# WARNING ABOUT GENERATED CODE # # This file is generated. See the contributing guide for more information: # https://github.com/aws/aws-sdk-ruby/blob/master/CONTRIBUTING.md # # WARNING ABOUT GENERATED CODE require 'seahorse/client/plugins/content_length.rb' require 'aws-sdk-core/plugins/credentials_configuration.rb' require 'aws-sdk-core/plugins/logging.rb' require 'aws-sdk-core/plugins/param_converter.rb' require 'aws-sdk-core/plugins/param_validator.rb' require 'aws-sdk-core/plugins/user_agent.rb' require 'aws-sdk-core/plugins/helpful_socket_errors.rb' require 'aws-sdk-core/plugins/retry_errors.rb' require 'aws-sdk-core/plugins/global_configuration.rb' require 'aws-sdk-core/plugins/regional_endpoint.rb' require 'aws-sdk-core/plugins/endpoint_discovery.rb' require 'aws-sdk-core/plugins/endpoint_pattern.rb' require 'aws-sdk-core/plugins/response_paging.rb' require 'aws-sdk-core/plugins/stub_responses.rb' require 'aws-sdk-core/plugins/idempotency_token.rb' require 'aws-sdk-core/plugins/jsonvalue_converter.rb' require 'aws-sdk-core/plugins/client_metrics_plugin.rb' require 'aws-sdk-core/plugins/client_metrics_send_plugin.rb' require 'aws-sdk-core/plugins/transfer_encoding.rb' require 'aws-sdk-core/plugins/signature_v4.rb' require 'aws-sdk-core/plugins/protocols/json_rpc.rb' require 'aws-sdk-machinelearning/plugins/predict_endpoint.rb' Aws::Plugins::GlobalConfiguration.add_identifier(:machinelearning) module Aws::MachineLearning class Client < Seahorse::Client::Base include Aws::ClientStubs @identifier = :machinelearning set_api(ClientApi::API) add_plugin(Seahorse::Client::Plugins::ContentLength) add_plugin(Aws::Plugins::CredentialsConfiguration) add_plugin(Aws::Plugins::Logging) add_plugin(Aws::Plugins::ParamConverter) add_plugin(Aws::Plugins::ParamValidator) add_plugin(Aws::Plugins::UserAgent) add_plugin(Aws::Plugins::HelpfulSocketErrors) add_plugin(Aws::Plugins::RetryErrors) add_plugin(Aws::Plugins::GlobalConfiguration) add_plugin(Aws::Plugins::RegionalEndpoint) add_plugin(Aws::Plugins::EndpointDiscovery) add_plugin(Aws::Plugins::EndpointPattern) add_plugin(Aws::Plugins::ResponsePaging) add_plugin(Aws::Plugins::StubResponses) add_plugin(Aws::Plugins::IdempotencyToken) add_plugin(Aws::Plugins::JsonvalueConverter) add_plugin(Aws::Plugins::ClientMetricsPlugin) add_plugin(Aws::Plugins::ClientMetricsSendPlugin) add_plugin(Aws::Plugins::TransferEncoding) add_plugin(Aws::Plugins::SignatureV4) add_plugin(Aws::Plugins::Protocols::JsonRpc) add_plugin(Aws::MachineLearning::Plugins::PredictEndpoint) # @overload initialize(options) # @param [Hash] options # @option options [required, Aws::CredentialProvider] :credentials # Your AWS credentials. This can be an instance of any one of the # following classes: # # * `Aws::Credentials` - Used for configuring static, non-refreshing # credentials. # # * `Aws::InstanceProfileCredentials` - Used for loading credentials # from an EC2 IMDS on an EC2 instance. # # * `Aws::SharedCredentials` - Used for loading credentials from a # shared file, such as `~/.aws/config`. # # * `Aws::AssumeRoleCredentials` - Used when you need to assume a role. # # When `:credentials` are not configured directly, the following # locations will be searched for credentials: # # * `Aws.config[:credentials]` # * The `:access_key_id`, `:secret_access_key`, and `:session_token` options. # * ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY'] # * `~/.aws/credentials` # * `~/.aws/config` # * EC2 IMDS instance profile - When used by default, the timeouts are # very aggressive. Construct and pass an instance of # `Aws::InstanceProfileCredentails` to enable retries and extended # timeouts. # # @option options [required, String] :region # The AWS region to connect to. The configured `:region` is # used to determine the service `:endpoint`. When not passed, # a default `:region` is search for in the following locations: # # * `Aws.config[:region]` # * `ENV['AWS_REGION']` # * `ENV['AMAZON_REGION']` # * `ENV['AWS_DEFAULT_REGION']` # * `~/.aws/credentials` # * `~/.aws/config` # # @option options [String] :access_key_id # # @option options [Boolean] :active_endpoint_cache (false) # When set to `true`, a thread polling for endpoints will be running in # the background every 60 secs (default). Defaults to `false`. # # @option options [Boolean] :client_side_monitoring (false) # When `true`, client-side metrics will be collected for all API requests from # this client. # # @option options [String] :client_side_monitoring_client_id ("") # Allows you to provide an identifier for this client which will be attached to # all generated client side metrics. Defaults to an empty string. # # @option options [String] :client_side_monitoring_host ("127.0.0.1") # Allows you to specify the DNS hostname or IPv4 or IPv6 address that the client # side monitoring agent is running on, where client metrics will be published via UDP. # # @option options [Integer] :client_side_monitoring_port (31000) # Required for publishing client metrics. The port that the client side monitoring # agent is running on, where client metrics will be published via UDP. # # @option options [Aws::ClientSideMonitoring::Publisher] :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) # Allows you to provide a custom client-side monitoring publisher class. By default, # will use the Client Side Monitoring Agent Publisher. # # @option options [Boolean] :convert_params (true) # When `true`, an attempt is made to coerce request parameters into # the required types. # # @option options [Boolean] :disable_host_prefix_injection (false) # Set to true to disable SDK automatically adding host prefix # to default service endpoint when available. # # @option options [String] :endpoint # The client endpoint is normally constructed from the `:region` # option. You should only configure an `:endpoint` when connecting # to test endpoints. This should be avalid HTTP(S) URI. # # @option options [Integer] :endpoint_cache_max_entries (1000) # Used for the maximum size limit of the LRU cache storing endpoints data # for endpoint discovery enabled operations. Defaults to 1000. # # @option options [Integer] :endpoint_cache_max_threads (10) # Used for the maximum threads in use for polling endpoints to be cached, defaults to 10. # # @option options [Integer] :endpoint_cache_poll_interval (60) # When :endpoint_discovery and :active_endpoint_cache is enabled, # Use this option to config the time interval in seconds for making # requests fetching endpoints information. Defaults to 60 sec. # # @option options [Boolean] :endpoint_discovery (false) # When set to `true`, endpoint discovery will be enabled for operations when available. Defaults to `false`. # # @option options [Aws::Log::Formatter] :log_formatter (Aws::Log::Formatter.default) # The log formatter. # # @option options [Symbol] :log_level (:info) # The log level to send messages to the `:logger` at. # # @option options [Logger] :logger # The Logger instance to send log messages to. If this option # is not set, logging will be disabled. # # @option options [String] :profile ("default") # Used when loading credentials from the shared credentials file # at HOME/.aws/credentials. When not specified, 'default' is used. # # @option options [Float] :retry_base_delay (0.3) # The base delay in seconds used by the default backoff function. # # @option options [Symbol] :retry_jitter (:none) # A delay randomiser function used by the default backoff function. Some predefined functions can be referenced by name - :none, :equal, :full, otherwise a Proc that takes and returns a number. # # @see https://www.awsarchitectureblog.com/2015/03/backoff.html # # @option options [Integer] :retry_limit (3) # The maximum number of times to retry failed requests. Only # ~ 500 level server errors and certain ~ 400 level client errors # are retried. Generally, these are throttling errors, data # checksum errors, networking errors, timeout errors and auth # errors from expired credentials. # # @option options [Integer] :retry_max_delay (0) # The maximum number of seconds to delay between retries (0 for no limit) used by the default backoff function. # # @option options [String] :secret_access_key # # @option options [String] :session_token # # @option options [Boolean] :simple_json (false) # Disables request parameter conversion, validation, and formatting. # Also disable response data type conversions. This option is useful # when you want to ensure the highest level of performance by # avoiding overhead of walking request parameters and response data # structures. # # When `:simple_json` is enabled, the request parameters hash must # be formatted exactly as the DynamoDB API expects. # # @option options [Boolean] :stub_responses (false) # Causes the client to return stubbed responses. By default # fake responses are generated and returned. You can specify # the response data to return or errors to raise by calling # {ClientStubs#stub_responses}. See {ClientStubs} for more information. # # ** Please note ** When response stubbing is enabled, no HTTP # requests are made, and retries are disabled. # # @option options [Boolean] :validate_params (true) # When `true`, request parameters are validated before # sending the request. # # @option options [URI::HTTP,String] :http_proxy A proxy to send # requests through. Formatted like 'http://proxy.com:123'. # # @option options [Float] :http_open_timeout (15) The number of # seconds to wait when opening a HTTP session before rasing a # `Timeout::Error`. # # @option options [Integer] :http_read_timeout (60) The default # number of seconds to wait for response data. This value can # safely be set # per-request on the session yeidled by {#session_for}. # # @option options [Float] :http_idle_timeout (5) The number of # seconds a connection is allowed to sit idble before it is # considered stale. Stale connections are closed and removed # from the pool before making a request. # # @option options [Float] :http_continue_timeout (1) The number of # seconds to wait for a 100-continue response before sending the # request body. This option has no effect unless the request has # "Expect" header set to "100-continue". Defaults to `nil` which # disables this behaviour. This value can safely be set per # request on the session yeidled by {#session_for}. # # @option options [Boolean] :http_wire_trace (false) When `true`, # HTTP debug output will be sent to the `:logger`. # # @option options [Boolean] :ssl_verify_peer (true) When `true`, # SSL peer certificates are verified when establishing a # connection. # # @option options [String] :ssl_ca_bundle Full path to the SSL # certificate authority bundle file that should be used when # verifying peer certificates. If you do not pass # `:ssl_ca_bundle` or `:ssl_ca_directory` the the system default # will be used if available. # # @option options [String] :ssl_ca_directory Full path of the # directory that contains the unbundled SSL certificate # authority files for verifying peer certificates. If you do # not pass `:ssl_ca_bundle` or `:ssl_ca_directory` the the # system default will be used if available. # def initialize(*args) super end # @!group API Operations # Adds one or more tags to an object, up to a limit of 10. Each tag # consists of a key and an optional value. If you add a tag using a key # that is already associated with the ML object, `AddTags` updates the # tag's value. # # @option params [required, Array] :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. # # @option params [required, String] :resource_id # The ID of the ML object to tag. For example, `exampleModelId`. # # @option params [required, String] :resource_type # The type of the ML object to tag. # # @return [Types::AddTagsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::AddTagsOutput#resource_id #resource_id} => String # * {Types::AddTagsOutput#resource_type #resource_type} => String # # @example Request syntax with placeholder values # # resp = client.add_tags({ # tags: [ # required # { # key: "TagKey", # value: "TagValue", # }, # ], # resource_id: "EntityId", # required # resource_type: "BatchPrediction", # required, accepts BatchPrediction, DataSource, Evaluation, MLModel # }) # # @example Response structure # # resp.resource_id #=> String # resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel" # # @overload add_tags(params = {}) # @param [Hash] params ({}) def add_tags(params = {}, options = {}) req = build_request(:add_tags, params) req.send_request(options) end # Generates predictions for a group of observations. The observations to # process exist in one or more data files referenced by a `DataSource`. # This operation creates a new `BatchPrediction`, and uses an `MLModel` # and the data files referenced by the `DataSource` as information # sources. # # `CreateBatchPrediction` is an asynchronous operation. In response to # `CreateBatchPrediction`, Amazon Machine Learning (Amazon ML) # immediately returns and sets the `BatchPrediction` status to # `PENDING`. After the `BatchPrediction` completes, Amazon ML sets the # status to `COMPLETED`. # # You can poll for status updates by using the GetBatchPrediction # operation and checking the `Status` parameter of the result. After the # `COMPLETED` status appears, the results are available in the location # specified by the `OutputUri` parameter. # # @option params [required, String] :batch_prediction_id # A user-supplied ID that uniquely identifies the `BatchPrediction`. # # @option params [String] :batch_prediction_name # A user-supplied name or description of the `BatchPrediction`. # `BatchPredictionName` can only use the UTF-8 character set. # # @option params [required, String] :ml_model_id # The ID of the `MLModel` that will generate predictions for the group # of observations. # # @option params [required, String] :batch_prediction_data_source_id # The ID of the `DataSource` that points to the group of observations to # predict. # # @option params [required, String] :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]: http://docs.aws.amazon.com/machine-learning/latest/dg # # @return [Types::CreateBatchPredictionOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::CreateBatchPredictionOutput#batch_prediction_id #batch_prediction_id} => String # # @example Request syntax with placeholder values # # resp = client.create_batch_prediction({ # batch_prediction_id: "EntityId", # required # batch_prediction_name: "EntityName", # ml_model_id: "EntityId", # required # batch_prediction_data_source_id: "EntityId", # required # output_uri: "S3Url", # required # }) # # @example Response structure # # resp.batch_prediction_id #=> String # # @overload create_batch_prediction(params = {}) # @param [Hash] params ({}) def create_batch_prediction(params = {}, options = {}) req = build_request(:create_batch_prediction, params) req.send_request(options) end # Creates a `DataSource` object from an [ Amazon Relational Database # Service][1] (Amazon RDS). A `DataSource` references data that can be # used to perform `CreateMLModel`, `CreateEvaluation`, or # `CreateBatchPrediction` operations. # # `CreateDataSourceFromRDS` is an asynchronous operation. In response to # `CreateDataSourceFromRDS`, Amazon Machine Learning (Amazon ML) # immediately returns and sets the `DataSource` status to `PENDING`. # After the `DataSource` is created and ready for use, Amazon ML sets # the `Status` parameter to `COMPLETED`. `DataSource` in the `COMPLETED` # or `PENDING` state can be used only to perform `>CreateMLModel`>, # `CreateEvaluation`, or `CreateBatchPrediction` operations. # # If Amazon ML cannot accept the input source, it sets the `Status` # parameter to `FAILED` and includes an error message in the `Message` # attribute of the `GetDataSource` operation response. # # # # [1]: http://aws.amazon.com/rds/ # # @option params [required, String] :data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. # Typically, an Amazon Resource Number (ARN) becomes the ID for a # `DataSource`. # # @option params [String] :data_source_name # A user-supplied name or description of the `DataSource`. # # @option params [required, Types::RDSDataSpec] :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]: http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html # # @option params [required, String] :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. # # @option params [Boolean] :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 [Types::CreateDataSourceFromRDSOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::CreateDataSourceFromRDSOutput#data_source_id #data_source_id} => String # # @example Request syntax with placeholder values # # resp = client.create_data_source_from_rds({ # data_source_id: "EntityId", # required # data_source_name: "EntityName", # rds_data: { # required # database_information: { # required # instance_identifier: "RDSInstanceIdentifier", # required # database_name: "RDSDatabaseName", # required # }, # select_sql_query: "RDSSelectSqlQuery", # required # database_credentials: { # required # username: "RDSDatabaseUsername", # required # password: "RDSDatabasePassword", # required # }, # s3_staging_location: "S3Url", # required # data_rearrangement: "DataRearrangement", # data_schema: "DataSchema", # data_schema_uri: "S3Url", # resource_role: "EDPResourceRole", # required # service_role: "EDPServiceRole", # required # subnet_id: "EDPSubnetId", # required # security_group_ids: ["EDPSecurityGroupId"], # required # }, # role_arn: "RoleARN", # required # compute_statistics: false, # }) # # @example Response structure # # resp.data_source_id #=> String # # @overload create_data_source_from_rds(params = {}) # @param [Hash] params ({}) def create_data_source_from_rds(params = {}, options = {}) req = build_request(:create_data_source_from_rds, params) req.send_request(options) end # Creates a `DataSource` from a database hosted on an Amazon Redshift # cluster. A `DataSource` references data that can be used to perform # either `CreateMLModel`, `CreateEvaluation`, or `CreateBatchPrediction` # operations. # # `CreateDataSourceFromRedshift` is an asynchronous operation. In # response to `CreateDataSourceFromRedshift`, Amazon Machine Learning # (Amazon ML) immediately returns