From 1ca484c762614998d2571dc04f4ffd2ac0d94938 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Sun, 26 Nov 2017 11:09:25 -0500
Subject: [PATCH] more efficient peptide encoding in calibrate_percentile_ranks
---
.../class1_affinity_prediction/class1_affinity_predictor.py | 5 ++++-
1 file changed, 4 insertions(+), 1 deletion(-)
diff --git a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
index e61d46f7..5d6df8c6 100644
--- a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
@@ -6,6 +6,7 @@ from os.path import join, exists
from six import string_types
import logging
import warnings
+import sys
import numpy
import pandas
@@ -532,12 +533,14 @@ class Class1AffinityPredictor(object):
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(peptides, allele=allele)
+ predictions = self.predict(encoded_peptides, allele=allele)
msg("Generated %d predictions in %0.2f sec." % (
len(predictions), time.time() - start))
transform = PercentRankTransform()
--
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