def test_on_train(path_model, n_combination_question = 3, n_combination_answer = 3, n_word_question = 5):
model = gensim.models.Word2Vec.load(path_model)
path_train = 'data/training_set.tsv'
n_total = 0
n_correct = 0
set_stopword = ('a', 'an', 'the', 'are', 'is', 'that', 'this', 'will', 'is', 'are', 'was', 'were', 'of', 'for')
d_word_count = util.load_d_word_count()
for index, line in enumerate(open(path_train)):
n_total += 1
if index == 0:
continue
else:
lst = line.lower().strip('\n').split('\t')
question = lst[1].split(' ')
answer = lst[2]
lst_choice = [l.split(' ') for l in lst[3:]]
#question_u = list(set(question).difference(set_stopword))
d = {}
for word in question:
word = word.strip('?').strip('.').strip(',').strip('!')
if d_word_count.has_key(word):
d[word] = d_word_count[word]
else:
d[word] = 0
# Only consider the word with lowest frequency.
sort = sorted(d.iteritems(), key = lambda dd : dd[1])
question_u = [s[0] for s in sort[:n_word_question]]
lst_com_q = util.combination_index(len(question_u), n_combination_question)
max = -1000000
answer_p = ''
words_focus_question = []
words_focus_choice = []
for com_q in lst_com_q:
vec_q = np.sum([get_vector_from_model(model, question_u[i]) for i in com_q], axis = 0)
for i_choice in range(4):
choice_u =list(set(lst_choice[i_choice]))
lst_com_choice = util.combination_index(len(choice_u), n_combination_answer)
for com_c in lst_com_choice:
vec_c = np.sum([get_vector_from_model(model, choice_u[i]) for i in com_c], axis = 0)
score = vec_q.dot(vec_c)
if score > max:
words_focus_question = [question_u[i] for i in com_q]
words_focus_choice = [choice_u[i] for i in com_c]
max = score
if i_choice == 0:
answer_p = 'a'
elif i_choice == 1:
answer_p = 'b'
elif i_choice == 2:
answer_p = 'c'
elif i_choice == 3:
answer_p = 'd'
if answer == answer_p:
n_correct += 1
print ' '.join(lst) + '\n' + "Focus Words in Question: " + ' '.join(words_focus_question) + '\n' + "Focus Words in Choice: " + ' '.join(words_focus_choice)
print 'Predicted Answer: ' + answer_p
print str(n_correct) + ' / ' + str(n_total) + '\t' + str(n_correct * 1.0 / n_total)
print n_correct * 1.0 / n_total
def test_on_validation(path_model, n_combination_question = 3, n_combination_answer = 3, n_word_question = 5):
model = gensim.models.Word2Vec.load(path_model)
path_train = 'data/validation_set.tsv'
n_combination_question = 4
n_combination_answer = 3
n_word_question = 5
n_total = 0
n_correct = 0
#set_stopword = ('a', 'an', 'the', 'are', 'is', 'that', 'this', 'will', 'is', 'are', 'was', 'were', 'of', 'for')
d_word_count = util.load_d_word_count()
for index, line in enumerate(open(path_train)):
n_total += 1
if index == 0:
continue
else:
lst = line.lower().strip('\n').split('\t')
question = lst[1].split(' ')
lst_choice = [l.split(' ') for l in lst[2:]]
#question_u = list(set(question).difference(set_stopword))
d = {}
for word in question:
#word = word.strip('?').strip('.').strip(',').strip('!')
word = util.norm_word(word)
if d_word_count.has_key(word):
d[word] = d_word_count[word]
else:
d[word] = 0
sort = sorted(d.iteritems(), key = lambda dd : dd[1])
question_u = [s[0] for s in sort[:n_word_question]]
lst_com_q = util.combination_index(len(question_u), n_combination_question)
max = -1000000
answer_p = ''
for com_q in lst_com_q:
vec_q = np.sum([get_vector_from_model(model, question_u[i]) for i in com_q], axis = 0)
for i_choice in range(4):
choice_u =list(set(lst_choice[i_choice]))
lst_com_choice = util.combination_index(len(choice_u), n_combination_answer)
for com_c in lst_com_choice:
vec_c = np.sum([get_vector_from_model(model, choice_u[i]) for i in com_c], axis = 0)
score = vec_q.dot(vec_c)
if score > max:
max = score
if i_choice == 0:
answer_p = 'A'
elif i_choice == 1:
answer_p = 'B'
elif i_choice == 2:
answer_p = 'C'
elif i_choice == 3:
answer_p = 'D'
print "%s,%s" % (lst[0], answer_p)
def get_max_occurence(lst_set_sentence, question, lst_choice, d_word_count,
set_stopword, n_word_question, n_combination_question,
n_combination_answer):
answer_p = ''
MAX = -1
lst_word_focus_q = []
lst_word_focus_c = []
lst_word_question_u = list(set(map(util.norm_word, question.split())))
d = {}
for word in question:
#word = word.strip('?').strip('.').strip(',').strip('!')
