def test_prob_t_a_given_s(self):
# arrange
src_sentence = ["ich", "esse", "ja", "gern", "räucherschinken"]
trg_sentence = ["i", "love", "to", "eat", "smoked", "ham"]
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]],
)
distortion_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float)))
)
distortion_table[1][1][5][6] = 0.97 # i -> ich
distortion_table[2][4][5][6] = 0.97 # love -> gern
distortion_table[3][0][5][6] = 0.97 # to -> NULL
distortion_table[4][2][5][6] = 0.97 # eat -> esse
distortion_table[5][5][5][6] = 0.97 # smoked -> räucherschinken
distortion_table[6][5][5][6] = 0.97 # ham -> räucherschinken
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,
"distortion_table": distortion_table,
"fertility_table": fertility_table,
"alignment_table": None,
}
model3 = IBMModel3(corpus, 0, probabilities)
# act
probability = model3.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
distortion = 0.97 * 0.97 * 0.97 * 0.97 * 0.97 * 0.97
expected_probability = (
null_generation * fertility * lexical_translation * distortion
)
self.assertEqual(round(probability, 4), round(expected_probability, 4))
def test_prob_t_a_given_s(self):
# arrange
src_sentence = ["ich", 'esse', 'ja', 'gern', 'räucherschinken']
trg_sentence = ['i', 'love', 'to', 'eat', 'smoked', 'ham']
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]],
)
distortion_table = defaultdict(lambda: defaultdict(lambda: defaultdict(
lambda: defaultdict(float))))
distortion_table[1][1][5][6] = 0.97 # i -> ich
distortion_table[2][4][5][6] = 0.97 # love -> gern
distortion_table[3][0][5][6] = 0.97 # to -> NULL
distortion_table[4][2][5][6] = 0.97 # eat -> esse
distortion_table[5][5][5][6] = 0.97 # smoked -> räucherschinken
distortion_table[6][5][5][6] = 0.97 # ham -> räucherschinken
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,
'distortion_table': distortion_table,
'fertility_table': fertility_table,
'alignment_table': None,
}
model3 = IBMModel3(corpus, 0, probabilities)
# act
probability = model3.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
distortion = 0.97 * 0.97 * 0.97 * 0.97 * 0.97 * 0.97
expected_probability = (null_generation * fertility *
lexical_translation * distortion)
self.assertEqual(round(probability, 4), round(expected_probability, 4))
def test_set_uniform_distortion_probabilities(self):
# arrange
corpus = [
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
]
model3 = IBMModel3(corpus, 0)
# act
model3.set_uniform_probabilities(corpus)
# assert
# expected_prob = 1.0 / length of target sentence
self.assertEqual(model3.distortion_table[1][0][3][2], 1.0 / 2)
self.assertEqual(model3.distortion_table[4][2][2][4], 1.0 / 4)
def test_set_uniform_distortion_probabilities_of_non_domain_values(self):
# arrange
corpus = [
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
]
model3 = IBMModel3(corpus, 0)
# act
model3.set_uniform_probabilities(corpus)
# assert
# examine i and j values that are not in the training data domain
self.assertEqual(model3.distortion_table[0][0][3][2], IBMModel.MIN_PROB)
self.assertEqual(model3.distortion_table[9][2][2][4], IBMModel.MIN_PROB)
self.assertEqual(model3.distortion_table[2][9][2][4], IBMModel.MIN_PROB)
def __init__(
self,
sentence_aligned_corpus,
iterations,
source_word_classes,
target_word_classes,
probability_tables=None,
):
"""
Train on ``sentence_aligned_corpus`` and create a lexical
translation model, distortion models, a fertility model, and a
model for generating NULL-aligned words.
Translation direction is from ``AlignedSent.mots`` to
``AlignedSent.words``.
:param sentence_aligned_corpus: Sentence-aligned parallel corpus
:type sentence_aligned_corpus: list(AlignedSent)
:param iterations: Number of iterations to run training algorithm
:type iterations: int
:param source_word_classes: Lookup table that maps a source word
to its word class, the latter represented by an integer id
:type source_word_classes: dict[str]: int
:param target_word_classes: Lookup table that maps a target word
to its word class, the latter represented by an integer id
:type target_word_classes: dict[str]: int
:param probability_tables: Optional. Use this to pass in custom
probability values. If not specified, probabilities will be
set to a uniform distribution, or some other sensible value.
If specified, all the following entries must be present:
``translation_table``, ``alignment_table``,
``fertility_table``, ``p1``, ``head_distortion_table``,
``non_head_distortion_table``. See ``IBMModel`` and
``IBMModel4`` for the type and purpose of these tables.
:type probability_tables: dict[str]: object
"""
super(IBMModel4, self).__init__(sentence_aligned_corpus)
self.reset_probabilities()
self.src_classes = source_word_classes
self.trg_classes = target_word_classes
if probability_tables is None:
# Get probabilities from IBM model 3
ibm3 = IBMModel3(sentence_aligned_corpus, iterations)
self.translation_table = ibm3.translation_table
self.alignment_table = ibm3.alignment_table
self.fertility_table = ibm3.fertility_table
self.p1 = ibm3.p1
self.set_uniform_probabilities(sentence_aligned_corpus)
else:
# Set user-defined probabilities
self.translation_table = probability_tables["translation_table"]
self.alignment_table = probability_tables["alignment_table"]
self.fertility_table = probability_tables["fertility_table"]
self.p1 = probability_tables["p1"]
self.head_distortion_table = probability_tables[
"head_distortion_table"]
self.non_head_distortion_table = probability_tables[
"non_head_distortion_table"]
for n in range(0, iterations):
self.train(sentence_aligned_corpus)
# Structures: 1-paragraph alignment only, 2-sentence alignment based on paragraphs, 3-direct sentence alignment
structures = {1: "para", 2: "psent", 3: "sent"}
struct_num = 1
for i in range(1, 44):
en_path = 'translation-dashboard/data/en-ba-' + structures[
struct_num] + '-align/en-chapter-' + str(i) + '.txt'
ba_path = 'translation-dashboard/data/en-ba-' + structures[
struct_num] + '-align/ba-chapter-' + str(i) + '.txt'
aligned_paras.extend(para_as_sent(en_path, ba_path))
wc += word_count(en_path)
# print (wc.freq("i"))
num_iterations = 20
start = timer()
model = IBMModel3(aligned_paras, num_iterations)
end = timer()
timeelapsed = end - start # timer will only evaluate time taken to run IBM Models
with open('align_models/ibm-model-runtimes.csv', 'a',
encoding='utf-8') as output_file:
output_writer = csv.writer(output_file, delimiter='\t')
output_writer.writerow([
"3",
str(num_iterations), timeelapsed,
socket.gethostname(), 'struct' + str(struct_num)
])
output_file.close()
# Save model and word count
with open('align_models/ibm3.model', 'wb') as m_file:
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))