and sets the `DataSource` status to # `PENDING`. After the `DataSource` is created and ready for use, Amazon # ML sets the `Status` parameter to `COMPLETED`. `DataSource` in # `COMPLETED` or `PENDING` states can be used to perform only # `CreateMLModel`, `CreateEvaluation`, or `CreateBatchPrediction` # operations. # # If Amazon ML can't accept the input source, it sets the `Status` # parameter to `FAILED` and includes an error message in the `Message` # attribute of the `GetDataSource` operation response. # # The observations should be contained in the database hosted on an # Amazon Redshift cluster and should be specified by a `SelectSqlQuery` # query. Amazon ML executes an `Unload` command in Amazon Redshift to # transfer the result set of the `SelectSqlQuery` query to # `S3StagingLocation`. # # After the `DataSource` has been created, it's ready for use in # evaluations and batch predictions. If you plan to use the `DataSource` # to train an `MLModel`, the `DataSource` also requires a recipe. A # recipe describes how each input variable will be used in training an # `MLModel`. Will the variable be included or excluded from training? # Will the variable be manipulated; for example, will it be combined # with another variable or will it be split apart into word # combinations? The recipe provides answers to these questions. # # You can't change an existing datasource, but you can copy and modify # the settings from an existing Amazon Redshift datasource to create a # new datasource. To do so, call `GetDataSource` for an existing # datasource and copy the values to a `CreateDataSource` call. Change # the settings that you want to change and make sure that all required # fields have the appropriate values. # # # # @option params [required, String] :data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. # # @option params [String] :data_source_name # A user-supplied name or description of the `DataSource`. # # @option params [required, Types::RedshiftDataSpec] :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\}\}"` # # @option params [required, String] :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` # # @option params [Boolean] :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 [Types::CreateDataSourceFromRedshiftOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::CreateDataSourceFromRedshiftOutput#data_source_id #data_source_id} => String # # @example Request syntax with placeholder values # # resp = client.create_data_source_from_redshift({ # data_source_id: "EntityId", # required # data_source_name: "EntityName", # data_spec: { # required # database_information: { # required # database_name: "RedshiftDatabaseName", # required # cluster_identifier: "RedshiftClusterIdentifier", # required # }, # select_sql_query: "RedshiftSelectSqlQuery", # required # database_credentials: { # required # username: "RedshiftDatabaseUsername", # required # password: "RedshiftDatabasePassword", # required # }, # s3_staging_location: "S3Url", # required # data_rearrangement: "DataRearrangement", # data_schema: "DataSchema", # data_schema_uri: "S3Url", # }, # role_arn: "RoleARN", # required # compute_statistics: false, # }) # # @example Response structure # # resp.data_source_id #=> String # # @overload create_data_source_from_redshift(params = {}) # @param [Hash] params ({}) def create_data_source_from_redshift(params = {}, options = {}) req = build_request(:create_data_source_from_redshift, params) req.send_request(options) end # Creates a `DataSource` object. A `DataSource` references data that can # be used to perform `CreateMLModel`, `CreateEvaluation`, or # `CreateBatchPrediction` operations. # # `CreateDataSourceFromS3` is an asynchronous operation. In response to # `CreateDataSourceFromS3`, Amazon Machine Learning (Amazon ML) # immediately returns and sets the `DataSource` status to `PENDING`. # After the `DataSource` has been created and is ready for use, Amazon # ML sets the `Status` parameter to `COMPLETED`. `DataSource` in the # `COMPLETED` or `PENDING` state can be used to perform only # `CreateMLModel`, `CreateEvaluation` or `CreateBatchPrediction` # operations. # # If Amazon ML can't accept the input source, it sets the `Status` # parameter to `FAILED` and includes an error message in the `Message` # attribute of the `GetDataSource` operation response. # # The observation data used in a `DataSource` should be ready to use; # that is, it should have a consistent structure, and missing data # values should be kept to a minimum. The observation data must reside # in one or more .csv files in an Amazon Simple Storage Service (Amazon # S3) location, along with a schema that describes the data items by # name and type. The same schema must be used for all of the data files # referenced by the `DataSource`. # # After the `DataSource` has been created, it's ready to use in # evaluations and batch predictions. If you plan to use the `DataSource` # to train an `MLModel`, the `DataSource` also needs a recipe. A recipe # describes how each input variable will be used in training an # `MLModel`. Will the variable be included or excluded from training? # Will the variable be manipulated; for example, will it be combined # with another variable or will it be split apart into word # combinations? The recipe provides answers to these questions. # # @option params [required, String] :data_source_id # A user-supplied identifier that uniquely identifies the `DataSource`. # # @option params [String] :data_source_name # A user-supplied name or description of the `DataSource`. # # @option params [required, Types::S3DataSpec] :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\}\}"` # # @option params [Boolean] :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 [Types::CreateDataSourceFromS3Output] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::CreateDataSourceFromS3Output#data_source_id #data_source_id} => String # # @example Request syntax with placeholder values # # resp = client.create_data_source_from_s3({ # data_source_id: "EntityId", # required # data_source_name: "EntityName", # data_spec: { # required # data_location_s3: "S3Url", # required # data_rearrangement: "DataRearrangement", # data_schema: "DataSchema", # data_schema_location_s3: "S3Url", # }, # compute_statistics: false, # }) # # @example Response structure # # resp.data_source_id #=> String # # @overload create_data_source_from_s3(params = {}) # @param [Hash] params ({}) def create_data_source_from_s3(params = {}, options = {}) req = build_request(:create_data_source_from_s3, params) req.send_request(options) end # Creates a new `Evaluation` of an `MLModel`. An `MLModel` is evaluated # on a set of observations associated to a `DataSource`. Like a # `DataSource` for an `MLModel`, the `DataSource` for an `Evaluation` # contains values for the `Target Variable`. The `Evaluation` compares # the predicted result for each observation to the actual outcome and # provides a summary so that you know how effective the `MLModel` # functions on the test data. Evaluation generates a relevant # performance metric, such as BinaryAUC, RegressionRMSE or # MulticlassAvgFScore based on the corresponding `MLModelType`\: # `BINARY`, `REGRESSION` or `MULTICLASS`. # # `CreateEvaluation` is an asynchronous operation. In response to # `CreateEvaluation`, Amazon Machine Learning (Amazon ML) immediately # returns and sets the evaluation status to `PENDING`. After the # `Evaluation` is created and ready for use, Amazon ML sets the status # to `COMPLETED`. # # You can use the `GetEvaluation` operation to check progress of the # evaluation during the creation operation. # # @option params [required, String] :evaluation_id # A user-supplied ID that uniquely identifies the `Evaluation`. # # @option params [String] :evaluation_name # A user-supplied name or description of the `Evaluation`. # # @option params [required, String] :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`. # # @option params [required, String] :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 [Types::CreateEvaluationOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::CreateEvaluationOutput#evaluation_id #evaluation_id} => String # # @example Request syntax with placeholder values # # resp = client.create_evaluation({ # evaluation_id: "EntityId", # required # evaluation_name: "EntityName", # ml_model_id: "EntityId", # required # evaluation_data_source_id: "EntityId", # required # }) # # @example Response structure # # resp.evaluation_id #=> String # # @overload create_evaluation(params = {}) # @param [Hash] params ({}) def create_evaluation(params = {}, options = {}) req = build_request(:create_evaluation, params) req.send_request(options) end # Creates a new `MLModel` using the `DataSource` and the recipe as # information sources. # # An `MLModel` is nearly immutable. Users can update only the # `MLModelName` and the `ScoreThreshold` in an `MLModel` without # creating a new `MLModel`. # # `CreateMLModel` is an asynchronous operation. In response to # `CreateMLModel`, Amazon Machine Learning (Amazon ML) immediately # returns and sets