def predict(self, peptides, **kwargs):
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides):peptides}
else:
if any(not isinstance(p, Peptide) for p in peptides):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs = {str(p):p for p in peptides}
#group peptides by length and
result = {self.name:{}}
for length, peps in itertools.groupby(pep_seqs.iterkeys(), key= lambda x: len(x)):
#load svm model
if length not in self.supportedLength:
warnings.warn("Peptide length of %i is not supported by %s"%(length,self.name))
continue
encoding = self.encode(peps)
model_path = pkg_resources.resource_filename("Fred2.Data.svms.%s"%self.name, "%s_%i"%(self.name, length))
model = svmlight.read_model(model_path)
pred = svmlight.classify(model, encoding.values())
result[self.name] = {}
for pep, score in itertools.izip(encoding.keys(), pred):
result[self.name][pep_seqs[pep]] = score
if not result[self.name]:
raise ValueError("No predictions could be made with "+self.name+" for given input.")
df_result = TAPPredictionResult.from_dict(result)
return df_result
def predict(self, peptides, **kwargs):
def __load_model(length):
model = "%s_%i"%(self.name, length)
return getattr( __import__("Fred2.Data.TAPPSSMMatrices", fromlist=[model]), model)
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides):peptides}
else:
if any(not isinstance(p, Peptide) for p in peptides):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs = {str(p):p for p in peptides}
result = {self.name:{}}
for length, peps in itertools.groupby(pep_seqs.iterkeys(), key= lambda x: len(x)):
try:
pssm = __load_model(length)
except ImportError:
warnings.warn("No model found for %s with length %i"%(self.name, length))
continue
result = {self.name:{}}
for p in peps:
score = sum(pssm[i].get(aa, 0.0) for i, aa in enumerate(p))+pssm.get(-1,{}).get("con", 0)
result[self.name][pep_seqs[p]] = score
if not result[self.name]:
raise ValueError("No predictions could be made for given input.")
df_result = TAPPredictionResult.from_dict(result)
return df_result
def predict(self, peptides, **kwargs):
"""
Returns TAP predictions for given :class:`~Fred2.Core.Peptide.Peptide`.
:param peptides: A single :class:`~Fred2.Core.Peptide.Peptide` or a list of :class:`~Fred2.Core.Peptide.Peptide`
:type peptides: list(:class:`~Fred2.Core.Peptide.Peptide`) or :class:`~Fred2.Core.Peptide.Peptide`
:return: Returns a :class:`~Fred2.Core.Result.TAPPredictionResult` object with the prediction results
:rtype: :class:`~Fred2.Core.Result.TAPPredictionResult`
"""
def __load_model(length):
model = "%s_%i" % (self.name, length)
return getattr(
__import__("Fred2.Data.pssms." + self.name + ".mat." + model,
fromlist=[model]), model)
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides): peptides}
else:
pep_seqs = {}
for p in peptides:
if not isinstance(p, Peptide):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs[str(p)] = p
result = {self.name: {}}
pep_groups = list(pep_seqs.keys())
pep_groups.sort(key=len)
for length, peps in itertools.groupby(pep_groups, key=len):
if length < 9:
warnings.warn("No model found for %s with length %i" %
(self.name, length))
continue
try:
pssm = __load_model(9)
except ImportError:
warnings.warn("No model found for %s with length %i" %
(self.name, length))
continue
for p in peps:
if length <= 9:
score = sum(pssm[i].get(aa, 0.0) for i, aa in enumerate(p))
else:
score = sum(pssm[i].get(p[i], 0.0)
for i in range(3)) + pssm[8].get(p[-1], 0.0)
result[self.name][pep_seqs[p]] = score
if not result[self.name]:
raise ValueError("No predictions could be made for given input.")
df_result = TAPPredictionResult.from_dict(result)
return df_result
def predict(self, peptides, **kwargs):
"""
Returns TAP predictions for given :class:`~Fred2.Core.Peptide.Peptide`.
