async def train_core(domain_file="domain.yml",
model_directory="models",
model_name="current",
training_data_file="data/stories.md"):
agent = Agent(
domain_file,
policies=[
MemoizationPolicy(max_history=3),
MappingPolicy(),
RestaurantPolicy(batch_size=100, epochs=100, validation_split=0.2),
],
)
training_data_file = "data/tiny_stories.md"
training_data = await agent.load_data(training_data_file,
augmentation_factor=10)
# show_training_data(training_data)
# print(type(training_data))
# print(training_data)
for data in training_data:
print(type(data))
# viz_domain(data.domain)
# viz_TrackerWithCachedStates(data, view_domain=False)
# exit()
agent.train(training_data)
# Attention: agent.persist stores the model and all meta data into a folder.
# The folder itself is not zipped.
model_path = os.path.join(model_directory, model_name, "core")
agent.persist(model_path)
logger.info(f"Model trained. Stored in '{model_path}'.")
return model_path
async def train_core(
domain_file: Text = "domain.yml",
model_directory: Text = "models",
model_name: Text = "current",
training_data_file: Text = "data/stories.md",
):
agent = Agent(
domain_file,
policies=[
MemoizationPolicy(max_history=3),
MappingPolicy(),
RestaurantPolicy(batch_size=100, epochs=100, validation_split=0.2),
],
)
training_data = await agent.load_data(training_data_file, augmentation_factor=10)
agent.train(training_data)
# Attention: agent.persist stores the model and all meta data into a folder.
# The folder itself is not zipped.
model_path = os.path.join(model_directory, model_name, "core")
agent.persist(model_path)
logger.info(f"Model trained. Stored in '{model_path}'.")
return model_path
def train_dialogue(domain_file="domain.yml",model_path="./Model/dialogue",training_data_file="Stories.md"):
agent = Agent(domain_file,policies=[MemoizationPolicy(max_history=5),RestaurantPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(training_data,epochs=100,
batch_size=5,
validation_split=0.2
)
agent.persist(model_path)
return agent
def train_core(domain_file="restaurant_domain.yml",
model_path="models/dialogue",
training_data_file="data/train_babi_stories.md"):
policies_array = [
[MemoizationPolicy(max_history=3)], #gut
[MemoizationPolicy(max_history=5)], #gut
[AugmentedMemoizationPolicy()], #gut
[RestaurantPolicy()], #gut
[KerasPolicy()], #gut
[FallbackPolicy()], #gut
[FallbackPolicy(core_threshold=0.5)], #gut
[SklearnPolicy(scoring=['accuracy','f1'])], #gut
[MemoizationPolicy(max_history=3), RestaurantPolicy()], #gut
[AugmentedMemoizationPolicy(), RestaurantPolicy()], #gut
[MemoizationPolicy(max_history=3), KerasPolicy()], #gut
[AugmentedMemoizationPolicy(), KerasPolicy()], #gut
[MemoizationPolicy(max_history=3), SklearnPolicy(scoring=['accuracy','f1'])], #gut
[AugmentedMemoizationPolicy(), SklearnPolicy(scoring=['accuracy','f1'])], #gut
[MemoizationPolicy(max_history=3), KerasPolicy(), SklearnPolicy()], #gut
[MemoizationPolicy(max_history=3), RestaurantPolicy(), SklearnPolicy()], #gut
[AugmentedMemoizationPolicy(), RestaurantPolicy(), SklearnPolicy(scoring=['accuracy','f1'])] #gut
]
index_policies = -4
print("Training policies: [%s]" % ",".join([x.__class__.__name__ for x in policies_array[index_policies]]))
agent = Agent(domain_file,policies_array[index_policies])
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="mydomain.yml",
model_path="models/dialogue",
training_data_file="data/mystories.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
RestaurantPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=400,
batch_size=100,
validation_split=0.2)
agent.persist(model_path)
# agent.visualize(training_data_file,output_file="graph.png", max_history=2)
return agent
def train_dialogue(domain_file="restaurant_domain.yml",
model_path="models/dialogue",
training_data_file="data/babi_stories.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=3),
RestaurantPolicy(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="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(domain_file="../config/fund_domain.yml",
model_path="../models/dialogue/sl",
training_data_file="../data/fund_stories.md"):
agent = Agent(domain_file,
policies=[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(domain_file="../rasa/jarvis_dom.yml",
model_path="nlu_dialogue/models/jarvis_nlu",
training_data_file="../rasa/jarvis_story.md"):
fallback = FallbackPolicy(fallback_action_name="utter_default",
core_threshold=0.46,
nlu_threshold=0.46)
agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=3),
RestaurantPolicy(), fallback
])
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