diff --git a/downloads-generation/models_class1_unselected/GENERATE.sh b/downloads-generation/models_class1_unselected/GENERATE.sh
index f04e23ddf9d603a6a255a86b1b9d0f6bf132e241..adf5c7ae230c44aecf78bc6506a4ec0f13464211 100755
--- a/downloads-generation/models_class1_unselected/GENERATE.sh
+++ b/downloads-generation/models_class1_unselected/GENERATE.sh
@@ -17,8 +17,6 @@ rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
mkdir "$SCRATCH_DIR/$DOWNLOAD_NAME"
# Send stdout and stderr to a logfile included with the archive.
-exec > >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt")
-exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
# Log some environment info
date
diff --git a/mhcflurry/class1_affinity_predictor.py b/mhcflurry/class1_affinity_predictor.py
index aa41f0cf739f340293f6710b45ab29a54869c4fa..4a4144ea6ba7b9dbd1491c1b24346390e8cb080f 100644
--- a/mhcflurry/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_predictor.py
@@ -156,6 +156,56 @@ class Class1AffinityPredictor(object):
allele_to_fixed_length_sequence=allele_to_fixed_length_sequence
)
+ def merge_in_place(self, others):
+ """
+ Add the models present other predictors into the current predictor.
+
+ Parameters
+ ----------
+ others : list of Class1AffinityPredictor
+ Other predictors to merge into the current predictor.
+
+ Returns
+ -------
+ list of string : names of newly added models
+ """
+
+ new_model_names = []
+ for predictor in others:
+ for model in predictor.class1_pan_allele_models:
+ model_name = self.model_name(
+ "pan-class1",
+ len(self.class1_pan_allele_models))
+ self.class1_pan_allele_models.append(model)
+ row = pandas.Series(collections.OrderedDict([
+ ("model_name", model_name),
+ ("allele", "pan-class1"),
+ ("config_json", json.dumps(model.get_config())),
+ ("model", model),
+ ])).to_frame().T
+ self.manifest_df = pandas.concat(
+ [self.manifest_df, row], ignore_index=True)
+ new_model_names.append(model_name)
+
+ for allele in predictor.allele_to_allele_specific_models:
+ if allele not in self.allele_to_allele_specific_models:
+ self.allele_to_allele_specific_models[allele] = []
+ current_models = self.allele_to_allele_specific_models[allele]
+ for model in predictor.allele_to_allele_specific_models[allele]:
+ model_name = self.model_name(allele, len(current_models))
+ row = pandas.Series(collections.OrderedDict([
+ ("model_name", model_name),
+ ("allele", allele),
+ ("config_json", json.dumps(model.get_config())),
+ ("model", model),
+ ])).to_frame().T
+ self.manifest_df = pandas.concat(
+ [self.manifest_df, row], ignore_index=True)
+ current_models.append(model)
+ new_model_names.append(model_name)
+
+ return new_model_names
+
@property
def supported_alleles(self):
"""
diff --git a/mhcflurry/train_allele_specific_models_command.py b/mhcflurry/train_allele_specific_models_command.py
index dd180995443112238b6b43dd2f4e22d1d0085161..1711a8a2097a52ed575ecf428de6eabe88c88e00 100644
--- a/mhcflurry/train_allele_specific_models_command.py
+++ b/mhcflurry/train_allele_specific_models_command.py
@@ -125,7 +125,13 @@ parser.add_argument(
default=1000,
help="Maximum number of workers to assign to a GPU. Additional tasks will "
"run on CPU.")
-
+parser.add_argument(
+ "--save-interval",
+ type=float,
+ metavar="N",
+ default=60,
+ help="Write models to disk every N seconds. Only affects parallel runs; "
+ "serial runs write each model to disk as it is trained.")
def run(argv=sys.argv[1:]):
global GLOBAL_DATA
@@ -136,6 +142,8 @@ def run(argv=sys.argv[1:]):
args = parser.parse_args(argv)
+ args.out_models_dir = os.path.abspath(args.out_models_dir)
+
configure_logging(verbose=args.verbosity > 1)
hyperparameters_lst = yaml.load(open(args.hyperparameters))
@@ -223,7 +231,7 @@ def run(argv=sys.argv[1:]):
processes=num_workers)
print("Started pool of %d workers: %s" % (num_workers, str(worker_pool)))
- if args.out_models_dir and not os.path.exists(args.out_models_dir):
+ if not os.path.exists(args.out_models_dir):
print("Attempting to create directory: %s" % args.out_models_dir)
os.mkdir(args.out_models_dir)
print("Done.")
