def ignor_test_online_training(self):
"""
test online training.
"""
test_config = "tests/data/test_config/test_config.json"
config = AnnotatorConfig(test_config)
# init trainer first
trainer = Trainer(config)
# load all data for test, in actual data should get from user label
with io.open(config["org_data"], encoding="utf-8-sig") as f:
data = simplejson.loads(f.read())
validate_local_data(data)
data_set = data.get("data_set", list())
# faker user labeled data, user has labeled 50 texts.
faker_user_labeled_data = data_set[:50]
# 950 text to predict and rank
unlabeled_data = data_set[50:]
# now test online training
examples = []
for e in faker_user_labeled_data:
data = e.copy()
if "text" in data:
del data["text"]
examples.append(Message(e["text"], data))
new_labeled_data = TrainingData(examples)
# full amount train and persist model
interpreter = trainer.train(new_labeled_data)
trainer.persist(config['path'],
config['project'],
config['fixed_model_name'])
# predict unlabeled dataset and ranking
predicted_results = []
for unlabeled_data in unlabeled_data:
predict = interpreter.parse(unlabeled_data["text"])
predicted_results.append(predict)
# sort predict result
# predicted result format as
# {
# 'classifylabel': {'name': 'spam', 'confidence': 0.5701943777626447},
# 'classifylabel_ranking': [{'name': 'spam', 'confidence': 0.5701943777626447},
# {'name': 'notspam', 'confidence': 0.42980562223735524}],
# 'text': '我是一个垃圾邮件'
# }
confidence_threshold = config["confidence_threshold"]
ranking_candidates = [text for text in predicted_results \
if text.get("classifylabel").get("confidence") < confidence_threshold]
for candidate in ranking_candidates:
assert candidate.get("classifylabel").get("confidence") < confidence_threshold
def ignore_test_train_with_empty_data(self):
"""
test train with empty train data
:return:
"""
test_config = "tests/data/test_config/test_config.json"
config = AnnotatorConfig(test_config)
trainer = Trainer(config)
assert len(trainer.pipeline) > 0
# create tmp train set
train_data = TrainingData([])
# rm tmp train set
trainer.train(train_data)
# test persist and load
persisted_path = trainer.persist(config['path'],
config['project'],
config['fixed_model_name'])
interpreter_loaded = Interpreter.load(persisted_path, config)
assert interpreter_loaded.pipeline
assert interpreter_loaded.parse("hello") is not None
assert interpreter_loaded.parse("Hello today is Monday, again!") is not None
# remove tmp models
shutil.rmtree(config['path'], ignore_errors=False)
def ignore_test_pipeline_flow(self):
"""
test trainer's train func for pipeline
:return:
"""
test_config = "tests/data/test_config/test_config.json"
config = AnnotatorConfig(test_config)
trainer = Trainer(config)
assert len(trainer.pipeline) > 0
# create tmp train set
tmp_path = create_tmp_test_jsonfile("tmp.json")
train_data = load_local_data(tmp_path)
# rm tmp train set
rm_tmp_file("tmp.json")
interpreter = trainer.train(train_data)
assert interpreter is not None
out1 = interpreter.parse(("点连接拿红包啦"))
# test persist and load
persisted_path = trainer.persist(config['path'],
config['project'],
config['fixed_model_name'])
interpreter_loaded = Interpreter.load(persisted_path, config)
out2 = interpreter_loaded.parse("点连接拿红包啦")
assert out1.get("classifylabel").get("name") == out2.get("classifylabel").get("name")
# remove tmp models
shutil.rmtree(config['path'], ignore_errors=True)
def ignore_test_load_and_persist_without_train(self):
"""
test save and load model without train
:return:
"""
test_config = "tests/data/test_config/test_config.json"
config = AnnotatorConfig(test_config)
trainer = Trainer(config)
assert len(trainer.pipeline) > 0
# create tmp train set
tmp_path = create_tmp_test_jsonfile("tmp.json")
train_data = load_local_data(tmp_path)
# rm tmp train set
rm_tmp_file("tmp.json")
# interpreter = trainer.train(train_data)
# test persist and load
persisted_path = trainer.persist(config['path'],
config['project'],
config['fixed_model_name'])
interpreter_loaded = Interpreter.load(persisted_path, config)
assert interpreter_loaded.pipeline
assert interpreter_loaded.parse("hello") is not None
