MachineLearning *************** Client ====== class MachineLearning.Client A low-level client representing Amazon Machine Learning Definition of the public APIs exposed by Amazon Machine Learning: import boto3 client = boto3.client('machinelearning') These are the available methods: * add_tags * can_paginate * close * create_batch_prediction * create_data_source_from_rds * create_data_source_from_redshift * create_data_source_from_s3 * create_evaluation * create_ml_model * create_realtime_endpoint * delete_batch_prediction * delete_data_source * delete_evaluation * delete_ml_model * delete_realtime_endpoint * delete_tags * describe_batch_predictions * describe_data_sources * describe_evaluations * describe_ml_models * describe_tags * get_batch_prediction * get_data_source * get_evaluation * get_ml_model * get_paginator * get_waiter * predict * update_batch_prediction * update_data_source * update_evaluation * update_ml_model Paginators ========== Paginators are available on a client instance via the "get_paginator" method. For more detailed instructions and examples on the usage of paginators, see the paginators user guide. The available paginators are: * DescribeBatchPredictions * DescribeDataSources * DescribeEvaluations * DescribeMLModels Waiters ======= Waiters are available on a client instance via the "get_waiter" method. For more detailed instructions and examples on the usage or waiters, see the waiters user guide. The available waiters are: * BatchPredictionAvailable * DataSourceAvailable * EvaluationAvailable * MLModelAvailable MachineLearning / Waiter / DataSourceAvailable DataSourceAvailable ******************* class MachineLearning.Waiter.DataSourceAvailable waiter = client.get_waiter('data_source_available') wait(**kwargs) Polls "MachineLearning.Client.describe_data_sources()" every 30 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( FilterVariable='CreatedAt'|'LastUpdatedAt'|'Status'|'Name'|'DataLocationS3'|'IAMUser', EQ='string', GT='string', LT='string', GE='string', LE='string', NE='string', Prefix='string', SortOrder='asc'|'dsc', NextToken='string', Limit=123, WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **FilterVariable** (*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 * **SortOrder** (*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". * **NextToken** (*string*) -- The ID of the page in the paginated results. * **Limit** (*integer*) -- The maximum number of "DataSource" to include in the result. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 30 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None MachineLearning / Waiter / EvaluationAvailable EvaluationAvailable ******************* class MachineLearning.Waiter.EvaluationAvailable waiter = client.get_waiter('evaluation_available') wait(**kwargs) Polls "MachineLearning.Client.describe_evaluations()" every 30 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( FilterVariable='CreatedAt'|'LastUpdatedAt'|'Status'|'Name'|'IAMUser'|'MLModelId'|'DataSourceId'|'DataURI', EQ='string', GT='string', LT='string', GE='string', LE='string', NE='string', Prefix='string', SortOrder='asc'|'dsc', NextToken='string', Limit=123, WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **FilterVariable** (*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 * **SortOrder** (*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". * **NextToken** (*string*) -- The ID of the page in the paginated results. * **Limit** (*integer*) -- The maximum number of "Evaluation" to include in the result. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 30 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None MachineLearning / Waiter / BatchPredictionAvailable BatchPredictionAvailable ************************ class MachineLearning.Waiter.BatchPredictionAvailable waiter = client.get_waiter('batch_prediction_available') wait(**kwargs) Polls "MachineLearning.Client.describe_batch_predictions()" every 30 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( FilterVariable='CreatedAt'|'LastUpdatedAt'|'Status'|'Name'|'IAMUser'|'MLModelId'|'DataSourceId'|'DataURI', EQ='string', GT='string', LT='string', GE='string', LE='string', NE='string', Prefix='string', SortOrder='asc'|'dsc', NextToken='string', Limit=123, WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **FilterVariable** (*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 * **SortOrder** (*string*) -- A two-value parameter that determines the sequence of the resulting list of >>``<>``<>``<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. See also: AWS API Documentation **Request Syntax** response = client.create_data_source_from_rds( DataSourceId='string', DataSourceName='string', RDSData={ 'DatabaseInformation': { 'InstanceIdentifier': 'string', 'DatabaseName': 'string' }, 'SelectSqlQuery': 'string', 'DatabaseCredentials': { 'Username': 'string', 'Password': 'string' }, 'S3StagingLocation': 'string', 'DataRearrangement': 'string', 'DataSchema': 'string', 'DataSchemaUri': 'string', 'ResourceRole': 'string', 'ServiceRole': 'string', 'SubnetId': 'string', 'SecurityGroupIds': [ 'string', ] }, RoleARN='string', ComputeStatistics=True|False ) Parameters: * **DataSourceId** (*string*) -- **[REQUIRED]** A user-supplied ID that uniquely identifies the "DataSource". Typically, an Amazon Resource Number (ARN) becomes the ID for a "DataSource". * **DataSourceName** (*string*) -- A user-supplied name or description of the "DataSource". * **RDSData** (*dict*) -- **[REQUIRED]** 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}}"" * **DatabaseInformation** *(dict) --* **[REQUIRED]** Describes the "DatabaseName" and "InstanceIdentifier" of an Amazon RDS database. * **InstanceIdentifier** *(string) --* **[REQUIRED]** The ID of an RDS DB instance. * **DatabaseName** *(string) --* **[REQUIRED]** The name of a database hosted on an RDS DB instance. * **SelectSqlQuery** *(string) --* **[REQUIRED]** The query that is used to retrieve the observation data for the "DataSource". * **DatabaseCredentials** *(dict) --* **[REQUIRED]** The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database. * **Username** *(string) --* **[REQUIRED]** The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an "RDSSelectSqlQuery" query. * **Password** *(string) --* **[REQUIRED]** The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the "RDSSelectQuery" query. * **S3StagingLocation** *(string) --* **[REQUIRED]** The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using "SelectSqlQuery" is stored in this location. * **DataRearrangement** *(string) --* A JSON string that represents the splitting and rearrangement processing to be applied to a "DataSource". If the "DataRearrangement" parameter is not provided, all of the input data is used to create the "Datasource". There are multiple parameters that control what data is used to create a datasource: * "percentBegin" Use "percentBegin" to indicate the beginning of the range of the data used to create the Datasource. If you do not include "percentBegin" and "percentEnd", Amazon ML includes all of the data when creating the datasource. * "percentEnd" Use "percentEnd" to indicate the end of the range of the data used to create the Datasource. If you do not include "percentBegin" and "percentEnd", Amazon ML includes all of the data when creating the datasource. * "complement" The "complement" parameter instructs Amazon ML to use the data that is not included in the range of "percentBegin" to "percentEnd" to create a datasource. The "complement" parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for "percentBegin" and "percentEnd", along with the "complement" parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: "{"splitting":{"percentBegin":0, "percentEnd":25}}" Datasource for training: "{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}" * "strategy" To change how Amazon ML splits the data for a datasource, use the "strategy" parameter. The default value for the "strategy" parameter is "sequential", meaning that Amazon ML takes all of the data records between the "percentBegin" and "percentEnd" parameters for the datasource, in the order that the records appear in the input data. The following two "DataRearrangement" lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: "{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}" Datasource for training: "{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}" To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the "strategy" parameter to "random" and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between "percentBegin" and "percentEnd". Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two "DataRearrangement" lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: "{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}" Datasource for training: "{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}" * **DataSchema** *(string) --* A JSON string that represents the schema for an Amazon RDS "DataSource". The "DataSchema" defines the structure of the observation data in the data file(s) referenced in the "DataSource". A "DataSchema" is not required if you specify a "DataSchemaUri" Define your "DataSchema" as a series of key-value pairs. "attributes" and "excludedVariableNames" have an array of key-value pairs for their value. Use the following format to define your "DataSchema". { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] } * **DataSchemaUri** *(string) --* The Amazon S3 location of the "DataSchema". * **ResourceRole** *(string) --* **[REQUIRED]** The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines. * **ServiceRole** *(string) --* **[REQUIRED]** The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines. * **SubnetId** *(string) --* **[REQUIRED]** The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3. * **SecurityGroupIds** *(list) --* **[REQUIRED]** The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task. * *(string) --* * **RoleARN** (*string*) -- **[REQUIRED]** 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. * **ComputeStatistics** (*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. Return type: dict Returns: **Response Syntax** { 'DataSourceId': 'string' } **Response Structure** * *(dict) --* Represents the output of a "CreateDataSourceFromRDS" operation, and is an acknowledgement that Amazon ML received the request. The "CreateDataSourceFromRDS"> operation