From 38adfb4d8f310f7f3399e4149271b1a8bc7aef82 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Tue, 28 Nov 2017 10:28:21 -0500
Subject: [PATCH] Change prediction batch_size from 32 to 4096 for better
performance
---
.../class1_neural_network.py | 11 ++++++++---
test/test_class1_affinity_predictor.py | 1 -
test/test_train_allele_specific_models_command.py | 4 ++--
3 files changed, 10 insertions(+), 6 deletions(-)
diff --git a/mhcflurry/class1_affinity_prediction/class1_neural_network.py b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
index f37827f3..73a9670a 100644
--- a/mhcflurry/class1_affinity_prediction/class1_neural_network.py
+++ b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
@@ -479,7 +479,7 @@ class Class1NeuralNetwork(object):
break
self.fit_seconds = time.time() - start
- def predict(self, peptides, allele_pseudosequences=None):
+ def predict(self, peptides, allele_pseudosequences=None, batch_size=4096):
"""
Predict affinities
@@ -490,6 +490,9 @@ class Class1NeuralNetwork(object):
allele_pseudosequences : EncodableSequences or list of string, optional
Only required when this model is a pan-allele model
+ batch_size : int
+ batch_size passed to Keras
+
Returns
-------
numpy.array of nM affinity predictions
@@ -501,8 +504,10 @@ class Class1NeuralNetwork(object):
pseudosequences_input = self.pseudosequence_to_network_input(
allele_pseudosequences)
x_dict['pseudosequence'] = pseudosequences_input
- (predictions,) = numpy.array(
- self.network(borrow=True).predict(x_dict), dtype="float64").T
+
+ network = self.network(borrow=True)
+ raw_predictions = network.predict(x_dict, batch_size=batch_size)
+ predictions = numpy.array(raw_predictions, dtype = "float64")[:,0]
return to_ic50(predictions)
def compile(self):
diff --git a/test/test_class1_affinity_predictor.py b/test/test_class1_affinity_predictor.py
index 3ab6277c..65f1f587 100644
--- a/test/test_class1_affinity_predictor.py
+++ b/test/test_class1_affinity_predictor.py
@@ -132,7 +132,6 @@ def test_class1_affinity_predictor_a0205_memorize_training_data():
dense_layer_l1_regularization=0.0,
dropout_probability=0.0)
- # First test a Class1NeuralNetwork, then a Class1AffinityPredictor.
allele = "HLA-A*02:05"
df = pandas.read_csv(
diff --git a/test/test_train_allele_specific_models_command.py b/test/test_train_allele_specific_models_command.py
index f08963a1..19cde4f2 100644
--- a/test/test_train_allele_specific_models_command.py
+++ b/test/test_train_allele_specific_models_command.py
@@ -60,9 +60,9 @@ def test_run():
args = [
"--data", get_path("data_curated", "curated_training_data.csv.bz2"),
"--hyperparameters", hyperparameters_filename,
- "--min-measurements-per-allele", "9000",
+ "--allele", "HLA-A*02:01", "HLA-A*01:01", "HLA-A*03:01",
"--out-models-dir", models_dir,
- "--percent-rank-calibration-num-peptides-per-length", "1000",
+ "--percent-rank-calibration-num-peptides-per-length", "10000",
]
print("Running with args: %s" % args)
train_allele_specific_models_command.run(args)
--
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