def get_AL_predict(test_feature, choose_feature, unlabel_feature, test_query, choose_query, choose_answer, unlabel_query, unlabel_answer, rec_api_test, rec_api_choose, rec_api_unlabel, w2v, idf):
unlabel_feedback_info = feedback.get_feedback_inf(unlabel_query, choose_query, choose_answer, rec_api_unlabel, w2v, idf)
label_feedback_info = feedback.get_feedback_inf(choose_query, choose_query, choose_answer, rec_api_choose, w2v, idf)
X_train, y_train = braid_AL.get_active_data(unlabel_feedback_info, unlabel_feature)
X_feedback, y_feedback = braid_AL.get_active_data(label_feedback_info, choose_feature)
# initializing the active learner
learner = ActiveLearner(
estimator=KNeighborsClassifier(n_neighbors=4),
# estimator=LogisticRegression(penalty='l1', solver='liblinear'),
X_training=X_feedback, y_training=y_feedback
)
length = len(rec_api_test)
predict, sel_query, add_unlabel_feature = [], [], []
if len(unlabel_query) > 0:
# pool-based sampling
n_queries = 40
for idx in range(n_queries):
query_idx, query_instance = uncertainty_sampling(classifier=learner, X=X_train)
idx = int(query_idx/10)
learner.teach(
X=X_train[query_idx].reshape(1, -1),
y=y_train[query_idx].reshape(1, )
)
# add queried instance into FR
choose_query.append(unlabel_query[idx])
choose_answer.append(unlabel_answer[idx])
rec_api_choose.extend(rec_api_unlabel[idx*10:idx*10+10])
choose_feature.extend(unlabel_feature[idx*10:idx*10+10])
# remove queried instance from pool
for i in range(10):
X_train = np.delete(X_train, idx*10, axis=0)
y_train = np.delete(y_train, idx*10)
del unlabel_query[idx]
del unlabel_answer[idx]
del rec_api_unlabel[idx*10:idx*10+10]
del unlabel_feature[idx*10:idx*10+10]
if len(X_train) == 0:
break
add_label_feedback_info = feedback.get_feedback_inf(choose_query, choose_query, choose_answer, rec_api_choose, w2v, idf)
new_X_feedback, new_y_feedback = braid_AL.get_active_data(add_label_feedback_info, choose_feature)
learner = ActiveLearner(
estimator=KNeighborsClassifier(n_neighbors=4),
# estimator=LogisticRegression(penalty='l1', solver='liblinear'),
X_training=new_X_feedback, y_training=new_y_feedback
)
feedback_info = feedback.get_feedback_inf(test_query, choose_query, choose_answer, rec_api_test, w2v, idf)
X = split_data.get_test_feature_matrix(feedback_info, test_feature)
X_test = np.array(X)
# 用反馈数据学习过后的模型来预测测试数据
for query_idx in range(length):
try:
y_pre = learner.predict_proba(X=X_test[query_idx].reshape(1, -1))
except ValueError:
predict = [0.0 for n in range(length)]
else:
predict.append(float(y_pre[0, 1]))
return predict, X, new_X_feedback, new_y_feedback
def process_input(msg='how to convert int to string?'):
global rerank
global responseToClient
global g_query_str
global feedback_inf
global api_feature
global sort
query = msg
query_matrix, query_idf_vector = feedback.load_matrix(query, w2v, idf)
top_questions = recommendation.get_topk_questions(query, query_matrix, query_idf_vector, questions, 50, parent)
recommended_api = recommendation.recommend_api(query_matrix, query_idf_vector, top_questions, questions, javadoc,javadoc_dict_methods,-1)
# recommended_api = recommendation.recommend_api_class(query_matrix, query_idf_vector, top_questions, questions, javadoc,javadoc_dict_classes,-1)
# combine api_relevant feature with FF
pos = -1
rec_api = []
api_dict_desc = {}
x, api_feature, responseToClient = [], [], []
for i,api in enumerate(recommended_api):
# print('Rank',i+1,':',api)
rec_api.append(api)
# recommendation.summarize_api_method(api,top_questions,questions,javadoc,javadoc_dict_methods)
api_descriptions, questions_titles = recommendation.summarize_api_method(api, top_questions, questions, javadoc,
javadoc_dict_methods)
api_dict_desc[api] = api_descriptions
sum_inf, api_inf, api_desc_inf = text2feat(api, api_descriptions, w2v, idf, query_matrix, query_idf_vector)
api_feature.append(sum_inf)
# print(api_feature)
if i == 9:
break
# print('##################')
start1 = time.time()
# feedback info of user query from SO
fr = open('../data/feedback_all.csv', 'r')
