def main():
output_format = "plaintext"
lang_a = sys.argv[1]
lang_b = sys.argv[2]
model_path = os.path.abspath(sys.argv[3])
nltk.data.path += [model_path]
model = YalignModel.load(model_path)
pairing = read_pairing(open(sys.argv[4]), lang_a, lang_b)
src_needed = set([a for a, _ in pairing])
tgt_needed = set([a for _, a in pairing])
src_articles = read_articles(open(sys.argv[5]), src_needed)
tgt_articles = read_articles(open(sys.argv[6]), tgt_needed)
for src, tgt in pairing:
try:
text_a = "\n".join(src_articles[src])
text_b = "\n".join(tgt_articles[tgt])
document_a = text_to_document(text_a, lang_a)
document_b = text_to_document(text_b, lang_b)
pairs = model.align(document_a, document_b)
sys.stderr.write(u"{0} pairs in {1}-{2}\n".format(len(pairs), src, tgt).encode("utf-8"))
write_plaintext(sys.stdout, pairs)
except KeyError:
sys.stderr.write(u"KeyError with {0}-{1}\n".format(src, tgt).encode("utf-8"))
continue
def setUp(self):
random.seed(hash("Y U NO?"))
base_path = os.path.dirname(os.path.abspath(__file__))
word_scores = os.path.join(base_path, "data",
"test_word_scores_big.csv")
parallel_corpus = os.path.join(base_path, "data", "parallel-en-es.txt")
A, B = parallel_corpus_to_documents(parallel_corpus)
A = A[:25]
B = B[:25]
self.alignments = list(training_alignments_from_documents(A, B))
self.A, self.B, self.correct_alignments = \
list(training_scrambling_from_documents(A, B))
# Word score
word_pair_score = WordPairScore(word_scores)
# Sentence Score
sentence_pair_score = SentencePairScore()
sentence_pair_score.train(self.alignments, word_pair_score)
# Yalign model
self.min_ = sentence_pair_score.min_bound
self.max_ = sentence_pair_score.max_bound
gap_penalty = (self.min_ + self.max_) / 2.0
document_aligner = SequenceAligner(sentence_pair_score, gap_penalty)
self.model = YalignModel(document_aligner, 1)
def test_save_load_and_align(self):
doc1 = [Sentence([u"House"]), Sentence([u"asoidfhuioasgh"])]
doc2 = [Sentence([u"Casa"])]
result_before_save = self.model.align(doc1, doc2)
# Save
tmp_folder = tempfile.mkdtemp()
self.model.save(tmp_folder)
# Load
new_model = YalignModel.load(tmp_folder)
result_after_load = new_model.align(doc1, doc2)
self.assertEqual(result_before_save, result_after_load)
self.assertEqual(self.model.threshold, new_model.threshold)
self.assertEqual(self.model.document_pair_aligner.penalty, new_model.document_pair_aligner.penalty)
def test_save_load_and_align(self):
doc1 = [Sentence([u"House"]), Sentence([u"asoidfhuioasgh"])]
doc2 = [Sentence([u"Casa"])]
result_before_save = self.model.align(doc1, doc2)
# Save
tmp_folder = tempfile.mkdtemp()
self.model.save(tmp_folder)
# Load
new_model = YalignModel.load(tmp_folder)
result_after_load = new_model.align(doc1, doc2)
self.assertEqual(result_before_save, result_after_load)
self.assertEqual(self.model.threshold, new_model.threshold)
self.assertEqual(self.model.document_pair_aligner.penalty,
new_model.document_pair_aligner.penalty)
def setUp(self):
word_scores = os.path.join(data_path, "test_word_scores_big.csv")
self.parallel_corpus = os.path.join(data_path, "parallel-en-es.txt")
# Documents
A, B = parallel_corpus_to_documents(self.parallel_corpus)
self.document_a = A[:30]
self.document_b = B[:30]
training = training_alignments_from_documents(self.document_a,
self.document_b)
# Word score
word_pair_score = WordPairScore(word_scores)
# Sentence Score
sentence_pair_score = SentencePairScore()
sentence_pair_score.train(training, word_pair_score)
# Yalign model
document_aligner = SequenceAligner(sentence_pair_score, 0.49)
self.model = YalignModel(document_aligner)
def test_command_tool(self):
if self.cmdline is None:
return
tmpdir = tempfile.mkdtemp()
_, tmpfile = tempfile.mkstemp()
self.model.save(tmpdir)
cmd = self.cmdline.format(corpus=self.parallel_corpus, model=tmpdir)
outputfh = open(tmpfile, "w")
subprocess.call(cmd, shell=True, stdout=outputfh)
outputfh = open(tmpfile)
output = outputfh.read()
A, B = parallel_corpus_to_documents(self.parallel_corpus)
model = YalignModel.load(tmpdir)
value = self.alignment_function(A, B, model)
self.assertIn("{}%".format(value), output)
def test_command_tool(self):
if self.cmdline is None:
return
tmpdir = tempfile.mkdtemp()
_, tmpfile = tempfile.mkstemp()
self.model.save(tmpdir)
cmd = self.cmdline.format(corpus=self.parallel_corpus, model=tmpdir)
outputfh = open(tmpfile, "w")
subprocess.call(cmd, shell=True, stdout=outputfh)
outputfh = open(tmpfile)
output = outputfh.read()
A, B = parallel_corpus_to_documents(self.parallel_corpus)
model = YalignModel.load(tmpdir)
value = self.alignment_function(A, B, model)
self.assertIn("{}%".format(value), output)
def setUp(self):
random.seed(hash("Y U NO?"))
