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An interface between molecules and machine learning

Project description

MolML
=====
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A library to interface molecules and machine learning. The goal of this library is to be a simple way to convert molecules into a vector representation for later use with libraries such as [scikit-learn](http://scikit-learn.org/). This is done using a similar API scheme.

All of the coordinates are assumed to be in angstroms.


Example Usage
=============

```python
>>> from molml.features import CoulombMatrix
>>> feat = CoulombMatrix()
>>> H2 = (
... ['H', 'H'],
... [
... [0.0, 0.0, 0.0],
... [1.0, 0.0, 0.0],
... ]
... )
>>> HCN = (
... ['H', 'C', 'N'],
... [
... [-1.0, 0.0, 0.0],
... [ 0.0, 0.0, 0.0],
... [ 1.0, 0.0, 0.0],
... ]
... )
>>> feat.fit([H2, HCN])
CoulombMatrix(input_type='list', n_jobs=1)
>>> feat.transform([H2])
array([[ 0.5, 1. , 0. , 1. , 0.5, 0. , 0. , 0. , 0. ]])
>>> feat.transform([H2, HCN])
array([[ 0.5 , 1. , 0. , 1. , 0.5 ,
0. , 0. , 0. , 0. ],
[ 0.5 , 6. , 3.5 , 6. , 36.8581052,
42. , 3.5 , 42. , 53.3587074]])
```

For more examples, look in the [examples](https://github.com/crcollins/molml/tree/master/examples). Note: To run some of the examples scikit-learn>=0.16.0 is required.


Dependencies
============

MolML requires python 2.7, numpy, scipy, and pathos. The specific versions that have been tested are numpy 1.9.1, scipy 0.15.1, and pathos 0.2.0, but newer versions should work.


Install
=======

Once the dependeicies are installed, the package can be installed with pip.

$ pip install molml

Or for the bleeding edge version, you can use

$ pip install git+git://github.com/crcollins/molml


Development
===========

To install a development version, just clone the git repo.

$ git clone https://github.com/crcollins/molml

Pull requests and bug reports are welcomed!


Testing
=======

To run the tests, make sure that `nose` is installed and then run:

$ nosetests

To include coverage information, make sure that `coverage` is installed and then run:

$ nosetests --with-coverage --cover-package=molml --cover-erase

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