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        "author": "Marinka Zitnik",
        "author_email": "marinka.zitnik@fri.uni-lj.si",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Software Development"
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        "description": ".. -*- mode: rst -*-\n\n=============\nscikit-fusion\n=============\n\n|Travis|_\n\n.. |Travis| image:: https://travis-ci.org/marinkaz/scikit-fusion.svg?branch=master\n.. _Travis: https://travis-ci.org/marinkaz/scikit-fusion\n\n*scikit-fusion* is a Python module for data fusion based on recent collective latent\nfactor models.\n\nDependencies\n============\n\nscikit-fusion is tested to work under Python 3.\n\nThe required dependencies to build the software are Numpy >= 1.7, SciPy >= 0.12,\nPyGraphviz >= 1.3 (needed only for drawing data fusion graphs) and Joblib >= 0.8.4.\n\nInstall\n=======\n\nThis package uses distutils, which is the default way of installing\npython modules. To install in your home directory, use::\n\n    python setup.py install --user\n\nTo install for all users on Unix/Linux::\n\n    python setup.py build\n    sudo python setup.py install\n\nFor development mode use::\n\n    python setup.py develop\n\nUsage\n=====\n\nLet's generate three random data matrices describing three different object types::\n\n     >>> import numpy as np\n     >>> R12 = np.random.rand(50, 100)\n     >>> R13 = np.random.rand(50, 40)\n     >>> R23 = np.random.rand(100, 40)\n\nNext, we define our data fusion graph::\n\n     >>> from skfusion import fusion\n     >>> t1 = fusion.ObjectType('Type 1', 10)\n     >>> t2 = fusion.ObjectType('Type 2', 20)\n     >>> t3 = fusion.ObjectType('Type 3', 30)\n     >>> relations = [fusion.Relation(R12, t1, t2),\n                      fusion.Relation(R13, t1, t3),\n                      fusion.Relation(R23, t2, t3)]\n     >>> fusion_graph = fusion.FusionGraph()\n     >>> fusion_graph.add_relations_from(relations)\n\nand then collectively infer the latent data model::\n\n     >>> fuser = fusion.Dfmf()\n     >>> fuser.fuse(fusion_graph)\n     >>> print(fuser.factor(t1).shape)\n     (50, 10)\n\n\nAfterwards new data might arrive::\n\n     >>> new_R12 = np.random.rand(10, 100)\n     >>> new_R13 = np.random.rand(10, 40)\n\nfor which we define the fusion graph::\n\n     >>> new_relations = [fusion.Relation(new_R12, t1, t2),\n                          fusion.Relation(new_R13, t1, t3)]\n     >>> new_graph = fusion.FusionGraph(new_relations)\n\nand transform new objects to the latent space induced by the ``fuser``::\n\n     >>> transformer = fusion.DfmfTransform()\n     >>> transformer.transform(t1, new_graph, fuser)\n     >>> print(transformer.factor(t1).shape)\n     (10, 10)\n\n****\n\nscikit-fusion is distributed with a few working data fusion scenarios::\n\n    >>> from skfusion import datasets\n    >>> dicty = datasets.load_dicty()\n    >>> print(dicty)\n    FusionGraph(Object types: 3, Relations: 3)\n    >>> print(dicty.object_types)\n    {ObjectType(GO term), ObjectType(Experimental condition), ObjectType(Gene)}\n    >>> print(dicty.relations)\n    {Relation(ObjectType(Gene), ObjectType(GO term)),\n     Relation(ObjectType(Gene), ObjectType(Gene)),\n     Relation(ObjectType(Gene), ObjectType(Experimental condition))}\n\nRelevant links\n==============\n\n- Official source code repo: https://github.com/marinkaz/scikit-fusion\n- HTML documentation: TBA\n- Download releases: https://github.com/marinkaz/scikit-fusion/releases\n- Issue tracker: https://github.com/marinkaz/scikit-fusion/issues\n\n****\n\n- Data fusion by matrix factorization: http://dx.doi.org/10.1109/TPAMI.2014.2343973\n- Discovering disease-disease associations by fusing systems-level molecular data: http://www.nature.com/srep/2013/131115/srep03202/full/srep03202.html\n- Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold: http://www.worldscientific.com/doi/pdf/10.1142/9789814583220_0038\n- Matrix factorization-based data fusion for drug-induced liver injury prediction: http://www.tandfonline.com/doi/abs/10.4161/sysb.29072\n- Survival regression by data fusion: http://www.tandfonline.com/doi/abs/10.1080/21628130.2015.1016702",
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