MachineLearning / Client / create_data_source_from_s3

create_data_source_from_s3#

MachineLearning.Client.create_data_source_from_s3(**kwargs)#

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.

See also: AWS API Documentation

Request Syntax

response = client.create_data_source_from_s3(
    DataSourceId='string',
    DataSourceName='string',
    DataSpec={
        'DataLocationS3': 'string',
        'DataRearrangement': 'string',
        'DataSchema': 'string',
        'DataSchemaLocationS3': 'string'
    },
    ComputeStatistics=True|False
)
Parameters:

  • DataSourceId (string) –

    [REQUIRED]

    A user-supplied identifier that uniquely identifies the DataSource.

  • DataSourceName (string) – A user-supplied name or description of the DataSource.

  • DataSpec (dict) –

    [REQUIRED]

    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}}"

    • DataLocationS3 (string) – [REQUIRED]

      The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.

    • 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 S3 DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

      You must provide either the DataSchema or the DataSchemaLocationS3.

      Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

      { “version”: “1.0”,

      ”recordAnnotationFieldName”: “F1”,

      ”recordWeightFieldName”: “F2”,

      ”targetFieldName”: “F3”,

      ”dataFormat”: “CSV”,

      ”dataFileContainsHeader”: true,

      ”attributes”: [

      { “fieldName”: “F1”, “fieldType”: “TEXT” }, { “fieldName”: “F2”, “fieldType”: “NUMERIC” }, { “fieldName”: “F3”, “fieldType”: “CATEGORICAL” }, { “fieldName”: “F4”, “fieldType”: “NUMERIC” }, { “fieldName”: “F5”, “fieldType”: “CATEGORICAL” }, { “fieldName”: “F6”, “fieldType”: “TEXT” }, { “fieldName”: “F7”, “fieldType”: “WEIGHTED_INT_SEQUENCE” }, { “fieldName”: “F8”, “fieldType”: “WEIGHTED_STRING_SEQUENCE” } ],

      ”excludedVariableNames”: [ “F6” ] }

    • DataSchemaLocationS3 (string) –

      Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3.

  • 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 CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.

    The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

    • 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.IdempotentParameterMismatchException