:param peptides: A single :class:`~Fred2.Core.Peptide.Peptide` or a list of :class:`~Fred2.Core.Peptide.Peptide`
:type peptides: list(:class:`~Fred2.Core.Peptide.Peptide`) or :class:`~Fred2.Core.Peptide.Peptide`
:return: Returns a :class:`~Fred2.Core.Result.TAPPredictionResult` object with the prediction results
:rtype: :class:`~Fred2.Core.Result.TAPPredictionResult`
"""
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides): peptides}
else:
pep_seqs = {}
for p in peptides:
if not isinstance(p, Peptide):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs[str(p)] = p
#group peptides by length and
chunksize = len(pep_seqs)
if 'chunks' in kwargs:
chunksize = kwargs['chunks']
result = {self.name: {}}
pep_groups = list(pep_seqs.keys())
pep_groups.sort(key=len)
for length, peps in itertools.groupby(pep_groups, key=len):
#load svm model
if length not in self.supportedLength:
warnings.warn("Peptide length of %i is not supported by %s" %
(length, self.name))
continue
peps = list(peps)
for i in range(0, len(peps), chunksize):
encoding = self.encode(peps[i:i + chunksize])
model_path = pkg_resources.resource_filename(
"Fred2.Data.svms.%s" % self.name,
"%s_%i" % (self.name, length))
model = svmlight.read_model(model_path)
pred = svmlight.classify(model, list(encoding.values()))
for pep, score in zip(list(encoding.keys()), pred):
result[self.name][pep_seqs[pep]] = score
if not result[self.name]:
raise ValueError("No predictions could be made with " + self.name +
" for given input.")
df_result = TAPPredictionResult.from_dict(result)
return df_result
def predict(self, peptides, **kwargs):
"""
Returns TAP predictions for given :class:`~Fred2.Core.Peptide.Peptide`.
:param peptides: A single :class:`~Fred2.Core.Peptide.Peptide` or a list of :class:`~Fred2.Core.Peptide.Peptide`
:type peptides: list(:class:`~Fred2.Core.Peptide.Peptide`) or :class:`~Fred2.Core.Peptide.Peptide`
:return: Returns a :class:`~Fred2.Core.Result.TAPPredictionResult` object with the prediction results
:rtype: :class:`~Fred2.Core.Result.TAPPredictionResult`
"""
def __load_model(length):
model = "%s_%i"%(self.name, length)
return getattr(__import__("Fred2.Data.pssms."+self.name+".mat."+model, fromlist=[model]), model)
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides): peptides}
else:
pep_seqs = {}
for p in peptides:
if not isinstance(p, Peptide):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs[str(p)] = p
result = {self.name: {}}
pep_groups = pep_seqs.keys()
pep_groups.sort(key=len)
for length, peps in itertools.groupby(pep_groups, key=len):
if length < 9:
warnings.warn("No model found for %s with length %i"%(self.name, length))
continue
try:
pssm = __load_model(9)
except ImportError:
warnings.warn("No model found for %s with length %i"%(self.name, length))
continue
for p in peps:
if length <= 9:
score = sum(pssm[i].get(aa, 0.0) for i, aa in enumerate(p))
else:
score = sum(pssm[i].get(p[i], 0.0) for i in xrange(3))+pssm[8].get(p[-1], 0.0)
result[self.name][pep_seqs[p]] = score
if not result[self.name]:
raise ValueError("No predictions could be made for given input.")
df_result = TAPPredictionResult.from_dict(result)
return df_result
def predict(self, peptides, **kwargs):
"""
Returns TAP predictions for given :class:`~Fred2.Core.Peptide.Peptide`.
:param peptides: A single :class:`~Fred2.Core.Peptide.Peptide` or a list of :class:`~Fred2.Core.Peptide.Peptide`
:type peptides: list(:class:`~Fred2.Core.Peptide.Peptide`) or :class:`~Fred2.Core.Peptide.Peptide`
:return: Returns a :class:`~Fred2.Core.Result.TAPPredictionResult` object with the prediction results
:rtype: :class:`~Fred2.Core.Result.TAPPredictionResult`
"""
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides):peptides}
else:
pep_seqs = {}
for p in peptides:
if not isinstance(p, Peptide):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs[str(p)] = p
#group peptides by length and
chunksize = len(pep_seqs)
if 'chunks' in kwargs:
chunksize = kwargs['chunks']
result = {self.name: {}}
pep_groups = pep_seqs.keys()
pep_groups.sort(key=len)
for length, peps in itertools.groupby(pep_groups, key=len):
#load svm model
if length not in self.supportedLength:
warnings.warn("Peptide length of %i is not supported by %s"%(length,self.name))
continue
peps = list(peps)
for i in xrange(0, len(peps), chunksize):
encoding = self.encode(peps[i:i+chunksize])
model_path = pkg_resources.resource_filename("Fred2.Data.svms.%s"%self.name, "%s_%i"%(self.name, length))
model = svmlight.read_model(model_path)
pred = svmlight.classify(model, encoding.values())
for pep, score in itertools.izip(encoding.keys(), pred):
result[self.name][pep_seqs[pep]] = score
if not result[self.name]:
raise ValueError("No predictions could be made with "+self.name+" for given input.")
df_result = TAPPredictionResult.from_dict(result)
return df_result
