of an Amazon Redshift `DataSource`. # # @!attribute [rw] database_information # Describes the `DatabaseName` and `ClusterIdentifier` for an Amazon # Redshift `DataSource`. # @return [Types::RedshiftDatabase] # # @!attribute [rw] select_sql_query # Describes the SQL Query to execute on an Amazon Redshift database # for an Amazon Redshift `DataSource`. # @return [String] # # @!attribute [rw] database_credentials # Describes AWS Identity and Access Management (IAM) credentials that # are used connect to the Amazon Redshift database. # @return [Types::RedshiftDatabaseCredentials] # # @!attribute [rw] s3_staging_location # Describes an Amazon S3 location to store the result set of the # `SelectSqlQuery` query. # @return [String] # # @!attribute [rw] data_rearrangement # 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"\}\}` # @return [String] # # @!attribute [rw] data_schema # 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": \[ "F6" \] \\} # @return [String] # # @!attribute [rw] data_schema_uri # Describes the schema location for an Amazon Redshift `DataSource`. # @return [String] # class RedshiftDataSpec < Struct.new( :database_information, :select_sql_query, :database_credentials, :s3_staging_location, :data_rearrangement, :data_schema, :data_schema_uri) SENSITIVE = [] include Aws::Structure end # Describes the database details required to connect to an Amazon # Redshift database. # # @!attribute [rw] database_name # The name of a database hosted on an Amazon Redshift cluster. # @return [String] # # @!attribute [rw] cluster_identifier # The ID of an Amazon Redshift cluster. # @return [String] # class RedshiftDatabase < Struct.new( :database_name, :cluster_identifier) SENSITIVE = [] include Aws::Structure end # Describes the database credentials for connecting to a database on an # Amazon Redshift cluster. # # @!attribute [rw] username # 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][1]. # # # # [1]: https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html # @return [String] # # @!attribute [rw] password # 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][1]. # # # # [1]: https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html # @return [String] # class RedshiftDatabaseCredentials < Struct.new( :username, :password) SENSITIVE = [:password] include Aws::Structure end # Describes the `DataSource` details specific to Amazon Redshift. # # @!attribute [rw] redshift_database # Describes the database details required to connect to an Amazon # Redshift database. # @return [Types::RedshiftDatabase] # # @!attribute [rw] database_user_name # 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][1]. # # # # [1]: https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html # @return [String] # # @!attribute [rw] select_sql_query # The SQL query that is specified during CreateDataSourceFromRedshift. # Returns only if `Verbose` is true in GetDataSourceInput. # @return [String] # class RedshiftMetadata < Struct.new( :redshift_database, :database_user_name, :select_sql_query) SENSITIVE = [] include Aws::Structure end # A specified resource cannot be located. # # @!attribute [rw] message # @return [String] # # @!attribute [rw] code # @return [Integer] # class ResourceNotFoundException < Struct.new( :message, :code) SENSITIVE = [] include Aws::Structure end # Describes the data specification of a `DataSource`. # # @!attribute [rw] data_location_s3 # 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. # @return [String] # # @!attribute [rw] data_rearrangement # 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"\}\}` # @return [String] # # @!attribute [rw] data_schema # 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": \[ "F6" \] \\} # @return [String] # # @!attribute [rw] data_schema_location_s3 # Describes the schema location in Amazon S3. You must provide either # the `DataSchema` or the `DataSchemaLocationS3`. # @return [String] # class S3DataSpec < Struct.new( :data_location_s3, :data_rearrangement, :data_schema, :data_schema_location_s3) SENSITIVE = [] include Aws::Structure end # A custom key-value pair associated with an ML object, such as an ML # model. # # @!attribute [rw] key # A unique identifier for the tag. Valid characters include Unicode # letters, digits, white space, \_, ., /, =, +, -, %, and @. # @return [String] # # @!attribute [rw] value # An optional string, typically used to describe or define the tag. # Valid characters include Unicode letters, digits, white space, \_, # ., /, =, +, -, %, and @. # @return [String] # class Tag < Struct.new( :key, :value) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] message # @return [String] # class TagLimitExceededException < Struct.new( :message) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] batch_prediction_id # The ID assigned to the `BatchPrediction` during creation. # @return [String] # # @!attribute [rw] batch_prediction_name # A new user-supplied name or description of the `BatchPrediction`. # @return [String] # class UpdateBatchPredictionInput < Struct.new( :batch_prediction_id, :batch_prediction_name) SENSITIVE = [] include Aws::Structure end # Represents the output of an `UpdateBatchPrediction` operation. # # You can see the updated content by using the `GetBatchPrediction` # operation. # # @!attribute [rw] batch_prediction_id # The ID assigned to the `BatchPrediction` during creation. This value # should be identical to the value of the `BatchPredictionId` in the # request. # @return [String] # class UpdateBatchPredictionOutput < Struct.new( :batch_prediction_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] data_source_id # The ID assigned to the `DataSource` during creation. # @return [String] # # @!attribute [rw] data_source_name # A new user-supplied name or description of the `DataSource` that # will replace the current description. # @return [String] # class UpdateDataSourceInput < Struct.new( :data_source_id, :data_source_name) SENSITIVE = [] include Aws::Structure end # Represents the output of an `UpdateDataSource` operation. # # You can see the updated content by using the `GetBatchPrediction` # operation. # # @!attribute [rw] data_source_id # The ID assigned to the `DataSource` during creation. This value # should be identical to the value of the `DataSourceID` in the # request. # @return [String] # class UpdateDataSourceOutput < Struct.new( :data_source_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] evaluation_id # The ID assigned to the `Evaluation` during creation. # @return [String] # # @!attribute [rw] evaluation_name # A new user-supplied name or description of the `Evaluation` that # will replace the current content. # @return [String] # class UpdateEvaluationInput < Struct.new( :evaluation_id, :evaluation_name) SENSITIVE = [] include Aws::Structure end # Represents the output of an `UpdateEvaluation` operation. # # You can see the updated content by using the `GetEvaluation` # operation. # # @!attribute [rw] evaluation_id # The ID assigned to the `Evaluation` during creation. This value # should be identical to the value of the `Evaluation` in the request. # @return [String] # class UpdateEvaluationOutput < Struct.new( :evaluation_id) SENSITIVE = [] include Aws::Structure end # @!attribute [rw] ml_model_id # The ID assigned to the `MLModel` during creation. # @return [String] # # @!attribute [rw] ml_model_name # A user-supplied name or description of the `MLModel`. # @return [String] # # @!attribute [rw] score_threshold # 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`. # @return [Float] # class UpdateMLModelInput < Struct.new( :ml_model_id, :ml_model_name, :score_threshold) SENSITIVE = [] include Aws::Structure end # Represents the output of an `UpdateMLModel` operation. # # You can see the updated content by using the `GetMLModel` operation. # # @!attribute [rw] ml_model_id # The ID assigned to the `MLModel` during creation. This value should # be identical to the value of the `MLModelID` in the request. # @return [String] # class UpdateMLModelOutput < Struct.new( :ml_model_id) SENSITIVE = [] include Aws::Structure end end end