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

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

.. _sphx_glr_auto_examples_plot_pipeline_example.py:


====================================================
Example of building a graph classification pipeline.
====================================================

Script makes use of :class:`grakel.ShortestPath`




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

 Out:

 .. code-block:: none


    Accuracy: 82.45%






|


.. code-block:: default

    from __future__ import print_function
    print(__doc__)

    import numpy as np

    from sklearn.svm import SVC
    from sklearn.model_selection import GridSearchCV
    from sklearn.model_selection import cross_val_predict
    from sklearn.pipeline import make_pipeline
    from sklearn.metrics import accuracy_score

    from grakel.datasets import fetch_dataset
    from grakel.kernels import ShortestPath

    # Loads the Mutag dataset from:
    MUTAG = fetch_dataset("MUTAG", verbose=False)
    G, y = MUTAG.data, MUTAG.target

    # Values of C parameter of SVM
    C_grid = (10. ** np.arange(-4,6,1) / len(G)).tolist()

    # Creates pipeline
    estimator = make_pipeline(
        ShortestPath(normalize=True),
        GridSearchCV(SVC(kernel='precomputed'), dict(C=C_grid),
                     scoring='accuracy', cv=10))

    # Performs cross-validation and computes accuracy
    n_folds = 10
    acc = accuracy_score(y, cross_val_predict(estimator, G, y, cv=n_folds))
    print("Accuracy:", str(round(acc*100, 2)) + "%")

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

   **Total running time of the script:** ( 0 minutes  6.481 seconds)


.. _sphx_glr_download_auto_examples_plot_pipeline_example.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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