An interface between molecules and machine learning
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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.
>>> 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]])
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.
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
To install a development version, just clone the git repo.
$ git clone https://github.com/crcollins/molml
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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