def _write_nlu_to_file(
export_nlu_path: Text,
evts: List[Dict[Text, Any]]
) -> None:
"""Write the nlu data of the sender_id to the file paths."""
msgs = _collect_messages(evts)
# noinspection PyBroadException
try:
previous_examples = load_data(export_nlu_path)
except Exception as e:
logger.exception("An exception occurred while trying to load the "
"NLU data.")
export_nlu_path = questionary.text(
message="Could not load existing NLU data, please "
"specify where to store NLU data learned in "
"this session (this will overwrite any "
"existing file). {}".format(str(e)),
default=PATHS["backup"]).ask()
if export_nlu_path is None:
return
previous_examples = TrainingData()
nlu_data = previous_examples.merge(TrainingData(msgs))
with io.open(export_nlu_path, 'w', encoding="utf-8") as f:
if _guess_format(export_nlu_path) in {"md", "unk"}:
f.write(nlu_data.as_markdown())
else:
f.write(nlu_data.as_json())
def _write_nlu_to_file(
export_nlu_path: Text,
evts: List[Dict[Text, Any]]
) -> None:
"""Write the nlu data of the sender_id to the file paths."""
msgs = _collect_messages(evts)
# noinspection PyBroadException
try:
previous_examples = load_data(export_nlu_path)
except Exception:
questions = [{"name": "export nlu",
"type": "input",
"message": "Could not load existing NLU data, please "
"specify where to store NLU data learned in "
"this session (this will overwrite any "
"existing file)",
"default": PATHS["backup"]}]
answers = prompt(questions)
export_nlu_path = answers["export nlu"]
previous_examples = TrainingData()
nlu_data = previous_examples.merge(TrainingData(msgs))
with io.open(export_nlu_path, 'w', encoding="utf-8") as f:
if _guess_format(export_nlu_path) in {"md", "unk"}:
f.write(nlu_data.as_markdown())
else:
f.write(nlu_data.as_json())
def split_nlu_data(args):
from rasa_nlu.training_data.loading import load_data
data_path = get_validated_path(args.nlu, "nlu", DEFAULT_DATA_PATH)
data_path = data.get_nlu_directory(data_path)
nlu_data = load_data(data_path)
train, test = nlu_data.train_test_split(args.training_fraction)
train.persist(args.out, filename="training_data.json")
test.persist(args.out, filename="test_data.json")
def train_nlu():
from rasa_nlu import utils, config
from rasa_nlu.training_data.loading import load_data
from rasa_nlu.model import Trainer
from rasa_nlu.config import RasaNLUModelConfig
nlu_config = config.load(os.path.join(current_dir, "sample/nlu_config.yml"))
nlu_trainer = Trainer(nlu_config)
nlu_training_data = load_data(os.path.join(current_dir, "sample/nlu"))
nlu_trainer.train(nlu_training_data)
nlu_trainer.persist(os.path.join(current_dir, "sample/models/current/nlu"), fixed_model_name="default")
def train_nlu():
from rasa_nlu.training_data.loading import load_data
from rasa_nlu import config
from rasa_nlu.model import Trainer
training_data = load_data("data/train_file_news.json")
trainer = Trainer(config.load("data/nlu_model_config.yml"))
trainer.train(training_data)
model_directory = trainer.persist("models/",
project_name="news",
fixed_model_name="demo")
return model_directory
def train_nlu():
from rasa_nlu.training_data.loading import load_data # 新api,会将目录下的所有文件合并
from rasa_nlu.config import RasaNLUModelConfig#新 API
from rasa_nlu.model import Trainer
from rasa_nlu.config import load
# 生成46634意图样本，22003实体样本
training_data = load_data("nlu_data/train_data")
trainer = Trainer(load("pipeline_config.yaml"))# load的返回值就是一个RasaNLUModelConfig对象，而且其初始化需要传入的不是文件名，而是读取的配置文件内容，一个字典
trainer.train(training_data)
model_directory = trainer.persist("models/", project_name="nlu",fixed_model_name="model_ner_reg_all")
# model_directory = trainer.persist("models/", project_name="ivr", fixed_model_name="demo")
return model_directory
def evaluate(self, data, project=None, model=None):
# type: (Text, Optional[Text], Optional[Text]) -> Dict[Text, Any]
"""Perform a model evaluation."""
project = project or RasaNLUModelConfig.DEFAULT_PROJECT_NAME
model = model or None
file_name = utils.create_temporary_file(data, "_training_data")
test_data = load_data(file_name)
if project not in self.project_store:
raise InvalidProjectError("Project {} could not "
"be found".format(project))
preds_json = self.parse_training_examples(test_data.intent_examples,
project,
model)
predictions = [
{"text": e.text,
"intent": e.data.get("intent"),
"predicted": p.get("intent", {}).get("name"),
"confidence": p.get("intent", {}).get("confidence")}
for e, p in zip(test_data.intent_examples, preds_json)
]
y_true = [e.data.get("intent") for e in test_data.intent_examples]
y_true = clean_intent_labels(y_true)
y_pred = [p.get("intent", {}).get("name") for p in preds_json]
y_pred = clean_intent_labels(y_pred)
report, precision, f1, accuracy = get_evaluation_metrics(y_true,
y_pred)
return {
"intent_evaluation": {
"report": report,
"predictions": predictions,
"precision": precision,
"f1_score": f1,
"accuracy": accuracy}
}
def evaluate(self, data, project=None, model=None):
# type: (Text, Optional[Text], Optional[Text]) -> Dict[Text, Any]
"""Perform a model evaluation."""
project = project or RasaNLUModelConfig.DEFAULT_PROJECT_NAME
model = model or None
file_name = utils.create_temporary_file(data, "_training_data")
test_data = load_data(file_name)
if project not in self.project_store:
raise InvalidProjectError("Project {} could not "
"be found".format(project))
preds_json = self.parse_training_examples(test_data.intent_examples,
project,
model)
predictions = [
{"text": e.text,
"intent": e.data.get("intent"),
"predicted": p.get("intent", {}).get("name"),
"confidence": p.get("intent", {}).get("confidence")}
for e, p in zip(test_data.intent_examples, preds_json)
]
y_true = [e.data.get("intent") for e in test_data.intent_examples]
y_true = clean_intent_labels(y_true)
y_pred = [p.get("intent", {}).get("name") for p in preds_json]
y_pred = clean_intent_labels(y_pred)
report, precision, f1, accuracy = get_evaluation_metrics(y_true,
y_pred)
return {
"intent_evaluation": {
"report": report,
"predictions": predictions,
"precision": precision,
"f1_score": f1,
"accuracy": accuracy}
}
