def train_dialogue(domain_file, model_path, training_folder):
agent = Agent(domain_file, policies=[
MemoizationPolicy(max_history=6),
KerasPolicy(MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(),
max_history=6)),
FallbackPolicy(nlu_threshold=0.8, core_threshold=0.3)])
training_data = agent.load_data(training_folder)
agent.train(training_data, epochs=100)
agent.persist(model_path)
def test_agent_and_persist():
policies = config.load('policies.yml')
policies[0] = KerasPolicy(epochs=2) # Keep training times low
agent = Agent('domain.yml', policies=policies)
training_data = agent.load_data('data/stories.md')
agent.train(training_data, validation_split=0.0)
agent.persist('./tests/models/dialogue')
loaded = Agent.load('./tests/models/dialogue')
assert agent.handle_text('/greet') is not None
assert loaded.domain.action_names == agent.domain.action_names
assert loaded.domain.intents == agent.domain.intents
assert loaded.domain.entities == agent.domain.entities
assert loaded.domain.templates == agent.domain.templates
def train_dialogue(domain_file="restaurant_domain.yml", model_path="models/dialogue", training_data_file="data/babi_stories.md"): agent = Agent(domain_file,[MemoizationPolicy(max_history=3), RestaurantPolicy()]) training_data = agent.load_data(training_data_file) agent.train( training_data, epochs=400, batch_size=100, validation_split=0.2 ) agent.persist(model_path) return agent
def train_dialogue(doman_file="../weather_domain.yml",
model_path="../models/dialogue",
training_data_file="../data/stories.md"):
agent = Agent(domain=doman_file,
policies=[MemoizationPolicy(),
KerasPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=300,
batch_size=50,
validation_split=0.2,
augmentation_factor=50)
agent.persist(model_path)
return agent
def train_dialogue(domain_file="../../../models/domain.yml",
model_path="../../../models/models/dialogue",
training_data_file="../../../models/stories.md"):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=3),
FastSearchPolicy(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 train_dialogue(domain_file="mobile_domain.yml",
model_path="projects/dialogue",
training_data_file="data/mobile_story.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy(), fallback])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
epochs=200,
batch_size=16,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='restaurant_domain.yml',
model_path="models/dialogue",
training_data_file='data/babi_stories.md'):
featurizer = MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(),
max_history=5)
agent = Agent(
domain_file,
policies=[MemoizationPolicy(max_history=5),
KerasPolicy(featurizer)])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
epochs=400,
batch_size=100,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file="order_domain.yml",
model_path="models/dialogue",
training_data_file="data/babi_stories.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), OrderPolicy()])
agent.train(
training_data_file,
max_history=3,
epochs=400,
batch_size=100,
validation_split=0.2
)
agent.persist(model_path)
return agent
def train_dialog(dialog_training_data_file,
domain_file,
path_to_model='models/dialogue'):
logging.basicConfig(level='INFO')
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
core_threshold=0.3,
nlu_threshold=0.3)
agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=1),
KerasPolicy(epochs=200, batch_size=20), fallback
])
training_data = agent.load_data(dialog_training_data_file)
agent.train(training_data, augmentation_factor=50, validation_split=0.2)
agent.persist(path_to_model)
def train_dialogue(domain_file = 'restaurant_domain.yml', model_path = './models/dialogue', training_data_file = './data/core_stories.md'): featurizer = MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(), max_history=5) agent = Agent(domain_file, policies = [MemoizationPolicy(max_history = 5), KerasPolicy(featurizer)]) agent.train( training_data_file, epochs = 300, batch_size = 50, validation_split = 0.2, augmentation_factor = 50) agent.persist(model_path) return agent
def train_babi_dm():
training_data_file = 'data/babi_task5_trn_rasa_with_slots.md'
model_path = 'models/policy/current'
agent = Agent("../restaurant_domain.yml",
policies=[MemoizationPolicy(),
RestaurantPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=100,
batch_size=50,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
def train_dialog(domain_file, training_data_file, model_dir, interpreter):
_agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=6),
KerasPolicy(MaxHistoryTrackerFeaturizer(
BinarySingleStateFeaturizer(), max_history=6),
augmentation_factor=50,
epochs=300,
batch_size=50,
validation_split=0.2)
],
interpreter=interpreter)
_training_data = _agent.load_data(training_data_file)
_agent.train(_training_data)
_agent.persist(model_dir)
return _agent
def train_dialogue(domain_file='bank_domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=150, batch_size=50),
AugmentedMemoizationPolicy(max_history=3)
])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent
def train_dialogue(domain_file="./dialog_data/domain.yml",
model_path="./models/current/dialogue",
training_data_file="wine_stories.md"):
agent = Agent(
domain_file,
policies=[MemoizationPolicy(max_history=3),
KerasPolicy(), fallback])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
epochs=300,
