An interface between molecules and machine learning
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. This is done using a similar API scheme.
All of the coordinates are assumed to be in angstroms.
>>> 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. Note: To run some of the examples scikit-learn>=0.16.0 is required.
MolML works with both Python 2 and Python 3 and depends on numpy, scipy, and pathos (and future for Python 2). The specific versions that have been tested are Python 2.7/3.4/3.5, numpy 1.9.1, scipy 0.15.1, and pathos 0.2.0, but newer versions should work.
NOTE: Due to an issue with multiprocess (a pathos dependency), the minimum version of python that will work is 2.7.4. For full details see this link. Without this, the parallel computation of features will fail.
Once the dependencies 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
To install a development version, just clone the git repo.
$ git clone https://github.com/crcollins/molml $ # cd to molml and setup some virtualenv $ pip install -r requirements.txt $ pip install -r requirements-dev.txt
Pull requests and bug reports are welcomed!
To run the tests, make sure that nose is installed and then run:
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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