From eee968c980d2412c8b8fd3560e5c231b14c06aaf Mon Sep 17 00:00:00 2001
From: Alex Rubinsteyn <alex.rubinsteyn@gmail.com>
Date: Tue, 19 Apr 2016 13:26:59 -0400
Subject: [PATCH] training script seems to work
---
.../train-class1-allele-specific-models.py | 57 +++++++++++++------
1 file changed, 39 insertions(+), 18 deletions(-)
diff --git a/scripts/train-class1-allele-specific-models.py b/scripts/train-class1-allele-specific-models.py
index 260674d2..7a6bd17e 100755
--- a/scripts/train-class1-allele-specific-models.py
+++ b/scripts/train-class1-allele-specific-models.py
@@ -80,12 +80,18 @@ parser.add_argument(
help="Don't train predictors for alleles with fewer samples than this",
type=int)
+parser.add_argument(
+ "--alleles",
+ default=[],
+ nargs="+",
+ type=normalize_allele_name)
+
# add options for neural network hyperparameters
parser = add_hyperparameter_arguments_to_parser(parser)
if __name__ == "__main__":
args = parser.parse_args()
-
+ print(args)
if not exists(args.output_dir):
makedirs(args.output_dir)
@@ -103,7 +109,29 @@ if __name__ == "__main__":
Y_all = np.concatenate([group.Y for group in allele_groups.values()])
print("Total Dataset size = %d" % len(Y_all))
- for allele_name, allele_data in allele_groups.items():
+ # if user didn't specify alleles then train models for all available alleles
+ alleles = args.alleles
+
+ if not alleles:
+ alleles = sorted(allele_groups.keys())
+
+ for allele_name in alleles:
+ allele_name = normalize_allele_name(allele_name)
+ if allele_name.isdigit():
+ print("Skipping allele %s" % (allele_name,))
+ continue
+
+ allele_data = allele_groups[allele_name]
+ X = allele_data.X_index
+ Y = allele_data.Y
+
+ n_allele = len(allele_data.Y)
+ assert len(X) == n_allele
+
+ print("\n=== Training predictor for %s: %d samples" % (
+ allele_name,
+ n_allele))
+
model = Class1BindingPredictor.from_hyperparameters(
name=allele_name,
peptide_length=9,
@@ -115,12 +143,6 @@ if __name__ == "__main__":
init=args.initialization,
dropout_probability=args.dropout,
learning_rate=args.learning_rate)
- allele_name = normalize_allele_name(allele_name)
- if allele_name.isdigit():
- print("Skipping allele %s" % (allele_name,))
- continue
- n_allele = len(allele_data.Y)
- print("%s: total count = %d" % (allele_name, n_allele))
json_filename = allele_name + ".json"
json_path = join(args.output_dir, json_filename)
@@ -139,19 +161,18 @@ if __name__ == "__main__":
if exists(json_path):
print("-- removing old model description %s" % json_path)
remove(json_path)
+
if exists(hdf_path):
print("-- removing old weights file %s" % hdf_path)
remove(hdf_path)
model.fit(
- allele_data.X,
+ allele_data.X_index,
allele_data.Y,
- nb_epoch=args.training_epochs,
- show_accuracy=True)
- print("Saving model description for %s to %s" % (
- allele_name, json_path))
- with open(json_path, "w") as f:
- f.write(model.to_json())
- print("Saving model weights for %s to %s" % (
- allele_name, hdf_path))
- model.save_weights(hdf_path)
+ n_training_epochs=args.training_epochs,
+ verbose=True)
+
+ model.to_disk(
+ model_json_path=json_path,
+ weights_hdf_path=hdf_path,
+ overwrite=args.overwrite)
--
GitLab