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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_erdos_renyi.py:


===========================================================================
Graph classification on a randomly generated dataset of Erdos-Renyi graphs.
===========================================================================

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

.. GENERATED FROM PYTHON SOURCE LINES 8-56

.. code-block:: default

    from __future__ import print_function
    print(__doc__)

    import numpy as np

    from random import random

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

    from grakel import Graph
    from grakel.kernels import ShortestPath

    # Generates 3 sets of Erdos-Renyi graphs. Each edge is included in the graph with probability p
    # independent from every other edge. The probability p is set equal to 0.25, 0.5 and 0.75 for 
    # the graphs of the 1st, 2nd and 3rd set, respectivery
    Gs = list()
    y = list()
    probs = [0.25, 0.5, 0.75]
    for i in range(len(probs)):
    	for j in range(5, 35):
    		edges = list()
    		for n1 in range(j):
    			for n2 in range(n1+1, j):
    				if random() <= probs[i]:
    					edges.append((n1, n2))
    					edges.append((n2, n1))

    		Gs.append(Graph(edges))
    		y.append(i)

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

    # Uses the shortest path kernel to generate the kernel matrices
    gk = ShortestPath(normalize=True, with_labels=False)
    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_erdos_renyi.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: erdos_renyi.py <erdos_renyi.py>`



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

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


.. only:: html

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

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