scikit-fusion 0.2

Dependencies

scikit-fusion is tested to work under Python 3.

The required dependencies to build the software are Numpy >= 1.7, SciPy >= 0.12, PyGraphviz >= 1.3 (needed only for drawing data fusion graphs) and Joblib >= 0.8.4.

Install

This package uses distutils, which is the default way of installing python modules. To install in your home directory, use:

python setup.py install --user

To install for all users on Unix/Linux:

python setup.py build
sudo python setup.py install

For development mode use:

python setup.py develop

Usage

Let’s generate three random data matrices describing three different object types:

>>> import numpy as np
>>> R12 = np.random.rand(50, 100)
>>> R13 = np.random.rand(50, 40)
>>> R23 = np.random.rand(100, 40)

Next, we define our data fusion graph:

>>> from skfusion import fusion
>>> t1 = fusion.ObjectType('Type 1', 10)
>>> t2 = fusion.ObjectType('Type 2', 20)
>>> t3 = fusion.ObjectType('Type 3', 30)
>>> relations = [fusion.Relation(R12, t1, t2),
                 fusion.Relation(R13, t1, t3),
                 fusion.Relation(R23, t2, t3)]
>>> fusion_graph = fusion.FusionGraph()
>>> fusion_graph.add_relations_from(relations)

and then collectively infer the latent data model:

>>> fuser = fusion.Dfmf()
>>> fuser.fuse(fusion_graph)
>>> print(fuser.factor(t1).shape)
(50, 10)

Afterwards new data might arrive:

>>> new_R12 = np.random.rand(10, 100)
>>> new_R13 = np.random.rand(10, 40)

for which we define the fusion graph:

>>> new_relations = [fusion.Relation(new_R12, t1, t2),
                     fusion.Relation(new_R13, t1, t3)]
>>> new_graph = fusion.FusionGraph(new_relations)

and transform new objects to the latent space induced by the fuser:

>>> transformer = fusion.DfmfTransform()
>>> transformer.transform(t1, new_graph, fuser)
>>> print(transformer.factor(t1).shape)
(10, 10)

scikit-fusion is distributed with a few working data fusion scenarios:

>>> from skfusion import datasets
>>> dicty = datasets.load_dicty()
>>> print(dicty)
FusionGraph(Object types: 3, Relations: 3)
>>> print(dicty.object_types)
{ObjectType(GO term), ObjectType(Experimental condition), ObjectType(Gene)}
>>> print(dicty.relations)
{Relation(ObjectType(Gene), ObjectType(GO term)),
 Relation(ObjectType(Gene), ObjectType(Gene)),
 Relation(ObjectType(Gene), ObjectType(Experimental condition))}

Relevant links

• Official source code repo: https://github.com/marinkaz/scikit-fusion

• HTML documentation: TBA

• Download releases: https://github.com/marinkaz/scikit-fusion/releases

• Issue tracker: https://github.com/marinkaz/scikit-fusion/issues

• Data fusion by matrix factorization: http://dx.doi.org/10.1109/TPAMI.2014.2343973

• Discovering disease-disease associations by fusing systems-level molecular data: http://www.nature.com/srep/2013/131115/srep03202/full/srep03202.html

• 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

• Matrix factorization-based data fusion for drug-induced liver injury prediction: http://www.tandfonline.com/doi/abs/10.4161/sysb.29072

• Survival regression by data fusion: http://www.tandfonline.com/doi/abs/10.1080/21628130.2015.1016702