def test_prune(self):
# arrange
alignment_infos = [
AlignmentInfo((1, 1), None, None, None),
AlignmentInfo((1, 2), None, None, None),
AlignmentInfo((2, 1), None, None, None),
AlignmentInfo((2, 2), None, None, None),
AlignmentInfo((0, 0), None, None, None),
]
min_factor = IBMModel5.MIN_SCORE_FACTOR
best_score = 0.9
scores = {
(1, 1): min(min_factor * 1.5, 1) * best_score, # above threshold
(1, 2): best_score,
(2, 1): min_factor * best_score, # at threshold
(2, 2): min_factor * best_score * 0.5, # low score
(0, 0): min(min_factor * 1.1, 1) * 1.2, # above threshold
}
corpus = [AlignedSent(['a'], ['b'])]
original_prob_function = IBMModel4.model4_prob_t_a_given_s
# mock static method
IBMModel4.model4_prob_t_a_given_s = staticmethod(
lambda a, model: scores[a.alignment]
)
model5 = IBMModel5(corpus, 0, None, None)
# act
pruned_alignments = model5.prune(alignment_infos)
# assert
self.assertEqual(len(pruned_alignments), 3)
# restore static method
IBMModel4.model4_prob_t_a_given_s = original_prob_function
def test_set_uniform_vacancy_probabilities_of_non_domain_values(self):
# arrange
src_classes = {'schinken': 0, 'eier': 0, 'spam': 1}
trg_classes = {'ham': 0, 'eggs': 1, 'spam': 2}
corpus = [
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
]
model5 = IBMModel5(corpus, 0, src_classes, trg_classes)
# act
model5.set_uniform_probabilities(corpus)
# assert
# examine dv and max_v values that are not in the training data domain
self.assertEqual(model5.head_vacancy_table[5][4][0], IBMModel.MIN_PROB)
self.assertEqual(model5.head_vacancy_table[-4][1][2], IBMModel.MIN_PROB)
self.assertEqual(model5.head_vacancy_table[4][0][0], IBMModel.MIN_PROB)
self.assertEqual(model5.non_head_vacancy_table[5][4][0], IBMModel.MIN_PROB)
self.assertEqual(model5.non_head_vacancy_table[-4][1][2], IBMModel.MIN_PROB)
def test_set_uniform_vacancy_probabilities_of_max_displacements(self):
# arrange
src_classes = {'schinken': 0, 'eier': 0, 'spam': 1}
trg_classes = {'ham': 0, 'eggs': 1, 'spam': 2}
corpus = [
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
]
model5 = IBMModel5(corpus, 0, src_classes, trg_classes)
# act
model5.set_uniform_probabilities(corpus)
# assert
# number of vacancy difference values =
# 2 * number of words in longest target sentence
expected_prob = 1.0 / (2 * 4)
# examine the boundary values for (dv, max_v, trg_class)
self.assertEqual(model5.head_vacancy_table[4][4][0], expected_prob)
self.assertEqual(model5.head_vacancy_table[-3][1][2], expected_prob)
self.assertEqual(model5.non_head_vacancy_table[4][4][0], expected_prob)
self.assertEqual(model5.non_head_vacancy_table[-3][1][2], expected_prob)
def test_prob_t_a_given_s(self):
# arrange
src_sentence = ["ich", 'esse', 'ja', 'gern', 'räucherschinken']
trg_sentence = ['i', 'love', 'to', 'eat', 'smoked', 'ham']
src_classes = {'räucherschinken': 0, 'ja': 1, 'ich': 2, 'esse': 3, 'gern': 4}
trg_classes = {'ham': 0, 'smoked': 1, 'i': 3, 'love': 4, 'to': 2, 'eat': 4}
corpus = [AlignedSent(trg_sentence, src_sentence)]
alignment_info = AlignmentInfo(
(0, 1, 4, 0, 2, 5, 5),
[None] + src_sentence,
['UNUSED'] + trg_sentence,
[[3], [1], [4], [], [2], [5, 6]],
)
head_vacancy_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(float))
)
head_vacancy_table[1 - 0][6][3] = 0.97 # ich -> i
head_vacancy_table[3 - 0][5][4] = 0.97 # esse -> eat
head_vacancy_table[1 - 2][4][4] = 0.97 # gern -> love
head_vacancy_table[2 - 0][2][1] = 0.97 # räucherschinken -> smoked
non_head_vacancy_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(float))
)
non_head_vacancy_table[1 - 0][1][0] = 0.96 # räucherschinken -> ham
translation_table = defaultdict(lambda: defaultdict(float))
translation_table['i']['ich'] = 0.98
translation_table['love']['gern'] = 0.98
translation_table['to'][None] = 0.98
translation_table['eat']['esse'] = 0.98
translation_table['smoked']['räucherschinken'] = 0.98
translation_table['ham']['räucherschinken'] = 0.98
fertility_table = defaultdict(lambda: defaultdict(float))
fertility_table[1]['ich'] = 0.99
fertility_table[1]['esse'] = 0.99
fertility_table[0]['ja'] = 0.99
fertility_table[1]['gern'] = 0.99
fertility_table[2]['räucherschinken'] = 0.999
fertility_table[1][None] = 0.99
probabilities = {
'p1': 0.167,
'translation_table': translation_table,
'fertility_table': fertility_table,
'head_vacancy_table': head_vacancy_table,
'non_head_vacancy_table': non_head_vacancy_table,