word = util.norm_word(word)
if d_word_count.has_key(word):
d[word] = d_word_count[word]
else:
d[word] = 0
sort = sorted(d.iteritems(), key=lambda dd: dd[1])
question_u = [s[0] for s in sort[:n_word_question]]
lst_com_q = util.combination_index(len(lst_word_question_u),
n_combination_question)
for com_q in lst_com_q:
lst_word_question = [lst_word_question_u[i] for i in com_q]
for index_c, choice in enumerate(lst_choice):
# Filter stop words in choice in order to prevent them from calculating cooccurence.
lst_word_choice_u = list(
set(map(util.norm_word,
choice.split())).difference(set_stopword))
lst_com_c = util.combination_index(len(lst_word_choice_u),
n_combination_answer)
for com_c in lst_com_c:
lst_word_choice = [lst_word_choice_u[i] for i in com_c]
n_cooccurence = get_cooccurence(lst_set_sentence,
lst_word_question,
lst_word_choice)
if n_cooccurence > MAX:
MAX = n_cooccurence
lst_word_focus_q = lst_word_question
lst_word_focus_c = lst_word_choice
if index_c == 0:
answer_p = 'A'
elif index_c == 1:
answer_p = 'B'
elif index_c == 2:
answer_p = 'C'
elif index_c == 3:
answer_p = 'D'
return answer_p, MAX, lst_word_focus_q, lst_word_focus_c
def get_max_occurence(lst_set_sentence, question, lst_choice, d_word_count, set_stopword, n_word_question, n_combination_question, n_combination_answer):
answer_p = ''
MAX = -1
lst_word_focus_q = []
lst_word_focus_c = []
lst_word_question_u = list(set(map(util.norm_word, question.split())))
d = {}
for word in question:
#word = word.strip('?').strip('.').strip(',').strip('!')
word = util.norm_word(word)
if d_word_count.has_key(word):
d[word] = d_word_count[word]
else:
d[word] = 0
sort = sorted(d.iteritems(), key = lambda dd : dd[1])
question_u = [s[0] for s in sort[:n_word_question]]
lst_com_q = util.combination_index(len(lst_word_question_u), n_combination_question)
for com_q in lst_com_q:
lst_word_question = [lst_word_question_u[i] for i in com_q]
for index_c, choice in enumerate(lst_choice):
# Filter stop words in choice in order to prevent them from calculating cooccurence.
lst_word_choice_u = list(set(map(util.norm_word, choice.split())).difference(set_stopword))
lst_com_c = util.combination_index(len(lst_word_choice_u), n_combination_answer)
for com_c in lst_com_c:
lst_word_choice = [lst_word_choice_u[i] for i in com_c]
n_cooccurence = get_cooccurence(lst_set_sentence, lst_word_question, lst_word_choice)
if n_cooccurence > MAX :
MAX = n_cooccurence
lst_word_focus_q = lst_word_question
lst_word_focus_c = lst_word_choice
if index_c == 0:
answer_p = 'A'
elif index_c == 1:
answer_p = 'B'
elif index_c == 2:
answer_p = 'C'
elif index_c == 3:
answer_p = 'D'
return answer_p, MAX, lst_word_focus_q, lst_word_focus_c
def test_on_train(path_model,
n_combination_question=3,
n_combination_answer=3,
n_word_question=5):
model = gensim.models.Word2Vec.load(path_model)
path_train = 'data/training_set.tsv'
n_total = 0
n_correct = 0
set_stopword = ('a', 'an', 'the', 'are', 'is', 'that', 'this', 'will',
'is', 'are', 'was', 'were', 'of', 'for')
d_word_count = util.load_d_word_count()
for index, line in enumerate(open(path_train)):
n_total += 1
if index == 0:
continue
else:
lst = line.lower().strip('\n').split('\t')
question = lst[1].split(' ')
answer = lst[2]
lst_choice = [l.split(' ') for l in lst[3:]]
#question_u = list(set(question).difference(set_stopword))
d = {}
for word in question:
word = word.strip('?').strip('.').strip(',').strip('!')