reader = csv.reader(fr)
so_query, so_answer = [], []
for row in reader:
so_query.append(row[0])
so_answer.append(row[1:])
# feedback info of user query from FR
fr = open('../data/feedback_rec.csv', 'r')
reader = csv.reader(fr)
choose_query, choose_answer = [], []
for row in reader:
choose_query.append(row[0])
choose_answer.append(row[1:])
feedback_inf = feedback.get_feedback_inf(query, choose_query, choose_answer, rec_api, w2v, idf)
# FV = RF+FF
for i in range(len(api_feature)):
sum = api_feature[i]
sum.extend(feedback_inf[i])
x.append(sum)
# feature info of FR
fr = open('../data/feedback_feature_rec.csv', 'r')
reader = csv.reader(fr)
y_feature, x_feautre, api_relevant_feature, rec_api_choose = [], [], [], []
for row in reader:
# y_feature.append(row[0])
x_feautre.append(row[:-1])
api_relevant_feature.append(row[1:3])
rec_api_choose.append(row[-1])
#feature info of SO
fr = open('../data/get_feature_method.csv', 'r')
reader = csv.reader(fr)
unlabel_feature, rec_api_unlabel = [], []
for row in reader:
# y_feature.append(row[0])
unlabel_feature.append(row[:-1])
rec_api_unlabel.append(row[-1])
# AL_choose_feature, AL_unlabel_feature = split_data.get_choose(AL_train_feature, choose)
pred2, add_x_FR, add_x_FV, add_y_FV = get_AL_predict(x, x_feautre, unlabel_feature, query, choose_query, choose_answer, so_query, so_answer, rec_api, rec_api_choose, rec_api_unlabel, w2v, idf)
pred1 = braid_LTR.get_LTR_predict(add_x_FR, add_x_FV, add_y_FV)
rem = -10
rec, rec_LTR, rec_AL = [], [], []
sort, sort_LTR, sort_AL = [], [], []
pred = []
sum_pred1, sum_pred2 = 0, 0
for i in range(len(api_feature)):
sum_pred1 += pred1[i]+5
sum_pred2 += pred2[i]
al_idx = []
rerank_al = sorted(pred2, reverse=True)
for i in range(len(api_feature)):
temp = rerank_al.index(pred2[i])+1
while temp in al_idx:
temp += 1
al_idx.append(temp)
m = 0.6
for num in range(len(api_feature)):
sum = (pred1[num]+5)/len(api_feature) + m*pred2[num]/al_idx[num]
pred.append(sum)
for i in range(len(api_feature)):
sort.append(pred.index(max(pred)) + 1)
sort_LTR.append(pred1.index(max(pred1)) + 1)
sort_AL.append(pred2.index(max(pred2)) + 1)
rec.append(max(pred))
rec_LTR.append(max(pred1))
rec_AL.append(max(pred2))
pred[pred.index(max(pred))] = rem
pred1[pred1.index(max(pred1))] = rem
pred2[pred2.index(max(pred2))] = rem
# 将api重新排序，输出相关结果
for i in sort:
api_mod = rec_api[i-1]
print(sort.index(i) + 1, api_mod)
api_obj = {'id':sort.index(i) + 1, 'api':api_mod, 'desc':api_dict_desc[api_mod] }
rerank.append(api_mod)
responseToClient.append(api_obj)
print(type(rerank))
print(json.dumps(rerank))
print(rerank)
print(responseToClient)
return responseToClient
test_query, test_answer, train_query, train_answer, test_feature, train_feature, rec_api_test, rec_api_train = split_data.get_test_train(
)
print('test_answer', test_answer)
LTR_train_feature = get_LTR_feature(train_answer, rec_api_train,
train_feature)
num_choose = 373
top1, top3, top5, map, mrr = 0, 0, 0, 0, 0
# iteration begin
for round in range(1):
choose_query, choose_answer, rec_api_choose, unlabel_query, unlabel_answer, rec_api_unlabel, choose = split_data.split_choose_unlabel(
train_query, train_answer, rec_api_train, num_choose)
choose_feature, unlabel_feature = split_data.get_choose(
LTR_train_feature, choose)
train_feedback_info = feedback.get_feedback_inf(
choose_query, choose_query, choose_answer, rec_api_choose, w2v,
idf)
test_feedback_info = feedback.get_feedback_inf(test_query,
choose_query,
choose_answer,
rec_api_test, w2v, idf)
# print(11, len(train_feedback_info), len(LTR_train_feature), len(choose_feature), len(unlabel_feature))
train_x_FV, train_y_FV = split_data.get_train_feature_matrix(
train_feedback_info, choose_feature)
test_feature = np.array(test_feature)
y_predict = get_LTR_predict_LTR(test_feature, test_feedback_info,
choose_feature, train_feedback_info)
rank_mod, rankall = [], []
for n in range(len(test_query)):
# feedback info of user query from SO
fr = open('../data/feedback_all.csv', 'r')
reader = csv.reader(fr)
so_query, so_answer = [], []
