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)
