def test_generate_sent_1gram(self):
ngram = NGram(1, self.sents)
generator = NGramGenerator(ngram)
voc = {'el', 'gato', 'come', 'pescado', '.', 'la', 'gata', 'salmón'}
for i in range(100):
sent = generator.generate_sent()
self.assertTrue(set(sent).issubset(voc))
def test_generate_sent_1gram(self):
ngram = NGram(1, self.sents)
generator = NGramGenerator(ngram)
voc = {'el', 'gato', 'come', 'pescado', '.', 'la', 'gata', 'salmón'}
for i in range(100):
sent = generator.generate_sent()
self.assertTrue(set(sent).issubset(voc))
def test_generate_token(self):
ngram = NGram(2, self.sents)
generator = NGramGenerator(ngram)
for i in range(100):
# after 'el' always comes 'gato':
token = generator.generate_token(('el',))
self.assertEqual(token, 'gato')
# after 'come' may come 'pescado' or 'salmón'
token = generator.generate_token(('come',))
self.assertTrue(token in ['pescado', 'salmón'])
def test_generate_token(self):
ngram = NGram(2, self.sents)
generator = NGramGenerator(ngram)
for i in range(100):
# after 'el' always comes 'gato':
token = generator.generate_token(('el', ))
self.assertEqual(token, 'gato')
# after 'come' may come 'pescado' or 'salmón'
token = generator.generate_token(('come', ))
self.assertTrue(token in ['pescado', 'salmón'])
def test_generate_sent_2gram(self):
ngram = NGram(2, self.sents)
generator = NGramGenerator(ngram)
# all the possible generated sentences for 2-grams:
sents = [
'el gato come pescado .',
'la gata come salmón .',
'el gato come salmón .',
'la gata come pescado .',
]
for i in range(100):
sent = generator.generate_sent()
self.assertTrue(' '.join(sent) in sents, sent)
def test_generate_sent_2gram(self):
ngram = NGram(2, self.sents)
generator = NGramGenerator(ngram)
# all the possible generated sentences for 2-grams:
sents = [
'el gato come pescado .',
'la gata come salmón .',
'el gato come salmón .',
'la gata come pescado .',
]
for i in range(100):
sent = generator.generate_sent()
self.assertTrue(' '.join(sent) in sents, sent)
def test_init_3gram(self):
ngram = NGram(3, self.sents)
generator = NGramGenerator(ngram)
probs = {
('<s>', '<s>'): {'el': 0.5, 'la': 0.5},
('<s>', 'el',): {'gato': 1.0},
('el', 'gato'): {'come': 1.0},
('gato', 'come'): {'pescado': 1.0},
('come', 'pescado'): {'.': 1.0},
('pescado', '.'): {'</s>': 1.0},
('<s>', 'la'): {'gata': 1.0},
('la', 'gata'): {'come': 1.0},
('gata', 'come'): {'salmón': 1.0},
('come', 'salmón'): {'.': 1.0},
('salmón', '.'): {'</s>': 1.0},
}
sorted_probs = {
('<s>', '<s>'): [('el', 0.5), ('la', 0.5)],
('<s>', 'el',): [('gato', 1.0)],
('el', 'gato'): [('come', 1.0)],
('gato', 'come'): [('pescado', 1.0)],
('come', 'pescado'): [('.', 1.0)],
('pescado', '.'): [('</s>', 1.0)],
('<s>', 'la'): [('gata', 1.0)],
('la', 'gata'): [('come', 1.0)],
('gata', 'come'): [('salmón', 1.0)],
('come', 'salmón'): [('.', 1.0)],
('salmón', '.'): [('</s>', 1.0)],
}
self.assertEqual(dict(generator.probs), probs)
self.assertEqual(generator.sorted_probs, sorted_probs)
def test_generate_sent_3and4gram(self):
ngram = NGram(3, self.sents4)
ngram2 = NGram(4, self.sents4)
generator = NGramGenerator(ngram)
generator2 = NGramGenerator(ngram2)
# all the possible generated sentences for 3 or 4-grams:
sents = [
'la casa se construye y el corre y la gata come ensalada',
'el corre y la gata come pescado y duerme',
'la casa se construye y el corre y la gata come ensalada',
'la casa se construye y el corre y la gata come pescado y duerme',
'la casa se construye y el corre',
'la gata come pescado y duerme',
'el corre y la gata come ensalada',
'el corre',
'la gata come ensalada',
'la casa se construye y el corre',
'la gata come pescado y duerme',
]
for i in range(1000):
sent = generator.generate_sent()
sent2 = generator2.generate_sent()
self.assertTrue(' '.join(sent) in sents)
self.assertTrue(' '.join(sent2) in sents)
def test_generate_token_3and4gram(self):
ngram = NGram(3, self.sents3)
ngram2 = NGram(4, self.sents3)
generator = NGramGenerator(ngram)
generator2 = NGramGenerator(ngram2)
for i in range(100):
# after 'come pescado' always comes 'y'
token = generator.generate_token(('come', 'pescado'))
self.assertEqual(token, 'y')
# after 'come pescado y' always comes 'duerme'
token = generator2.generate_token(('come', 'pescado', 'y'))
self.assertEqual(token, 'duerme')
# sentence may come start with 'el' or 'la'
token = generator.generate_token(('<s>', '<s>'))
self.assertTrue(token in ['el', 'la'])
token = generator2.generate_token(('<s>', '<s>', '<s>'))
self.assertTrue(token in ['el', 'la'])
def test_init_1gram(self):
ngram = NGram(1, self.sents)
generator = NGramGenerator(ngram)
probs = {
(): {
'el': 1 / 12.0,
'gato': 1 / 12.0,
'come': 2 / 12.0,
'pescado': 1 / 12.0,
'.': 2 / 12.0,
'</s>': 2 / 12.0,
'la': 1 / 12.0,
'gata': 1 / 12.0,
'salmón': 1 / 12.0,
}
}
self.assertEqual(dict(generator.probs), probs)
Options:
-i <file> Language model file.