for row in reader:
so_query.append(row[0])
so_answer.append(row[1:])
# feedback info of user query from FR
fr = open('../data/feedback_rec.csv', 'r')
reader = csv.reader(fr)
choose_query, choose_answer = [], []
for row in reader:
choose_query.append(row[0])
choose_answer.append(row[1:])
feedback_inf = feedback.get_feedback_inf(query, choose_query,
choose_answer, rec_api, w2v, idf)
# FV = RF+FF
for i in range(len(api_feature)):
sum = api_feature[i]
sum.extend(feedback_inf[i])
x.append(sum)
# feature info of FR
fr = open('../data/feedback_feature_rec.csv', 'r')
reader = csv.reader(fr)
y_feature, x_feautre, api_relevant_feature, rec_api_choose = [], [], [], []
for row in reader:
# y_feature.append(row[0])
x_feautre.append(row[:-1])
api_relevant_feature.append(row[1:3])
def get_AL_predict(test_feature, choose_feature, unlabel_feature, test_query, choose_query, choose_answer, unlabel_query, unlabel_answer, rec_api_test, rec_api_choose, rec_api_unlabel, w2v, idf):
unlabel_feedback_info = feedback.get_feedback_inf(unlabel_query, choose_query, choose_answer, rec_api_unlabel, w2v, idf)
label_feedback_info = feedback.get_feedback_inf(choose_query, choose_query, choose_answer, rec_api_choose, w2v, idf)
X_train, y_train = get_active_data(unlabel_feedback_info, unlabel_feature)
X_feedback, y_feedback = get_active_data(label_feedback_info, choose_feature)
# initializing the active learner
learner = ActiveLearner(
# estimator=KNeighborsClassifier(n_neighbors=4),
estimator=LogisticRegression(penalty='l1', solver='liblinear'),
X_training=X_feedback, y_training=y_feedback
)
predict, sel_query, add_unlabel_feature = [], [], []
if len(unlabel_query) > 0:
# pool-based sampling
n_queries = 100
sel_idx, sel_label = [], []
for idx in range(n_queries):
# query_idx, query_instance = learner.query(X=X_train)
query_idx, query_instance = uncertainty_sampling(classifier=learner, X=X_train)
idx = int(query_idx/10)
# print(idx, len(X_train))
# print('uncertain', query_idx, X_train[query_idx], y_train[query_idx])
learner.teach(
X=X_train[query_idx].reshape(1, -1),
y=y_train[query_idx].reshape(1, )
)
# add queried instance into FR
choose_query.append(unlabel_query[idx])
choose_answer.append(unlabel_answer[idx])
rec_api_choose.extend(rec_api_unlabel[idx*10:idx*10+10])
choose_feature.extend(unlabel_feature[idx*10:idx*10+10])
# learner.teach(
# X=new_X_train.reshape(1, -1),
# y=new_y_train.reshape(1, )
# )
# print(unlabel_query[idx], unlabel_query[idx], rec_api_unlabel[idx*10:idx*10+10], rec_api_unlabel[idx*10:idx*10+10])
# remove queried instance from pool
for i in range(10):
X_train = np.delete(X_train, idx*10, axis=0)
y_train = np.delete(y_train, idx*10)
del unlabel_query[idx]
del unlabel_answer[idx]
del rec_api_unlabel[idx*10:idx*10+10]
del unlabel_feature[idx*10:idx*10+10]
if len(X_train) == 0:
break
add_label_feedback_info = feedback.get_feedback_inf(choose_query, choose_query, choose_answer, rec_api_choose, w2v, idf)
new_X_feedback, new_y_feedback = get_active_data(add_label_feedback_info, choose_feature)
learner = ActiveLearner(
# estimator=KNeighborsClassifier(n_neighbors=4),
estimator=LogisticRegression(penalty='l1', solver='liblinear'),
X_training=new_X_feedback, y_training=new_y_feedback
)
feedback_info = feedback.get_feedback_inf(test_query, choose_query, choose_answer, rec_api_test, w2v, idf)
X = split_data.get_test_feature_matrix(feedback_info, test_feature)
X_test = np.array(X)
# 用反馈数据学习过后的模型来预测测试数据
for query_idx in range(400):
y_pre = learner.predict_proba(X=X_test[query_idx].reshape(1, -1))
predict.append(float(y_pre[0, 1]))
# predict.append(math.log(float(y_pre[0, 1])+1))
# predict.extend(y_pre.tolist())
x = X_test[query_idx].reshape(1, -1)
# print(predict)
# print('new_choose', len(choose_query), len(choose_answer))
# fw = open('../data/add_FR.csv', 'a+', newline='')
# writer = csv.writer(fw)
# for i, fr_q in enumerate(choose_query):
# writer.writerow((fr_q, choose_answer[i]))
# fw.close()
return predict, X, new_X_feedback, new_y_feedback #sorted(sel_query)
