docs-archive/apache-airflow-providers-amazon/5.0.0/_sources/_api/tests/system/providers/amazon/aws/example_sagemaker/index.rst.txt

:py:mod:`tests.system.providers.amazon.aws.example_sagemaker`
=============================================================

.. py:module:: tests.system.providers.amazon.aws.example_sagemaker


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   tests.system.providers.amazon.aws.example_sagemaker.set_up
   tests.system.providers.amazon.aws.example_sagemaker.delete_ecr_repository
   tests.system.providers.amazon.aws.example_sagemaker.delete_logs



Attributes
~~~~~~~~~~

.. autoapisummary::

   tests.system.providers.amazon.aws.example_sagemaker.DAG_ID
   tests.system.providers.amazon.aws.example_sagemaker.ROLE_ARN_KEY
   tests.system.providers.amazon.aws.example_sagemaker.KNN_IMAGE_URI_KEY
   tests.system.providers.amazon.aws.example_sagemaker.sys_test_context_task
   tests.system.providers.amazon.aws.example_sagemaker.DATASET
   tests.system.providers.amazon.aws.example_sagemaker.SAMPLE_SIZE
   tests.system.providers.amazon.aws.example_sagemaker.PREPROCESS_SCRIPT_TEMPLATE
   tests.system.providers.amazon.aws.example_sagemaker.test_context
   tests.system.providers.amazon.aws.example_sagemaker.test_run


.. py:data:: DAG_ID
   :annotation: = example_sagemaker

   

.. py:data:: ROLE_ARN_KEY
   :annotation: = ROLE_ARN

   

.. py:data:: KNN_IMAGE_URI_KEY
   :annotation: = KNN_IMAGE_URI

   

.. py:data:: sys_test_context_task
   

   

.. py:data:: DATASET
   :annotation: = Multiline-String

    .. raw:: html

        <details><summary>Show Value</summary>

    .. code-block:: text
        :linenos:

        
                5.1,3.5,1.4,0.2,Iris-setosa
                4.9,3.0,1.4,0.2,Iris-setosa
                7.0,3.2,4.7,1.4,Iris-versicolor
                6.4,3.2,4.5,1.5,Iris-versicolor
                4.9,2.5,4.5,1.7,Iris-virginica
                7.3,2.9,6.3,1.8,Iris-virginica
                

    .. raw:: html

        </details>

   

.. py:data:: SAMPLE_SIZE
   

   

.. py:data:: PREPROCESS_SCRIPT_TEMPLATE
   :annotation: = Multiline-String

    .. raw:: html

        <details><summary>Show Value</summary>

    .. code-block:: text
        :linenos:

        
        import boto3
        import numpy as np
        import pandas as pd

        def main():
            # Load the Iris dataset from {input_path}/input.csv, split it into train/test
            # subsets, and write them to {output_path}/ for the Processing Operator.

            columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
            iris = pd.read_csv('{input_path}/input.csv', names=columns)

            # Process data
            iris['species'] = iris['species'].replace({{'Iris-virginica': 0, 'Iris-versicolor': 1, 'Iris-setosa': 2}})
            iris = iris[['species', 'sepal_length', 'sepal_width', 'petal_length', 'petal_width']]

            # Split into test and train data
            iris_train, iris_test = np.split(
                iris.sample(frac=1, random_state=np.random.RandomState()), [int(0.7 * len(iris))]
            )

            # Remove the "answers" from the test set
            iris_test.drop(['species'], axis=1, inplace=True)

            # Write the splits to disk
            iris_train.to_csv('{output_path}/train.csv', index=False, header=False)
            iris_test.to_csv('{output_path}/test.csv', index=False, header=False)

            print('Preprocessing Done.')

        if __name__ == "__main__":
            main()

            

    .. raw:: html

        </details>

   

.. py:function:: set_up(env_id, knn_image_uri, role_arn)


.. py:function:: delete_ecr_repository(repository_name)


.. py:function:: delete_logs(env_id)


.. py:data:: test_context
   

   

.. py:data:: test_run