.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/graph_based/plot_1_dl2vec.py"
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.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_graph_based_plot_1_dl2vec.py>`
        to download the full example code.

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

.. _sphx_glr_examples_graph_based_plot_1_dl2vec.py:


DL2Vec
========

This example corresponds to the paper `Predicting candidate genes from phenotypes, functions and anatomical site of expression <https://doi.org/10.1093/bioinformatics/btaa879>`_. 

This work is a graph-based machine-learning method to learn from biomedical ontologies. This method works by transforming the ontology into a graph following a set of rules. Random walks are generated from the obtained graph and then processed by a Word2Vec model, which generates 
embeddings of the original ontology classes. This algorithm is applied to generate numerical representations of genes and diseases based on the background knowledge found in the Gene Ontology, which was extended to incorporate phenotypes, functions of the gene products and anatomical location of gene expression. The representations of genes and diseases are then used to predict candidate genes for a given disease.

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To show an example of DL2Vec, we need 3 components:

- The ontology projector
- The random walks generator
- The Word2Vec model

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.. code-block:: Python


    import sys
    sys.path.append('../../')
    import mowl
    mowl.init_jvm("10g")

    from mowl.datasets.builtin import GDADatasetV2
    from mowl.models import RandomWalkPlusW2VModel
    from mowl.projection import DL2VecProjector
    from mowl.walking import DeepWalk
    from gensim.models.word2vec import LineSentence
    from gensim.models import Word2Vec








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Instantiating the dataset and the model

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.. code-block:: Python


    dataset = GDADatasetV2()

    print(f"Number of classes: f{len(dataset.classes)}")

    model = RandomWalkPlusW2VModel(dataset)
    model.set_projector(DL2VecProjector())
    model.set_walker(DeepWalk(5, 5, 0.1, workers=4))
    model.set_w2v_model(vector_size=5, epochs=2, window=5, min_count=1, workers=4)
    model.train()




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

 .. code-block:: none

    Number of classes: f197774




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Evaluating the model

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.. code-block:: Python


    from mowl.evaluation import GDAEvaluator
    model.set_evaluator(GDAEvaluator)
    model.evaluate(dataset.testing)









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

   **Total running time of the script:** (1 minutes 11.823 seconds)

**Estimated memory usage:**  5145 MB


.. _sphx_glr_download_examples_graph_based_plot_1_dl2vec.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_1_dl2vec.ipynb <plot_1_dl2vec.ipynb>`

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

      :download:`Download Python source code: plot_1_dl2vec.py <plot_1_dl2vec.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_1_dl2vec.zip <plot_1_dl2vec.zip>`


.. only:: html

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

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