batch_size=100,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file="restaurant_domain.yml",
model_path="models/dialogue",
training_data_file="data/restaurant_story.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
RestaurantPolicy()])
agent.train(training_data_file,
max_history=2,
epochs=200,
batch_size=16,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue_model(domain_file, stories_file, output_path,
nlu_model_path=None,
endpoints=None,
max_history=None,
dump_flattened_stories=False,
kwargs=None):
if not kwargs:
kwargs = {}
action_endpoint = utils.read_endpoint_config(endpoints, "action_endpoint")
fallback_args, kwargs = utils.extract_args(kwargs,
{"nlu_threshold",
"core_threshold",
"fallback_action_name"})
policies = [
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))]
agent = Agent(domain_file,
action_endpoint=action_endpoint,
interpreter=nlu_model_path,
policies=policies)
data_load_args, kwargs = utils.extract_args(kwargs,
{"use_story_concatenation",
"unique_last_num_states",
"augmentation_factor",
"remove_duplicates",
"debug_plots"})
training_data = agent.load_data(stories_file, **data_load_args)
agent.train(training_data, **kwargs)
agent.persist(output_path, dump_flattened_stories)
return agent
def train_dialogue_model(domain_file, stories_file, output_path,
nlu_model_path=None,
endpoints=None,
max_history=None,
dump_flattened_stories=False,
kwargs=None):
if not kwargs:
kwargs = {}
action_endpoint = utils.read_endpoint_config(endpoints, "action_endpoint")
fallback_args, kwargs = utils.extract_args(kwargs,
{"nlu_threshold",
"core_threshold",
"fallback_action_name"})
policies = [
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))]
agent = Agent(domain_file,
action_endpoint=action_endpoint,
interpreter=nlu_model_path,
policies=policies)
data_load_args, kwargs = utils.extract_args(kwargs,
{"use_story_concatenation",
"unique_last_num_states",
"augmentation_factor",
"remove_duplicates",
"debug_plots"})
training_data = agent.load_data(stories_file, **data_load_args)
agent.train(training_data, **kwargs)
agent.persist(output_path, dump_flattened_stories)
return agent
def train_online(project='Lambton'):
domain_file = "../Chatbots/projects/" + project + "/domain.json",
model_path = "../Chatbots/projects/" + project + "models/dialogue",
#training_data_file="projects/"+project+"/stories/stories.md"
training_data_file = "../Chatbots/projects/" + project + "/stories/stories.md"
agent = Agent(domain_file, policies=[MemoizationPolicy(), nuRobotPolicy()])
agent.train_online(training_data_file,
input_channel=ConsoleInputChannel(),
max_history=3,
batch_size=50,
epochs=200,
max_training_samples=300)
agent.persist(model_path)
return agent
def train_dialog(
dialog_training_data_file,
domain_file,
path_to_model='/home/tcs/chatbot_project_phase_1/models/dialogue'):
logging.basicConfig(level='INFO')
# first we will give the domain file, and policy to the agent.
agent = Agent(domain_file, policies=[MemoizationPolicy(max_history=1)])
# then we will give dialog training data file to it.
training_data = agent.load_data(dialog_training_data_file)
# here we will train the dialogue into the agent using the loaded the training data , define out values to the variables to # tweak the training
agent.train(training_data,
augmentation_factor=50,
epochs=200,
batch_size=10,
validation_split=0.2)
# then we save the dialogue model
agent.persist(path_to_model)
def train_dialogue(domain_file="mobile_domain.yml",
model_path="projects/dialogue",
training_data_file="data/mobile_story.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), KerasPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(
training_data,
epochs=200,
batch_size=16,
augmentation_factor=50,
validation_split=0.2
)
agent.persist(model_path)
return agent
def train_diag(domain_file="dynamo_domain.yml",
training_data_file='data/dialouge_stories/stories.md',
model_path='models/dialouge'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=2), KerasPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
augmentation_factor= 50,
epochs = 500,
batch_size = 50,
validation_split = 0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='domain.yml',
model_path='./models/dialogue',
training_data_file='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=4),
BotPolicy(), fallback])
data = agent.load_data(training_data_file)
agent.train(data, epochs=70, batch_size=8, validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue_model(domain_file, stories_file, output_path,
use_online_learning, nlu_model_path, kwargs):
agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy()])
if use_online_learning:
if nlu_model_path:
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
else:
agent.interpreter = RegexInterpreter()
agent.train_online(stories_file,
input_channel=ConsoleInputChannel(),
epochs=10,
model_path=output_path)
else:
agent.train(stories_file, validation_split=0.1, **kwargs)
agent.persist(output_path)
def train_dialogue(domain_file = './config/domain.yml',
model_path = './models/dialogue',
training_data_file = './data/stories.md'):
fallback = FallbackPolicy(fallback_action_name="utter_fallback",
core_threshold=0.3,
nlu_threshold=0.3)
agent = Agent(domain_file, policies = [MemoizationPolicy(), KerasPolicy(),fallback])
data = agent.load_data(training_data_file)
agent.train(
data,
epochs = 300,
batch_size = 50,
