def test_count_2gram(self):
ngram = InterpolatedNGram(2, self.sents, gamma=1.0)
counts = {
(): 12,
('el',): 1,
('gato',): 1,
('come',): 2,
('pescado',): 1,
('.',): 2,
('</s>',): 2,
('la',): 1,
('gata',): 1,
('salmón',): 1,
('<s>', 'el'): 1,
('el', 'gato'): 1,
('gato', 'come'): 1,
('come', 'pescado'): 1,
('pescado', '.'): 1,
('.', '</s>'): 2,
('<s>', 'la'): 1,
('la', 'gata'): 1,
('gata', 'come'): 1,
('come', 'salmón'): 1,
('salmón', '.'): 1,
}
for gram, c in counts.items():
self.assertEqual(ngram.count(gram), c, gram)
def test_cond_prob_1gram_no_addone(self):
model = InterpolatedNGram(1, self.sents, gamma=1.0, addone=False)
# behaves just like unsmoothed n-gram
probs = {
'pescado': 1 / 12.0,
'come': 2 / 12.0,
'salame': 0.0,
}
for token, p in probs.items():
self.assertAlmostEqual(model.cond_prob(token), p, msg=token)
def test_cond_prob_2gram_no_addone(self):
gamma = 1.0
model = InterpolatedNGram(2, self.sents, gamma, addone=False)
c1 = 2.0 # count for 'come' (and '.')
l1 = c1 / (c1 + gamma)
probs = {
('pescado', 'come'): l1 * 0.5 + (1.0 - l1) * 1 / 12.0,
('salmón', 'come'): l1 * 0.5 + (1.0 - l1) * 1 / 12.0,
('salame', 'come'): 0.0,
('</s>', '.'): l1 * 1.0 + (1.0 - l1) * 2 / 12.0,
}
for (token, prev), p in probs.items():
self.assertAlmostEqual(model.cond_prob(token, [prev]), p, msg=token)
def test_held_out(self):
model = InterpolatedNGram(1, self.sents)
# only first sentence (second sentence is held-out data)
counts = {
(): 6,
('el',): 1,
('gato',): 1,
('come',): 1,
('pescado',): 1,
('.',): 1,
('</s>',): 1,
}
for gram, c in counts.items():
self.assertEqual(model.count(gram), c, gram)
def test_count_1gram(self):
model = InterpolatedNGram(1, self.sents, gamma=1.0)
counts = {
(): 12,
('el',): 1,
('gato',): 1,
('come',): 2,
('pescado',): 1,
('.',): 2,
('</s>',): 2,
('la',): 1,
('gata',): 1,
('salmón',): 1,
}
for gram, c in counts.items():
self.assertEqual(model.count(gram), c, gram)
def test_norm_1gram(self):
models = [
InterpolatedNGram(1, self.sents, gamma=1.0, addone=False),
InterpolatedNGram(1, self.sents, gamma=5.0, addone=False),
InterpolatedNGram(1, self.sents, gamma=10.0, addone=False),
InterpolatedNGram(1, self.sents, gamma=50.0, addone=False),
InterpolatedNGram(1, self.sents, gamma=100.0, addone=False),
InterpolatedNGram(1, self.sents, gamma=1.0, addone=True),
InterpolatedNGram(1, self.sents, gamma=5.0, addone=True),
InterpolatedNGram(1, self.sents, gamma=10.0, addone=True),
InterpolatedNGram(1, self.sents, gamma=50.0, addone=True),
InterpolatedNGram(1, self.sents, gamma=100.0, addone=True),
]
tokens = {
'el', 'gato', 'come', 'pescado', '.', 'la', 'gata', 'salmón',
'</s>'
}
for model in models:
prob_sum = sum(model.cond_prob(token) for token in tokens)
# prob_sum < 1.0 or almost equal to 1.0:
self.assertAlmostLessEqual(prob_sum, 1.0)
tokenizer = RegexpTokenizer(pattern)
root = '.'
corpus = PlaintextCorpusReader(root, 'books\.txt', word_tokenizer=tokenizer)
sents = corpus.sents()
# train the model
n = int(opts['-n'])
if opts['-m'] == 'addone':
model = AddOneNGram(n, sents)
elif opts['-m'] == 'inter':
gamma = opts['-g']
if gamma is None:
model = InterpolatedNGram(n, sents, None, False)
else:
model = InterpolatedNGram(n, sents, gamma, False)
elif opts['-m'] == 'interaddone':
gamma = opts['-g']
if gamma is None:
model = InterpolatedNGram(n, sents, None, True)
else:
model = InterpolatedNGram(n, sents, gamma, True)
else:
model = NGram(n, sents)
# save it
filename = opts['-o']
f = open(filename, 'wb')
pickle.dump(model, f)
word_tokenizer=tokenizer,
sent_tokenizer=sent_tokenizer)
# sents will be a tokens' list of the corpus
sents = corpus.sents()
# train the model
type_model = opts['-m']
n = int(opts['-n'])
if type_model == 'ngram':
model = NGram(n, sents)
print(str(n) + '-gram will be ready')
elif type_model == 'addone':
model = AddOneNGram(n, sents)
print(str(n) + '-addone will be ready')
elif type_model == 'interpolated':
model = InterpolatedNGram(n, sents)
print(str(n) + '-interpolated will be ready')
elif type_model == 'backoff':
model = BackOffNGram(n, sents)
print(str(n) + '-backoff will be ready')
else:
print('modelo erroneo')
exit(0)
# save it
filename = opts['-o']
f = open(filename, 'wb')
# to load a object pickle.load(file)
# dump save the object in bytes
pickle.dump(model, f)
f.close()
# order of the model
n = int(opts['-n'])
# model type
m = str(opts['-m'])
filename = opts['-o']
# train the model
if m == "ngram":
print("NGram Model selected")
model = NGram(n, sents)
elif m == "addone":
print("AddOne NGram Model selected")
model = AddOneNGram(n, sents)
elif m == "interpolated":
print("Interpolated NGram Model selected")
model = InterpolatedNGram(n, sents, addone=True)
elif m == "backoff":
print("BackOff NGram Model selected")
model = BackOffNGram(n, sents, addone=True)
else:
print("Bad Model Type")
print(help())
exit()
print("n: %d\nOutput file: %s\n" % (n, filename))
# save it
f = open(filename, 'wb')
pickle.dump(model, f)
f.close()
