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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_node_attributed_dataset.py:


=======================================================================
Graph classification on a dataset that contains node-attributed graphs.
=======================================================================

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

.. GENERATED FROM PYTHON SOURCE LINES 8-40

.. code-block:: default

    from __future__ import print_function
    print(__doc__)

    import numpy as np

    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC
    from sklearn.metrics import accuracy_score

    from grakel.datasets import fetch_dataset
    from grakel.kernels import PropagationAttr

    # Loads the ENZYMES dataset
    ENZYMES_attr = fetch_dataset("ENZYMES", prefer_attr_nodes=True, verbose=False)
    G, y = ENZYMES_attr.data, ENZYMES_attr.target

    # Splits the dataset into a training and a test set
    G_train, G_test, y_train, y_test = train_test_split(G, y, test_size=0.1, random_state=42)

    # Uses the graphhopper kernel to generate the kernel matrices
    gk = PropagationAttr(normalize=True)
    K_train = gk.fit_transform(G_train)
    K_test = gk.transform(G_test)

    # Uses the SVM classifier to perform classification
    clf = SVC(kernel="precomputed")
    clf.fit(K_train, y_train)
    y_pred = clf.predict(K_test)

    # Computes and prints the classification accuracy
    acc = accuracy_score(y_test, y_pred)
    print("Accuracy:", str(round(acc*100, 2)) + "%")


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

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


.. _sphx_glr_download_auto_examples_node_attributed_dataset.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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