is asynchronous. You can poll for updates by using the "GetBatchPrediction" operation and checking the "Status" parameter. You can inspect the "Message" when "Status" shows up as "FAILED". You can also check the progress of the copy operation by going to the "DataPipeline" console and looking up the pipeline using the "pipelineId" from the describe call. * **DataSourceId** *(string) --* A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the "DataSourceID" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.InternalServerException" * "MachineLearning.Client.exceptions.IdempotentParameterMismatchEx ception" MachineLearning / Client / create_realtime_endpoint create_realtime_endpoint ************************ MachineLearning.Client.create_realtime_endpoint(**kwargs) 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". See also: AWS API Documentation **Request Syntax** response = client.create_realtime_endpoint( MLModelId='string' ) Parameters: **MLModelId** (*string*) -- **[REQUIRED]** The ID assigned to the "MLModel" during creation. Return type: dict Returns: **Response Syntax** { 'MLModelId': 'string', 'RealtimeEndpointInfo': { 'PeakRequestsPerSecond': 123, 'CreatedAt': datetime(2015, 1, 1), 'EndpointUrl': 'string', 'EndpointStatus': 'NONE'|'READY'|'UPDATING'|'FAILED' } } **Response Structure** * *(dict) --* Represents the output of an "CreateRealtimeEndpoint" operation. The result contains the "MLModelId" and the endpoint information for the "MLModel". **Note:** The endpoint information includes the URI of the "MLModel"; that is, the location to send online prediction requests for the specified "MLModel". * **MLModelId** *(string) --* A user-supplied ID that uniquely identifies the "MLModel". This value should be identical to the value of the "MLModelId" in the request. * **RealtimeEndpointInfo** *(dict) --* The endpoint information of the "MLModel" * **PeakRequestsPerSecond** *(integer) --* The maximum processing rate for the real-time endpoint for "MLModel", measured in incoming requests per second. * **CreatedAt** *(datetime) --* The time that the request to create the real-time endpoint for the "MLModel" was received. The time is expressed in epoch time. * **EndpointUrl** *(string) --* The URI that specifies where to send real-time prediction requests for the "MLModel". **Note:** The application must wait until the real-time endpoint is ready before using this URI. * **EndpointStatus** *(string) --* The current status of the real-time endpoint for the "MLModel". This element can have one of the following values: * "NONE" - Endpoint does not exist or was previously deleted. * "READY" - Endpoint is ready to be used for real-time predictions. * "UPDATING" - Updating/creating the endpoint. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / delete_data_source delete_data_source ****************** MachineLearning.Client.delete_data_source(**kwargs) 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. See also: AWS API Documentation **Request Syntax** response = client.delete_data_source( DataSourceId='string' ) Parameters: **DataSourceId** (*string*) -- **[REQUIRED]** A user-supplied ID that uniquely identifies the "DataSource". Return type: dict Returns: **Response Syntax** { 'DataSourceId': 'string' } **Response Structure** * *(dict) --* Represents the output of a "DeleteDataSource" operation. * **DataSourceId** *(string) --* A user-supplied ID that uniquely identifies the "DataSource". This value should be identical to the value of the "DataSourceID" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / describe_evaluations describe_evaluations ******************** MachineLearning.Client.describe_evaluations(**kwargs) Returns a list of "DescribeEvaluations" that match the search criteria in the request. See also: AWS API Documentation **Request Syntax** response = client.describe_evaluations( FilterVariable='CreatedAt'|'LastUpdatedAt'|'Status'|'Name'|'IAMUser'|'MLModelId'|'DataSourceId'|'DataURI', EQ='string', GT='string', LT='string', GE='string', LE='string', NE='string', Prefix='string', SortOrder='asc'|'dsc', NextToken='string', Limit=123 ) Parameters: * **FilterVariable** (*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 * **SortOrder** (*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". * **NextToken** (*string*) -- The ID of the page in the paginated results. * **Limit** (*integer*) -- The maximum number of "Evaluation" to include in the result. Return type: dict Returns: **Response Syntax** { 'Results': [ { 'EvaluationId': 'string', 'MLModelId': 'string', 'EvaluationDataSourceId': 'string', 'InputDataLocationS3': 'string', 'CreatedByIamUser': 'string', 'CreatedAt': datetime(2015, 1, 1), 'LastUpdatedAt': datetime(2015, 1, 1), 'Name': 'string', 'Status': 'PENDING'|'INPROGRESS'|'FAILED'|'COMPLETED'|'DELETED', 'PerformanceMetrics': { 'Properties': { 'string': 'string' } }, 'Message': 'string', 'ComputeTime': 123, 'FinishedAt': datetime(2015, 1, 1), 'StartedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* Represents the query results from a "DescribeEvaluations" operation. The content is essentially a list of "Evaluation". * **Results** *(list) --* A list of "Evaluation" that meet the search criteria. * *(dict) --* Represents the output of "GetEvaluation" operation. The content consists of the detailed metadata and data file information and the current status of the "Evaluation". * **EvaluationId** *(string) --* The ID that is assigned to the "Evaluation" at creation. * **MLModelId** *(string) --* The ID of the "MLModel" that is the focus of the evaluation. * **EvaluationDataSourceId** *(string) --* The ID of the "DataSource" that is used to evaluate the "MLModel". * **InputDataLocationS3** *(string) --* The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation. * **CreatedByIamUser** *(string) --* The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. * **CreatedAt** *(datetime) --* The time that the "Evaluation" was created. The time is expressed in epoch time. * **LastUpdatedAt** *(datetime) --* The time of the most recent edit to the "Evaluation". The time is expressed in epoch time. * **Name** *(string) --* A user-supplied name or description of the "Evaluation". * **Status** *(string) --* The status of the evaluation. This element can have one of the following values: * "PENDING" - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an "MLModel". * "INPROGRESS" - The evaluation is underway. * "FAILED" - The request to evaluate an "MLModel" did not run to completion. It is not usable. * "COMPLETED" - The evaluation process completed successfully. * "DELETED" - The "Evaluation" is marked as deleted. It is not usable. * **PerformanceMetrics** *(dict) --* Measurements of how well the "MLModel" performed, using observations referenced by the "DataSource". One of the following metrics is returned, based on the type of the "MLModel": * BinaryAUC: A binary "MLModel" uses the Area Under the Curve (AUC) technique to measure performance. * RegressionRMSE: A regression "MLModel" uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. * MulticlassAvgFScore: A multiclass "MLModel" uses the F1 score technique to measure performance. For more information about performance metrics, please see the Amazon Machine Learning Developer Guide. * **Properties** *(dict) --* * *(string) --* * *(string) --* * **Message** *(string) --* A description of the most recent details about evaluating the "MLModel". * **ComputeTime** *(integer) --* Long integer type that is a 64-bit signed number. * **FinishedAt** *(datetime) --* A timestamp represented in epoch time. * **StartedAt** *(datetime) --* A timestamp represented in epoch time. * **NextToken** *(string) --* The ID of the next page in the paginated results that indicates at least one more page follows. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / get_evaluation get_evaluation ************** MachineLearning.Client.get_evaluation(**kwargs) Returns an "Evaluation" that includes metadata as well as the current status of the "Evaluation". See also: AWS API Documentation **Request Syntax** response = client.get_evaluation( EvaluationId='string' ) Parameters: **EvaluationId** (*string*) -- **[REQUIRED]** The ID of the "Evaluation" to retrieve. The evaluation of each "MLModel" is recorded and cataloged. The ID provides the means to access the information. Return type: dict Returns: **Response Syntax** { 'EvaluationId': 'string', 'MLModelId': 'string', 'EvaluationDataSourceId': 'string', 'InputDataLocationS3': 'string', 'CreatedByIamUser': 'string', 'CreatedAt': datetime(2015, 1, 1), 'LastUpdatedAt': datetime(2015, 1, 1), 'Name': 'string', 'Status': 'PENDING'|'INPROGRESS'|'FAILED'|'COMPLETED'|'DELETED', 'PerformanceMetrics': { 'Properties': { 'string': 'string' } }, 'LogUri': 'string', 'Message': 'string', 'ComputeTime': 123, 'FinishedAt': datetime(2015, 1, 1), 'StartedAt': datetime(2015, 1, 1) } **Response Structure** * *(dict) --* Represents the output of a "GetEvaluation" operation and describes an "Evaluation". * **EvaluationId** *(string) --* The evaluation ID which is same as the "EvaluationId" in the request. * **MLModelId** *(string) --* The ID of the "MLModel" that was the focus of the evaluation. * **EvaluationDataSourceId** *(string) --* The "DataSource" used for this evaluation. * **InputDataLocationS3** *(string) --* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). * **CreatedByIamUser** *(string) --* The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. * **CreatedAt** *(datetime) --* The time that the "Evaluation" was created. The time is expressed in epoch time. * **LastUpdatedAt** *(datetime) --* The time of the most recent edit to the "Evaluation". The time is expressed in epoch time. * **Name** *(string) --* A user-supplied name or description of the "Evaluation". * **Status** *(string) --* The status of the evaluation. This element can have one of the following values: * "PENDING" - Amazon Machine Language (Amazon ML) submitted a request to evaluate an "MLModel". * "INPROGRESS" - The evaluation is underway. * "FAILED" - The request to evaluate an "MLModel" did not run to completion. It is not usable. * "COMPLETED" - The evaluation process completed successfully. * "DELETED" - The "Evaluation" is marked as deleted. It is not usable. * **PerformanceMetrics** *(dict) --* Measurements of how well the "MLModel" performed