def list_prediction(self, species, to_this_composition=True):
"""
Args:
species:
list of species
to_this_composition:
If true, substitutions with this as a final composition
will be found. If false, substitutions with this as a
starting composition will be found (these are slightly
different)
Returns:
List of predictions in the form of dictionaries.
If to_this_composition is true, the values of the dictionary
will be from the list species. If false, the keys will be
from that list.
"""
for sp in species:
if smart_element_or_specie(sp) not in self.p.species:
raise ValueError("the species {} is not allowed for the"
"probability model you are using".format(sp))
max_probabilities = []
for s1 in species:
if to_this_composition:
max_p = max(
[self.p.cond_prob(s2, s1) for s2 in self.p.species])
else:
max_p = max(
[self.p.cond_prob(s1, s2) for s2 in self.p.species])
max_probabilities.append(max_p)
output = []
def _recurse(output_prob, output_species):
best_case_prob = list(max_probabilities)
best_case_prob[:len(output_prob)] = output_prob
if reduce(mul, best_case_prob) > self.threshold:
if len(output_species) == len(species):
odict = {'probability': reduce(mul, best_case_prob)}
if to_this_composition:
odict['substitutions'] = dict(
zip(output_species, species))
else:
odict['substitutions'] = dict(
zip(species, output_species))
if len(output_species) == len(set(output_species)):
output.append(odict)
return
for sp in self.p.species:
i = len(output_prob)
if to_this_composition:
prob = self.p.cond_prob(sp, species[i])
else:
prob = self.p.cond_prob(species[i], sp)
_recurse(output_prob + [prob], output_species + [sp])
_recurse([], [])
logging.info('{} substitutions found'.format(len(output)))
return output
def list_prediction(self, species, to_this_composition = True):
"""
Args:
species:
list of species
to_this_composition:
If true, substitutions with this as a final composition
will be found. If false, substitutions with this as a
starting composition will be found (these are slightly
different)
Returns:
List of predictions in the form of dictionaries.
If to_this_composition is true, the values of the dictionary
will be from the list species. If false, the keys will be
from that list.
"""
for sp in species:
if smart_element_or_specie(sp) not in self.p.species:
raise ValueError("the species {} is not allowed for the"
"probability model you are using".format(sp))
max_probabilities = []
for s1 in species:
if to_this_composition:
max_p = max([self.p.cond_prob(s2, s1) for s2 in self.p.species])
else:
max_p = max([self.p.cond_prob(s1, s2) for s2 in self.p.species])
max_probabilities.append(max_p)
output = []
def _recurse(output_prob, output_species):
best_case_prob = list(max_probabilities)
best_case_prob[:len(output_prob)] = output_prob
if reduce(mul, best_case_prob) > self.threshold:
if len(output_species) == len(species):
odict = {'probability': reduce(mul, best_case_prob)}
if to_this_composition:
odict['substitutions'] = dict(zip(output_species, species))
else:
odict['substitutions'] = dict(zip(species, output_species))
if len(output_species) == len(set(output_species)):
output.append(odict)
return
for sp in self.p.species:
i = len(output_prob)
if to_this_composition:
prob = self.p.cond_prob(sp, species[i])
else:
prob = self.p.cond_prob(species[i], sp)
_recurse(output_prob + [prob], output_species + [sp])
_recurse([], [])
logging.info('{} substitutions found'.format(len(output)))
return output
def get_px(self, sp):
return self._px[smart_element_or_specie(sp)]
def get_lambda(self, s1, s2):
k = frozenset([smart_element_or_specie(s1),
smart_element_or_specie(s2)])
return self._l.get(k, self.alpha)
def get_px(self, sp):
return self._px[smart_element_or_specie(sp)]
def get_lambda(self, s1, s2):
k = frozenset(
[smart_element_or_specie(s1),
smart_element_or_specie(s2)])
return self._l.get(k, self.alpha)