the `MLModel` status to `PENDING`. After the # `MLModel` has been created and ready is for use, Amazon ML sets the # status to `COMPLETED`. # # You can use the `GetMLModel` operation to check the progress of the # `MLModel` during the creation operation. # # `CreateMLModel` requires a `DataSource` with computed statistics, # which can be created by setting `ComputeStatistics` to `true` in # `CreateDataSourceFromRDS`, `CreateDataSourceFromS3`, or # `CreateDataSourceFromRedshift` operations. # # @option params [required, String] :ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. # # @option params [String] :ml_model_name # A user-supplied name or description of the `MLModel`. # # @option params [required, String] :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]: http://docs.aws.amazon.com/machine-learning/latest/dg # # @option params [Hash] :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. # # @option params [required, String] :training_data_source_id # The `DataSource` that points to the training data. # # @option params [String] :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. # # @option params [String] :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 [Types::CreateMLModelOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::CreateMLModelOutput#ml_model_id #ml_model_id} => String # # @example Request syntax with placeholder values # # resp = client.create_ml_model({ # ml_model_id: "EntityId", # required # ml_model_name: "EntityName", # ml_model_type: "REGRESSION", # required, accepts REGRESSION, BINARY, MULTICLASS # parameters: { # "StringType" => "StringType", # }, # training_data_source_id: "EntityId", # required # recipe: "Recipe", # recipe_uri: "S3Url", # }) # # @example Response structure # # resp.ml_model_id #=> String # # @overload create_ml_model(params = {}) # @param [Hash] params ({}) def create_ml_model(params = {}, options = {}) req = build_request(:create_ml_model, params) req.send_request(options) end # Creates a real-time endpoint for the `MLModel`. The endpoint contains # the URI of the `MLModel`; that is, the location to send real-time # prediction requests for the specified `MLModel`. # # @option params [required, String] :ml_model_id # The ID assigned to the `MLModel` during creation. # # @return [Types::CreateRealtimeEndpointOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::CreateRealtimeEndpointOutput#ml_model_id #ml_model_id} => String # * {Types::CreateRealtimeEndpointOutput#realtime_endpoint_info #realtime_endpoint_info} => Types::RealtimeEndpointInfo # # @example Request syntax with placeholder values # # resp = client.create_realtime_endpoint({ # ml_model_id: "EntityId", # required # }) # # @example Response structure # # resp.ml_model_id #=> String # resp.realtime_endpoint_info.peak_requests_per_second #=> Integer # resp.realtime_endpoint_info.created_at #=> Time # resp.realtime_endpoint_info.endpoint_url #=> String # resp.realtime_endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED" # # @overload create_realtime_endpoint(params = {}) # @param [Hash] params ({}) def create_realtime_endpoint(params = {}, options = {}) req = build_request(:create_realtime_endpoint, params) req.send_request(options) end # Assigns the DELETED status to a `BatchPrediction`, rendering it # unusable. # # After using the `DeleteBatchPrediction` operation, you can use the # GetBatchPrediction operation to verify that the status of the # `BatchPrediction` changed to DELETED. # # **Caution:** The result of the `DeleteBatchPrediction` operation is # irreversible. # # @option params [required, String] :batch_prediction_id # A user-supplied ID that uniquely identifies the `BatchPrediction`. # # @return [Types::DeleteBatchPredictionOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DeleteBatchPredictionOutput#batch_prediction_id #batch_prediction_id} => String # # @example Request syntax with placeholder values # # resp = client.delete_batch_prediction({ # batch_prediction_id: "EntityId", # required # }) # # @example Response structure # # resp.batch_prediction_id #=> String # # @overload delete_batch_prediction(params = {}) # @param [Hash] params ({}) def delete_batch_prediction(params = {}, options = {}) req = build_request(:delete_batch_prediction, params) req.send_request(options) end # Assigns the DELETED status to a `DataSource`, rendering it unusable. # # After using the `DeleteDataSource` operation, you can use the # GetDataSource operation to verify that the status of the `DataSource` # changed to DELETED. # # **Caution:** The results of the `DeleteDataSource` operation are # irreversible. # # @option params [required, String] :data_source_id # A user-supplied ID that uniquely identifies the `DataSource`. # # @return [Types::DeleteDataSourceOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DeleteDataSourceOutput#data_source_id #data_source_id} => String # # @example Request syntax with placeholder values # # resp = client.delete_data_source({ # data_source_id: "EntityId", # required # }) # # @example Response structure # # resp.data_source_id #=> String # # @overload delete_data_source(params = {}) # @param [Hash] params ({}) def delete_data_source(params = {}, options = {}) req = build_request(:delete_data_source, params) req.send_request(options) end # Assigns the `DELETED` status to an `Evaluation`, rendering it # unusable. # # After invoking the `DeleteEvaluation` operation, you can use the # `GetEvaluation` operation to verify that the status of the # `Evaluation` changed to `DELETED`. # # Caution The results of the `DeleteEvaluation` operation are irreversible. # # # # @option params [required, String] :evaluation_id # A user-supplied ID that uniquely identifies the `Evaluation` to # delete. # # @return [Types::DeleteEvaluationOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DeleteEvaluationOutput#evaluation_id #evaluation_id} => String # # @example Request syntax with placeholder values # # resp = client.delete_evaluation({ # evaluation_id: "EntityId", # required # }) # # @example Response structure # # resp.evaluation_id #=> String # # @overload delete_evaluation(params = {}) # @param [Hash] params ({}) def delete_evaluation(params = {}, options = {}) req = build_request(:delete_evaluation, params) req.send_request(options) end # Assigns the `DELETED` status to an `MLModel`, rendering it unusable. # # After using the `DeleteMLModel` operation, you can use the # `GetMLModel` operation to verify that the status of the `MLModel` # changed to DELETED. # # **Caution:** The result of the `DeleteMLModel` operation is # irreversible. # # @option params [required, String] :ml_model_id # A user-supplied ID that uniquely identifies the `MLModel`. # # @return [Types::DeleteMLModelOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DeleteMLModelOutput#ml_model_id #ml_model_id} => String # # @example Request syntax with placeholder values # # resp = client.delete_ml_model({ # ml_model_id: "EntityId", # required # }) # # @example Response structure # # resp.ml_model_id #=> String # # @overload delete_ml_model(params = {}) # @param [Hash] params ({}) def delete_ml_model(params = {}, options = {}) req = build_request(:delete_ml_model, params) req.send_request(options) end # Deletes a real time endpoint of an `MLModel`. # # @option params [required, String] :ml_model_id # The ID assigned to the `MLModel` during creation. # # @return [Types::DeleteRealtimeEndpointOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DeleteRealtimeEndpointOutput#ml_model_id #ml_model_id} => String # * {Types::DeleteRealtimeEndpointOutput#realtime_endpoint_info #realtime_endpoint_info} => Types::RealtimeEndpointInfo # # @example Request syntax with placeholder values # # resp = client.delete_realtime_endpoint({ # ml_model_id: "EntityId", # required # }) # # @example Response structure # # resp.ml_model_id #=> String # resp.realtime_endpoint_info.peak_requests_per_second #=> Integer # resp.realtime_endpoint_info.created_at #=> Time # resp.realtime_endpoint_info.endpoint_url #=> String # resp.realtime_endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED" # # @overload delete_realtime_endpoint(params = {}) # @param [Hash] params ({}) def delete_realtime_endpoint(params = {}, options = {}) req = build_request(:delete_realtime_endpoint, params) req.send_request(options) end # Deletes the specified tags associated with an ML object. After this # operation is complete, you can't recover deleted tags. # # If you specify a tag that doesn't exist, Amazon ML ignores it. # # @option params [required, Array] :tag_keys # One or more tags to delete. # # @option params [required, String] :resource_id # The ID of the tagged ML object. For example, `exampleModelId`. # # @option params [required, String] :resource_type # The type of the tagged ML object. # # @return [Types::DeleteTagsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DeleteTagsOutput#resource_id #resource_id} => String # * {Types::DeleteTagsOutput#resource_type #resource_type} => String # # @example Request syntax with placeholder values # # resp = client.delete_tags({ # tag_keys: ["TagKey"], # required # resource_id: "EntityId", # required # resource_type: "BatchPrediction", # required, accepts BatchPrediction, DataSource, Evaluation, MLModel # }) # # @example Response structure # # resp.resource_id #=> String # resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel" # # @overload delete_tags(params = {}) # @param [Hash] params ({}) def delete_tags(params = {}, options = {}) req = build_request(:delete_tags, params) req.send_request(options) end # Returns a list of `BatchPrediction` operations that match the search # criteria in the request. # # @option params [String] :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. # # @option params [String] :eq # The equal to operator. The `BatchPrediction` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # # @option params [String] :gt # The greater than operator. The `BatchPrediction` results will have # `FilterVariable` values that are greater than the value specified with # `GT`. # # @option params [String] :lt # The less than operator. The `BatchPrediction` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # # @option params [String] :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`. # # @option params [String] :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`. # # @option params [String] :ne # The not equal to operator. The `BatchPrediction` results will have # `FilterVariable` values not equal to the value specified with `NE`. # # @option params [String] :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 # # @option params [String] :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`. # # @option params [String] :next_token # An ID of the page in the paginated results. # # @option params [Integer] :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 [Types::DescribeBatchPredictionsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DescribeBatchPredictionsOutput#results #results} => Array<Types::BatchPrediction> # * {Types::DescribeBatchPredictionsOutput#next_token #next_token} => String # # @example Request syntax with placeholder values # # resp = client.describe_batch_predictions({ # filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, IAMUser, MLModelId, DataSourceId, DataURI # eq: "ComparatorValue", # gt: "ComparatorValue", # lt: "ComparatorValue", # ge: "ComparatorValue", # le: "ComparatorValue", # ne: "ComparatorValue", # prefix: "ComparatorValue", # sort_order: "asc", # accepts asc, dsc # next_token: "StringType", # limit: 1, # }) # # @example Response structure # # resp.results #=> Array # resp.results[0].batch_prediction_id #=> String # resp.results[0].ml_model_id #=> String # resp.results[0].batch_prediction_data_source_id #=> String # resp.results[0].input_data_location_s3 #=> String # resp.results[0].created_by_iam_user #=> String # resp.results[0].created_at #=> Time # resp.results[0].last_updated_at #=> Time # resp.results[0].name #=> String # resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.results[0].output_uri #=> String # resp.results[0].message #=> String # resp.results[0].compute_time #=> Integer # resp.results[0].finished_at #=> Time # resp.results[0].started_at #=> Time # resp.results[0].total_record_count #=> Integer # resp.results[0].invalid_record_count #=> Integer # resp.next_token #=> String # # @overload describe_batch_predictions(params = {}) # @param [Hash] params ({}) def describe_batch_predictions(params = {}, options = {}) req = build_request(:describe_batch_predictions, params) req.send_request(options) end # Returns a list of `DataSource` that match the search criteria in the # request. # # @option params [String] :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. # # @option params [String] :eq # The equal to operator. The `DataSource` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # # @option params [String] :gt # The greater than operator. The `DataSource` results will have # `FilterVariable` values that are greater than the value specified with # `GT`. # # @option params [String] :lt # The less than operator. The `DataSource` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # # @option params [String] :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`. # # @option params [String] :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`. # # @option params [String] :ne # The not equal to operator. The `DataSource` results will have # `FilterVariable` values not equal to the value specified with `NE`. # # @option params [String] :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 # # @option params [String] :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`. # # @option params [String] :next_token # The ID of the page in the paginated results. # # @option params [Integer] :limit # The maximum number of `DataSource` to include in the result. # # @return [Types::DescribeDataSourcesOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DescribeDataSourcesOutput#results #results} => Array<Types::DataSource> # * {Types::DescribeDataSourcesOutput#next_token #next_token} => String # # @example Request syntax with placeholder values # # resp = client.describe_data_sources({ # filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, DataLocationS3, IAMUser # eq: "ComparatorValue", # gt: "ComparatorValue", # lt: "ComparatorValue", # ge: "ComparatorValue", # le: "ComparatorValue", # ne: "ComparatorValue", # prefix: "ComparatorValue", # sort_order: "asc", # accepts asc, dsc # next_token: "StringType", # limit: 1, # }) # # @example Response structure # # resp.results #=> Array # resp.results[0].data_source_id #=> String # resp.results[0].data_location_s3 #=> String # resp.results[0].data_rearrangement #=> String # resp.results[0].created_by_iam_user #=> String # resp.results[0].created_at #=> Time # resp.results[0].last_updated_at #=> Time # resp.results[0].data_size_in_bytes #=> Integer # resp.results[0].number_of_files #=> Integer # resp.results[0].name #=> String # resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.results[0].message #=> String # resp.results[0].redshift_metadata.redshift_database.database_name #=> String # resp.results[0].redshift_metadata.redshift_database.cluster_identifier #=> String # resp.results[0].redshift_metadata.database_user_name #=> String # resp.results[0].redshift_metadata.select_sql_query #=> String # resp.results[0].rds_metadata.database.instance_identifier #=> String # resp.results[0].rds_metadata.database.database_name #=> String # resp.results[0].rds_metadata.database_user_name #=> String # resp.results[0].rds_metadata.select_sql_query #=> String # resp.results[0].rds_metadata.resource_role #=> String # resp.results[0].rds_metadata.service_role #=> String # resp.results[0].rds_metadata.data_pipeline_id #=> String # resp.results[0].role_arn #=> String # resp.results[0].compute_statistics #=> Boolean # resp.results[0].compute_time #=> Integer # resp.results[0].finished_at #=> Time # resp.results[0].started_at #=> Time # resp.next_token #=> String # # @overload describe_data_sources(params = {}) # @param [Hash] params ({}) def describe_data_sources(params = {}, options = {}) req = build_request(:describe_data_sources, params) req.send_request(options) end # Returns a list of `DescribeEvaluations` that match the search criteria # in the request. # # @option params [String] :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. # # @option params [String] :eq # The equal to operator. The `Evaluation` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # # @option params [String] :gt # The greater than operator. The `Evaluation` results will have # `FilterVariable` values that are greater than the value specified with # `GT`. # # @option params [String] :lt # The less than operator. The `Evaluation` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # # @option params [String] :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`. # # @option params [String] :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`. # # @option params [String] :ne # The not equal to operator. The `Evaluation` results will have # `FilterVariable` values not equal to the value specified with `NE`. # # @option params [String] :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 # # @option params [String] :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`. # # @option params [String] :next_token # The ID of the page in the paginated results. # # @option params [Integer] :limit # The maximum number of `Evaluation` to include in the result. # # @return [Types::DescribeEvaluationsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DescribeEvaluationsOutput#results #results} => Array<Types::Evaluation> # * {Types::DescribeEvaluationsOutput#next_token #next_token} => String # # @example Request syntax with placeholder values # # resp = client.describe_evaluations({ # filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, IAMUser, MLModelId, DataSourceId, DataURI # eq: "ComparatorValue", # gt: "ComparatorValue", # lt: "ComparatorValue", # ge: "ComparatorValue", # le: "ComparatorValue", # ne: "ComparatorValue", # prefix: "ComparatorValue", # sort_order: "asc", # accepts