From ebc915a664016650414fa3d55c3e241fb35b5d2f Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Sat, 27 Jan 2018 19:13:13 -0500
Subject: [PATCH] memory usage hack
---
mhcflurry/class1_affinity_predictor.py | 75 ++-----------------
.../train_allele_specific_models_command.py | 8 +-
2 files changed, 9 insertions(+), 74 deletions(-)
diff --git a/mhcflurry/class1_affinity_predictor.py b/mhcflurry/class1_affinity_predictor.py
index 10ac0e55..aee7d2e5 100644
--- a/mhcflurry/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_predictor.py
@@ -584,76 +584,6 @@ class Class1AffinityPredictor(object):
return models
- def calibrate_percentile_ranks(
- self,
- peptides=None,
- num_peptides_per_length=int(1e5),
- alleles=None,
- bins=None,
- quiet=False,
- worker_pool=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, 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.
- quiet : boolean
- If False (default), status updates will be printed to stdout.
- """
- 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))
-
-
-
- if quiet:
- def msg(s):
- pass
- else:
- def msg(s):
- print(s)
- sys.stdout.flush()
-
- encoded_peptides = EncodableSequences.create(peptides)
- for (i, allele) in enumerate(alleles):
- msg("Calibrating percentile ranks for allele %03d/%03d: %s" % (
- i + 1, len(alleles), allele))
- start = time.time()
- predictions = self.predict(encoded_peptides, allele=allele)
- msg("Generated %d predictions in %0.2f sec." % (
- len(predictions), time.time() - start))
- transform = PercentRankTransform()
- transform.fit(predictions, bins=bins)
- self.allele_to_percent_rank_transform[allele] = transform
- msg("Done calibrating allele %s in %0.2f sec." % (
- allele, time.time() - start))
-
def percentile_ranks(self, affinities, allele=None, alleles=None, throw=True):
"""
Return percentile ranks for the given ic50 affinities and alleles.
@@ -1003,6 +933,11 @@ class Class1AffinityPredictor(object):
if worker_pool and len(alleles) > 1:
# Run in parallel
+
+ # Performance hack.
+ for network in self.neural_networks:
+ network.peptides_to_network_input(encoded_peptides)
+
do_work = partial(
_calibrate_percentile_ranks,
predictor=self,
diff --git a/mhcflurry/train_allele_specific_models_command.py b/mhcflurry/train_allele_specific_models_command.py
index 82763d6e..dfd77d4c 100644
--- a/mhcflurry/train_allele_specific_models_command.py
+++ b/mhcflurry/train_allele_specific_models_command.py
@@ -223,8 +223,8 @@ def run(argv=sys.argv[1:]):
print("*" * 30)
training_time = time.time() - start
- print("Trained affinity predictor with %d networks in %0.2f sec." % (
- len(predictor.neural_networks), training_time))
+ print("Trained affinity predictor with %d networks in %0.2f min." % (
+ len(predictor.neural_networks), training_time / 60.0))
print("*" * 30)
if args.percent_rank_calibration_num_peptides_per_length > 0:
@@ -242,8 +242,8 @@ def run(argv=sys.argv[1:]):
worker_pool.close()
worker_pool.join()
- print("Train time: %0.2f sec. Percent rank calibration time: %0.2f sec." % (
- training_time, percent_rank_calibration_time))
+ print("Train time: %0.2f min. Percent rank calibration time: %0.2f min." % (
+ training_time / 60.0, percent_rank_calibration_time / 60.0))
print("Predictor written to: %s" % args.out_models_dir)
--
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