assert interpreter_loaded.parse("Hello today is Monday, again!") is not None
# remove tmp models
shutil.rmtree(config['path'], ignore_errors=False)
def ignore_test_trainer_persist(self):
"""
test pipeline persist, metadata will be saved
:return:
"""
test_config = "tests/data/test_config/test_config.json"
config = AnnotatorConfig(test_config)
trainer = Trainer(config)
assert len(trainer.pipeline) > 0
# char_tokenizer component should been created
assert trainer.pipeline[0] is not None
# create tmp train set
tmp_path = create_tmp_test_jsonfile("tmp.json")
train_data = load_local_data(tmp_path)
# rm tmp train set
rm_tmp_file("tmp.json")
trainer.train(train_data)
persisted_path = trainer.persist(config['path'],
config['project'],
config['fixed_model_name'])
# load persisted metadata
metadata_path = os.path.join(persisted_path, 'metadata.json')
with io.open(metadata_path) as f:
metadata = json.load(f)
assert 'trained_at' in metadata
# rm tmp files and dirs
shutil.rmtree(config['path'], ignore_errors=False)
def test_load_and_persist_without_train(self):
"""
test save and load model without train
:return:
"""
test_config = "tests/data/test_config.json"
config = AnnotatorConfig(test_config)
trainer = Trainer(config)
assert len(trainer.pipeline) > 0
# create tmp train set
tmp_path = create_tmp_test_jsonfile("tmp.json")
train_data = load_local_data(tmp_path)
# rm tmp train set
rm_tmp_file("tmp.json")
# interpreter = trainer.train(train_data)
# test persist and load
persisted_path = trainer.persist(config['path'], config['project'],
config['fixed_model_name'])
interpreter_loaded = Interpreter.load(persisted_path, config)
assert interpreter_loaded.pipeline
assert interpreter_loaded.parse("hello") is not None
assert interpreter_loaded.parse(
"Hello today is Monday, again!") is not None
# remove tmp models
shutil.rmtree(config['path'], ignore_errors=True)
def test_trainer_persist(self):
"""
test pipeline persist, metadata will be saved
:return:
"""
test_config = "tests/data/test_config.json"
config = AnnotatorConfig(test_config)
trainer = Trainer(config)
assert len(trainer.pipeline) > 0
# char_tokenizer component should been created
assert trainer.pipeline[0] is not None
# create tmp train set
tmp_path = create_tmp_test_jsonfile("tmp.json")
train_data = load_local_data(tmp_path)
# rm tmp train set
rm_tmp_file("tmp.json")
trainer.train(train_data)
persisted_path = trainer.persist(config['path'], config['project'],
config['fixed_model_name'])
# load persisted metadata
metadata_path = os.path.join(persisted_path, 'metadata.json')
with io.open(metadata_path) as f:
metadata = json.load(f)
assert 'trained_at' in metadata
# rm tmp files and dirs
shutil.rmtree(config['path'], ignore_errors=True)
def do_train(
config, # type: AnnotatorConfig
component_builder=None # type: Optional[ComponentBuilder]
):
# type: (...) -> Tuple[Trainer, Interpreter, Text]
"""Loads the trainer and the data and runs the training of the model."""
# Ensure we are training a model that we can save in the end
# WARN: there is still a race condition if a model with the same name is
# trained in another subprocess
trainer = Trainer(config, component_builder)
training_data = load_local_data(config['org_data'])
interpreter = trainer.train(training_data)
persisted_path = trainer.persist(config['path'], config['project'],
config['fixed_model_name'])
return trainer, interpreter, persisted_path
class ActiveLearner(object):
"""
implement of active learning core, this class is mainly the wrapper of trainer and interpreter.
you can use ActiveLearner as follow:
active_leaner = ActiveLearner(config)
data_to_train = get_data_from_user_label()
active_leaner.train(data_to_train)
data_to_rank = get_data_from_db()
low_confidence_data = active_leaner.process_batch(data_to_rank)
"""
def __init__(self, config):
"""
init of ActiveLearner
"""
self.config = config
self.trainer = Trainer(config)
self.train_data = TrainingData([])
self.new_data_count = 0
self.batch_num = config.get("batch_num", 20)
self.db = DBManager(config)
self.interpreter = None
def train(self, data_set):
"""
train data set
:param data_set: format as [{"id": 1, "text": "我是测试", "label": "spam"}, .....]
