From 57edfb49466c66eece94c6ece9df840b1ecc0b9b Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Sat, 7 Sep 2019 13:47:40 -0400
Subject: [PATCH] fix
---
.travis.yml | 2 --
mhcflurry/class1_affinity_predictor.py | 14 ++++++++++----
test/test_network_merging.py | 8 ++++++--
3 files changed, 16 insertions(+), 8 deletions(-)
diff --git a/.travis.yml b/.travis.yml
index 08b33360..d1ec644f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -35,8 +35,6 @@ install:
env:
global:
- PYTHONHASHSEED=0
- - CUDA_VISIBLE_DEVICES="" # for tensorflow
- matrix:
- KERAS_BACKEND=tensorflow
script:
# download data and models, then run tests
diff --git a/mhcflurry/class1_affinity_predictor.py b/mhcflurry/class1_affinity_predictor.py
index 662b0cf8..d464a372 100644
--- a/mhcflurry/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_predictor.py
@@ -418,7 +418,7 @@ class Class1AffinityPredictor(object):
logging.info("Wrote: %s", percent_ranks_path)
@staticmethod
- def load(models_dir=None, max_models=None):
+ def load(models_dir=None, max_models=None, optimization_level=None):
"""
Deserialize a predictor from a directory on disk.
@@ -431,12 +431,18 @@ class Class1AffinityPredictor(object):
max_models : int, optional
Maximum number of `Class1NeuralNetwork` instances to load
+ optimization_level : int
+ If >0, model optimization will be attempted. Defaults to value of
+ environment variable MHCFLURRY_OPTIMIZATION_LEVEL.
+
Returns
-------
`Class1AffinityPredictor` instance
"""
if models_dir is None:
models_dir = get_default_class1_models_dir()
+ if optimization_level is None:
+ optimization_level = OPTIMIZATION_LEVEL
manifest_path = join(models_dir, "manifest.csv")
manifest_df = pandas.read_csv(manifest_path, nrows=max_models)
@@ -497,11 +503,11 @@ class Class1AffinityPredictor(object):
manifest_df=manifest_df,
allele_to_percent_rank_transform=allele_to_percent_rank_transform,
)
- if OPTIMIZATION_LEVEL >= 1:
- logging.info("Optimizing models")
+ if optimization_level >= 1:
optimized = result.optimize()
logging.info(
- "Optimization %s", ("succeeded" if optimized else "failed"))
+ "Model optimization %s",
+ "succeeded" if optimized else "not supported for these models")
return result
def optimize(self):
diff --git a/test/test_network_merging.py b/test/test_network_merging.py
index 1f083e8a..4c486975 100644
--- a/test/test_network_merging.py
+++ b/test/test_network_merging.py
@@ -14,13 +14,16 @@ from mhcflurry.common import random_peptides
from mhcflurry.downloads import get_path
ALLELE_SPECIFIC_PREDICTOR = Class1AffinityPredictor.load(
- get_path("models_class1", "models"))
+ get_path("models_class1", "models"), optimization_level=0)
PAN_ALLELE_PREDICTOR = Class1AffinityPredictor.load(
- get_path("models_class1_pan", "models.with_mass_spec"))
+ get_path("models_class1_pan", "models.with_mass_spec"),
+ optimization_level=0)
def test_merge():
+ assert len(PAN_ALLELE_PREDICTOR.class1_pan_allele_models) > 1
+
peptides = random_peptides(100, length=9)
peptides.extend(random_peptides(100, length=10))
peptides = pandas.Series(peptides).sample(frac=1.0)
@@ -40,3 +43,4 @@ def test_merge():
)
predictions2 = merged_predictor.predict(peptides=peptides, alleles=alleles)
numpy.testing.assert_allclose(predictions1, predictions2, atol=0.1)
+
--
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