'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` is created and
ready for use, Amazon ML sets the `Status` parameter to
`COMPLETED`. `DataSource` in `COMPLETED` or `PENDING` status
can only be used to perform CreateMLModel, CreateEvaluation or
CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the
`Status` parameter to `FAILED` and includes an error message
in the `Message` attribute of the GetDataSource operation
response.
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) bucket, 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` requires
another item: a recipe. A recipe describes the observation
variables that participate in training an `MLModel`. A recipe
describes how each input variable will be used in training.
Will the variable be included or excluded from training? Will
the variable be manipulated, for example, combined with
another variable, or split apart into word combinations? The
recipe provides answers to these questions. For more
information, see the `Amazon Machine Learning Developer
Guide`_.
:type data_source_id: string
:param data_source_id: A user-supplied identifier that uniquely
identifies the `DataSource`.
:type data_source_name: string
:param data_source_name: A user-supplied name or description of the
`DataSource`.
:type data_spec: dict
:param data_spec:
The data specification of a `DataSource`:
+ DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location
of the observation data.
+ DataSchemaLocationS3 - 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 representing the splitting
requirement of a `Datasource`. Sample - ` "{"randomSeed":"some-
random-seed",
"splitting":{"percentBegin":10,"percentEnd":60}}"`
:type compute_statistics: boolean
:param compute_statistics: The compute statistics for a `DataSource`.
The statistics are generated from the observation data referenced
by a `DataSource`. Amazon ML uses the statistics internally during
an `MLModel` training. This parameter must be set to `True` if the
``DataSource `` needs to be used for `MLModel` training'

def create_data_source_from_s3(self, data_source_id, data_spec, data_source_name=None, compute_statistics=None):

    params = {'DataSourceId': data_source_id, 'DataSpec': data_spec}
    if (data_source_name is not None):
        params['DataSourceName'] = data_source_name
    if (compute_statistics is not None):
        params['ComputeStatistics'] = compute_statistics
    return self.make_request(action='CreateDataSourceFromS3', body=json.dumps(params))
