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Class: Aws::MachineLearning::Client

Inherits:
Seahorse::Client::Base show all
Includes:
ClientStubs
Defined in:
gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

API Operations collapse

Instance Method Summary collapse

Methods included from ClientStubs

#stub_data, #stub_responses

Methods inherited from Seahorse::Client::Base

add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

Constructor Details

#initialize(*args) ⇒ Client

Returns a new instance of Client

Parameters:

  • options (Hash)

    a customizable set of options



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 154

def initialize(*args)
  super
end

Instance Method Details

#add_tags(params = {}) ⇒ Types::AddTagsOutput

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.

Examples:

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
})

Response structure


resp.resource_id #=> String
resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :tags (required, Array<Types::Tag>)

    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.

  • :resource_id (required, String)

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

  • :resource_type (required, String)

    The type of the ML object to tag.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 201

def add_tags(params = {}, options = {})
  req = build_request(:add_tags, params)
  req.send_request(options)
end

#create_batch_prediction(params = {}) ⇒ Types::CreateBatchPredictionOutput

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.

Examples:

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
})

Response structure


resp.batch_prediction_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :batch_prediction_id (required, String)

    A user-supplied ID that uniquely identifies the BatchPrediction.

  • :batch_prediction_name (String)

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

  • :ml_model_id (required, String)

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

  • :batch_prediction_data_source_id (required, String)

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

  • :output_uri (required, String)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 272

def create_batch_prediction(params = {}, options = {})
  req = build_request(:create_batch_prediction, params)
  req.send_request(options)
end

#create_data_source_from_rds(params = {}) ⇒ Types::CreateDataSourceFromRDSOutput

Creates a DataSource object from an Amazon Relational Database Service (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.

Examples:

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,
})

Response structure


resp.data_source_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :data_source_id (required, String)

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

  • :data_source_name (String)

    A user-supplied name or description of the DataSource.

  • :rds_data (required, Types::RDSDataSpec)

    The data specification of an Amazon RDS DataSource:

    • DatabaseInformation - * DatabaseName - The name of the Amazon RDS database.

      • InstanceIdentifier - A unique identifier for the Amazon RDS database instance.
    • DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.

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

    • ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

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

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

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

    • DataSchemaUri - The Amazon S3 location of the DataSchema.

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

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

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

  • :role_arn (required, String)

    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.

  • :compute_statistics (Boolean)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 406

def create_data_source_from_rds(params = {}, options = {})
  req = build_request(:create_data_source_from_rds, params)
  req.send_request(options)
end

#create_data_source_from_redshift(params = {}) ⇒ Types::CreateDataSourceFromRedshiftOutput

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.

<?oxy_insert_start author="laurama" timestamp="20160406T153842-0700">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.

<?oxy_insert_end>

Examples:

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,
})

Response structure


resp.data_source_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :data_source_id (required, String)

    A user-supplied ID that uniquely identifies the DataSource.

  • :data_source_name (String)

    A user-supplied name or description of the DataSource.

  • :data_spec (required, Types::RedshiftDataSpec)

    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\}\}"

  • :role_arn (required, String)

    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

  • :compute_statistics (Boolean)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 541

def create_data_source_from_redshift(params = {}, options = {})
  req = build_request(:create_data_source_from_redshift, params)
  req.send_request(options)
end

#create_data_source_from_s3(params = {}) ⇒ Types::CreateDataSourceFromS3Output

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.

Examples:

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,
})

Response structure


resp.data_source_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :data_source_id (required, String)

    A user-supplied identifier that uniquely identifies the DataSource.

  • :data_source_name (String)

    A user-supplied name or description of the DataSource.

  • :data_spec (required, Types::S3DataSpec)

    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\}\}"

  • :compute_statistics (Boolean)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 633

def create_data_source_from_s3(params = {}, options = {})
  req = build_request(:create_data_source_from_s3, params)
  req.send_request(options)
end

#create_evaluation(params = {}) ⇒ Types::CreateEvaluationOutput

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.

Examples:

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
})

Response structure


resp.evaluation_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :evaluation_id (required, String)

    A user-supplied ID that uniquely identifies the Evaluation.

  • :evaluation_name (String)

    A user-supplied name or description of the Evaluation.

  • :ml_model_id (required, String)

    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.

  • :evaluation_data_source_id (required, String)

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

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 693

def create_evaluation(params = {}, options = {})
  req = build_request(:create_evaluation, params)
  req.send_request(options)
end

#create_ml_model(params = {}) ⇒ Types::CreateMLModelOutput

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.

Examples:

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",
})

Response structure


resp.ml_model_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :ml_model_id (required, String)

    A user-supplied ID that uniquely identifies the MLModel.

