def ask_question(qs_input, top_k):
"""
Ask one question and generate response for tfidf, lm and cnn
"""
print("Question : %s" % qs_input)
print("Top k : : %d" % top_k)
random.seed(12345)
retrieval_data_start_time = time.clock()
questions, pred_questions, answers, pred_answers = Data.read_pred_data(
"Data/pred_QA-pair.csv")
# Build word --> sentence dictionary
word_sentence_dict = Data.generate_word_sentence_dict(pred_questions)
print("Retrieval Data Finished")
retrieval_data_end_time = time.clock()
print("Retrieval Data cost %f" %
(retrieval_data_end_time - retrieval_data_start_time))
response_start_time = time.clock()
lm = LM(questions, pred_questions, answers, pred_answers,
word_sentence_dict)
tfidf = TFIDF(questions, pred_questions, answers, pred_answers,
word_sentence_dict)
cnn = CNN(questions,
pred_questions,
answers,
pred_answers,
word_sentence_dict,
isTrain=False)
_, lm_response = lm.ask_response(qs_input, top_k=top_k)
tfidf_response_id, tfidf_response = tfidf.ask_response(qs_input,
top_k=top_k * 10)
cnn_response = cnn.ask_response(qs_input, top_k, tfidf_response_id)
for i in range(top_k):
print("LM response %d: %s" % (i + 1, lm_response[i]))
for i in range(top_k):
print("TFIDF response %d: %s" % (i + 1, tfidf_response[i]))
for i in range(top_k):
print("CNN response %d: %s" % (i + 1, cnn_response[i]))
print("Response Finished")
response_end_time = time.clock()
print("Response cost %f" % (response_end_time - response_start_time))
def cnn_output(input_file_name, output_file_name, output_num, top_k):
"""
Generate cnn outputs
"""
random.seed(12345)
retrieval_data_start_time = time.clock()
questions, pred_questions, answers, pred_answers = Data.read_pred_data(
input_file_name)
# Build word --> sentence dictionary
word_sentence_dict = Data.generate_word_sentence_dict(pred_questions)
print("Retrieval Data Finished")
retrieval_data_end_time = time.clock()
print("Retrieval Data cost %f" %
(retrieval_data_end_time - retrieval_data_start_time))
cnn_response_start_time = time.clock()
tfidf = TFIDF(questions, pred_questions, answers, pred_answers,
word_sentence_dict)
cnn = CNN(questions,
pred_questions,
answers,
pred_answers,
word_sentence_dict,
isTrain=False)
if output_file_name.split(".")[-1] == "txt":
output = open(output_file_name, "w")
for i in range(output_num):
qs_index = int(random.random() * len(questions))
qs_input = questions[qs_index].encode("utf-8")
output.write("Question : %s\n" % qs_input)
tfidf_response_id, tfidf_response = tfidf.ask_response(
qs_input, top_k * 10)
cnn_response = cnn.ask_response(qs_input, top_k, tfidf_response_id)
for i in range(top_k):
output.write("CNN response %d: %s\n" %
(i + 1, cnn_response[i].encode("utf-8")))
output.write("\n")
output.close()
cnn_response_end_time = time.clock()
print("CNN response cost %f" %
(cnn_response_end_time - cnn_response_start_time))
if output_file_name.split(".")[-1] == "csv":
with open(
output_file_name,
'w',
) as csvfile:
fieldnames = ['Question']
fieldnames.extend(["Reply " + str(i + 1) for i in range(top_k)])
fieldnames.append("Score")
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for i in range(output_num):
dict = {"Score": ""}
qs_index = int(random.random() * len(questions))
qs_input = questions[qs_index].encode("utf-8")
dict["Question"] = qs_input
tfidf_response_id, tfidf_response = tfidf.ask_response(
qs_input, top_k * 10)
cnn_response = cnn.ask_response(qs_input, top_k,
tfidf_response_id)
for i in range(min(top_k, len(cnn_response))):
dict["Reply " +
str(i + 1)] = cnn_response[i].encode("utf-8")
writer.writerow(dict)