using observations referenced by the "DataSource". One of the following metric is returned based on the type of the "MLModel": * BinaryAUC: A binary "MLModel" uses the Area Under the Curve (AUC) technique to measure performance. * RegressionRMSE: A regression "MLModel" uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. * MulticlassAvgFScore: A multiclass "MLModel" uses the F1 score technique to measure performance. For more information about performance metrics, please see the Amazon Machine Learning Developer Guide. * **Properties** *(dict) --* * *(string) --* * *(string) --* * **LogUri** *(string) --* A link to the file that contains logs of the "CreateEvaluation" operation. * **Message** *(string) --* A description of the most recent details about evaluating the "MLModel". * **ComputeTime** *(integer) --* The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the "Evaluation", normalized and scaled on computation resources. "ComputeTime" is only available if the "Evaluation" is in the "COMPLETED" state. * **FinishedAt** *(datetime) --* The epoch time when Amazon Machine Learning marked the "Evaluation" as "COMPLETED" or "FAILED". "FinishedAt" is only available when the "Evaluation" is in the "COMPLETED" or "FAILED" state. * **StartedAt** *(datetime) --* The epoch time when Amazon Machine Learning marked the "Evaluation" as "INPROGRESS". "StartedAt" isn't available if the "Evaluation" is in the "PENDING" state. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / update_data_source update_data_source ****************** MachineLearning.Client.update_data_source(**kwargs) Updates the "DataSourceName" of a "DataSource". You can use the "GetDataSource" operation to view the contents of the updated data element. See also: AWS API Documentation **Request Syntax** response = client.update_data_source( DataSourceId='string', DataSourceName='string' ) Parameters: * **DataSourceId** (*string*) -- **[REQUIRED]** The ID assigned to the "DataSource" during creation. * **DataSourceName** (*string*) -- **[REQUIRED]** A new user-supplied name or description of the "DataSource" that will replace the current description. Return type: dict Returns: **Response Syntax** { 'DataSourceId': 'string' } **Response Structure** * *(dict) --* Represents the output of an "UpdateDataSource" operation. You can see the updated content by using the "GetBatchPrediction" operation. * **DataSourceId** *(string) --* The ID assigned to the "DataSource" during creation. This value should be identical to the value of the "DataSourceID" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / predict predict ******* MachineLearning.Client.predict(**kwargs) 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. See also: AWS API Documentation **Request Syntax** response = client.predict( MLModelId='string', Record={ 'string': 'string' }, PredictEndpoint='string' ) Parameters: * **MLModelId** (*string*) -- **[REQUIRED]** A unique identifier of the "MLModel". * **Record** (*dict*) -- **[REQUIRED]** A map of variable name-value pairs that represent an observation. * *(string) --* The name of a variable. Currently it's used to specify the name of the target value, label, weight, and tags. * *(string) --* The value of a variable. Currently it's used to specify values of the target value, weights, and tag variables and for filtering variable values. * **PredictEndpoint** (*string*) -- **[REQUIRED]** Return type: dict Returns: **Response Syntax** { 'Prediction': { 'predictedLabel': 'string', 'predictedValue': ..., 'predictedScores': { 'string': ... }, 'details': { 'string': 'string' } } } **Response Structure** * *(dict) --* * **Prediction** *(dict) --* The output from a "Predict" operation: * "Details" - Contains the following attributes: "DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS" "DetailsAttributes.ALGORITHM - SGD" * "PredictedLabel" - Present for either a "BINARY" or "MULTICLASS" "MLModel" request. * "PredictedScores" - Contains the raw classification score corresponding to each label. * "PredictedValue" - Present for a "REGRESSION" "MLModel" request. * **predictedLabel** *(string) --* The prediction label for either a "BINARY" or "MULTICLASS" "MLModel". * **predictedValue** *(float) --* The prediction value for "REGRESSION" "MLModel". * **predictedScores** *(dict) --* Provides the raw classification score corresponding to each label. * *(string) --* * *(float) --* * **details** *(dict) --* Provides any additional details regarding the prediction. * *(string) --* Contains the key values of "DetailsMap": * "PredictiveModelType" - Indicates the type of the "MLModel". * "Algorithm" - Indicates the algorithm that was used for the "MLModel". * *(string) --* **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.LimitExceededException" * "MachineLearning.Client.exceptions.InternalServerException" * "MachineLearning.Client.exceptions.PredictorNotMountedException" MachineLearning / Client / describe_tags describe_tags ************* MachineLearning.Client.describe_tags(**kwargs) Describes one or more of the tags for your Amazon ML object. See also: AWS API Documentation **Request Syntax** response = client.describe_tags( ResourceId='string', ResourceType='BatchPrediction'|'DataSource'|'Evaluation'|'MLModel' ) Parameters: * **ResourceId** (*string*) -- **[REQUIRED]** The ID of the ML object. For example, "exampleModelId". * **ResourceType** (*string*) -- **[REQUIRED]** The type of the ML object. Return type: dict Returns: **Response Syntax** { 'ResourceId': 'string', 'ResourceType': 'BatchPrediction'|'DataSource'|'Evaluation'|'MLModel', 'Tags': [ { 'Key': 'string', 'Value': 'string' }, ] } **Response Structure** * *(dict) --* Amazon ML returns the following elements. * **ResourceId** *(string) --* The ID of the tagged ML object. * **ResourceType** *(string) --* The type of the tagged ML object. * **Tags** *(list) --* A list of tags associated with the ML object. * *(dict) --* A custom key-value pair associated with an ML object, such as an ML model. * **Key** *(string) --* A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. * **Value** *(string) --* An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / get_waiter get_waiter ********** MachineLearning.Client.get_waiter(waiter_name) Returns an object that can wait for some condition. Parameters: **waiter_name** (*str*) -- The name of the waiter to get. See the waiters section of the service docs for a list of available waiters. Returns: The specified waiter object. Return type: "botocore.waiter.Waiter" MachineLearning / Client / delete_ml_model delete_ml_model *************** MachineLearning.Client.delete_ml_model(**kwargs) 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. See also: AWS API Documentation **Request Syntax** response = client.delete_ml_model( MLModelId='string' ) Parameters: **MLModelId** (*string*) -- **[REQUIRED]** A user-supplied ID that uniquely identifies the "MLModel". Return type: dict Returns: **Response Syntax** { 'MLModelId': 'string' } **Response Structure** * *(dict) --* Represents the output of a "DeleteMLModel" operation. You can use the "GetMLModel" operation and check the value of the "Status" parameter to see whether an "MLModel" is marked as "DELETED". * **MLModelId** *(string) --* A user-supplied ID that uniquely identifies the "MLModel". This value should be identical to the value of the "MLModelID" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / describe_ml_models describe_ml_models ****************** MachineLearning.Client.describe_ml_models(**kwargs) Returns a list of "MLModel" that match the search criteria in the request. See also: AWS API Documentation **Request Syntax** response = client.describe_ml_models( FilterVariable='CreatedAt'|'LastUpdatedAt'|'Status'|'Name'|'IAMUser'|'TrainingDataSourceId'|'RealtimeEndpointStatus'|'MLModelType'|'Algorithm'|'TrainingDataURI', EQ='string', GT='string', LT='string', GE='string', LE='string', NE='string', Prefix='string', SortOrder='asc'|'dsc', NextToken='string', Limit=123 ) Parameters: * **FilterVariable** (*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 * **SortOrder** (*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". * **NextToken** (*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". Return type: dict Returns: **Response Syntax** { 'Results': [ { 'MLModelId': 'string', 'TrainingDataSourceId': 'string', 'CreatedByIamUser': 'string', 'CreatedAt': datetime(2015, 1, 1), 'LastUpdatedAt': datetime(2015, 1, 1), 'Name': 'string', 'Status': 'PENDING'|'INPROGRESS'|'FAILED'|'COMPLETED'|'DELETED', 'SizeInBytes': 123, 'EndpointInfo': { 'PeakRequestsPerSecond': 123, 'CreatedAt': datetime(2015, 1, 1), 'EndpointUrl': 'string', 'EndpointStatus': 'NONE'|'READY'|'UPDATING'|'FAILED' }, 'TrainingParameters': { 'string': 'string' }, 'InputDataLocationS3': 'string', 'Algorithm': 'sgd', 'MLModelType': 'REGRESSION'|'BINARY'|'MULTICLASS', 'ScoreThreshold': ..., 'ScoreThresholdLastUpdatedAt': datetime(2015, 1, 1), 'Message': 'string', 'ComputeTime': 123, 'FinishedAt': datetime(2015, 1, 1), 'StartedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* Represents the output of a "DescribeMLModels" operation. The content is essentially a list of "MLModel". * **Results** *(list) --* A list of "MLModel" that meet the search criteria. * *(dict) --* Represents the output of a "GetMLModel" operation. The content consists of the detailed metadata and the current status of the "MLModel". * **MLModelId** *(string) --* The ID assigned to the "MLModel" at creation. * **TrainingDataSourceId** *(string) --* The ID of the training "DataSource". The "CreateMLModel" operation uses the "TrainingDataSourceId". * **CreatedByIamUser** *(string) --* The AWS user account from which the "MLModel" was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. * **CreatedAt** *(datetime) --* The time that the "MLModel" was created. The time is expressed in epoch time. * **LastUpdatedAt** *(datetime) --* The time of the most recent edit to the "MLModel". The time is expressed in epoch time. * **Name** *(string) --* A user-supplied name or description of the "MLModel". * **Status** *(string) --* The current status of an "MLModel". This element can have one of the following values: * "PENDING" - Amazon Machine Learning (Amazon ML) submitted a request to create an "MLModel". * "INPROGRESS" - The creation process is underway. * "FAILED" - The request