def test_vector(self):
ids = IdManager()
assert len(ids.vector()) == 0
ids.add_token('a')
assert len(ids.vector()) == 1
ids.add_token('b')
vec = ids.vector()
ids.assign(vec, 'b', 0.5)
assert vec == [0, 0.5]
def test_save_load(self):
ids1 = IdManager()
ids1.add_token('hi')
ids1.add_token('hello')
if not isdir('temp'):
os.mkdir('temp')
ids1.save('temp/temp')
ids2 = IdManager()
ids2.load('temp/temp')
vec1 = ids1.vector()
vec2 = ids2.vector()
ids1.assign(vec1, 'hello', 3)
ids2.assign(vec2, 'hello', 3)
assert vec1 == vec2
class SimpleIntent(object):
"""General intent used to match sentences or phrases"""
LENIENCE = 0.6
def __init__(self, name=''):
self.name = name
self.ids = IdManager(Ids)
self.net = None
def match(self, sent):
return max(0, self.net.run(self.vectorize(sent))[0])
def vectorize(self, sent):
vector = self.ids.vector()
unknown = 0
for token in sent:
if token in self.ids:
self.ids.assign(vector, token, 1.0)
else:
unknown += 1
if len(sent) > 0:
self.ids.assign(vector, Ids.unknown_tokens,
unknown / float(len(sent)))
self.ids.assign(vector, Ids.w_1, len(sent) / 1)
self.ids.assign(vector, Ids.w_2, len(sent) / 2.)
self.ids.assign(vector, Ids.w_3, len(sent) / 3.)
self.ids.assign(vector, Ids.w_4, len(sent) / 4.)
return vector
def configure_net(self):
self.net = fann.neural_net()
self.net.create_standard_array([len(self.ids), 10, 1])
self.net.set_activation_function_hidden(
fann.SIGMOID_SYMMETRIC_STEPWISE)
self.net.set_activation_function_output(
fann.SIGMOID_SYMMETRIC_STEPWISE)
self.net.set_train_stop_function(fann.STOPFUNC_BIT)
self.net.set_bit_fail_limit(0.1)
def train(self, train_data):
for sent in train_data.my_sents(self.name):
self.ids.add_sent(sent)
inputs = []
outputs = []
def add(vec, out):
inputs.append(self.vectorize(vec))
outputs.append([out])
def pollute(sent, p):
sent = sent[:]
for _ in range(int((len(sent) + 2) / 3)):
sent.insert(p, ':null:')
add(sent, self.LENIENCE)
def weight(sent):
def calc_weight(w):
return pow(len(w), 3.0)
total_weight = 0.0
for word in sent:
total_weight += calc_weight(word)
for word in sent:
weight = 0 if word.startswith('{') else calc_weight(word)
add([word], weight / total_weight)
for sent in train_data.my_sents(self.name):
add(sent, 1.0)
weight(sent)
if not any(word[0] == ':' for word in sent):
pollute(sent, 0)
pollute(sent, len(sent))
for sent in train_data.other_sents(self.name):
add(sent, 0.0)
add([], 0.0)
inputs, outputs = resolve_conflicts(inputs, outputs)
train_data = fann.training_data()
train_data.set_train_data(inputs, outputs)
for _ in range(10):
self.configure_net()
self.net.train_on_data(train_data, 1000, 0, 0)
self.net.test_data(train_data)
if self.net.get_bit_fail() == 0:
break
def save(self, prefix):
prefix += '.intent'
self.net.save(str(prefix + '.net')) # Must have str()
self.ids.save(prefix)
@classmethod
def from_file(cls, name, prefix):
prefix += '.intent'
self = cls(name)
self.net = fann.neural_net()
self.net.create_from_file(str(prefix + '.net')) # Must have str()
self.ids.load(prefix)
return self
class EntityEdge(object):
"""
Represents the left or right side of an entity (a PosIntent)
Args:
direction (int): -1 for left and +1 for right
token (str): token to attach to (something like {word})
intent_name (str): name of parent intent
"""
def __init__(self, direction, token, intent_name):
self.ids = IdManager(Ids)
self.intent_name = intent_name
self.token = token
self.dir = direction
self.net = None
def get_end(self, sent):
return len(sent) if self.dir > 0 else -1
def vectorize(self, sent, pos):
unknown = 0
vector = self.ids.vector()
end_pos = self.get_end(sent)
for i in range(pos + self.dir, end_pos, self.dir):
if sent[i] in self.ids:
self.ids.assign(vector, sent[i], 1.0 / abs(i - pos))
else:
unknown += 1
self.ids.assign(vector, Ids.end, 1.0 / abs(end_pos - pos))
return vector
def match(self, sent, pos):
return self.net.run(self.vectorize(sent, pos))[0]
def configure_net(self):
layers = [len(self.ids), 3, 1]
self.net = fann.neural_net()
self.net.create_standard_array(layers)
self.net.set_activation_function_hidden(
fann.SIGMOID_SYMMETRIC_STEPWISE)
self.net.set_activation_function_output(fann.SIGMOID_STEPWISE)
self.net.set_train_stop_function(fann.STOPFUNC_BIT)
self.net.set_bit_fail_limit(0.1)
def save(self, prefix):
prefix += '.' + {-1: 'l', +1: 'r'}[self.dir]
self.net.save(str(prefix + '.net')) # Must have str()
self.ids.save(prefix)
def load(self, prefix):
prefix += '.' + {-1: 'l', +1: 'r'}[self.dir]
self.net = fann.neural_net()
if not self.net.create_from_file(
str(prefix + '.net')): # Must have str()
raise FileNotFoundError(str(prefix + '.net'))
self.ids.load(prefix)
def train(self, train_data):
for sent in train_data.my_sents(self.intent_name):
if self.token in sent:
for i in range(
sent.index(self.token) + self.dir, self.get_end(sent),
self.dir):
if sent[i][0] != '{':
self.ids.add_token(sent[i])
inputs, outputs = [], []
def pollute(sent, i, out_val):
"""Simulates multiple token words in adjacent entities"""
for j, check_token in enumerate(sent):
d = j - i
if int(d > 0) - int(
d < 0) == self.dir and check_token.startswith('{'):
for pol_len in range(1, 4):
s = sent[:j] + [':0'] * pol_len + sent[j + 1:]
p = i + (pol_len - 1) * int(self.dir < 0)
inputs.append(self.vectorize(s, p))
outputs.append([out_val])
def add_sents(sents, out_fn):
for sent in sents:
for i, token in enumerate(sent):
out_val = out_fn(token)
inputs.append(self.vectorize(sent, i))
outputs.append([out_val])
if out_val == 1.0:
pollute(sent, i, 1.0)
add_sents(train_data.my_sents(self.intent_name),
lambda x: float(x == self.token))
add_sents(train_data.other_sents(self.intent_name), lambda x: 0.0)
inputs, outputs = resolve_conflicts(inputs, outputs)
data = fann.training_data()
data.set_train_data(inputs, outputs)
for _ in range(10):
self.configure_net()
self.net.train_on_data(data, 1000, 0, 0)
self.net.test_data(data)
if self.net.get_bit_fail() == 0:
break
def test_assign(self):
ids = IdManager(ids={'test': 0, 'word': 1})
vec = ids.vector()
ids.assign(vec, 'test', 0.7)
ids.assign(vec, 'word', 0.2)
assert vec == [0.7, 0.2]
