def run(self, model, training_set, epoch):
if self.collect_stats_frequency > 0 and epoch % self.collect_stats_frequency == 0:
validation_set = next(self.validation_sets)
other_values = {"lr": self.get_lr()}
stats = ma.CollectStatsFromModel(
model=model,
epoch=epoch,
training_set=training_set,
validation_set=validation_set,
writer=self._writer,
other_values=other_values,
logger=self.logger,
sample_size=self.collect_stats_params["sample_size"],
to_mol_func=uc.get_mol_func(
self.collect_stats_params["smiles_type"])).run()
self._metric_epochs.append(stats["nll_plot/jsd_joined"])
if isinstance(self.lr_scheduler,
torch.optim.lr_scheduler.ReduceLROnPlateau):
metric = np.mean(
self._metric_epochs[-self.lr_params["average_steps"]:])
self.lr_scheduler.step(metric, epoch=epoch)
else:
self.lr_scheduler.step(epoch=epoch)
lr_reached_min = (self.get_lr() < self.lr_params["min"])
if lr_reached_min or self.epochs == epoch \
or (self.save_frequency > 0 and (epoch % self.save_frequency == 0)):
model.save(self._model_path(epoch))
if self._writer and (epoch % self.WRITER_CACHE_EPOCHS == 0):
self._reset_writer()
return not lr_reached_min
def main():
"""Main function."""
args = parse_args()
randomize_func = functools.partial(uc.randomize_smiles,
random_type=args.random_type)
to_mol_func = uc.get_mol_func(args.smiles_type)
to_smiles_func = uc.get_smi_func(args.smiles_type)
mols_rdd = SC.textFile(args.input_smi_path) \
.repartition(args.num_partitions) \
.map(to_mol_func)\
.persist()
os.makedirs(args.output_smi_folder_path, exist_ok=True)
for i in range(args.num_files):
with open("{}/{:03d}.smi".format(args.output_smi_folder_path, i),
"w+") as out_file:
for smi in mols_rdd.map(
lambda mol: to_smiles_func(randomize_func(mol))).collect():
out_file.write("{}\n".format(smi))
def main():
"""Main function."""
args = parse_args()
model = mm.Model.load_from_file(args.model_path, mode="sampling")
training_set = list(uc.read_smi_file(args.training_set_path))
validation_set = list(uc.read_smi_file(args.validation_set_path))
writer = tbx.SummaryWriter(log_dir=args.log_path)
ma.CollectStatsFromModel(model,
args.epoch,
training_set,
validation_set,
writer,
sample_size=args.sample_size,
with_weights=args.with_weights,
to_mol_func=uc.get_mol_func(args.smiles_type),
logger=LOG).run()
writer.close()