class IntentContainer(object):
"""
Creates an IntentContainer object used to load and match intents
Args:
cache_dir (str): Place to put all saved neural networks
"""
def __init__(self, cache_dir):
os.makedirs(cache_dir, exist_ok=True)
self.cache_dir = cache_dir
self.must_train = False
self.intents = IntentManager(cache_dir)
self.entities = EntityManager(cache_dir)
self.padaos = padaos.IntentContainer()
self.train_thread = None # type: Thread
self.serialized_args = [
] # Arguments of all calls to register intents/entities
def clear(self):
os.makedirs(self.cache_dir, exist_ok=True)
self.must_train = False
self.intents = IntentManager(self.cache_dir)
self.entities = EntityManager(self.cache_dir)
self.padaos = padaos.IntentContainer()
self.train_thread = None
self.serialized_args = []
def instantiate_from_disk(self):
"""
Instantiates the necessary (internal) data structures when loading persisted model from disk.
This is done via injecting entities and intents back from cached file versions.
"""
entity_traindata = {}
intent_traindata = {}
# workaround: load training data for both entities and intents since
# padaos regex needs it for (re)compilation until TODO is cleared
for f in os.listdir(self.cache_dir):
if f.endswith('.entity'):
entity_name = f[0:f.find('.entity')]
with open(os.path.join(self.cache_dir, f), 'r') as d:
entity_traindata[entity_name] = [
line.strip() for line in d
]
elif f.endswith('.intent'):
intent_name = f[0:f.find('.intent')]
with open(os.path.join(self.cache_dir, f), 'r') as d:
intent_traindata[intent_name] = [
line.strip() for line in d
]
# TODO: padaos.compile (regex compilation) is redone when loading: find
# a way to persist regex, as well!
for f in os.listdir(self.cache_dir):
if f.startswith('{') and f.endswith('}.hash'):
entity_name = f[1:f.find('}.hash')]
self.add_entity(name=entity_name,
lines=entity_traindata[entity_name],
reload_cache=False,
must_train=False)
elif not f.startswith('{') and f.endswith('.hash'):
intent_name = f[0:f.find('.hash')]
self.add_intent(name=intent_name,
lines=intent_traindata[intent_name],
reload_cache=False,
must_train=False)
@_save_args
def add_intent(self, name, lines, reload_cache=False, must_train=True):
"""
Creates a new intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
lines (list<str>): All the sentences that should activate the intent
reload_cache: Whether to ignore cached intent if exists
"""
self.intents.add(name, lines, reload_cache, must_train)
self.padaos.add_intent(name, lines)
self.must_train = must_train
@_save_args
def add_entity(self, name, lines, reload_cache=False, must_train=True):
"""
Adds an entity that matches the given lines.
Example:
self.add_intent('weather', ['will it rain on {weekday}?'])
self.add_entity('weekday', ['monday', 'tuesday', 'wednesday']) # ...
Args:
name (str): The name of the entity
lines (list<str>): Lines of example extracted entities
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.add(Entity.wrap_name(name), lines, reload_cache,
must_train)
self.padaos.add_entity(name, lines)
self.must_train = must_train
@_save_args
def load_entity(self,
name,
file_name,
reload_cache=False,
must_train=True):
"""
Loads an entity, optionally checking the cache first
Args:
name (str): The associated name of the entity
file_name (str): The location of the entity file
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.load(Entity.wrap_name(name), file_name, reload_cache)
with open(file_name) as f:
self.padaos.add_entity(name, f.read().split('\n'))
self.must_train = must_train
@_save_args
def load_file(self, *args, **kwargs):
"""Legacy. Use load_intent instead"""
self.load_intent(*args, **kwargs)
@_save_args
def load_intent(self,
name,
file_name,
reload_cache=False,
must_train=True):
"""
Loads an intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
file_name (str): The location of the intent file
reload_cache (bool): Whether to refresh all of cache
"""
self.intents.load(name, file_name, reload_cache)
with open(file_name) as f:
self.padaos.add_intent(name, f.read().split('\n'))
self.must_train = must_train
@_save_args
def remove_intent(self, name):
"""Unload an intent"""
self.intents.remove(name)
self.padaos.remove_intent(name)
self.must_train = True
@_save_args
def remove_entity(self, name):
"""Unload an entity"""
