From eeabbafdf6a7593f3aaba38a4e73ce112aaa6393 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 16 May 2019 11:45:06 -0400
Subject: [PATCH] fix
---
.../models_class1_pan_unselected/GENERATE.sh | 2 +-
.../generate_hyperparameters.py | 6 +++++-
mhcflurry/train_pan_allele_models_command.py | 16 ++++++++++------
3 files changed, 16 insertions(+), 8 deletions(-)
diff --git a/downloads-generation/models_class1_pan_unselected/GENERATE.sh b/downloads-generation/models_class1_pan_unselected/GENERATE.sh
index 8475d01d..8724ed3f 100755
--- a/downloads-generation/models_class1_pan_unselected/GENERATE.sh
+++ b/downloads-generation/models_class1_pan_unselected/GENERATE.sh
@@ -5,7 +5,7 @@
set -e
set -x
-DOWNLOAD_NAME=models_class1_unselected
+DOWNLOAD_NAME=models_class1_pan
SCRATCH_DIR=${TMPDIR-/tmp}/mhcflurry-downloads-generation
SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
diff --git a/downloads-generation/models_class1_pan_unselected/generate_hyperparameters.py b/downloads-generation/models_class1_pan_unselected/generate_hyperparameters.py
index 6a9c2782..bfbd4432 100644
--- a/downloads-generation/models_class1_pan_unselected/generate_hyperparameters.py
+++ b/downloads-generation/models_class1_pan_unselected/generate_hyperparameters.py
@@ -39,7 +39,11 @@ base_hyperparameters = {
'random_negative_distribution_smoothing': 0.0,
'random_negative_match_distribution': True,
'random_negative_rate': 0.2,
- 'train_data': {},
+ 'train_data': {
+ 'pretrain': True,
+ 'pretrain_peptides_per_epoch': 1024,
+ 'pretrain_patience': 10,
+ },
'validation_split': 0.1,
}
diff --git a/mhcflurry/train_pan_allele_models_command.py b/mhcflurry/train_pan_allele_models_command.py
index 1520502a..3bdd9963 100644
--- a/mhcflurry/train_pan_allele_models_command.py
+++ b/mhcflurry/train_pan_allele_models_command.py
@@ -291,6 +291,10 @@ def main(args):
if args.max_epochs:
hyperparameters['max_epochs'] = args.max_epochs
+ if hyperparameters.get("train_data", {}).get("pretrain", False):
+ if not args.pretrain_data:
+ raise ValueError("--pretrain-data is required")
+
for fold in range(args.ensemble_size):
for replicate in range(args.num_replicates):
work_dict = {
@@ -386,11 +390,10 @@ def train_model(
num_replicates,
hyperparameters,
pretrain_data_filename,
- pretrain_patience=1,
- verbose=None,
- progress_print_interval=None,
- predictor=None,
- save_to=None):
+ verbose,
+ progress_print_interval,
+ predictor,
+ save_to):
if predictor is None:
predictor = Class1AffinityPredictor()
@@ -424,7 +427,7 @@ def train_model(
replicate_num + 1,
num_replicates))
- if pretrain_data_filename:
+ if hyperparameters.get("train_data", {}).get("pretrain", False):
iterator = pretrain_data_iterator(pretrain_data_filename, allele_encoding)
original_hyperparameters = dict(model.hyperparameters)
model.hyperparameters['minibatch_size'] = int(len(next(iterator)[-1]) / 100)
@@ -432,6 +435,7 @@ def train_model(
model.hyperparameters['validation_split'] = 0.0
model.hyperparameters['random_negative_rate'] = 0.0
model.hyperparameters['random_negative_constant'] = 0
+ pretrain_patience = hyperparameters["train_data"]["pretrain_patience"]
scores = []
best_score = float('inf')
best_score_epoch = 0
--
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