validation_split = 0.2)
agent.persist(model_path)
return agent
def train_online(project='Lambton'):
domain_file = './Core/models/' + project + '/dialogue/domain.yml'
model_path = './NLU/models/default/' + project,
training_data_file = './Core/models/' + project + '/stories/stories.md'
agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy()])
agent.train_online(training_data_file,
input_channel=ConsoleInputChannel(),
max_history=2,
batch_size=10,
epochs=250,
max_training_samples=300,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dm():
training_data_file = 'stories.md'
model_path = 'models/policy/current'
agent = Agent("domain.yml",
policies=[MemoizationPolicy(), MusicPlayerPolicy()])
agent.train(
training_data_file,
max_history=3,
epochs=100,
batch_size=50,
augmentation_factor=50,
validation_split=0.2
)
agent.persist(model_path)
def train_tickets_order_dm():
training_data_file = "../tickets/data/stories.md"
domain_path = '../tickets/domain.yml'
model_path = '../models/policy/current'
agent = Agent(domain=domain_path,
policies=[MemoizationPolicy(),
TicketsPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=100,
batch_size=50,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
def train_dialogue(domain_id="default"):
domain_file = "{}/{}/domain.yml".format(data_folder, domain_id)
model_path = "{}/{}/dialogue".format(model_folder, domain_id)
training_data_file = "{}/{}/stories.md".format(data_folder, domain_id)
agent = Agent(
domain_file,
policies=[MemoizationPolicy(max_history=3),
IntelleiPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
epochs=400,
batch_size=100,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='weather_domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), WeatherPolicy()])
agent.train(#TODO set the model parameters
training_data_file,
max_history = ,
epochs = ,
batch_size = ,
augmentation_factor = ,
validation_split =
)
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),
CustomPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(
training_data,
epochs=400,
batch_size=100,
validation_split=0.2
)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='domain.yml',
model_path='./models/dialogue',
training_data_file='stories.md'):
fallback = FallbackPolicy(fallback_action_name='utter_unclear',
core_threshold=0.2,
nlu_threshold=0.6)
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy(), fallback])
agent.train(training_data_file,
epochs=300,
batch_size=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='Sell4BidsBot_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=0.2)
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=200, 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="domain.yml",
model_path="models/dialogue",
training_data_file="data/core/stories.md"):
fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
nlu_threshold=0.5,
core_threshold=0.3)
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy(), fallback])
training_data = agent.load_data(training_data_file)
agent.train(training_data)
agent.persist(model_path)
return agent
def train_dialouge(domain_file='domain.yml',
output_model_path='./models/dialogue',
training_data_file='./data/stories.md'):
fallback = FallbackPolicy(fallback_action_name='action_default_fallback',
core_threshold = 0.3,
nlu_threshold=0.1)
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=50, batch_size=2),
fallback])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(output_model_path)
return agent
def test_agent_train(tmpdir, default_domain):
training_data_file = 'examples/moodbot/data/stories.md'
agent = Agent("examples/moodbot/domain.yml",
policies=[AugmentedMemoizationPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(training_data)
agent.persist(tmpdir.strpath)
loaded = Agent.load(tmpdir.strpath)
# test domain
assert loaded.domain.action_names == agent.domain.action_names
assert loaded.domain.intents == agent.domain.intents
assert loaded.domain.entities == agent.domain.entities
assert loaded.domain.templates == agent.domain.templates
assert [s.name for s in loaded.domain.slots] == \
[s.name for s in agent.domain.slots]
# test policies
assert type(loaded.policy_ensemble) is type(
agent.policy_ensemble) # nopep8
assert [type(p) for p in loaded.policy_ensemble.policies] == \
[type(p) for p in agent.policy_ensemble.policies]
def train_dialogue(domain_file="mobile_domain.yml",
model_path="models/dialogue",
training_data_file="data/mobile_edit_story.md"):
fallback = FallbackPolicy(
fallback_action_name="action_default_fallback",
nlu_threshold=0.5,
core_threshold=0.3
)
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=5),
MobilePolicy(), fallback])
training_data = agent.load_data(training_data_file)
agent.train(
training_data,
epochs=500,
batch_size=16,
validation_split=0.2
)
agent.persist(model_path)
return agent
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from rasa_core import utils
from rasa_core.agent import Agent
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
if __name__ == '__main__':
utils.configure_colored_logging(loglevel="INFO")
training_data_file = 'data/stories.md'
model_path = 'models/dialogue'
agent = Agent("concert_domain.yml",
policies=[MemoizationPolicy(), KerasPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(
training_data,
augmentation_factor=50,
max_history=2,
epochs=500,
batch_size=10,
validation_split=0.2
)
agent.persist(model_path)
