.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto/case_studies/feature_engineering/eda/notebook.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_case_studies_feature_engineering_eda_notebook.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_case_studies_feature_engineering_eda_notebook.py:


Flyte Pipeline in One Jupyter Notebook
=======================================

In this example, we will implement a simple pipeline that takes hyperparameters, does EDA, feature engineering, and measures the Gradient
Boosting model's performance using mean absolute error (MAE), all in one notebook.

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First, let's import the libraries we will use in this example.

.. GENERATED FROM PYTHON SOURCE LINES 11-17

.. code-block:: default

    import os
    import pathlib

    from flytekit import Resources, kwtypes, workflow
    from flytekitplugins.papermill import NotebookTask


.. GENERATED FROM PYTHON SOURCE LINES 18-34

We define a ``NotebookTask`` to run the `Jupyter notebook
<https://github.com/flyteorg/flytesnacks/blob/master/cookbook/case_studies/feature_engineering/eda/supermarket_regression.ipynb>`__.

.. list-table:: ``NotebookTask`` Parameters
   :widths: 25 25

   * - ``notebook_path``
     - Path to the Jupyter notebook file
   * - ``inputs``
     - Inputs to be sent to the notebook
   * - ``outputs``
     - Outputs to be returned from the notebook
   * - ``requests``
     - Specify compute resource requests for your task.

This notebook returns ``mae_score`` as the output.

.. GENERATED FROM PYTHON SOURCE LINES 34-50

.. code-block:: default

    nb = NotebookTask(
        name="pipeline-nb",
        notebook_path=os.path.join(
            pathlib.Path(__file__).parent.absolute(), "supermarket_regression.ipynb"
        ),
        inputs=kwtypes(
            n_estimators=int,
            max_depth=int,
            max_features=str,
            min_samples_split=int,
            random_state=int,
        ),
        outputs=kwtypes(mae_score=float),
        requests=Resources(mem="500Mi"),
    )


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Since a task need not be defined, we create a ``workflow`` and return the MAE score.

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.. code-block:: default



    @workflow
    def notebook_wf(
        n_estimators: int = 150,
        max_depth: int = 3,
        max_features: str = "sqrt",
        min_samples_split: int = 4,
        random_state: int = 2,
    ) -> float:
        output = nb(
            n_estimators=n_estimators,
            max_depth=max_depth,
            max_features=max_features,
            min_samples_split=min_samples_split,
            random_state=random_state,
        )
        return output.mae_score



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We can now run the notebook locally.

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.. code-block:: default

    if __name__ == "__main__":
        print(notebook_wf())


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_case_studies_feature_engineering_eda_notebook.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: notebook.py <notebook.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: notebook.ipynb <notebook.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_