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

    Click :ref:`here <sphx_glr_download_auto_examples_plot_7_example_DemuxModelsMux.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_7_example_DemuxModelsMux.py:


.. _example7:

Seventh Example: Dynamically built component using initialization parameters
----------------------------------------------------------------------------

We continue demonstrating several interesting features:
 1. How the user can choose to encapsulate several blocks into a PipeGraph and use it as a single unit in another PipeGraph
 2. How these components can be dynamically built on runtime depending on initialization parameters
 3. How these components can be dynamically built on runtime depending on input signal values during fit
 4. Using GridSearchCV to explore the best combination of hyperparameters




.. code-block:: python

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt

    from sklearn.linear_model import LinearRegression
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.mixture import GaussianMixture

    from pipegraph.base import PipeGraph, PipeGraph, Demultiplexer, Multiplexer, \
        RegressorsWithParametrizedNumberOfReplicas

    X_first = pd.Series(np.random.rand(100,))
    y_first = pd.Series(4 * X_first + 0.5*np.random.randn(100,))
    X_second = pd.Series(np.random.rand(100,) + 3)
    y_second = pd.Series(-4 * X_second + 0.5*np.random.randn(100,))
    X_third = pd.Series(np.random.rand(100,) + 6)
    y_third = pd.Series(2 * X_third + 0.5*np.random.randn(100,))

    X = pd.concat([X_first, X_second, X_third], axis=0).to_frame()
    y = pd.concat([y_first, y_second, y_third], axis=0).to_frame()







We can think of programatically changing the number of models inside this component.
First we do it by using initialization parameters in a :class:``PipeGraph`` subclass
we called :class:``pipegraph.standard_blocks.RegressorsWithParametrizedNumberOfReplicas``:



.. code-block:: python


    import inspect
    print(inspect.getsource(RegressorsWithParametrizedNumberOfReplicas))





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    class RegressorsWithParametrizedNumberOfReplicas(PipeGraph, RegressorMixin):
        def __init__(self, number_of_replicas=1, regressor=LinearRegression()):
            self.number_of_replicas = number_of_replicas
            self.regressor = regressor

            steps = ([('demux', Demultiplexer())] +
                     [('regressor_' + str(i), clone(regressor)) for i in range(number_of_replicas)] +
                     [('mux', Multiplexer())]
                     )

            connections = dict(demux={'X': 'X',
                                      'y': 'y',
                                      'selection': 'selection'})

            for i in range(number_of_replicas):
                connections['regressor_' + str(i)] = {'X': ('demux', 'X_' + str(i)),
                                                   'y': ('demux', 'y_' + str(i))}

            connections['mux'] = {str(i): ('regressor_' + str(i)) for i in range(number_of_replicas)}
            connections['mux']['selection'] = 'selection'
            super().__init__(steps=steps, fit_connections=connections)


Using this new component we can build a simplified PipeGraph:



.. code-block:: python


    scaler = MinMaxScaler()
    gaussian_mixture = GaussianMixture(n_components=3)
    models = RegressorsWithParametrizedNumberOfReplicas(number_of_replicas=3, regressor=LinearRegression())

    steps = [('scaler', scaler),
             ('classifier', gaussian_mixture),
             ('models', models), ]

    connections = {'scaler': {'X': 'X'},
                   'classifier': {'X': 'scaler'},
                   'models': {'X': 'scaler',
                              'y': 'y',
                              'selection': 'classifier'},
                   }

    pgraph = PipeGraph(steps=steps, fit_connections=connections)
    pgraph.fit(X, y)
    y_pred = pgraph.predict(X)
    plt.scatter(X, y)
    plt.scatter(X, y_pred)



.. image:: /auto_examples/images/sphx_glr_plot_7_example_DemuxModelsMux_001.png
    :class: sphx-glr-single-img




**Total running time of the script:** ( 0 minutes  0.037 seconds)


.. _sphx_glr_download_auto_examples_plot_7_example_DemuxModelsMux.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_7_example_DemuxModelsMux.py <plot_7_example_DemuxModelsMux.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_7_example_DemuxModelsMux.ipynb <plot_7_example_DemuxModelsMux.ipynb>`


.. only:: html

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

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