if d_word_count.has_key(word):
d[word] = d_word_count[word]
else:
d[word] = 0
# Only consider the word with lowest frequency.
sort = sorted(d.iteritems(), key=lambda dd: dd[1])
question_u = [s[0] for s in sort[:n_word_question]]
lst_com_q = util.combination_index(len(question_u),
n_combination_question)
max = -1000000
answer_p = ''
words_focus_question = []
words_focus_choice = []
for com_q in lst_com_q:
vec_q = np.sum([
get_vector_from_model(model, question_u[i]) for i in com_q
],
axis=0)
for i_choice in range(4):
choice_u = list(set(lst_choice[i_choice]))
lst_com_choice = util.combination_index(
len(choice_u), n_combination_answer)
for com_c in lst_com_choice:
vec_c = np.sum([
get_vector_from_model(model, choice_u[i])
for i in com_c
],
axis=0)
score = vec_q.dot(vec_c)
if score > max:
words_focus_question = [
question_u[i] for i in com_q
]
words_focus_choice = [choice_u[i] for i in com_c]
max = score
if i_choice == 0:
answer_p = 'a'
elif i_choice == 1:
answer_p = 'b'
elif i_choice == 2:
answer_p = 'c'
elif i_choice == 3:
answer_p = 'd'
if answer == answer_p:
n_correct += 1
print ' '.join(lst) + '\n' + "Focus Words in Question: " + ' '.join(
words_focus_question
) + '\n' + "Focus Words in Choice: " + ' '.join(words_focus_choice)
print 'Predicted Answer: ' + answer_p
print str(n_correct) + ' / ' + str(n_total) + '\t' + str(
n_correct * 1.0 / n_total)
print n_correct * 1.0 / n_total
def test_on_validation(path_model,
n_combination_question=3,
n_combination_answer=3,
n_word_question=5):
model = gensim.models.Word2Vec.load(path_model)
path_train = 'data/validation_set.tsv'
n_combination_question = 4
n_combination_answer = 3
n_word_question = 5
n_total = 0
n_correct = 0
#set_stopword = ('a', 'an', 'the', 'are', 'is', 'that', 'this', 'will', 'is', 'are', 'was', 'were', 'of', 'for')
d_word_count = util.load_d_word_count()
for index, line in enumerate(open(path_train)):
n_total += 1
if index == 0:
continue
else:
lst = line.lower().strip('\n').split('\t')
question = lst[1].split(' ')
lst_choice = [l.split(' ') for l in lst[2:]]
#question_u = list(set(question).difference(set_stopword))
d = {}
for word in question:
#word = word.strip('?').strip('.').strip(',').strip('!')
word = util.norm_word(word)
if d_word_count.has_key(word):
d[word] = d_word_count[word]
else:
d[word] = 0
sort = sorted(d.iteritems(), key=lambda dd: dd[1])
question_u = [s[0] for s in sort[:n_word_question]]
lst_com_q = util.combination_index(len(question_u),
n_combination_question)
max = -1000000
answer_p = ''
for com_q in lst_com_q:
vec_q = np.sum([
get_vector_from_model(model, question_u[i]) for i in com_q
],
axis=0)
for i_choice in range(4):
choice_u = list(set(lst_choice[i_choice]))
lst_com_choice = util.combination_index(
len(choice_u), n_combination_answer)
for com_c in lst_com_choice:
vec_c = np.sum([
get_vector_from_model(model, choice_u[i])
for i in com_c
],
axis=0)
score = vec_q.dot(vec_c)
if score > max:
max = score
if i_choice == 0:
answer_p = 'A'
elif i_choice == 1:
answer_p = 'B'
elif i_choice == 2:
answer_p = 'C'
elif i_choice == 3:
answer_p = 'D'
print "%s,%s" % (lst[0], answer_p)