RustRepoTrans / Dataset_Construction /match_function_throughBm25.py

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f0f4f2b over 1 year ago

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from rank_bm25 import BM25Plus
import os
import sys
import re
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer, WordNetLemmatizer


def read_corpus(corpus_files_path):
    corpus = []

    corpus_files = os.listdir(corpus_files_path)
    for corpus_file in corpus_files:
        with open(os.path.join(corpus_files_path, corpus_file), 'r') as input_file:
            tmp = input_file.read()

            corpus.append(tmp)
    
    return corpus

def normalize_text(text):
    # 大小写归一化
    text = text.lower()
    
    # 分词
    words = re.findall(r'\w+|[^\s\w]+', text)
    
    # 去除停用词
    stop_words = set(stopwords.words('english'))
    words = [word for word in words if word not in stop_words]
    
    # 词干提取
    stemmer = PorterStemmer()
    words = [stemmer.stem(word) for word in words]
    
    # 词形还原
    lemmatizer = WordNetLemmatizer()
    words = [lemmatizer.lemmatize(word) for word in words]
    
    return words


# 使用正则表达式进行分词
def tokenize_code(code):
    # 使用归一化
    return normalize_text(code)

def main():
    corpus_files_path = "functions"
    query_files_path = "functions_with_unitTest"
    match_results_path = "potential_function_pair"

    project = sys.argv[1]
    corpus_lang = sys.argv[2]
    query_lang = sys.argv[3]

    corpus_files_path = os.path.join(corpus_files_path, project, corpus_lang)
    query_files_path = os.path.join(query_files_path, project, query_lang)
    match_results_path = os.path.join(match_results_path, project, f"{query_lang}__{corpus_lang}")
    query_files = os.listdir(query_files_path)

    # 获取匹配池子
    corpus = read_corpus(corpus_files_path)
    tokenized_corpus = [tokenize_code(doc) for doc in corpus]
    bm25 = BM25Plus(tokenized_corpus)

    # 获取请求
    for query_file in query_files:
        with open(os.path.join(query_files_path, query_file), 'r') as input_file:
            query = input_file.read()

        # 对于每个请求计算前n个匹配结果
        # 放大函数名的权重
        tokenized_query = tokenize_code(query)
        
        # 获取相关性评分
        scores = bm25.get_scores(tokenized_query)
        # 获取最相关的前几个函数定义
        top_n = 10
        match_results_index = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:top_n]

        # 如果文件夹不存在，则创建它
        if not os.path.exists(match_results_path):
            os.makedirs(match_results_path)
        
        # 记录匹配结果
        with open(os.path.join(match_results_path, query_file), 'w') as output_file:
            output_file.write("<Target function>\n")
            output_file.write(query)
            output_file.write("\n</Target function>\n\n")
            # for match_result in match_results:
            #     output_file.write(match_result)
            #     output_file.write("\n")
            output_file.write("<Possible matching functions>\n")
            i = 1
            for index in match_results_index:
                output_file.write("<Function {}> \n{}\n</Function {}>\n\n".format(i, corpus[index], i))
                # output_file.write("Score: {}\n".format(scores[index]))
                i += 1
            output_file.write("</Possible matching functions>\n")
                

if __name__ == "__main__" :
    main()

	from rank_bm25 import BM25Plus
	import os
	import sys
	import re
	from nltk.corpus import stopwords
	from nltk.stem import PorterStemmer, WordNetLemmatizer


	def read_corpus(corpus_files_path):
	corpus = []

	corpus_files = os.listdir(corpus_files_path)
	for corpus_file in corpus_files:
	with open(os.path.join(corpus_files_path, corpus_file), 'r') as input_file:
	tmp = input_file.read()

	corpus.append(tmp)

	return corpus

	def normalize_text(text):
	# 大小写归一化
	text = text.lower()

	# 分词
	words = re.findall(r'\w+\|[^\s\w]+', text)

	# 去除停用词
	stop_words = set(stopwords.words('english'))
	words = [word for word in words if word not in stop_words]

	# 词干提取
	stemmer = PorterStemmer()
	words = [stemmer.stem(word) for word in words]

	# 词形还原
	lemmatizer = WordNetLemmatizer()
	words = [lemmatizer.lemmatize(word) for word in words]

	return words


	# 使用正则表达式进行分词
	def tokenize_code(code):
	# 使用归一化
	return normalize_text(code)

	def main():
	corpus_files_path = "functions"
	query_files_path = "functions_with_unitTest"
	match_results_path = "potential_function_pair"

	project = sys.argv[1]
	corpus_lang = sys.argv[2]
	query_lang = sys.argv[3]

	corpus_files_path = os.path.join(corpus_files_path, project, corpus_lang)
	query_files_path = os.path.join(query_files_path, project, query_lang)
	match_results_path = os.path.join(match_results_path, project, f"{query_lang}__{corpus_lang}")
	query_files = os.listdir(query_files_path)

	# 获取匹配池子
	corpus = read_corpus(corpus_files_path)
	tokenized_corpus = [tokenize_code(doc) for doc in corpus]
	bm25 = BM25Plus(tokenized_corpus)

	# 获取请求
	for query_file in query_files:
	with open(os.path.join(query_files_path, query_file), 'r') as input_file:
	query = input_file.read()

	# 对于每个请求计算前n个匹配结果
	# 放大函数名的权重
	tokenized_query = tokenize_code(query)

	# 获取相关性评分
	scores = bm25.get_scores(tokenized_query)
	# 获取最相关的前几个函数定义
	top_n = 10
	match_results_index = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:top_n]

	# 如果文件夹不存在，则创建它
	if not os.path.exists(match_results_path):
	os.makedirs(match_results_path)

	# 记录匹配结果
	with open(os.path.join(match_results_path, query_file), 'w') as output_file:
	output_file.write("<Target function>\n")
	output_file.write(query)
	output_file.write("\n</Target function>\n\n")
	# for match_result in match_results:
	# output_file.write(match_result)
	# output_file.write("\n")
	output_file.write("<Possible matching functions>\n")
	i = 1
	for index in match_results_index:
	output_file.write("<Function {}> \n{}\n</Function {}>\n\n".format(i, corpus[index], i))
	# output_file.write("Score: {}\n".format(scores[index]))
	i += 1
	output_file.write("</Possible matching functions>\n")


	if __name__ == "__main__" :
	main()