to create an "MLModel" didn't run to completion. The model isn't usable. * "COMPLETED" - The creation process completed successfully. * "DELETED" - The "MLModel" is marked as deleted. It isn't usable. * **SizeInBytes** *(integer) --* Long integer type that is a 64-bit signed number. * **EndpointInfo** *(dict) --* The current endpoint of the "MLModel". * **PeakRequestsPerSecond** *(integer) --* The maximum processing rate for the real-time endpoint for "MLModel", measured in incoming requests per second. * **CreatedAt** *(datetime) --* The time that the request to create the real-time endpoint for the "MLModel" was received. The time is expressed in epoch time. * **EndpointUrl** *(string) --* The URI that specifies where to send real-time prediction requests for the "MLModel". **Note:** The application must wait until the real- time endpoint is ready before using this URI. * **EndpointStatus** *(string) --* The current status of the real-time endpoint for the "MLModel". This element can have one of the following values: * "NONE" - Endpoint does not exist or was previously deleted. * "READY" - Endpoint is ready to be used for real-time predictions. * "UPDATING" - Updating/creating the endpoint. * **TrainingParameters** *(dict) --* A list of the training parameters in the "MLModel". The list is implemented as a map of key-value pairs. The following is the current set of training parameters: * "sgd.maxMLModelSizeInBytes" - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from "100000" to "2147483648". The default value is "33554432". * "sgd.maxPasses" - The number of times that the training process traverses the observations to build the "MLModel". The value is an integer that ranges from "1" to "10000". The default value is "10". * "sgd.shuffleType" - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are "auto" and "none". The default value is "none". * "sgd.l1RegularizationAmount" - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as "1.0E-08". The value is a double that ranges from "0" to "MAX_DOUBLE". The default is to not use L1 normalization. This parameter can't be used when "L2" is specified. Use this parameter sparingly. * "sgd.l2RegularizationAmount" - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as "1.0E-08". The value is a double that ranges from "0" to "MAX_DOUBLE". The default is to not use L2 normalization. This parameter can't be used when "L1" is specified. Use this parameter sparingly. * *(string) --* String type. * *(string) --* String type. * **InputDataLocationS3** *(string) --* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). * **Algorithm** *(string) --* The algorithm used to train the "MLModel". The following algorithm is supported: * "SGD" -- Stochastic gradient descent. The goal of "SGD" is to minimize the gradient of the loss function. * **MLModelType** *(string) --* Identifies the "MLModel" category. The following are the available types: * "REGRESSION" - Produces a numeric result. For example, "What price should a house be listed at?" * "BINARY" - Produces one of two possible results. For example, "Is this a child-friendly web site?". * "MULTICLASS" - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?". * **ScoreThreshold** *(float) --* * **ScoreThresholdLastUpdatedAt** *(datetime) --* The time of the most recent edit to the "ScoreThreshold". The time is expressed in epoch time. * **Message** *(string) --* A description of the most recent details about accessing the "MLModel". * **ComputeTime** *(integer) --* Long integer type that is a 64-bit signed number. * **FinishedAt** *(datetime) --* A timestamp represented in epoch time. * **StartedAt** *(datetime) --* A timestamp represented in epoch time. * **NextToken** *(string) --* The ID of the next page in the paginated results that indicates at least one more page follows. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / describe_batch_predictions describe_batch_predictions ************************** MachineLearning.Client.describe_batch_predictions(**kwargs) Returns a list of "BatchPrediction" operations that match the search criteria in the request. See also: AWS API Documentation **Request Syntax** response = client.describe_batch_predictions( FilterVariable='CreatedAt'|'LastUpdatedAt'|'Status'|'Name'|'IAMUser'|'MLModelId'|'DataSourceId'|'DataURI', EQ='string', GT='string', LT='string', GE='string', LE='string', NE='string', Prefix='string', SortOrder='asc'|'dsc', NextToken='string', Limit=123 ) Parameters: * **FilterVariable** (*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 * **SortOrder** (*string*) -- A two-value parameter that determines the sequence of the resulting list of >>``<>``<GetBatchPrediction" operation and checking the "Status" parameter of the result. * **BatchPredictionId** *(string) --* A user-supplied ID that uniquely identifies the "BatchPrediction". This value is identical to the value of the "BatchPredictionId" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.InternalServerException" * "MachineLearning.Client.exceptions.IdempotentParameterMismatchEx ception" MachineLearning / Client / update_batch_prediction update_batch_prediction *********************** MachineLearning.Client.update_batch_prediction(**kwargs) Updates the "BatchPredictionName" of a "BatchPrediction". You can use the "GetBatchPrediction" operation to view the contents of the updated data element. See also: AWS API Documentation **Request Syntax** response = client.update_batch_prediction( BatchPredictionId='string', BatchPredictionName='string' ) Parameters: * **BatchPredictionId** (*string*) -- **[REQUIRED]** The ID assigned to the "BatchPrediction" during creation. * **BatchPredictionName** (*string*) -- **[REQUIRED]** A new user-supplied name or description of the "BatchPrediction". Return type: dict Returns: **Response Syntax** { 'BatchPredictionId': 'string' } **Response Structure** * *(dict) --* Represents the output of an "UpdateBatchPrediction" operation. You can see the updated content by using the "GetBatchPrediction" operation. * **BatchPredictionId** *(string) --* The ID assigned to the "BatchPrediction" during creation. This value should be identical to the value of the "BatchPredictionId" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException" MachineLearning / Client / create_ml_model create_ml_model *************** MachineLearning.Client.create_ml_model(**kwargs) 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. See also: AWS API Documentation **Request Syntax** response = client.create_ml_model( MLModelId='string', MLModelName='string', MLModelType='REGRESSION'|'BINARY'|'MULTICLASS', Parameters={ 'string': 'string' }, TrainingDataSourceId='string', Recipe='string', RecipeUri='string' ) Parameters: * **MLModelId** (*string*) -- **[REQUIRED]** A user-supplied ID that uniquely identifies the "MLModel". * **MLModelName** (*string*) -- A user-supplied name or description of the "MLModel". * **MLModelType** (*string*) -- **[REQUIRED]** 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** (*dict*) -- A list of the training parameters in the "MLModel". The list is implemented as a map of key-value pairs. The following is the current set of training parameters: * "sgd.maxMLModelSizeInBytes" - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from "100000" to "2147483648". The default value is "33554432". * "sgd.maxPasses" - The number of times that the training process traverses the observations to build the "MLModel". The value is an integer that ranges from "1" to "10000". The default value is "10". * "sgd.shuffleType" - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are "auto" and "none". The default value is "none". We strongly recommend that you shuffle your data. * "sgd.l1RegularizationAmount" - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as "1.0E-08". The value is a double that ranges from "0" to "MAX_DOUBLE". The default is to not use L1 normalization. This parameter can't be used when "L2" is specified. Use this parameter sparingly. * "sgd.l2RegularizationAmount" - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as "1.0E-08". The value is a double that ranges from "0" to "MAX_DOUBLE". The default is to not use L2 normalization. This parameter can't be used when "L1" is specified. Use this parameter sparingly. * *(string) --* String type. * *(string) --* String type. * **TrainingDataSourceId** (*string*) -- **[REQUIRED]** 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. * **RecipeUri** (*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. Return type: dict Returns: **Response Syntax** { 'MLModelId': 'string' } **Response Structure** * *(dict) --* Represents the output of a "CreateMLModel" operation, and is an acknowledgement that Amazon ML received the request. The "CreateMLModel" operation is asynchronous. You can poll for status updates by using the "GetMLModel" operation and checking the "Status" parameter. * **MLModelId** *(string) --* A user-supplied ID that uniquely identifies the "MLModel". This value should be identical to the value of the "MLModelId" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.InternalServerException" * "MachineLearning.Client.exceptions.IdempotentParameterMismatchEx ception" MachineLearning / Client / delete_evaluation delete_evaluation ***************** MachineLearning.Client.delete_evaluation(**kwargs) 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. See also: AWS API Documentation **Request Syntax** response = client.delete_evaluation( EvaluationId='string' ) Parameters: **EvaluationId** (*string*) -- **[REQUIRED]** A user-supplied ID that uniquely identifies the "Evaluation" to delete. Return type: dict Returns: **Response Syntax** { 'EvaluationId': 'string' } **Response Structure** * *(dict) --* Represents the output of a "DeleteEvaluation" operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request. You can use the "GetEvaluation" operation and check the value of the "Status" parameter to see whether an "Evaluation" is marked as "DELETED". * **EvaluationId** *(string) --* A user-supplied ID that uniquely identifies the "Evaluation". This value should be identical to the value of the "EvaluationId" in the request. **Exceptions** * "MachineLearning.Client.exceptions.InvalidInputException" * "MachineLearning.Client.exceptions.ResourceNotFoundException" * "MachineLearning.Client.exceptions.InternalServerException"