'head_distortion_table': None,
'non_head_distortion_table': None,
'alignment_table': None,
}
model5 = IBMModel5(corpus, 0, src_classes, trg_classes, probabilities)
# act
probability = model5.prob_t_a_given_s(alignment_info)
# assert
null_generation = 5 * pow(0.167, 1) * pow(0.833, 4)
fertility = 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 2 * 0.999
lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
vacancy = 0.97 * 0.97 * 1 * 0.97 * 0.97 * 0.96
expected_probability = (
null_generation * fertility * lexical_translation * vacancy
)
self.assertEqual(round(probability, 4), round(expected_probability, 4))
def generateModels(qtype):
# dictionary, pwC, pdf = prepare_corpus("data/linkSO",recompute=False)
datadir = "data/linkSO"
all_questions = pd.read_csv(join(datadir, "linkso/topublish/" + qtype + "/" + qtype + "_qid2all.txt"), sep='\t', \
names=['qID', 'qHeader', 'qDescription', 'topVotedAnswer', 'type'])
similar_docs_file = pd.read_csv(join(datadir, "linkso/topublish/" + qtype + "/" + qtype + "_cosidf.txt"), sep='\t', \
names=['qID1', 'qID2', 'score', 'label'], skiprows=1)
filtered_rows = similar_docs_file[similar_docs_file[label] == 1]
filtered_columns = filtered_rows.filter(items=[question_id1, question_id2])
bitext_qH_qH = []
bitext_qD_qD = []
bitext_qHqD_qHqD = []
loop_counter = 0
for each_row in filtered_columns.itertuples():
q1ID = each_row[1]
q2ID = each_row[2]
q1_row = all_questions.loc[all_questions[question_id] == q1ID]
q1header = str(q1_row[question_header].values[0]).split()
q1desc = str(q1_row[question_description].values[0]).split()
q1ans = str(q1_row[top_answer].values[0]).split()
q2_row = all_questions.loc[all_questions[question_id] == q2ID]
q2header = str(q2_row[question_header].values[0]).split()
q2desc = str(q2_row[question_description].values[0]).split()
q2ans = str(q2_row[top_answer].values[0]).split()
# print("\nQ1 Header:", q1header)
# print("Q1 Desc:", q1desc)
# print("Q1 Answer:", q1ans)
# print("Q2:", q2header)
# print("Q2 Desc:", q2desc)
# print("Q2 Answer:", q2ans)
bitext_qH_qH.append(AlignedSent(q1header, q2header))
bitext_qD_qD.append(AlignedSent(q1desc, q2desc))
bitext_qHqD_qHqD.append(AlignedSent(q1header + q1desc, q2header + q2desc))
loop_counter += 1
# Model 1
print("Training Model1 QH QH..")
start = time.time()
ibmQH = IBMModel1(bitext_qH_qH, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQH_Model1_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQH, fout)
print("Training Model1 QD QD..")
start = time.time()
ibmQD = IBMModel1(bitext_qD_qD, 50)
print("Model QD QD trained.. In", time.time() - start, " seconds..")
with open('modelQDQD_Model1_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQD, fout)
print("Training Model1 QHQD QHQD..")
start = time.time()
ibmQHQD = IBMModel1(bitext_qHqD_qHqD, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQD_Model1_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQHQD, fout)
print(round(ibmQH.translation_table['html']['web'], 10))
print(round(ibmQD.translation_table['html']['web'], 10))
print(round(ibmQHQD.translation_table['html']['web'], 10))
# Model 2
print("Training Model2 QH QH..")
start = time.time()
ibmQH = IBMModel2(bitext_qH_qH, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQH_Model2_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQH, fout)
print("Training Model2 QD QD..")
start = time.time()
ibmQD = IBMModel2(bitext_qD_qD, 50)
print("Model QD QD trained.. In", time.time() - start, " seconds..")
with open('modelQDQD_Model2_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQD, fout)
print("Training Model2 QHQD QHQD..")
start = time.time()
ibmQHQD = IBMModel2(bitext_qHqD_qHqD, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQD_Model2_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQHQD, fout)
print(round(ibmQH.translation_table['html']['web'], 10))
print(round(ibmQD.translation_table['html']['web'], 10))
print(round(ibmQHQD.translation_table['html']['web'], 10))
# Model 3
print("Training Model3 QH QH..")
start = time.time()
ibmQH = IBMModel3(bitext_qH_qH, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQH_Model3_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQH, fout)
print("Training Model3 QD QD..")
start = time.time()
ibmQD = IBMModel3(bitext_qD_qD, 50)
print("Model QD QD trained.. In", time.time() - start, " seconds..")