@@ -243,23 +251,21 @@ def run(argv=sys.argv[1:]):
hyperparameters['max_epochs'] = args.max_epochs
for (i, allele) in enumerate(df.allele.unique()):
- for model_group in range(n_models):
- work_dict = {
- 'model_group': model_group,
- 'n_models': n_models,
- 'allele_num': i,
- 'n_alleles': len(alleles),
- 'hyperparameter_set_num': h,
- 'num_hyperparameter_sets': len(hyperparameters_lst),
- 'allele': allele,
- 'data': None, # subselect from GLOBAL_DATA["train_data"]
- 'hyperparameters': hyperparameters,
- 'verbose': args.verbosity,
- 'progress_print_interval': None if worker_pool else 5.0,
- 'predictor': predictor if not worker_pool else None,
- 'save_to': args.out_models_dir if not worker_pool else None,
- }
- work_items.append(work_dict)
+ work_dict = {
+ 'n_models': n_models,
+ 'allele_num': i,
+ 'n_alleles': len(alleles),
+ 'hyperparameter_set_num': h,
+ 'num_hyperparameter_sets': len(hyperparameters_lst),
+ 'allele': allele,
+ 'data': None, # subselect from GLOBAL_DATA["train_data"]
+ 'hyperparameters': hyperparameters,
+ 'verbose': args.verbosity,
+ 'progress_print_interval': None if worker_pool else 5.0,
+ 'predictor': predictor if not worker_pool else None,
+ 'save_to': args.out_models_dir if not worker_pool else None,
+ }
+ work_items.append(work_dict)
if worker_pool:
print("Processing %d work items in parallel." % len(work_items))
@@ -268,23 +274,35 @@ def run(argv=sys.argv[1:]):
# the order of the work.
random.shuffle(work_items)
- # We sort here so the predictors are in order of hyperparameter set num.
- # This is convenient so that the neural networks get merged for each
- # allele in the same order.
- predictors = [
- predictor for (_, predictor)
- in sorted(
- tqdm.tqdm(
- worker_pool.imap_unordered(
- train_model_entrypoint, work_items, chunksize=1),
- total=len(work_items)),
- key=lambda pair: pair[0])
- ]
+ results_generator = worker_pool.imap_unordered(
+ train_model_entrypoint, work_items, chunksize=1)
+
+ unsaved_predictors = []
+ last_save_time = time.time()
+ for new_predictor in tqdm.tqdm(results_generator, total=len(work_items)):
+ unsaved_predictors.append(new_predictor)
+
+ if time.time() > last_save_time + args.save_interval:
+ # Save current predictor.
+ save_start = time.time()
+ new_model_names = predictor.merge_in_place(unsaved_predictors)
+ predictor.save(
+ args.out_models_dir, model_names_to_write=new_model_names)
+ print(
+ "Saved predictor (%d models total) including %d new models "
+ "in %0.2f sec to %s" % (
+ len(predictor.neural_networks),
+ len(new_model_names),
+ time.time() - save_start,
+ args.out_models_dir))
+ unsaved_predictors.clear()
+ last_save_time = time.time()
+
+ print("Saving final predictor to: %s" % args.out_models_dir)
+ predictor.merge_in_place(unsaved_predictors)
+ predictor.save(args.out_models_dir) # write all models just to be sure
+ print("Done.")
- print("Merging %d predictors fit in parallel." % (len(predictors)))
- predictor = Class1AffinityPredictor.merge([predictor] + predictors)
- print("Saving merged predictor to: %s" % args.out_models_dir)
- predictor.save(args.out_models_dir)
else:
# Run in serial. In this case, every worker is passed the same predictor,
# which it adds models to, so no merging is required. It also saves
@@ -373,7 +391,6 @@ def train_model_entrypoint(item):
def train_model(
- model_group,
n_models,
allele_num,
n_alleles,
@@ -404,23 +421,18 @@ def train_model(
else:
raise ValueError("Unsupported subset: %s" % subset)
- hyperparameters.setdefault("train_data", {})["num_points"] = len(data)
-
progress_preamble = (
"[%2d / %2d hyperparameters] "
- "[%4d / %4d alleles] "
- "[%2d / %2d replicates]: %s " % (
+ "[%4d / %4d alleles] %s " % (
hyperparameter_set_num + 1,
num_hyperparameter_sets,
allele_num + 1,
n_alleles,
- model_group + 1,
- n_models,
allele))
train_data = data.sample(frac=1.0)
- (model,) = predictor.fit_allele_specific_predictors(
- n_models=1,
+ predictor.fit_allele_specific_predictors(
+ n_models=n_models,
architecture_hyperparameters_list=[hyperparameters],
allele=allele,
peptides=train_data.peptide.values,
@@ -433,16 +445,7 @@ def train_model(
progress_print_interval=progress_print_interval,
verbose=verbose)
- if allele_num == 0 and model_group == 0:
- # For the first model for the first allele, print the architecture.
- print("*** HYPERPARAMETER SET %d***" %
- (hyperparameter_set_num + 1))
- pprint(hyperparameters)
- print("*** ARCHITECTURE FOR HYPERPARAMETER SET %d***" %
- (hyperparameter_set_num + 1))
- model.network(borrow=True).summary()
-
- return (hyperparameter_set_num, predictor)
+ return predictor
def calibrate_percentile_ranks(allele, predictor, peptides=None):