-n <n> Number of sentences to generate.
-h --help Show this screen.
"""
import sys
sys.path.append("../../")
import pickle
from docopt import docopt
from languagemodeling.ngram import NGramGenerator
if __name__ == '__main__':
opts = docopt(__doc__)
n = int(opts['-n'])
i = str(opts['-i'])
f = open(i, 'rb')
model = pickle.load(f)
generator = NGramGenerator(model)
for _ in range(n):
sent = generator.generate_sent()
for token in sent:
print(token, end=" ")
print("\n")
n = int(opts['-n'])
filename = opts['-i']
# the output will be written in test/output.txt
file_output = open(os.path.join(DEFAULT_OUTPUT_DIR, 'output.txt'), 'w')
if filename:
# instance an n-gram object whith n={1,2,3,4}
# open the model to read
file_model = open(filename, 'rb')
# ngram is a model trained.
ngram = pickle.load(file_model)
# close the file
file_model.close()
# an instance of NGramGenerator with ngram
generator = NGramGenerator(ngram)
print('have just upload')
for _ in range(0, n):
list_sentence = generator.generate_sent()
# join list with spaces between word
file_output.write(' '.join(list_sentence))
# put an EOL
file_output.write('\r\n')
else:
for i in range(1, 5):
# open the model to read n={1,2,3,4, 5, 6, 7, 8}
file_model = open(str(i) + '-gram.txt', 'rb')
# ngram is a model trained.
ngram = pickle.load(file_model)
file_model.close()
# an instance of NGramGenerator with ngram
Generate natural language sentences using a language model.
Usage:
generate.py -i <file> -n <n>
generate.py -h | --help
Options:
-i <file> Language model file.
-n <n> Number of sentences to generate.
-h --help Show this screen.
"""
import pickle
from docopt import docopt
from languagemodeling.ngram import NGramGenerator
if __name__ == '__main__':
opts = docopt(__doc__)
# read options
path = str(opts['-i'])
n = int(opts['-n'])
# open model file
file = open(path, 'rb')
# load model file
model = pickle.load(file)
# create generator
generator = NGramGenerator(model)
# print sentences while generate them.
for _ in range(n):
print(' '.join(generator.generate_sent()) + "\n")
-i <file> Language model file.
-n <n> Number of sentences to generate.
-h --help Show this screen.
"""
import sys
sys.path.append("../../")
import pickle
from docopt import docopt
from languagemodeling.ngram import NGramGenerator
if __name__ == '__main__':
opts = docopt(__doc__)
n = int(opts['-n'])
i = str(opts['-i'])
f = open(i, 'rb')
model = pickle.load(f)
generator = NGramGenerator(model)
for _ in range(n):
sent = generator.generate_sent()
for token in sent:
print(token, end=" ")
print("\n")
-h --help Show this screen.
"""
from docopt import docopt
import pickle
import os.path
import sys
# Add ../../ to PYTHONPATH
sys.path.append(
os.path.join(
os.path.dirname(os.path.realpath(__file__)),
os.pardir, os.pardir))
from languagemodeling.ngram import NGramGenerator
if __name__ == '__main__':
opts = docopt(__doc__)
# load the model
filename = opts['-i']
with open(filename, 'rb') as f:
model = pickle.load(f)
sys.stderr.write('Loaded model\n')
# generate
n = int(opts['-n'])
generator = NGramGenerator(model)
sys.stderr.write('Initialized generator\n')
for i in range(n):
print('Sentence %s:' % i)
print(' '.join(generator.generate_sent()))
"""Generate natural language sentences using a language model.
Usage:
generate.py -i <file> -n <n>
generate.py -h | --help
Options:
-i <file> Language model file.
-n <n> Number of sentences to generate.
-h --help Show this screen.
"""
from docopt import docopt
import pickle
from languagemodeling.ngram import NGramGenerator
if __name__ == '__main__':
opts = docopt(__doc__)
filename = opts['-i']
f = open(filename, 'rb')
model = pickle.load(f)
f.close()
generator = NGramGenerator(model)
for _ in range(int(opts['-n'])):
sent = ' '.join(generator.generate_sent())
print(sent)
print("-------------------------------------------------------------")