self.entities.remove(name)
self.padaos.remove_entity(name)
def _train(self, *args, **kwargs):
t1 = Thread(target=self.intents.train,
args=args,
kwargs=kwargs,
daemon=True)
t2 = Thread(target=self.entities.train,
args=args,
kwargs=kwargs,
daemon=True)
t1.start()
t2.start()
t1.join()
t2.join()
self.entities.calc_ent_dict()
def train(self, debug=True, force=False, single_thread=False, timeout=20):
"""
Trains all the loaded intents that need to be updated
If a cache file exists with the same hash as the intent file,
the intent will not be trained and just loaded from file
Args:
debug (bool): Whether to print a message to stdout each time a new intent is trained
force (bool): Whether to force training if already finished
single_thread (bool): Whether to force running in a single thread
timeout (float): Seconds before cancelling training
Returns:
bool: True if training succeeded without timeout
"""
if not self.must_train and not force:
return
self.padaos.compile()
self.train_thread = Thread(target=self._train,
kwargs=dict(debug=debug,
single_thread=single_thread,
timeout=timeout),
daemon=True)
self.train_thread.start()
self.train_thread.join(timeout)
self.must_train = False
return not self.train_thread.is_alive()
def train_subprocess(self, *args, **kwargs):
"""
Trains in a subprocess which provides a timeout guarantees everything shuts down properly
Args:
See <train>
Returns:
bool: True for success, False if timed out
"""
ret = call([
sys.executable,
'-m',
'padatious',
'train',
self.cache_dir,
'-d',
json.dumps(self.serialized_args),
'-a',
json.dumps(args),
'-k',
json.dumps(kwargs),
])
if ret == 2:
raise TypeError('Invalid train arguments: {} {}'.format(
args, kwargs))
data = self.serialized_args
self.clear()
self.apply_training_args(data)
self.padaos.compile()
if ret == 0:
self.must_train = False
return True
elif ret == 10: # timeout
return False
else:
raise ValueError(
'Training failed and returned code: {}'.format(ret))
def calc_intents(self, query):
"""
Tests all the intents against the query and returns
data on how well each one matched against the query
Args:
query (str): Input sentence to test against intents
Returns:
list<MatchData>: List of intent matches
See calc_intent() for a description of the returned MatchData
"""
if self.must_train:
self.train()
intents = {} if self.train_thread and self.train_thread.is_alive(
) else {
i.name: i
for i in self.intents.calc_intents(query, self.entities)
}
sent = tokenize(query)
for perfect_match in self.padaos.calc_intents(query):
name = perfect_match['name']
intents[name] = MatchData(name,
sent,
matches=perfect_match['entities'],
conf=1.0)
return list(intents.values())
def calc_intent(self, query):
"""
Tests all the intents against the query and returns
match data of the best intent
Args:
query (str): Input sentence to test against intents
Returns:
MatchData: Best intent match
"""
matches = self.calc_intents(query)
if len(matches) == 0:
return MatchData('', '')
best_match = max(matches, key=lambda x: x.conf)
best_matches = (match for match in matches
if match.conf == best_match.conf)
return min(best_matches,
key=lambda x: sum(map(len, x.matches.values())))
def get_training_args(self):
return self.serialized_args
def apply_training_args(self, data):
for params in data:
func_name = params.pop('__name__')
getattr(self, func_name)(**params)
class IntentContainer(object):
"""
Creates an IntentContainer object used to load and match intents
Args:
cache_dir (str): Place to put all saved neural networks
"""
def __init__(self, cache_dir):
self.must_train = False
self.intents = IntentManager(cache_dir)
self.entities = EntityManager(cache_dir)
self.padaos = padaos.IntentContainer()
def add_intent(self, name, lines, reload_cache=False):
"""
Creates a new intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
lines (list<str>): All the sentences that should activate the intent
reload_cache: Whether to ignore cached intent if exists
"""
self.intents.add(name, lines, reload_cache)
self.padaos.add_intent(name, lines)
self.must_train = True
def add_entity(self, name, lines, reload_cache=False):
"""
Adds an entity that matches the given lines.
Example:
self.add_intent('weather', ['will it rain on {weekday}?'])
self.add_entity('{weekday}', ['monday', 'tuesday', 'wednesday']) # ...
