Newer
Older
loaded = numpy.load(filename)
weights = [
loaded["array_%d" % i]
for i in range(len(loaded.keys()))
]
loaded.close()
return weights
def calibrate_percentile_ranks(
self,
peptides=None,
num_peptides_per_length=int(1e5),
alleles=None,
bins=None):
"""
Compute the cumulative distribution of ic50 values for a set of alleles
over a large universe of random peptides, to enable computing quantiles in
this distribution later.
Parameters
----------
peptides : sequence of string or EncodableSequences, optional
Peptides to use
num_peptides_per_length : int, optional
If peptides argument is not specified, then num_peptides_per_length
peptides are randomly sampled from a uniform distribution for each
supported length
alleles : sequence of string, optional
Alleles to perform calibration for. If not specified all supported
alleles will be calibrated.
bins : object
Anything that can be passed to numpy.histogram's "bins" argument
can be used here, i.e. either an integer or a sequence giving bin
edges. This is in ic50 space.
Returns
----------
EncodableSequences : peptides used for calibration
"""
if bins is None:
bins = to_ic50(numpy.linspace(1, 0, 1000))
if alleles is None:
alleles = self.supported_alleles
if peptides is None:
peptides = []
lengths = range(
self.supported_peptide_lengths[0],
self.supported_peptide_lengths[1] + 1)
for length in lengths:
peptides.extend(
random_peptides(num_peptides_per_length, length))
encoded_peptides = EncodableSequences.create(peptides)
for (i, allele) in enumerate(alleles):
predictions = self.predict(encoded_peptides, allele=allele)
transform = PercentRankTransform()
transform.fit(predictions, bins=bins)
self.allele_to_percent_rank_transform[allele] = transform
return encoded_peptides
def filter_networks(self, predicate):
"""
Return a new Class1AffinityPredictor containing a subset of this
predictor's neural networks.
Parameters
----------
predicate : Class1NeuralNetwork -> boolean
Function specifying which neural networks to include
Returns
-------
Class1AffinityPredictor
"""
allele_to_allele_specific_models = {}
for (allele, models) in self.allele_to_allele_specific_models.items():
allele_to_allele_specific_models[allele] = [
m for m in models if predicate(m)
]
class1_pan_allele_models = [
m for m in self.class1_pan_allele_models if predicate(m)
]
return Class1AffinityPredictor(
allele_to_allele_specific_models=allele_to_allele_specific_models,
class1_pan_allele_models=class1_pan_allele_models,
allele_to_fixed_length_sequence=self.allele_to_fixed_length_sequence,
)