def eval_time_within_group(repo_group_names):
# NUM_TOP_REPOS = 100, [0,0,0,...]
sum_precision_list = [0] * compute.NUM_TOP_REPOS
sum_recall_list = [0] * compute.NUM_TOP_REPOS
sum_f1score_list = [0] * compute.NUM_TOP_REPOS
for current_repo_name in repo_group_names:
res_dict = compute.find_similar_repos_considering_time(current_repo_name, 8)
rank_list = [repo_name for repo_name, sim in
sorted(res_dict.items(), key=lambda x: -x[1])]
for depth in range(0, len(rank_list)):
precision, recall, f1score = eval(depth+1, rank_list, repo_group_names)
sum_precision_list[depth]+=precision
sum_recall_list[depth]+=recall
sum_f1score_list[depth]+=f1score
group_size = len(repo_group_names)
sum_precision_list = [x / group_size for x in sum_precision_list]
sum_recall_list = [x / group_size for x in sum_recall_list]
sum_f1score_list = [x / group_size for x in sum_f1score_list]
plot_f1score(f1score_list=sum_f1score_list, precision_list=sum_precision_list,
recall_list=sum_recall_list, title = 'repo group:'+str(repo_group_names))
plot_precision_recall(recall_list=sum_recall_list, precision_list=sum_precision_list,
title = 'repo group:'+str(repo_group_names))
def find_similar_repo(repo_name, method_name, rank=True):
res = {}
if method_name == "user_based_jaccard":
res = user_based_jaccard.find_similar_repos(repo_name)
elif method_name == "user_based_jaccard_withtime":
res = user_based_jaccard.find_similar_repos_considering_time(repo_name, 2)
elif method_name == "user_based_lda":
res = user_based_model.find_similar_repos(repo_name, "lda")
elif method_name == "user_based_tfidf":
res = user_based_model.find_similar_repos(repo_name, "tfidf")
elif method_name == "text_based_lda":
pass
elif method_name == "text_based_tfidf":
pass
if rank:
res = [repo_name for repo_name, similarity in sorted(res.items(), key=lambda item: -item[1])]
ranks = range(1, len(res) + 1)
res = dict(zip(res, ranks))
return res
def eval_time_with_range(test_repo_name):
# for time_range in [0.5, 1, 2, 4, 8]:
for time_range in [1, 2]:
precision_list = []
recall_list = []
f1score_list = []
res_dict = compute.find_similar_repos_considering_time(test_repo_name, time_range)
rank_list = [repo_name for repo_name, sim in
sorted(res_dict.items(), key=lambda x: -x[1])]
for depth in range(1, len(rank_list)+1):
precision, recall, f1score = eval(depth, rank_list, showcase_js)
precision_list.append(precision)
recall_list.append(recall)
f1score_list.append(f1score)
plot_f1score(f1score_list=f1score_list, precision_list=precision_list,
recall_list=recall_list, title='time_range='+str(time_range))
plot_precision_recall(recall_list=recall_list, precision_list=precision_list,
title='time_range='+str(time_range))
