Class: Aws::MachineLearning::Client

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

Class Attribute Summary collapse

API Operations collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(*args) ⇒ Client

Returns a new instance of Client.

Parameters:

  • options (Hash) —

    a customizable set of options



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

def initialize(*args)
  super
end

Class Attribute Details

.identifier ⇒ Object (readonly)

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future. 



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# File 'lib/aws-sdk-machinelearning/client.rb', line 2261

def identifier
  @identifier
end

Class Method Details

.errors_module ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future. 



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# File 'lib/aws-sdk-machinelearning/client.rb', line 2264

def errors_module
  Errors
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 'lib/aws-sdk-machinelearning/client.rb', line 201

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

#build_request(operation_name, params = {}) ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.

Parameters:

  • params ({}) (defaults to: {}) 


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# File 'lib/aws-sdk-machinelearning/client.rb', line 2120

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] = '1.0.0'
  Seahorse::Client::Request.new(handlers, context)
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].

    [1]: docs.aws.amazon.com/machine-learning/latest/dg

Returns:



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# File '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`&gt;, `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]: aws.amazon.com/rds/

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}”`

    [1]: docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html

  • :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 '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 '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 '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 '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].

    [1]: docs.aws.amazon.com/machine-learning/latest/dg

  • :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 '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 '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 '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 '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 markdown=“1”><title>Caution</title> The results of the ‘DeleteEvaluation` operation are irreversible.

</caution>

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 '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 '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 '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 '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 ‘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`.

  • :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 '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_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

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 '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 '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 '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 '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 '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_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

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 '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 '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 '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 markdown=“1”><title>Note</title> Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

</note>

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 '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 '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 '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 '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 '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 '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

#waiter_names ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.

Deprecated. 


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# File 'lib/aws-sdk-machinelearning/client.rb', line 2232

def waiter_names
  waiters.keys
end