From 3d1c89711c3346328578ab4a5ff8e97e2aeaeae6 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Sat, 27 Jan 2018 16:22:07 -0500
Subject: [PATCH] more logging
---
mhcflurry/train_allele_specific_models_command.py | 10 +++++++---
1 file changed, 7 insertions(+), 3 deletions(-)
diff --git a/mhcflurry/train_allele_specific_models_command.py b/mhcflurry/train_allele_specific_models_command.py
index 4cc6fe5f..f1db3a66 100644
--- a/mhcflurry/train_allele_specific_models_command.py
+++ b/mhcflurry/train_allele_specific_models_command.py
@@ -162,6 +162,7 @@ def run(argv=sys.argv[1:]):
os.mkdir(args.out_models_dir)
print("Done.")
+ start = time.time()
for (h, hyperparameters) in enumerate(hyperparameters_lst):
n_models = None
if 'n_models' in hyperparameters:
@@ -214,16 +215,20 @@ def run(argv=sys.argv[1:]):
# which it adds models to, so no merging is required. It also saves
# as it goes so no saving is required at the end.
start = time.time()
- data_trained_on = 0
for _ in tqdm.trange(len(work_items)):
item = work_items.pop(0) # want to keep freeing up memory
work_predictor = work_entrypoint(item)
assert work_predictor is predictor
+ print("*" * 30)
+ print("Trained %d networks in %0.2f sec." % (
+ len(predictor.neural_networks), time.time() - start))
+ print("*" * 30)
+
if args.percent_rank_calibration_num_peptides_per_length > 0:
- start = time.time()
print("Performing percent rank calibration.")
+ start = time.time()
predictor.calibrate_percentile_ranks(
num_peptides_per_length=args.percent_rank_calibration_num_peptides_per_length,
worker_pool=worker_pool)
@@ -292,6 +297,5 @@ def process_work(
return predictor
-
if __name__ == '__main__':
run()
--
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