def load_model(self, lmid):
type_, class_, name = lmid.split(":")
filename = self.get_filename(lmid)
if type_ == "lm":
if class_ == "system":
if pypredict.read_order(filename) == 1:
model = pypredict.UnigramModel()
else:
model = pypredict.DynamicModel()
elif class_ == "user":
model = pypredict.CachedDynamicModel()
elif class_ == "mem":
model = pypredict.DynamicModel()
else:
_logger.error(
"Unknown class component '{}' in lmid '{}'".format(
class_, lmid))
return None
else:
_logger.error("Unknown type component '{}' in lmid '{}'".format(
type_, lmid))
return None
if filename:
self.do_load_model(model, filename, class_)
return model
def setUp(self):
self._tmp_dir = tempfile.TemporaryDirectory(prefix="test_onboard_")
self._dir = self._tmp_dir.name
text = "word1 word2 word3 word4 word5 word6"
tokens, _spans = pypredict.tokenize_text(text)
# prepare contents of error-free models
self._model_contents = []
self._models = []
for i in range(0, self.MAX_ORDER):
order = i + 1
fn = os.path.join(self._dir, "order{}.lm".format(order))
if order == 1:
model = pypredict.UnigramModel()
else:
model = pypredict.DynamicModel(order)
model.learn_tokens(tokens)
model.save(fn)
with open(fn, encoding="UTF-8") as f:
lines = f.readlines()
self._models.append(model)
self._model_contents.append([fn, lines])