def __init__(self, assign_stereo_from_3d=False):
self.engine = Dragon(assign_stereo_from_3d=assign_stereo_from_3d)
def __init__(self, assign_stereo_from_3d=False):
self.engine = Dragon(assign_stereo_from_3d=assign_stereo_from_3d)
class DragonDescriptors(Featurizer):
"""
Calculate Dragon descriptors.
Parameters
----------
assign_stereo_from_3d : bool, optional (default False)
Assign stereochemistry from 3D coordinates. This will overwrite any
existing stereochemistry information on molecules.
"""
name = 'dragon'
def __init__(self, assign_stereo_from_3d=False):
self.engine = Dragon(assign_stereo_from_3d=assign_stereo_from_3d)
def featurize(self,
mols,
parallel=False,
client_kwargs=None,
view_flags=None):
"""
Calculate features for molecules.
Parameters
----------
mols : iterable
RDKit Mol objects.
parallel : bool, optional (default False)
Train subtrainers in parallel using IPython.parallel.
client_kwargs : dict, optional
Keyword arguments for IPython.parallel Client.
view_flags : dict, optional
Flags for IPython.parallel LoadBalancedView.
"""
if parallel:
from IPython.parallel import Client
if client_kwargs is None:
client_kwargs = {}
if view_flags is None:
view_flags = {}
client = Client(**client_kwargs)
client.direct_view().use_dill() # use dill
view = client.load_balanced_view()
view.set_flags(**view_flags)
call = view.map(self._featurize,
np.array_split(mols, len(client.direct_view())),
block=False)
features = call.get()
features = np.concatenate(features)
# get output from engines
call.display_outputs()
else:
features = self._featurize(mols)
return np.asarray(features)
def _featurize(self, mols):
"""
Calculate Dragon descriptors.
Parameters
----------
mols : array_like
Molecules.
"""
return self.engine.get_descriptors(mols)
class DragonDescriptors(Featurizer):
"""
Calculate Dragon descriptors.
Parameters
----------
assign_stereo_from_3d : bool, optional (default False)
Assign stereochemistry from 3D coordinates. This will overwrite any
existing stereochemistry information on molecules.
"""
name = 'dragon'
def __init__(self, assign_stereo_from_3d=False):
self.engine = Dragon(assign_stereo_from_3d=assign_stereo_from_3d)
def featurize(self, mols, parallel=False, client_kwargs=None,
view_flags=None):
"""
Calculate features for molecules.
Parameters
----------
mols : iterable
RDKit Mol objects.
parallel : bool, optional (default False)
Train subtrainers in parallel using IPython.parallel.
client_kwargs : dict, optional
Keyword arguments for IPython.parallel Client.
view_flags : dict, optional
Flags for IPython.parallel LoadBalancedView.
"""
if parallel:
from IPython.parallel import Client
if client_kwargs is None:
client_kwargs = {}
if view_flags is None:
view_flags = {}
client = Client(**client_kwargs)
client.direct_view().use_dill() # use dill
view = client.load_balanced_view()
view.set_flags(**view_flags)
call = view.map(
self._featurize,
np.array_split(mols, len(client.direct_view())), block=False)
features = call.get()
features = np.concatenate(features)
# get output from engines
call.display_outputs()
else:
features = self._featurize(mols)
return np.asarray(features)
def _featurize(self, mols):
"""
Calculate Dragon descriptors.
Parameters
----------
mols : array_like
Molecules.
"""
return self.engine.get_descriptors(mols)
