A Python module for data fusion built on top of factorized models.

## Project description scikit-fusion is a Python module for data fusion based on recent collective latent factor models.

## 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()
```

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
>>> 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))}
```

## Project details

This version 0.2.1 0.2 0.1