  • :ml_model_name (String)

    A user-supplied name or description of the MLModel.

  • :ml_model_type (required, String)

    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.

  • :parameters (Hash<String,String>)

    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 <?oxy_insert_start author="laurama" timestamp="20160329T131121-0700">strongly recommend that you shuffle your data.<?oxy_insert_end>

    • 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.

  • :training_data_source_id (required, String)

    The DataSource that points to the training data.

  • :recipe (String)

    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.

  • :recipe_uri (String)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 824

def create_ml_model(params = {}, options = {})
  req = build_request(:create_ml_model, params)
  req.send_request(options)
end

#create_realtime_endpoint(params = {}) ⇒ Types::CreateRealtimeEndpointOutput

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.

Examples:

Request syntax with placeholder values


resp = client.create_realtime_endpoint({
  ml_model_id: "EntityId", # required
})

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"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :ml_model_id (required, String)

    The ID assigned to the MLModel during creation.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 857

def create_realtime_endpoint(params = {}, options = {})
  req = build_request(:create_realtime_endpoint, params)
  req.send_request(options)
end

#delete_batch_prediction(params = {}) ⇒ Types::DeleteBatchPredictionOutput

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.

Examples:

Request syntax with placeholder values


resp = client.delete_batch_prediction({
  batch_prediction_id: "EntityId", # required
})

Response structure


resp.batch_prediction_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :batch_prediction_id (required, String)

    A user-supplied ID that uniquely identifies the BatchPrediction.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 891

def delete_batch_prediction(params = {}, options = {})
  req = build_request(:delete_batch_prediction, params)
  req.send_request(options)
end

#delete_data_source(params = {}) ⇒ Types::DeleteDataSourceOutput

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.

Examples:

Request syntax with placeholder values


resp = client.delete_data_source({
  data_source_id: "EntityId", # required
})

Response structure


resp.data_source_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :data_source_id (required, String)

    A user-supplied ID that uniquely identifies the DataSource.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 924

def delete_data_source(params = {}, options = {})
  req = build_request(:delete_data_source, params)
  req.send_request(options)
end

#delete_evaluation(params = {}) ⇒ Types::DeleteEvaluationOutput

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.

Examples:

Request syntax with placeholder values


resp = client.delete_evaluation({
  evaluation_id: "EntityId", # required
})

Response structure


resp.evaluation_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :evaluation_id (required, String)

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

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 960

def delete_evaluation(params = {}, options = {})
  req = build_request(:delete_evaluation, params)
  req.send_request(options)
end

#delete_ml_model(params = {}) ⇒ Types::DeleteMLModelOutput

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.

Examples:

Request syntax with placeholder values


resp = client.delete_ml_model({
  ml_model_id: "EntityId", # required
})

Response structure


resp.ml_model_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :ml_model_id (required, String)

    A user-supplied ID that uniquely identifies the MLModel.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 993

def delete_ml_model(params = {}, options = {})
  req = build_request(:delete_ml_model, params)
  req.send_request(options)
end

#delete_realtime_endpoint(params = {}) ⇒ Types::DeleteRealtimeEndpointOutput

Deletes a real time endpoint of an MLModel.

Examples:

Request syntax with placeholder values


resp = client.delete_realtime_endpoint({
  ml_model_id: "EntityId", # required
})

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"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :ml_model_id (required, String)

    The ID assigned to the MLModel during creation.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1024

def delete_realtime_endpoint(params = {}, options = {})
  req = build_request(:delete_realtime_endpoint, params)
  req.send_request(options)
end

#delete_tags(params = {}) ⇒ Types::DeleteTagsOutput

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.

Examples:

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
})

Response structure


resp.resource_id #=> String
resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :tag_keys (required, Array<String>)

    One or more tags to delete.

  • :resource_id (required, String)

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

  • :resource_type (required, String)

    The type of the tagged ML object.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1063

def delete_tags(params = {}, options = {})
  req = build_request(:delete_tags, params)
  req.send_request(options)
end

#describe_batch_predictions(params = {}) ⇒ Types::DescribeBatchPredictionsOutput

Returns a list of BatchPrediction operations that match the search criteria in the request.

Examples:

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,
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :filter_variable (String)

    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.
  • :eq (String)

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

  • :gt (String)

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

  • :lt (String)

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

  • :ge (String)

    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.

  • :le (String)

    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.

  • :ne (String)

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

  • :prefix (String)

    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

  • :sort_order (String)

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

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

    Results are sorted by FilterVariable.

  • :next_token (String)

    An ID of the page in the paginated results.