base_path = os.path.dirname(os.path.abspath(__file__))
word_scores = os.path.join(base_path, "data", "test_word_scores_big.csv")
parallel_corpus = os.path.join(base_path, "data", "parallel-en-es.txt")
A, B = parallel_corpus_to_documents(parallel_corpus)
A = A[:25]
B = B[:25]
self.alignments = list(training_alignments_from_documents(A, B))
self.A, self.B, self.correct_alignments = list(training_scrambling_from_documents(A, B))
# Word score
word_pair_score = WordPairScore(word_scores)
# Sentence Score
sentence_pair_score = SentencePairScore()
sentence_pair_score.train(self.alignments, word_pair_score)
# Yalign model
self.min_ = sentence_pair_score.min_bound
self.max_ = sentence_pair_score.max_bound
gap_penalty = (self.min_ + self.max_) / 2.0
document_aligner = SequenceAligner(sentence_pair_score, gap_penalty)
self.model = YalignModel(document_aligner, 1)
class TestYalignModel(unittest.TestCase):
def setUp(self):
random.seed(hash("Y U NO?"))
base_path = os.path.dirname(os.path.abspath(__file__))
word_scores = os.path.join(base_path, "data", "test_word_scores_big.csv")
parallel_corpus = os.path.join(base_path, "data", "parallel-en-es.txt")
A, B = parallel_corpus_to_documents(parallel_corpus)
A = A[:25]
B = B[:25]
self.alignments = list(training_alignments_from_documents(A, B))
self.A, self.B, self.correct_alignments = \
list(training_scrambling_from_documents(A, B))
# Word score
word_pair_score = WordPairScore(word_scores)
# Sentence Score
sentence_pair_score = SentencePairScore()
sentence_pair_score.train(self.alignments, word_pair_score)
# Yalign model
self.min_ = sentence_pair_score.min_bound
self.max_ = sentence_pair_score.max_bound
gap_penalty = (self.min_ + self.max_) / 2.0
document_aligner = SequenceAligner(sentence_pair_score, gap_penalty)
self.model = YalignModel(document_aligner, 1)
def test_save_file_created(self):
tmp_folder = tempfile.mkdtemp()
self.model.save(tmp_folder)
model_path = os.path.join(tmp_folder, "aligner.pickle")
metadata_path = os.path.join(tmp_folder, "metadata.json")
self.assertTrue(os.path.exists(model_path))
self.assertTrue(os.path.exists(metadata_path))
def test_save_load_and_align(self):
doc1 = [Sentence([u"House"]),
Sentence([u"asoidfhuioasgh"])]
doc2 = [Sentence([u"Casa"])]
result_before_save = self.model.align(doc1, doc2)
# Save
tmp_folder = tempfile.mkdtemp()
self.model.save(tmp_folder)
# Load
new_model = YalignModel.load(tmp_folder)
result_after_load = new_model.align(doc1, doc2)
self.assertEqual(result_before_save, result_after_load)
self.assertEqual(self.model.threshold, new_model.threshold)
self.assertEqual(self.model.document_pair_aligner.penalty,
new_model.document_pair_aligner.penalty)
def test_reasonable_alignment(self):
doc1 = [Sentence([u"House"]),
Sentence([u"asoidfhuioasgh"])]
doc2 = [Sentence([u"Casa"])]
result = self.model.align(doc1, doc2)
result = [(list(x), list(y)) for x, y in result]
self.assertIn((list(doc1[0]), list(doc2[0])), result)
def test_optimize_gap_penalty_and_threshold_finishes(self):
self.model.optimize_gap_penalty_and_threshold(self.A, self.B,
self.correct_alignments)
def test_optimize_gap_penalty_and_threshold_is_best(self):
def evaluate(penalty, threshold):
self.model.document_pair_aligner.penalty = penalty
self.model.threshold = threshold
predicted = self.model.align_indexes(self.A, self.B)
return F_score(predicted, self.correct_alignments)[0]
random.seed(hash("12345"))
self.model.optimize_gap_penalty_and_threshold(self.A, self.B,
self.correct_alignments)
best_score = evaluate(self.model.document_pair_aligner.penalty,
self.model.threshold)
for _ in xrange(50):
penalty = random.uniform(self.min_, self.max_ / 2.0)
threshold = random.uniform(self.min_, self.max_)
score = evaluate(penalty, threshold)
self.assertGreaterEqual(best_score, score)
class TestYalignModel(unittest.TestCase):
def setUp(self):
random.seed(hash("Y U NO?"))
