def learn_text(self, text, allow_new_words):
""" Count n-grams and add words to the auto-learn models. """
if self.auto_learn_models:
tokens, spans = pypredict.tokenize_text(text)
# There are too many false positives with trailing
# single quotes, remove them.
# Do this here, because we still want "it's", etc. to
# incrementally provide completions.
for i, token in enumerate(tokens):
if token.endswith("'"):
token = token[:-1]
if not token: # shouldn't happen
token = "<unk>"
tokens[i] = token
# if requested, drop unknown words
if allow_new_words:
token_sections = [tokens]
else:
token_sections = self._drop_new_words(tokens, spans,
self.persistent_models)
models = self._model_cache.get_models(self.auto_learn_models)
for model in models:
for tokens in token_sections:
model.learn_tokens(tokens)
_logger.info("learn_text: tokens=" + repr(token_sections))
# debug: save all learned text for later parameter optimization
if config.log_learn:
fn = os.path.join(config.user_dir, "learned_text.txt")
with open(fn, "a") as f:
f.write(text + "\n")
def learn_scratch_text(self, text):
""" Count n-grams and add words to the scratch models. """
tokens, spans = pypredict.tokenize_text(text)
models = self._model_cache.get_models(self.scratch_models)
for model in models:
# print("scratch learn", model, tokens)
model.learn_tokens(tokens, True)
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])
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])
def learn_scratch_text(self, text):
""" Count n-grams and add words to the scratch models. """
tokens, spans = pypredict.tokenize_text(text)
models = self._model_cache.get_models(self.scratch_models)
for model in models:
#print("scratch learn", model, tokens)
model.learn_tokens(tokens, True)
def learn_text(self, text, allow_new_words):
""" Count n-grams and add words to the auto-learn models. """
if self.auto_learn_models:
tokens, spans = pypredict.tokenize_text(text)
# Remove trailing single quote, too many false positives.
# Do this here, because we still want "it's", etc. to
# incrementally provide completions.
for i, token in enumerate(tokens):
if token.endswith("'"):
token = token[:-1]
if not token: # shouldn't happen
token = "<unk>"
tokens[i] = token
models = self._model_cache.get_models(self.auto_learn_models)
for model in models:
model.learn_tokens(tokens, allow_new_words)
_logger.info("learn_text: tokens=" + repr(tokens[:10]))
# debug: save all learned text for later parameter optimization
if config.log_learn:
fn = os.path.join(config.user_dir, "learned_text.txt")
with open(fn, "a") as f:
f.write(text + "\n")
def tokenize_text(self, text):
"""
Let the service find the words in text.
"""
if 1:
# avoid the D-Bus round-trip while we can
tokens, spans = pypredict.tokenize_text(text)
else:
tokens, spans = self._call_method("tokenize_text", ([], []), text)
return tokens, spans
def tokenize_text(self, text):
"""
Let the service find the words in text.
"""
if 1:
# avoid the D-Bus round-trip while we can
tokens, spans = pypredict.tokenize_text(text)
else:
tokens, spans = self._call_method("tokenize_text", ([], []),
text)
return tokens, spans
def tokenize_text(self, text):
"""
Let the service find the words in text.
"""
tokens, spans = pypredict.tokenize_text(text)
return tokens, spans
def tokenize_text(self, text):
"""
Let the service find the words in text.
"""
tokens, spans = pypredict.tokenize_text(text)
return tokens, spans