asc, dsc # next_token: "StringType", # limit: 1, # }) # # @example Response structure # # resp.results #=> Array # resp.results[0].evaluation_id #=> String # resp.results[0].ml_model_id #=> String # resp.results[0].evaluation_data_source_id #=> String # resp.results[0].input_data_location_s3 #=> String # resp.results[0].created_by_iam_user #=> String # resp.results[0].created_at #=> Time # resp.results[0].last_updated_at #=> Time # resp.results[0].name #=> String # resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.results[0].performance_metrics.properties #=> Hash # resp.results[0].performance_metrics.properties["PerformanceMetricsPropertyKey"] #=> String # resp.results[0].message #=> String # resp.results[0].compute_time #=> Integer # resp.results[0].finished_at #=> Time # resp.results[0].started_at #=> Time # resp.next_token #=> String # # @overload describe_evaluations(params = {}) # @param [Hash] params ({}) def describe_evaluations(params = {}, options = {}) req = build_request(:describe_evaluations, params) req.send_request(options) end # Returns a list of `MLModel` that match the search criteria in the # request. # # @option params [String] :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. # # @option params [String] :eq # The equal to operator. The `MLModel` results will have # `FilterVariable` values that exactly match the value specified with # `EQ`. # # @option params [String] :gt # The greater than operator. The `MLModel` results will have # `FilterVariable` values that are greater than the value specified with # `GT`. # # @option params [String] :lt # The less than operator. The `MLModel` results will have # `FilterVariable` values that are less than the value specified with # `LT`. # # @option params [String] :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`. # # @option params [String] :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`. # # @option params [String] :ne # The not equal to operator. The `MLModel` results will have # `FilterVariable` values not equal to the value specified with `NE`. # # @option params [String] :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 # # @option params [String] :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`. # # @option params [String] :next_token # The ID of the page in the paginated results. # # @option params [Integer] :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 [Types::DescribeMLModelsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DescribeMLModelsOutput#results #results} => Array<Types::MLModel> # * {Types::DescribeMLModelsOutput#next_token #next_token} => String # # @example Request syntax with placeholder values # # resp = client.describe_ml_models({ # filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, IAMUser, TrainingDataSourceId, RealtimeEndpointStatus, MLModelType, Algorithm, TrainingDataURI # eq: "ComparatorValue", # gt: "ComparatorValue", # lt: "ComparatorValue", # ge: "ComparatorValue", # le: "ComparatorValue", # ne: "ComparatorValue", # prefix: "ComparatorValue", # sort_order: "asc", # accepts asc, dsc # next_token: "StringType", # limit: 1, # }) # # @example Response structure # # resp.results #=> Array # resp.results[0].ml_model_id #=> String # resp.results[0].training_data_source_id #=> String # resp.results[0].created_by_iam_user #=> String # resp.results[0].created_at #=> Time # resp.results[0].last_updated_at #=> Time # resp.results[0].name #=> String # resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.results[0].size_in_bytes #=> Integer # resp.results[0].endpoint_info.peak_requests_per_second #=> Integer # resp.results[0].endpoint_info.created_at #=> Time # resp.results[0].endpoint_info.endpoint_url #=> String # resp.results[0].endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED" # resp.results[0].training_parameters #=> Hash # resp.results[0].training_parameters["StringType"] #=> String # resp.results[0].input_data_location_s3 #=> String # resp.results[0].algorithm #=> String, one of "sgd" # resp.results[0].ml_model_type #=> String, one of "REGRESSION", "BINARY", "MULTICLASS" # resp.results[0].score_threshold #=> Float # resp.results[0].score_threshold_last_updated_at #=> Time # resp.results[0].message #=> String # resp.results[0].compute_time #=> Integer # resp.results[0].finished_at #=> Time # resp.results[0].started_at #=> Time # resp.next_token #=> String # # @overload describe_ml_models(params = {}) # @param [Hash] params ({}) def describe_ml_models(params = {}, options = {}) req = build_request(:describe_ml_models, params) req.send_request(options) end # Describes one or more of the tags for your Amazon ML object. # # @option params [required, String] :resource_id # The ID of the ML object. For example, `exampleModelId`. # # @option params [required, String] :resource_type # The type of the ML object. # # @return [Types::DescribeTagsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::DescribeTagsOutput#resource_id #resource_id} => String # * {Types::DescribeTagsOutput#resource_type #resource_type} => String # * {Types::DescribeTagsOutput#tags #tags} => Array<Types::Tag> # # @example Request syntax with placeholder values # # resp = client.describe_tags({ # resource_id: "EntityId", # required # resource_type: "BatchPrediction", # required, accepts BatchPrediction, DataSource, Evaluation, MLModel # }) # # @example Response structure # # resp.resource_id #=> String # resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel" # resp.tags #=> Array # resp.tags[0].key #=> String # resp.tags[0].value #=> String # # @overload describe_tags(params = {}) # @param [Hash] params ({}) def describe_tags(params = {}, options = {}) req = build_request(:describe_tags, params) req.send_request(options) end # Returns a `BatchPrediction` that includes detailed metadata, status, # and data file information for a `Batch Prediction` request. # # @option params [required, String] :batch_prediction_id # An ID assigned to the `BatchPrediction` at creation. # # @return [Types::GetBatchPredictionOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::GetBatchPredictionOutput#batch_prediction_id #batch_prediction_id} => String # * {Types::GetBatchPredictionOutput#ml_model_id #ml_model_id} => String # * {Types::GetBatchPredictionOutput#batch_prediction_data_source_id #batch_prediction_data_source_id} => String # * {Types::GetBatchPredictionOutput#input_data_location_s3 #input_data_location_s3} => String # * {Types::GetBatchPredictionOutput#created_by_iam_user #created_by_iam_user} => String # * {Types::GetBatchPredictionOutput#created_at #created_at} => Time # * {Types::GetBatchPredictionOutput#last_updated_at #last_updated_at} => Time # * {Types::GetBatchPredictionOutput#name #name} => String # * {Types::GetBatchPredictionOutput#status #status} => String # * {Types::GetBatchPredictionOutput#output_uri #output_uri} => String # * {Types::GetBatchPredictionOutput#log_uri #log_uri} => String # * {Types::GetBatchPredictionOutput#message #message} => String # * {Types::GetBatchPredictionOutput#compute_time #compute_time} => Integer # * {Types::GetBatchPredictionOutput#finished_at #finished_at} => Time # * {Types::GetBatchPredictionOutput#started_at #started_at} => Time # * {Types::GetBatchPredictionOutput#total_record_count #total_record_count} => Integer # * {Types::GetBatchPredictionOutput#invalid_record_count #invalid_record_count} => Integer # # @example Request syntax with placeholder values # # resp = client.get_batch_prediction({ # batch_prediction_id: "EntityId", # required # }) # # @example Response structure # # resp.batch_prediction_id #=> String # resp.ml_model_id #=> String # resp.batch_prediction_data_source_id #=> String # resp.input_data_location_s3 #=> String # resp.created_by_iam_user #=> String # resp.created_at #=> Time # resp.last_updated_at #=> Time # resp.name #=> String # resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.output_uri #=> String # resp.log_uri #=> String # resp.message #=> String # resp.compute_time #=> Integer # resp.finished_at #=> Time # resp.started_at #=> Time # resp.total_record_count #=> Integer # resp.invalid_record_count #=> Integer # # @overload get_batch_prediction(params = {}) # @param [Hash] params ({}) def get_batch_prediction(params = {}, options = {}) req = build_request(:get_batch_prediction, params) req.send_request(options) end # Returns a `DataSource` that includes metadata and data file # information, as well as the current status of the `DataSource`. # # `GetDataSource` provides results in normal or verbose format. The # verbose format adds the schema description and the list of files # pointed to by the DataSource to the normal format. # # @option params [required, String] :data_source_id # The ID assigned to the `DataSource` at creation. # # @option params [Boolean] :verbose # Specifies whether the `GetDataSource` operation should return # `DataSourceSchema`. # # If true, `DataSourceSchema` is returned. # # If false, `DataSourceSchema` is not returned. # # @return [Types::GetDataSourceOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::GetDataSourceOutput#data_source_id #data_source_id} => String # * {Types::GetDataSourceOutput#data_location_s3 #data_location_s3} => String # * {Types::GetDataSourceOutput#data_rearrangement #data_rearrangement} => String # * {Types::GetDataSourceOutput#created_by_iam_user #created_by_iam_user} => String # * {Types::GetDataSourceOutput#created_at #created_at} => Time # * {Types::GetDataSourceOutput#last_updated_at #last_updated_at} => Time # * {Types::GetDataSourceOutput#data_size_in_bytes #data_size_in_bytes} => Integer # * {Types::GetDataSourceOutput#number_of_files #number_of_files} => Integer # * {Types::GetDataSourceOutput#name #name} => String # * {Types::GetDataSourceOutput#status #status} => String # * {Types::GetDataSourceOutput#log_uri #log_uri} => String # * {Types::GetDataSourceOutput#message #message} => String # * {Types::GetDataSourceOutput#redshift_metadata #redshift_metadata} => Types::RedshiftMetadata # * {Types::GetDataSourceOutput#rds_metadata #rds_metadata} => Types::RDSMetadata # * {Types::GetDataSourceOutput#role_arn #role_arn} => String # * {Types::GetDataSourceOutput#compute_statistics #compute_statistics} => Boolean # * {Types::GetDataSourceOutput#compute_time #compute_time} => Integer # * {Types::GetDataSourceOutput#finished_at #finished_at} => Time # * {Types::GetDataSourceOutput#started_at #started_at} => Time # * {Types::GetDataSourceOutput#data_source_schema #data_source_schema} => String # # @example Request syntax with placeholder values # # resp = client.get_data_source({ # data_source_id: "EntityId", # required # verbose: false, # }) # # @example Response structure # # resp.data_source_id #=> String # resp.data_location_s3 #=> String # resp.data_rearrangement #=> String # resp.created_by_iam_user #=> String # resp.created_at #=> Time # resp.last_updated_at #=> Time # resp.data_size_in_bytes #=> Integer # resp.number_of_files #=> Integer # resp.name #=> String # resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.log_uri #=> String # resp.message #=> String # resp.redshift_metadata.redshift_database.database_name #=> String # resp.redshift_metadata.redshift_database.cluster_identifier #=> String # resp.redshift_metadata.database_user_name #=> String # resp.redshift_metadata.select_sql_query #=> String # resp.rds_metadata.database.instance_identifier #=> String # resp.rds_metadata.database.database_name #=> String # resp.rds_metadata.database_user_name #=> String # resp.rds_metadata.select_sql_query #=> String # resp.rds_metadata.resource_role #=> String # resp.rds_metadata.service_role #=> String # resp.rds_metadata.data_pipeline_id #=> String # resp.role_arn #=> String # resp.compute_statistics #=> Boolean # resp.compute_time #=> Integer # resp.finished_at #=> Time # resp.started_at #=> Time # resp.data_source_schema #=> String # # @overload get_data_source(params = {}) # @param [Hash] params ({}) def get_data_source(params = {}, options = {}) req = build_request(:get_data_source, params) req.send_request(options) end # Returns an `Evaluation` that includes metadata as well as the current # status of the `Evaluation`. # # @option params [required, String] :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 [Types::GetEvaluationOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::GetEvaluationOutput#evaluation_id #evaluation_id} => String # * {Types::GetEvaluationOutput#ml_model_id #ml_model_id} => String # * {Types::GetEvaluationOutput#evaluation_data_source_id #evaluation_data_source_id} => String # * {Types::GetEvaluationOutput#input_data_location_s3 #input_data_location_s3} => String # * {Types::GetEvaluationOutput#created_by_iam_user #created_by_iam_user} => String # * {Types::GetEvaluationOutput#created_at #created_at} => Time # * {Types::GetEvaluationOutput#last_updated_at #last_updated_at} => Time # * {Types::GetEvaluationOutput#name #name} => String # * {Types::GetEvaluationOutput#status #status} => String # * {Types::GetEvaluationOutput#performance_metrics #performance_metrics} => Types::PerformanceMetrics # * {Types::GetEvaluationOutput#log_uri #log_uri} => String # * {Types::GetEvaluationOutput#message #message} => String # * {Types::GetEvaluationOutput#compute_time #compute_time} => Integer # * {Types::GetEvaluationOutput#finished_at #finished_at} => Time # * {Types::GetEvaluationOutput#started_at #started_at} => Time # # @example Request syntax with placeholder values # # resp = client.get_evaluation({ # evaluation_id: "EntityId", # required # }) # # @example Response structure # # resp.evaluation_id #=> String # resp.ml_model_id #=> String # resp.evaluation_data_source_id #=> String # resp.input_data_location_s3 #=> String # resp.created_by_iam_user #=> String # resp.created_at #=> Time # resp.last_updated_at #=> Time # resp.name #=> String # resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.performance_metrics.properties #=> Hash # resp.performance_metrics.properties["PerformanceMetricsPropertyKey"] #=> String # resp.log_uri #=> String # resp.message #=> String # resp.compute_time #=> Integer # resp.finished_at #=> Time # resp.started_at #=> Time # # @overload get_evaluation(params = {}) # @param [Hash] params ({}) def get_evaluation(params = {}, options = {}) req = build_request(:get_evaluation, params) req.send_request(options) end # Returns an `MLModel` that includes detailed metadata, data source # information, and the current status of the `MLModel`. # # `GetMLModel` provides results in normal or verbose format. # # @option params [required, String] :ml_model_id # The ID assigned to the `MLModel` at creation. # # @option params [Boolean] :verbose # Specifies whether the `GetMLModel` operation should return `Recipe`. # # If true, `Recipe` is returned. # # If false, `Recipe` is not returned. # # @return [Types::GetMLModelOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::GetMLModelOutput#ml_model_id #ml_model_id} => String # * {Types::GetMLModelOutput#training_data_source_id #training_data_source_id} => String # * {Types::GetMLModelOutput#created_by_iam_user #created_by_iam_user} => String # * {Types::GetMLModelOutput#created_at #created_at} => Time # * {Types::GetMLModelOutput#last_updated_at #last_updated_at} => Time # * {Types::GetMLModelOutput#name #name} => String # * {Types::GetMLModelOutput#status #status} => String # * {Types::GetMLModelOutput#size_in_bytes #size_in_bytes} => Integer # * {Types::GetMLModelOutput#endpoint_info #endpoint_info} => Types::RealtimeEndpointInfo # * {Types::GetMLModelOutput#training_parameters #training_parameters} => Hash<String,String> # * {Types::GetMLModelOutput#input_data_location_s3 #input_data_location_s3} => String # * {Types::GetMLModelOutput#ml_model_type #ml_model_type} => String # * {Types::GetMLModelOutput#score_threshold #score_threshold} => Float # * {Types::GetMLModelOutput#score_threshold_last_updated_at #score_threshold_last_updated_at} => Time # * {Types::GetMLModelOutput#log_uri #log_uri} => String # * {Types::GetMLModelOutput#message #message} => String # * {Types::GetMLModelOutput#compute_time #compute_time} => Integer # * {Types::GetMLModelOutput#finished_at #finished_at} => Time # * {Types::GetMLModelOutput#started_at #started_at} => Time # * {Types::GetMLModelOutput#recipe #recipe} => String # * {Types::GetMLModelOutput#schema #schema} => String # # @example Request syntax with placeholder values # # resp = client.get_ml_model({ # ml_model_id: "EntityId", # required # verbose: false, # }) # # @example Response structure # # resp.ml_model_id #=> String # resp.training_data_source_id #=> String # resp.created_by_iam_user #=> String # resp.created_at #=> Time # resp.last_updated_at #=> Time # resp.name #=> String # resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" # resp.size_in_bytes #=> Integer # resp.endpoint_info.peak_requests_per_second #=> Integer # resp.endpoint_info.created_at #=> Time # resp.endpoint_info.endpoint_url #=> String # resp.endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED" # resp.training_parameters #=> Hash # resp.training_parameters["StringType"] #=> String # resp.input_data_location_s3 #=> String # resp.ml_model_type #=> String, one of "REGRESSION", "BINARY", "MULTICLASS" # resp.score_threshold #=> Float # resp.score_threshold_last_updated_at #=> Time # resp.log_uri #=> String # resp.message #=> String # resp.compute_time #=> Integer # resp.finished_at #=> Time # resp.started_at #=> Time # resp.recipe #=> String # resp.schema #=> String # # @overload get_ml_model(params = {}) # @param [Hash] params ({}) def get_ml_model(params = {}, options = {}) req = build_request(:get_ml_model, params) req.send_request(options) end # Generates a prediction for the observation using the specified `ML # Model`. # # Note Not all response parameters will be populated. Whether a response # parameter is populated depends on the type of model requested. # # # # @option params [required, String] :ml_model_id # A unique identifier of the `MLModel`. # # @option params [required, Hash] :record # A map of variable name-value pairs that represent an observation. # # @option params [required, String] :predict_endpoint # # @return [Types::PredictOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::PredictOutput#prediction #prediction} => Types::Prediction # # @example Request syntax with placeholder values # # resp = client.predict({ # ml_model_id: "EntityId", # required # record: { # required # "VariableName" => "VariableValue", # }, # predict_endpoint: "VipURL", # required # }) # # @example Response structure # # resp.prediction.predicted_label #=> String # resp.prediction.predicted_value #=> Float # resp.prediction.predicted_scores #=> Hash # resp.prediction.predicted_scores["Label"] #=> Float # resp.prediction.details #=> Hash # resp.prediction.details["DetailsAttributes"] #=> String # # @overload predict(params = {}) # @param [Hash] params ({}) def predict(params = {}, options = {}) req = build_request(:predict, params) req.send_request(options) end # Updates the `BatchPredictionName` of a `BatchPrediction`. # # You can use the `GetBatchPrediction` operation to view the contents of # the updated data element. # # @option params [required, String] :batch_prediction_id # The ID assigned to the `BatchPrediction` during creation. # # @option params [required, String] :batch_prediction_name # A new user-supplied name or description of the `BatchPrediction`. # # @return [Types::UpdateBatchPredictionOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::UpdateBatchPredictionOutput#batch_prediction_id #batch_prediction_id} => String # # @example Request syntax with placeholder values # # resp = client.update_batch_prediction({ # batch_prediction_id: "EntityId", # required # batch_prediction_name: "EntityName", # required # }) # # @example Response structure # # resp.batch_prediction_id #=> String # # @overload update_batch_prediction(params = {}) # @param [Hash] params ({}) def update_batch_prediction(params = {}, options = {}) req = build_request(:update_batch_prediction, params) req.send_request(options) end # Updates the `DataSourceName` of a `DataSource`. # # You can use the `GetDataSource` operation to view the contents of the # updated data element. # # @option params [required, String] :data_source_id # The ID assigned to the `DataSource` during creation. # # @option params [required, String] :data_source_name # A new user-supplied name or description of the `DataSource` that will # replace the current description. # # @return [Types::UpdateDataSourceOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::UpdateDataSourceOutput#data_source_id #data_source_id} => String # # @example Request syntax with placeholder values # # resp = client.update_data_source({ # data_source_id: "EntityId", # required # data_source_name: "EntityName", # required # }) # # @example Response structure # # resp.data_source_id #=> String # # @overload update_data_source(params = {}) # @param [Hash] params ({}) def update_data_source(params = {}, options = {}) req = build_request(:update_data_source, params) req.send_request(options) end # Updates the `EvaluationName` of an `Evaluation`. # # You can use the `GetEvaluation` operation to view the contents of the # updated data element. # # @option params [required, String] :evaluation_id # The ID assigned to the `Evaluation` during creation. # # @option params [required, String] :evaluation_name # A new user-supplied name or description of the `Evaluation` that will # replace the current content. # # @return [Types::UpdateEvaluationOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::UpdateEvaluationOutput#evaluation_id #evaluation_id} => String # # @example Request syntax with placeholder values # # resp = client.update_evaluation({ # evaluation_id: "EntityId", # required # evaluation_name: "EntityName", # required # }) # # @example Response structure # # resp.evaluation_id #=> String # # @overload update_evaluation(params = {}) # @param [Hash] params ({}) def update_evaluation(params = {}, options = {}) req = build_request(:update_evaluation, params) req.send_request(options) end # Updates the `MLModelName` and the `ScoreThreshold` of an `MLModel`. # # You can use the `GetMLModel` operation to view the contents of the # updated data element. # # @option params [required, String] :ml_model_id # The ID assigned to the `MLModel` during creation. # # @option params [String] :ml_model_name # A user-supplied name or description of the `MLModel`. # # @option params [Float] :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 [Types::UpdateMLModelOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods: # # * {Types::UpdateMLModelOutput#ml_model_id #ml_model_id} => String # # @example Request syntax with placeholder values # # resp = client.update_ml_model({ # ml_model_id: "EntityId", # required # ml_model_name: "EntityName", # score_threshold: 1.0, # }) # # @example Response structure # # resp.ml_model_id #=> String # # @overload update_ml_model(params = {}) # @param [Hash] params ({}) def update_ml_model(params = {}, options = {}) req = build_request(:update_ml_model, params) req.send_request(options) end # @!endgroup # @param params ({}) # @api private def build_request(operation_name, params = {}) handlers = @handlers.for(operation_name) context = Seahorse::Client::RequestContext.new( operation_name: operation_name, operation: config.api.operation(operation_name), client: self, params: params, config: config) context[:gem_name] = 'aws-sdk-machinelearning' context[:gem_version] = '1.16.0' Seahorse::Client::Request.new(handlers, context) end # Polls an API operation until a resource enters a desired state. # # ## Basic Usage # # A waiter will call an API operation until: # # * It is successful # * It enters a terminal state # * It makes the maximum number of attempts # # In between attempts, the waiter will sleep. # # # polls in a loop, sleeping between attempts # client.wait_until(waiter_name, params) # # ## Configuration # # You can configure the maximum number of polling attempts, and the # delay (in seconds) between each polling attempt. You can pass # configuration as the final arguments hash. # # # poll for ~25 seconds # client.wait_until(waiter_name, params, { # max_attempts: 5, # delay: 5, # }) # # ## Callbacks # # You can be notified before each polling attempt and before each # delay. If you throw `:success` or `:failure` from these callbacks, # it will terminate the waiter. # # started_at = Time.now # client.wait_until(waiter_name, params, { # # # disable max attempts # max_attempts: nil, # # # poll for 1 hour, instead of a number of attempts # before_wait: -> (attempts, response) do # throw :failure if Time.now - started_at > 3600 # end # }) # # ## Handling Errors # # When a waiter is unsuccessful, it will raise an error. # All of the failure errors extend from # {Aws::Waiters::Errors::WaiterFailed}. # # begin # client.wait_until(...) # rescue Aws::Waiters::Errors::WaiterFailed # # resource did not enter the desired state in time # end # # ## Valid Waiters # # The following table lists the valid waiter names, the operations they call, # and the default `:delay` and `:max_attempts` values. # # | waiter_name | params | :delay | :max_attempts | # | -------------------------- | ----------------------------- | -------- | ------------- | # | batch_prediction_available | {#describe_batch_predictions} | 30 | 60 | # | data_source_available | {#describe_data_sources} | 30 | 60 | # | evaluation_available | {#describe_evaluations} | 30 | 60 | # | ml_model_available | {#describe_ml_models} | 30 | 60 | # # @raise [Errors::FailureStateError] Raised when the waiter terminates # because the waiter has entered a state that it will not transition # out of, preventing success. # # @raise [Errors::TooManyAttemptsError] Raised when the configured # maximum number of attempts have been made, and the waiter is not # yet successful. # # @raise [Errors::UnexpectedError] Raised when an error is encounted # while polling for a resource that is not expected. # # @raise [Errors::NoSuchWaiterError] Raised when you request to wait # for an unknown state. # # @return [Boolean] Returns `true` if the waiter was successful. # @param [Symbol] waiter_name # @param [Hash] params ({}) # @param [Hash] options ({}) # @option options [Integer] :max_attempts # @option options [Integer] :delay # @option options [Proc] :before_attempt # @option options [Proc] :before_wait def wait_until(waiter_name, params = {}, options = {}) w = waiter(waiter_name, options) yield(w.waiter) if block_given? # deprecated w.wait(params) end # @api private # @deprecated def waiter_names waiters.keys end private # @param [Symbol] waiter_name # @param [Hash] options ({}) def waiter(waiter_name, options = {}) waiter_class = waiters[waiter_name] if waiter_class waiter_class.new(options.merge(client: self)) else raise Aws::Waiters::Errors::NoSuchWaiterError.new(waiter_name, waiters.keys) end end def waiters { batch_prediction_available: Waiters::BatchPredictionAvailable, data_source_available: Waiters::DataSourceAvailable, evaluation_available: Waiters::EvaluationAvailable, ml_model_available: Waiters::MLModelAvailable } end class << self # @api private attr_reader :identifier # @api private def errors_module Errors end end end end