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

    Click :ref:`here <sphx_glr_download_auto_examples_plot_4_example_combination_of_classifiers.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_4_example_combination_of_classifiers.py:


.. _example4:

Fourth Example: Combination of classifiers
-------------------------------------------------

A set of classifiers is combined as input to a neural network. Additionally, the scaled inputs are injected as well to
the neural network. The data is firstly transformed by scaling its features.

Steps of the **PipeGraph**:

- **scaler**: A :class:`MinMaxScaler` data preprocessor
- **gaussian_nb**: A :class:`GaussianNB` classifier
- **svc**: A :class:`SVC` classifier
- **concat**: A :class:`Concatenator` custom class that appends the outputs of the :class:`GaussianNB`, :class:`SVC` classifiers, and the scaled inputs.
- **mlp**: A :class:`MLPClassifier` object

.. figure:: https://raw.githubusercontent.com/mcasl/PipeGraph/master/examples/images/Diapositiva4.png

    Figure 1. PipeGraph diagram showing the steps and their connections



.. code-block:: python

    import numpy as np
    import pandas as pd
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import GridSearchCV
    from pipegraph.base import PipeGraph, Concatenator
    import matplotlib.pyplot as plt
    from sklearn.datasets import load_iris
    from sklearn.naive_bayes import GaussianNB
    from sklearn.svm import SVC
    from sklearn.neural_network import MLPClassifier

    iris = load_iris()
    X = iris.data
    y = iris.target

    scaler = MinMaxScaler()
    gaussian_nb = GaussianNB()
    svc = SVC()
    mlp = MLPClassifier()
    concatenator = Concatenator()

    steps = [('scaler', scaler),
             ('gaussian_nb', gaussian_nb),
             ('svc', svc),
             ('concat', concatenator),
             ('mlp', mlp)]








In this example we use a :class:`PipeGraphClassifier` because the result is a classification and we want to take advantage of Scikit-Learn default scoring method for classifiers.



.. code-block:: python


    pgraph = PipeGraph(steps=steps)
    (pgraph.inject(sink='scaler', sink_var='X', source='_External', source_var='X')
           .inject('gaussian_nb', 'X', 'scaler')
           .inject('gaussian_nb', 'y', source_var='y')
           .inject('svc', 'X', 'scaler')
           .inject('svc', 'y', source_var='y')
           .inject('concat', 'X1', 'scaler')
           .inject('concat', 'X2', 'gaussian_nb')
           .inject('concat', 'X3', 'svc')
           .inject('mlp', 'X', 'concat')
           .inject('mlp', 'y', source_var='y')
    )

    param_grid = {'svc__C': [0.1, 0.5, 1.0],
                  'mlp__hidden_layer_sizes': [(3,), (6,), (9,),],
                  'mlp__max_iter': [5000, 10000]}

    grid_search_classifier  = GridSearchCV(estimator=pgraph, param_grid=param_grid, refit=True)
    grid_search_classifier.fit(X, y)
    y_pred = grid_search_classifier.predict(X)

    grid_search_classifier.best_estimator_.get_params()


    # Code for plotting the confusion matrix taken
    #  from 'Python Data Science Handbook' by Jake VanderPlas
    from sklearn.metrics import confusion_matrix
    import seaborn as sns; sns.set()  # for plot styling

    mat = confusion_matrix(y_pred, y)
    sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)
    plt.xlabel('true label')
    plt.ylabel('predicted label');

    plt.show()





.. image:: /auto_examples/images/sphx_glr_plot_4_example_combination_of_classifiers_001.png
    :class: sphx-glr-single-img




This example displayed complex data injections that are successfully managed by **PipeGraph**.
Next example :ref:`on <example5>`.


**Total running time of the script:** ( 0 minutes  16.432 seconds)


.. _sphx_glr_download_auto_examples_plot_4_example_combination_of_classifiers.py:


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



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     :download:`Download Jupyter notebook: plot_4_example_combination_of_classifiers.ipynb <plot_4_example_combination_of_classifiers.ipynb>`


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

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