:return:
"""
config = self.config
examples = []
for e in data_set:
data = e.copy()
if "text" in data:
del data["text"]
examples.append(Message(e["text"], data))
train_data = TrainingData(examples)
self.interpreter = self.trainer.train(train_data)
# overwrite save model TODO
self.trainer.persist(config['path'], config['project'],
config['fixed_model_name'])
def process_one(self, id):
"""
predict one according id
:param id:
:return:
"""
data = self.db.get_row({"id": id})
predict = self.interpreter.parse(data["text"])
return predict
def process_batch(self, ids):
"""
process batch text according ids
:param ids:
:return:
"""
datas = self.db.get_row_by_ids(ids)
predicted_results = []
for unlabeled_data in datas:
predict = self.interpreter.parse(unlabeled_data["text"])
if predict:
unlabeled_data.update(predict)
predicted_results.append(unlabeled_data)
return predicted_results
def process_texts(self, texts):
"""
process texts
:param texts: format as [{"id": 1, "text": "我是测试"}, {"id": 2, "text": "我是测试2"}, ...]
:return: format as [{'id':-, 'text':-, 'classifylabel':-, 'classifylabel_ranking':-}, ...]
"""
if self.interpreter is None:
logger.warning(
"model has not been trained, nothing will be predicted.")
return []
predicted_results = []
for unlabeled_data in texts:
predict = self.interpreter.parse(unlabeled_data["text"])
if predict:
unlabeled_data.update(predict)
predicted_results.append(unlabeled_data)
return predicted_results
class ActiveLearner(object):
"""
implement of active learning core, this class is mainly the wrapper of trainer and interpreter.
you can use ActiveLearner as follow:
active_leaner = ActiveLearner(config)
data_to_train = get_data_from_user_label()
active_leaner.train(data_to_train)
data_to_rank = get_data_from_db()
low_confidence_data = active_leaner.process_batch(data_to_rank)
"""
def __init__(self, config):
"""
init of ActiveLearner
"""
self.config = config
self.trainer = Trainer(config)
self.train_data = TrainingData([])
self.new_data_count = 0
self.batch_num = config.get("batch_num", 20)
self.db = DBManager(config)
self.interpreter = None
def train(self, data_set):
"""
train data set
:param data_set: format as [{"id": 1, "text": "我是测试", "label": "spam"}, .....]
:return:
"""
config = self.config
examples = []
for e in data_set:
data = e.copy()
if "text" in data:
del data["text"]
examples.append(Message(e["text"], data))
train_data = TrainingData(examples)
self.interpreter = self.trainer.train(train_data)
# overwrite save model TODO
self.trainer.persist(config['path'],
config['project'],
config['fixed_model_name'])
def process_one(self, id):
"""
predict one according id
:param id:
:return:
"""
data = self.db.get_row({"id": id})
predict = self.interpreter.parse(data["text"])
return predict
def process_batch(self, ids):
"""
process batch text according ids
:param ids:
:return:
"""
datas = self.db.get_row_by_ids(ids)
predicted_results = []
for unlabeled_data in datas:
predict = self.interpreter.parse(unlabeled_data["text"])
if predict:
unlabeled_data.update(predict)
predicted_results.append(unlabeled_data)
return predicted_results
def process_texts(self, texts):
"""
process texts
:param texts: format as [{"id": 1, "text": "我是测试"}, {"id": 2, "text": "我是测试2"}, ...]
:return: format as [{'id':-, 'text':-, 'classifylabel':-, 'classifylabel_ranking':-}, ...]
"""
if self.interpreter is None:
logger.warning("model has not been trained, nothing will be predicted.")
return []
predicted_results = []
for unlabeled_data in texts:
predict = self.interpreter.parse(unlabeled_data["text"])
if predict:
unlabeled_data.update(predict)
predicted_results.append(unlabeled_data)
return predicted_results
