def train_dialogue(domain_file, stories_file, model_dir):
#assign domain to agent
#agent = Agent(domain_file, policies=[KerasPolicy(), FallbackPolicy(), MemoizationPolicy(), FormPolicy()])
agent = Agent(domain_file,
policies=[KerasPolicy(),
MemoizationPolicy(),
FormPolicy()])
#load story
training_data = agent.load_data(stories_file)
#train agent
agent.train(training_data)
#create model folder and store dialoge
agent.persist(model_dir)
def train_dialogue(domain_file='domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=5, epochs=100, batch_size=25),
FormPolicy()
])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent
def train_dialogue(domain_file="domain.yml",
model_path="models/dialogue",
training_data_file="data/stories.md"):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=3),
FormPolicy(),
jobPolicy(batch_size=100,
epochs=400,
validation_split=0.2)
])
training_data = agent.load_data(training_data_file)
agent.train(training_data)
agent.persist(model_path)
return agent
def run_interactive_online(interpreter,
domain_file="domain.yml",
training_data_file='stories.md'):
action_endpoint = EndpointConfig(url="http://localhost:5005/webhook")
agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=2),
KerasPolicy(max_history=3, epochs=3, batch_size=50),
FallbackPolicy(),
FormPolicy()
],
interpreter=interpreter,
action_endpoint=action_endpoint)
data = agent.load_data(training_data_file)
agent.train(data)
interactive.run_interactive_learning(agent, training_data_file)
return agent
def default_policies(cls, fallback_args, max_history):
# type: (Dict[Text, Any], int) -> List[Policy]
"""Load the default policy setup consisting of
FallbackPolicy, MemoizationPolicy and KerasPolicy."""
return [
FallbackPolicy(
fallback_args.get("nlu_threshold",
DEFAULT_NLU_FALLBACK_THRESHOLD),
fallback_args.get("core_threshold",
DEFAULT_CORE_FALLBACK_THRESHOLD),
fallback_args.get("fallback_action_name",
DEFAULT_FALLBACK_ACTION)),
MemoizationPolicy(max_history=max_history),
KerasPolicy(
MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(),
max_history=max_history)),
FormPolicy()
]
def run_bot_online(interpreter, domain_file, training_data_file):
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
core_threshold=0.3,
nlu_threshold=0.3)
agent = Agent(domain=domain_file,
policies=[
MemoizationPolicy(max_history=6),
KerasPolicy(max_history=6, epochs=200), fallback,
FormPolicy()
],
interpreter=interpreter,
action_endpoint=action_endpoint)
data = agent.load_data(training_data_file)
agent.train(data)
interactive.run_interactive_learning(agent, training_data_file)
return agent
def train_dialogue(
domain_file="domain.yml",
model_path="./models/dialogue",
training_data_file="./data/stories.md"):
fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
core_threshold=0.4,
nlu_threshold=0.4)
agent = Agent(
domain_file,
policies=[
FormPolicy(),
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=500, batch_size=50),
fallback
],
)
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent
def train_dialogue(domain_file="adobe_domain.yml",
model_path="models/dialogue",
training_data_file="data/stories.md"):
fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
core_threshold=0.3,
nlu_threshold=0.3)
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=5),
AdobePolicy(epochs=200, batch_size=32,
validation_split=0.2,
max_history=4),
fallback,
FormPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(
training_data
)
agent.persist(model_path)
return agent
def train_dialogue(
domain_file='/home/saradindu/dev/Work-II/Happsales/assistant_domain.yml',
model_path='/home/saradindu/dev/Work-II/Happsales/models/dialogue',
training_data_file='/home/saradindu/dev/Work-II/Happsales/data/stories.md'
):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
FormPolicy(),
MappingPolicy(),
FallbackPolicy(
nlu_threshold=0.4,
core_threshold=0.3,
fallback_action_name="action_default_fallback"),
KerasPolicy(max_history=3, epochs=200, batch_size=50)
])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent
def train_dialogue_keras(domain_file="mobile_domain.yml",
model_path="models/dialogue_keras",
training_data_file="data/mobile_edit_story.md"):
fallback = FallbackPolicy(
fallback_action_name="action_unknown_intent",
nlu_threshold=0.7,
core_threshold=0.3
)
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=8),
MobilePolicy(epochs=100, batch_size=16, max_history=8),
FormPolicy(),
fallback])
training_data = agent.load_data(training_data_file)
agent.train(
training_data,
validation_split=0.2
)
agent.persist(model_path)
return agent
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
import logging
from rasa_core.agent import Agent
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.form_policy import FormPolicy
from rasa_core.policies.memoization import MemoizationPolicy
if __name__ == '__main__':
logging.basicConfig(level='INFO')
training_data_file = './data/stories.md'
model_path = './models/dialogue'
memoization_policy = MemoizationPolicy(max_history=4)
keras_policy = KerasPolicy(max_history=5, epochs=500)
agent = Agent('restaurant_domain.yml',
policies=[memoization_policy, keras_policy,
FormPolicy()])
training_data = agent.load_data(training_data_file, augmentation_factor=50)
agent.train(training_data)
agent.persist(model_path)
def create_policy(self, featurizer, priority):
p = FormPolicy(priority=priority)
return p
warnings.simplefilter('ignore', yaml.error.UnsafeLoaderWarning)
logging.basicConfig(level='INFO')
'''
training the nlu
'''
args1 = {"pipeline": "tensorflow_embedding"}
conf1 = RasaNLUModelConfig(args1)
trainer1 = Trainer(conf1)
#nlu for agent 1
training_data1 = load_data("./data1/nlu.md")
Interpreter1 = trainer1.train(training_data1)
model_directory1 = trainer1.persist('./models', fixed_model_name="ner_a1")
#core for agent1
domain_file = "domain1.yml"
training_data_file = './data1/stories.md'
model_path = './models/dialogue_agent_1'
agent = Agent(domain_file, policies=[MemoizationPolicy(max_history=3), KerasPolicy(max_history=3, epochs=500, batch_size=10), FormPolicy()])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy(max_history=3, epochs=500, batch_size=50)])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
def create_policy(self, featurizer):
p = FormPolicy()
return p
def train_dialog(dialog_training_data_file, domain_file, path_to_model='models/'):
logging.basicConfig(level='INFO')
fallback = FallbackPolicy(
fallback_action_name="utter_default", core_threshold=0.3, nlu_threshold=0.3)
# agent = Agent(domain_file,
# policies=[MemoizationPolicy(max_history=1),KerasPolicy(epochs=200,
# batch_size=20), fallback, FormPolicy])
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=1), KerasPolicy(epochs=200,
batch_size=20), FormPolicy(), fallback])
loop = asyncio.get_event_loop()
data = loop.run_until_complete(agent.load_data(dialog_training_data_file))
# training_data = agent.load_data(dialog_training_data_file)
agent.train(
data,
augmentation_factor=50,
validation_split=0.2)
agent.persist(path_to_model)