base_path = os.path.dirname(os.path.abspath(__file__))
word_scores = os.path.join(base_path, "data",
"test_word_scores_big.csv")
parallel_corpus = os.path.join(base_path, "data", "parallel-en-es.txt")
A, B = parallel_corpus_to_documents(parallel_corpus)
A = A[:25]
B = B[:25]
self.alignments = list(training_alignments_from_documents(A, B))
self.A, self.B, self.correct_alignments = \
list(training_scrambling_from_documents(A, B))
# Word score
word_pair_score = WordPairScore(word_scores)
# Sentence Score
sentence_pair_score = SentencePairScore()
sentence_pair_score.train(self.alignments, word_pair_score)
# Yalign model
self.min_ = sentence_pair_score.min_bound
self.max_ = sentence_pair_score.max_bound
gap_penalty = (self.min_ + self.max_) / 2.0
document_aligner = SequenceAligner(sentence_pair_score, gap_penalty)
self.model = YalignModel(document_aligner, 1)
def test_save_file_created(self):
tmp_folder = tempfile.mkdtemp()
self.model.save(tmp_folder)
model_path = os.path.join(tmp_folder, "aligner.pickle")
metadata_path = os.path.join(tmp_folder, "metadata.json")
self.assertTrue(os.path.exists(model_path))
self.assertTrue(os.path.exists(metadata_path))
def test_save_load_and_align(self):
doc1 = [Sentence([u"House"]), Sentence([u"asoidfhuioasgh"])]
doc2 = [Sentence([u"Casa"])]
result_before_save = self.model.align(doc1, doc2)
# Save
tmp_folder = tempfile.mkdtemp()
self.model.save(tmp_folder)
# Load
new_model = YalignModel.load(tmp_folder)
result_after_load = new_model.align(doc1, doc2)
self.assertEqual(result_before_save, result_after_load)
self.assertEqual(self.model.threshold, new_model.threshold)
self.assertEqual(self.model.document_pair_aligner.penalty,
new_model.document_pair_aligner.penalty)
def test_reasonable_alignment(self):
doc1 = [Sentence([u"House"]), Sentence([u"asoidfhuioasgh"])]
doc2 = [Sentence([u"Casa"])]
result = self.model.align(doc1, doc2)
result = [(list(x), list(y)) for x, y in result]
self.assertIn((list(doc1[0]), list(doc2[0])), result)
def test_optimize_gap_penalty_and_threshold_finishes(self):
self.model.optimize_gap_penalty_and_threshold(self.A, self.B,
self.correct_alignments)
def test_optimize_gap_penalty_and_threshold_is_best(self):
def evaluate(penalty, threshold):
self.model.document_pair_aligner.penalty = penalty
self.model.threshold = threshold
predicted = self.model.align_indexes(self.A, self.B)
return F_score(predicted, self.correct_alignments)[0]
random.seed(hash("12345"))
self.model.optimize_gap_penalty_and_threshold(self.A, self.B,
self.correct_alignments)
best_score = evaluate(self.model.document_pair_aligner.penalty,
self.model.threshold)
for _ in xrange(50):
penalty = random.uniform(self.min_, self.max_ / 2.0)
threshold = random.uniform(self.min_, self.max_)
score = evaluate(penalty, threshold)
self.assertGreaterEqual(best_score, score)
