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

    Click :ref:`here <sphx_glr_download_auto_examples_plot_1_example_two_nodes_in_linear_sequence.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_1_example_two_nodes_in_linear_sequence.py:


.. _example1:

First example: A simple linear workflow
-----------------------------------------

Let's start with a simple example that could be perfectly expressed using a :class:`Pipeline`. The data is transformed using a :class:`MinMaxScaler` step and the preprocessed data is fed to a linear model.

Steps of the **PipeGraph**:

- **scaler**: preprocesses the data using a :class:`MinMaxScaler` object
- **linear_model**: fits and predicts a :class:`LinearRegression` model

.. figure:: https://raw.githubusercontent.com/mcasl/PipeGraph/master/examples/images/Diapositiva1.png

    Figure 1. PipeGraph diagram showing the steps and their connections

.. _fig2:

.. figure:: https://raw.githubusercontent.com/mcasl/PipeGraph/master/examples/images/Diapositiva1-2.png

    Figure 2. Illustration of the connections of the PipeGraph


Firstly, we import the necessary libraries and create some artificial data



.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.linear_model import LinearRegression
    from sklearn.pipeline import Pipeline
    from pipegraph import PipeGraph

    X = np.random.rand(100, 1)
    y = 4 * X + 0.5*np.random.randn(100, 1)








Secondly, we define the steps of the **PipeGraph** as a list of tuples (label, sklearn object) as if we were defining a standard :class:`Pipeline`.



.. code-block:: python


    scaler = MinMaxScaler()
    linear_model = LinearRegression()
    steps = [('scaler', scaler),
            ('linear_model', linear_model)]







As the last step is a regressor, a :class:`PipeGraphRegressor` is instantiated.
 The results from applying ``fit`` and ``predict`` and predict data are shown.



.. code-block:: python


    pgraph = PipeGraph(steps=steps)
    pgraph.fit(X, y)
    y_pred = pgraph.predict(X)

    plt.scatter(X, y, label='Original Data')
    plt.scatter(X, y_pred, label='Predicted Data')
    plt.title('Plots of original and predicted data')
    plt.legend(loc='best')
    plt.grid(True)
    plt.xlabel('Index')
    plt.ylabel('Value of Data')
    plt.show()




.. image:: /auto_examples/images/sphx_glr_plot_1_example_two_nodes_in_linear_sequence_001.png
    :class: sphx-glr-single-img




This example described the basic configuration of a :class:`PipeGraphRegressor`. The :ref:`following examples <example2>` show more elaborated cases in order of increasing complexity.


**Total running time of the script:** ( 0 minutes  0.787 seconds)


.. _sphx_glr_download_auto_examples_plot_1_example_two_nodes_in_linear_sequence.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_1_example_two_nodes_in_linear_sequence.py <plot_1_example_two_nodes_in_linear_sequence.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_1_example_two_nodes_in_linear_sequence.ipynb <plot_1_example_two_nodes_in_linear_sequence.ipynb>`


.. only:: html

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

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