  • :limit (Integer)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1194

def describe_batch_predictions(params = {}, options = {})
  req = build_request(:describe_batch_predictions, params)
  req.send_request(options)
end

#describe_data_sources(params = {}) ⇒ Types::DescribeDataSourcesOutput

Returns a list of DataSource that match the search criteria in the request.

Examples:

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,
})

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_database.database_name #=> String
resp.results[0]..redshift_database.cluster_identifier #=> String
resp.results[0]..database_user_name #=> String
resp.results[0]..select_sql_query #=> String
resp.results[0]..database.instance_identifier #=> String
resp.results[0]..database.database_name #=> String
resp.results[0]..database_user_name #=> String
resp.results[0]..select_sql_query #=> String
resp.results[0]..resource_role #=> String
resp.results[0]..service_role #=> String
resp.results[0]..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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :filter_variable (String)

    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.
  • :eq (String)

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

  • :gt (String)

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

  • :lt (String)

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

  • :ge (String)

    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.

  • :le (String)

    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.

  • :ne (String)

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

  • :prefix (String)

    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

  • :sort_order (String)

    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.

  • :next_token (String)

    The ID of the page in the paginated results.

  • :limit (Integer)

    The maximum number of DataSource to include in the result.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1330

def describe_data_sources(params = {}, options = {})
  req = build_request(:describe_data_sources, params)
  req.send_request(options)
end

#describe_evaluations(params = {}) ⇒ Types::DescribeEvaluationsOutput

Returns a list of DescribeEvaluations that match the search criteria in the request.

Examples:

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,
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :filter_variable (String)

    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.
  • :eq (String)

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

  • :gt (String)

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

  • :lt (String)

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

  • :ge (String)

    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.

  • :le (String)

    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.

  • :ne (String)

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

  • :prefix (String)

    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

  • :sort_order (String)

    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.

  • :next_token (String)

    The ID of the page in the paginated results.

  • :limit (Integer)

    The maximum number of Evaluation to include in the result.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1459

def describe_evaluations(params = {}, options = {})
  req = build_request(:describe_evaluations, params)
  req.send_request(options)
end

#describe_ml_models(params = {}) ⇒ Types::DescribeMLModelsOutput

Returns a list of MLModel that match the search criteria in the request.

Examples:

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,
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :filter_variable (String)

    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.
  • :eq (String)

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

  • :gt (String)

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

  • :lt (String)

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

  • :ge (String)

    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.

  • :le (String)

    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.

  • :ne (String)

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

  • :prefix (String)

    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

  • :sort_order (String)

    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.

  • :next_token (String)

    The ID of the page in the paginated results.

  • :limit (Integer)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1599

def describe_ml_models(params = {}, options = {})
  req = build_request(:describe_ml_models, params)
  req.send_request(options)
end

#describe_tags(params = {}) ⇒ Types::DescribeTagsOutput

Describes one or more of the tags for your Amazon ML object.

Examples:

Request syntax with placeholder values


resp = client.describe_tags({
  resource_id: "EntityId", # required
  resource_type: "BatchPrediction", # required, accepts BatchPrediction, DataSource, Evaluation, MLModel
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :resource_id (required, String)

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

  • :resource_type (required, String)

    The type of the ML object.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1635

def describe_tags(params = {}, options = {})
  req = build_request(:describe_tags, params)
  req.send_request(options)
end

#get_batch_prediction(params = {}) ⇒ Types::GetBatchPredictionOutput

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Examples:

Request syntax with placeholder values


resp = client.get_batch_prediction({
  batch_prediction_id: "EntityId", # required
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :batch_prediction_id (required, String)

    An ID assigned to the BatchPrediction at creation.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1694

def get_batch_prediction(params = {}, options = {})
  req = build_request(:get_batch_prediction, params)
  req.send_request(options)
end

#get_data_source(params = {}) ⇒ Types::GetDataSourceOutput

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.

Examples:

Request syntax with placeholder values


resp = client.get_data_source({
  data_source_id: "EntityId", # required
  verbose: false,
})

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_database.database_name #=> String
resp..redshift_database.cluster_identifier #=> String
resp..database_user_name #=> String
resp..select_sql_query #=> String
resp..database.instance_identifier #=> String
resp..database.database_name #=> String
resp..database_user_name #=> String
resp..select_sql_query #=> String
resp..resource_role #=> String
resp..service_role #=> String
resp..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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :data_source_id (required, String)

    The ID assigned to the DataSource at creation.

  • :verbose (Boolean)

    Specifies whether the GetDataSource operation should return DataSourceSchema.

    If true, DataSourceSchema is returned.