Args:
name (str): The name of the entity
lines (list<str>): Lines of example extracted entities
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.add(Entity.wrap_name(name), lines, reload_cache)
self.padaos.add_entity(name, lines)
self.must_train = True
def load_entity(self, name, file_name, reload_cache=False):
"""
Loads an entity, optionally checking the cache first
Args:
name (str): The associated name of the entity
file_name (str): The location of the entity file
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.load(Entity.wrap_name(name), file_name, reload_cache)
with open(file_name) as f:
self.padaos.add_entity(name, f.read().split('\n'))
def load_file(self, *args, **kwargs):
"""Legacy. Use load_intent instead"""
self.load_intent(*args, **kwargs)
def load_intent(self, name, file_name, reload_cache=False):
"""
Loads an intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
file_name (str): The location of the intent file
reload_cache (bool): Whether to refresh all of cache
"""
self.intents.load(name, file_name, reload_cache)
with open(file_name) as f:
self.padaos.add_intent(name, f.read().split('\n'))
def remove_intent(self, name):
"""Unload an intent"""
self.intents.remove(name)
self.padaos.remove_intent(name)
self.must_train = True
def remove_entity(self, name):
"""Unload an entity"""
self.entities.remove(name)
self.padaos.remove_entity(name)
def train(self, *args, **kwargs):
"""
Trains all the loaded intents that need to be updated
If a cache file exists with the same hash as the intent file,
the intent will not be trained and just loaded from file
Args:
print_updates (bool): Whether to print a message to stdout
each time a new intent is trained
single_thread (bool): Whether to force running in a single thread
"""
self.intents.train(*args, **kwargs)
self.entities.train(*args, **kwargs)
self.entities.calc_ent_dict()
self.padaos.compile()
self.must_train = False
def calc_intents(self, query):
"""
Tests all the intents against the query and returns
data on how well each one matched against the query
Args:
query (str): Input sentence to test against intents
Returns:
list<MatchData>: List of intent matches
See calc_intent() for a description of the returned MatchData
"""
if self.must_train:
self.train()
intents = {
i.name: i
for i in self.intents.calc_intents(query, self.entities)
}
for perfect_match in self.padaos.calc_intents(query):
intent = intents.get(perfect_match['name'])
if intent:
intent.conf = 1.0
intent.matches = perfect_match['entities']
return list(intents.values())
def calc_intent(self, query):
"""
Tests all the intents against the query and returns
match data of the best intent
Args:
query (str): Input sentence to test against intents
Returns:
MatchData: Best intent match
"""
matches = self.calc_intents(query)
if len(matches) == 0:
return MatchData('', '')
best_match = max(matches, key=lambda x: x.conf)
best_matches = (match for match in matches
if match.conf == best_match.conf)
return min(best_matches,
key=lambda x: sum(map(len, x.matches.values())))
class IntentContainer(object):
"""
Creates an IntentContainer object used to load and match intents
Args:
cache_dir (str): Place to put all saved neural networks
"""
def __init__(self, cache_dir):
self.intents = IntentManager(cache_dir)
self.entities = EntityManager(cache_dir)
def add_intent(self, *args, **kwargs):
"""
Creates a new intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
lines (list<str>): All the sentences that should activate the intent
reload_cache: Whether to ignore cached intent if exists
"""
self.intents.add(*args, **kwargs)
def add_entity(self, name, *args, **kwargs):
"""
Adds an entity that matches the given lines.
Example:
self.add_intent('weather', ['will it rain on {weekday}?'])
self.add_entity('{weekday}', ['monday', 'tuesday', 'wednesday']) # ...
Args:
name (str): The name of the entity
lines (list<str>): Lines of example extracted entities
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.add(Entity.wrap_name(name), *args, **kwargs)
def load_entity(self, name, *args, **kwargs):
"""
Loads an entity, optionally checking the cache first
Args:
name (str): The associated name of the entity
file_name (str): The location of the entity file
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.load(Entity.wrap_name(name), *args, **kwargs)
def load_file(self, *args, **kwargs):
"""Legacy. Use load_intent instead"""
self.load_intent(*args, **kwargs)
def load_intent(self, *args, **kwargs):
"""
Loads an intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
file_name (str): The location of the intent file
reload_cache (bool): Whether to refresh all of cache
"""
self.intents.load(*args, **kwargs)
def train(self, *args, **kwargs):
"""
Trains all the loaded intents that need to be updated
If a cache file exists with the same hash as the intent file,
the intent will not be trained and just loaded from file
Args:
print_updates (bool): Whether to print a message to stdout
each time a new intent is trained
single_thread (bool): Whether to force running in a single thread
"""
self.intents.train(*args, **kwargs)
self.entities.train(*args, **kwargs)
self.entities.calc_ent_dict()
def calc_intents(self, query):
"""
Tests all the intents against the query and returns
data on how well each one matched against the query
Args:
query (str): Input sentence to test against intents
Returns:
list<MatchData>: List of intent matches
See calc_intent() for a description of the returned MatchData
"""
return self.intents.calc_intents(query, self.entities)
def calc_intent(self, query):
"""
Tests all the intents against the query and returns
match data of the best intent
Args:
query (str): Input sentence to test against intents
Returns:
MatchData: Best intent match
"""
return self.intents.calc_intent(query, self.entities)