import boto3

client = boto3.client('machinelearning')
print "connected to ml"
try:
    #datasource for testing

    response = client.create_data_source_from_s3(
        DataSourceId='test-1234-id-11a',
        DataSourceName='test-1234-test',
        DataSpec={
            'DataLocationS3': 's3://1234-ml/banking.csv',
        'DataRearrangement':"{\"splitting\":{\"percentBegin\":90,\"percentEnd\":100}}",
            'DataSchemaLocationS3': 's3://1234-ml/dataschema.json'
        },
        ComputeStatistics=True
    )
    print "data for testing created"
    #training data source
    response = client.create_data_source_from_s3(
        DataSourceId='train-1234-id-12a',
        DataSourceName='test-1234-data',
        DataSpec={
            'DataLocationS3': 's3://1234-ml/banking.csv',
        'DataRearrangement':"{\"splitting\":{\"percentBegin\":0,\"percentEnd\":90}}",
            'DataSchemaLocationS3': 's3://1234-ml/dataschema.json'
        },
        ComputeStatistics=True
    )
    print "data for training created"
    response = client.create_ml_model(
        MLModelId='ml-1234-model-1a',
        MLModelName='ml-1234-model-name',
        MLModelType='BINARY',
        TrainingDataSourceId='train-1234-id-12a'
    )
    print "model created "
    response = client.create_evaluation(
        EvaluationId='ml-1234-eval-1a',
        EvaluationName='ml-1234-eval-name',
        MLModelId='ml-1234-model-1a',
        EvaluationDataSourceId='test-1234-id-11a'
    )
    print "model evaluation "
    response = client.get_evaluation(
        EvaluationId='ml-1234-eval-1a'
    )
    print "getting evaluation "
    print response
except Exception as e:
        print(e)