    If false, DataSourceSchema is not returned.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1781

def get_data_source(params = {}, options = {})
  req = build_request(:get_data_source, params)
  req.send_request(options)
end

#get_evaluation(params = {}) ⇒ Types::GetEvaluationOutput

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Examples:

Request syntax with placeholder values


resp = client.get_evaluation({
  evaluation_id: "EntityId", # required
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :evaluation_id (required, String)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1839

def get_evaluation(params = {}, options = {})
  req = build_request(:get_evaluation, params)
  req.send_request(options)
end

#get_ml_model(params = {}) ⇒ Types::GetMLModelOutput

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.

Examples:

Request syntax with placeholder values


resp = client.get_ml_model({
  ml_model_id: "EntityId", # required
  verbose: false,
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :ml_model_id (required, String)

    The ID assigned to the MLModel at creation.

  • :verbose (Boolean)

    Specifies whether the GetMLModel operation should return Recipe.

    If true, Recipe is returned.

    If false, Recipe is not returned.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1920

def get_ml_model(params = {}, options = {})
  req = build_request(:get_ml_model, params)
  req.send_request(options)
end

#predict(params = {}) ⇒ Types::PredictOutput

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.

Examples:

Request syntax with placeholder values


resp = client.predict({
  ml_model_id: "EntityId", # required
  record: { # required
    "VariableName" => "VariableValue",
  },
  predict_endpoint: "VipURL", # required
})

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

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :ml_model_id (required, String)

    A unique identifier of the MLModel.

  • :record (required, Hash<String,String>)

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

  • :predict_endpoint (required, String)

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1966

def predict(params = {}, options = {})
  req = build_request(:predict, params)
  req.send_request(options)
end

#update_batch_prediction(params = {}) ⇒ Types::UpdateBatchPredictionOutput

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_batch_prediction({
  batch_prediction_id: "EntityId", # required
  batch_prediction_name: "EntityName", # required
})

Response structure


resp.batch_prediction_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :batch_prediction_id (required, String)

    The ID assigned to the BatchPrediction during creation.

  • :batch_prediction_name (required, String)

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

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1999

def update_batch_prediction(params = {}, options = {})
  req = build_request(:update_batch_prediction, params)
  req.send_request(options)
end

#update_data_source(params = {}) ⇒ Types::UpdateDataSourceOutput

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_data_source({
  data_source_id: "EntityId", # required
  data_source_name: "EntityName", # required
})

Response structure


resp.data_source_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :data_source_id (required, String)

    The ID assigned to the DataSource during creation.

  • :data_source_name (required, String)

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

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2033

def update_data_source(params = {}, options = {})
  req = build_request(:update_data_source, params)
  req.send_request(options)
end

#update_evaluation(params = {}) ⇒ Types::UpdateEvaluationOutput

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_evaluation({
  evaluation_id: "EntityId", # required
  evaluation_name: "EntityName", # required
})

Response structure


resp.evaluation_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :evaluation_id (required, String)

    The ID assigned to the Evaluation during creation.

  • :evaluation_name (required, String)

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

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2067

def update_evaluation(params = {}, options = {})
  req = build_request(:update_evaluation, params)
  req.send_request(options)
end

#update_ml_model(params = {}) ⇒ Types::UpdateMLModelOutput

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_ml_model({
  ml_model_id: "EntityId", # required
  ml_model_name: "EntityName",
  score_threshold: 1.0,
})

Response structure


resp.ml_model_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :ml_model_id (required, String)

    The ID assigned to the MLModel during creation.

  • :ml_model_name (String)

    A user-supplied name or description of the MLModel.

  • :score_threshold (Float)

    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.

Returns:



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2111

def update_ml_model(params = {}, options = {})
  req = build_request(:update_ml_model, params)
  req.send_request(options)
end

#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean

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.waiter_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 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

Parameters:

  • waiter_name (Symbol)
  • params (Hash) (defaults to: {})

    ({})

  • options (Hash) (defaults to: {})

    ({})

Options Hash (options):

  • :max_attempts (Integer)
  • :delay (Integer)
  • :before_attempt (Proc)
  • :before_wait (Proc)

Yields:

  • (w.waiter)

Returns:

  • (Boolean)

    Returns true if the waiter was successful.

Raises:

  • (Errors::FailureStateError)

    Raised when the waiter terminates because the waiter has entered a state that it will not transition out of, preventing success.

  • (Errors::TooManyAttemptsError)

    Raised when the configured maximum number of attempts have been made, and the waiter is not yet successful.

  • (Errors::UnexpectedError)

    Raised when an error is encounted while polling for a resource that is not expected.

  • (Errors::NoSuchWaiterError)

    Raised when you request to wait for an unknown state.



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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2224

def wait_until(waiter_name, params = {}, options = {})
  w = waiter(waiter_name, options)
  yield(w.waiter) if block_given? # deprecated
  w.wait(params)
end