def __init__(self, threshold=1e-2, **kwargs):
"""
Args:
kwargs:
args for SubstitutionProbability class
lambda_table, alpha
"""
self._kwargs = kwargs
self._threshold = threshold
self._substitutor = SubstitutionPredictor(threshold=threshold,
**kwargs)
class SubstitutionPredictorTransformation(AbstractTransformation):
"""
This transformation takes a structure and uses the structure
prediction module to find likely site substitutions.
"""
def __init__(self, threshold=1e-2, **kwargs):
"""
Args:
kwargs:
args for SubstitutionProbability class
lambda_table, alpha
"""
self._kwargs = kwargs
self._threshold = threshold
self._substitutor = SubstitutionPredictor(threshold=threshold,
**kwargs)
def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError("SubstitutionPredictorTransformation doesn't"
" support returning 1 structure")
preds = self._substitutor.composition_prediction(
structure.composition, to_this_composition=False)
preds.sort(key=lambda x: x['probability'], reverse=True)
outputs = []
for pred in preds:
st = SubstitutionTransformation(pred['substitutions'])
output = {'structure': st.apply_transformation(structure),
'probability': pred['probability'],
'threshold': self._threshold, 'substitutions': {}}
#dictionary keys have to be converted to strings for JSON
for key, value in pred['substitutions'].items():
output['substitutions'][str(key)] = str(value)
outputs.append(output)
return outputs
def __str__(self):
return "SubstitutionPredictorTransformation"
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
@property
def to_dict(self):
d = {"name": self.__class__.__name__, "version": __version__,
"init_args": self._kwargs, "@module": self.__class__.__module__,
"@class": self.__class__.__name__}
d["init_args"]["threshold"] = self._threshold
return d
def run_task(self, fw_spec):
db = SPStructuresMongoAdapter.auto_load()
tstructs = []
species = fw_spec['species']
t = fw_spec['threshold']
for p in SubstitutionPredictor(threshold=t).list_prediction(species):
subs = p['substitutions']
if len(set(subs.values())) < len(species):
continue
st = SubstitutionTransformation(subs)
target = map(str, subs.keys())
for snl in db.get_snls(target):
ts = TransformedStructure.from_snl(snl)
ts.append_transformation(st)
if ts.final_structure.charge == 0:
tstructs.append(ts)
transmuter = StandardTransmuter(tstructs)
f = RemoveDuplicatesFilter(structure_matcher=StructureMatcher(
comparator=ElementComparator(), primitive_cell=False))
transmuter.apply_filter(f)
results = []
for ts in transmuter.transformed_structures:
results.append(ts.to_snl([]).to_dict)
submissions = SPSubmissionsMongoAdapter.auto_load()
submissions.insert_results(fw_spec['submission_id'], results)
def __init__(self, threshold=1e-2, **kwargs):
"""
Args:
kwargs:
args for SubstitutionProbability class
lambda_table, alpha
"""
self._kwargs = kwargs
self._threshold = threshold
self._substitutor = SubstitutionPredictor(threshold=threshold, **kwargs)
class SubstitutionPredictorTransformation(AbstractTransformation):
"""
This transformation takes a structure and uses the structure
prediction module to find likely site substitutions.
Args:
threshold: Threshold for substitution.
**kwargs: Args for SubstitutionProbability class lambda_table, alpha
"""
def __init__(self, threshold=1e-2, **kwargs):
self.kwargs = kwargs
self.threshold = threshold
self._substitutor = SubstitutionPredictor(threshold=threshold,
**kwargs)
def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError("SubstitutionPredictorTransformation doesn't"
" support returning 1 structure")
preds = self._substitutor.composition_prediction(
structure.composition, to_this_composition=False)
preds.sort(key=lambda x: x['probability'], reverse=True)
outputs = []
for pred in preds:
st = SubstitutionTransformation(pred['substitutions'])
output = {
'structure': st.apply_transformation(structure),
'probability': pred['probability'],
'threshold': self.threshold,
'substitutions': {}
}
# dictionary keys have to be converted to strings for JSON
for key, value in pred['substitutions'].items():
output['substitutions'][str(key)] = str(value)
outputs.append(output)
return outputs
def __str__(self):
return "SubstitutionPredictorTransformation"
def __repr__(self):
return self.__str__()
@property
def inverse(self):
return None
@property
def is_one_to_many(self):
return True
def test_prediction(self):
sp = SubstitutionPredictor(threshold = 8e-3)
result = sp.list_prediction(['Na+', 'Cl-'], to_this_composition = True)[5]
cprob = sp.p.cond_prob_list(result['substitutions'].keys(),
result['substitutions'].values())
self.assertAlmostEqual(result['probability'], cprob)
self.assertEqual(set(result['substitutions'].values()), set(['Na+', 'Cl-']))
result = sp.list_prediction(['Na+', 'Cl-'], to_this_composition = False)[5]
cprob = sp.p.cond_prob_list(result['substitutions'].keys(),
result['substitutions'].values())
self.assertAlmostEqual(result['probability'], cprob)
self.assertNotEqual(set(result['substitutions'].values()),
set(['Na+', 'Cl-']))
c = Composition({'Ag2+' : 1, 'Cl-' : 2})
result = sp.composition_prediction(c, to_this_composition = True)[2]
self.assertEqual(set(result['substitutions'].values()), set(c.elements))
result = sp.composition_prediction(c, to_this_composition = False)[2]
self.assertEqual(set(result['substitutions'].keys()), set(c.elements))
def __init__(self, threshold=1e-2, **kwargs):
self._kwargs = kwargs
self._threshold = threshold
self._substitutor = SubstitutionPredictor(threshold=threshold,
**kwargs)
def __init__(self, threshold=1e-2, **kwargs):
self.kwargs = kwargs
self.threshold = threshold
self._substitutor = SubstitutionPredictor(threshold=threshold,
**kwargs)