with open('modelQDQD_Model3_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQD, fout)
print("Training Model3 QHQD QHQD..")
start = time.time()
ibmQHQD = IBMModel3(bitext_qHqD_qHqD, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQD_Model3_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQHQD, fout)
print(round(ibmQH.translation_table['html']['web'], 10))
print(round(ibmQD.translation_table['html']['web'], 10))
print(round(ibmQHQD.translation_table['html']['web'], 10))
# Model 4
print("Training Model4 QH QH..")
start = time.time()
ibmQH = IBMModel4(bitext_qH_qH, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQH_Model4_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQH, fout)
print("Training Model4 QD QD..")
start = time.time()
ibmQD = IBMModel4(bitext_qD_qD, 50)
print("Model QD QD trained.. In", time.time() - start, " seconds..")
with open('modelQDQD_Model4_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQD, fout)
print("Training Model4 QHQD QHQD..")
start = time.time()
ibmQHQD = IBMModel4(bitext_qHqD_qHqD, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQD_Model4_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQHQD, fout)
print(round(ibmQH.translation_table['html']['web'], 10))
print(round(ibmQD.translation_table['html']['web'], 10))
print(round(ibmQHQD.translation_table['html']['web'], 10))
# Model5
print("Training Model5 QH QH..")
start = time.time()
ibmQH = IBMModel5(bitext_qH_qH, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQH_Model5_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQH, fout)
print("Training Model5 QD QD..")
start = time.time()
ibmQD = IBMModel5(bitext_qD_qD, 50)
print("Model QD QD trained.. In", time.time() - start, " seconds..")
with open('modelQDQD_Model5_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQD, fout)
print("Training Model5 QHQD QHQD..")
start = time.time()
ibmQHQD = IBMModel5(bitext_qHqD_qHqD, 50)
print("Model QH QH trained.. In", time.time() - start, " seconds..")
with open('modelQHQD_Model5_' + qtype + '.pk', 'wb') as fout:
pickle.dump(ibmQHQD, fout)
print(round(ibmQH.translation_table['html']['web'], 10))
print(round(ibmQD.translation_table['html']['web'], 10))
print(round(ibmQHQD.translation_table['html']['web'], 10))
def test_prob_t_a_given_s(self):
# arrange
src_sentence = ["ich", "esse", "ja", "gern", "räucherschinken"]
trg_sentence = ["i", "love", "to", "eat", "smoked", "ham"]
src_classes = {
"räucherschinken": 0,
"ja": 1,
"ich": 2,
"esse": 3,
"gern": 4
}
trg_classes = {
"ham": 0,
"smoked": 1,
"i": 3,
"love": 4,
"to": 2,
"eat": 4
}
corpus = [AlignedSent(trg_sentence, src_sentence)]
alignment_info = AlignmentInfo(
(0, 1, 4, 0, 2, 5, 5),
[None] + src_sentence,
["UNUSED"] + trg_sentence,
[[3], [1], [4], [], [2], [5, 6]],
)
head_vacancy_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(float)))
head_vacancy_table[1 - 0][6][3] = 0.97 # ich -> i
head_vacancy_table[3 - 0][5][4] = 0.97 # esse -> eat
head_vacancy_table[1 - 2][4][4] = 0.97 # gern -> love
head_vacancy_table[2 - 0][2][1] = 0.97 # räucherschinken -> smoked
non_head_vacancy_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(float)))
non_head_vacancy_table[1 - 0][1][0] = 0.96 # räucherschinken -> ham
translation_table = defaultdict(lambda: defaultdict(float))
translation_table["i"]["ich"] = 0.98
translation_table["love"]["gern"] = 0.98
translation_table["to"][None] = 0.98
translation_table["eat"]["esse"] = 0.98
translation_table["smoked"]["räucherschinken"] = 0.98
translation_table["ham"]["räucherschinken"] = 0.98
fertility_table = defaultdict(lambda: defaultdict(float))
fertility_table[1]["ich"] = 0.99
fertility_table[1]["esse"] = 0.99
fertility_table[0]["ja"] = 0.99
fertility_table[1]["gern"] = 0.99
fertility_table[2]["räucherschinken"] = 0.999
fertility_table[1][None] = 0.99
probabilities = {
"p1": 0.167,
"translation_table": translation_table,
"fertility_table": fertility_table,
"head_vacancy_table": head_vacancy_table,
"non_head_vacancy_table": non_head_vacancy_table,
"head_distortion_table": None,
"non_head_distortion_table": None,
"alignment_table": None,
}
model5 = IBMModel5(corpus, 0, src_classes, trg_classes, probabilities)
# act
probability = model5.prob_t_a_given_s(alignment_info)
# assert
null_generation = 5 * pow(0.167, 1) * pow(0.833, 4)
fertility = 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 2 * 0.999
lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
vacancy = 0.97 * 0.97 * 1 * 0.97 * 0.97 * 0.96
expected_probability = (null_generation * fertility *
lexical_translation * vacancy)
self.assertEqual(round(probability, 4), round(expected_probability, 4))
