def preProcessBySlide(df, dates):
"""参数说明
df：读取的dataframe对象
dates:作为测试的年月四元组
"""
"""注意： 输入文件中已经带有列名了"""
"""处理NAN"""
df.dropna(how='any', inplace=True)
df.reset_index(drop=True, inplace=True)
df.fillna(value='', inplace=True)
"""对df添加一列标识训练集和测试集"""
df['label'] = df['pr_created_at'].apply(
lambda x: (time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_year == dates[2] and
time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_mon == dates[3]))
"""创建时间转化为时间戳"""
df['pr_created_at'] = df['pr_created_at'].apply(lambda x: time.mktime(time.strptime(x, "%Y-%m-%d %H:%M:%S")))
df['pr_created_at'] = df['pr_created_at'] / (24 * 3600)
"""先对输入数据做精简 只留下感兴趣的数据"""
df = df[['pr_number', 'pr_title', 'review_user_login', 'label', 'pr_created_at']].copy(deep=True)
print("before filter:", df.shape)
df.drop_duplicates(inplace=True)
print("after filter:", df.shape)
"""对人名字做数字处理"""
convertDict = DataProcessUtils.changeStringToNumber(df, ['review_user_login'])
"""先对tag做拆分"""
tagDict = dict(list(df.groupby('pr_number')))
"""先尝试所有信息团在一起"""
df = df[['pr_number', 'pr_title', 'label', 'pr_created_at']].copy(deep=True)
df.drop_duplicates(inplace=True)
df.reset_index(drop=True, inplace=True)
"""用于收集所有文本向量分词"""
stopwords = SplitWordHelper().getEnglishStopList() # 获取通用英语停用词
textList = []
for row in df.itertuples(index=False, name='Pandas'):
tempList = []
"""获取pull request的标题"""
pr_title = getattr(row, 'pr_title')
pr_title_word_list = [x for x in FleshReadableUtils.word_list(pr_title) if x not in stopwords]
"""初步尝试提取词干效果反而下降了 。。。。"""
"""对单词做提取词干"""
pr_title_word_list = nltkFunction.stemList(pr_title_word_list)
tempList.extend(pr_title_word_list)
textList.append(tempList)
print(textList.__len__())
"""对分词列表建立字典 并提取特征数"""
dictionary = corpora.Dictionary(textList)
print('词典：', dictionary)
feature_cnt = len(dictionary.token2id)
print("词典特征数：", feature_cnt)
"""根据词典建立语料库"""
corpus = [dictionary.doc2bow(text) for text in textList]
# print('语料库:', corpus)
"""语料库训练TF-IDF模型"""
tfidf = models.TfidfModel(corpus)
"""再次遍历数据，形成向量，向量是稀疏矩阵的形式"""
wordVectors = []
for i in range(0, df.shape[0]):
wordVectors.append(dict(tfidf[dictionary.doc2bow(textList[i])]))
"""对已经有的本文特征向量和标签做训练集和测试集的拆分"""
trainData_index = df.loc[df['label'] == False].index
testData_index = df.loc[df['label'] == True].index
"""训练集"""
train_data = [wordVectors[x] for x in trainData_index]
"""测试集"""
test_data = [wordVectors[x] for x in testData_index]
"""填充为向量"""
train_data = DataProcessUtils.convertFeatureDictToDataFrame(train_data, featureNum=feature_cnt)
test_data = DataProcessUtils.convertFeatureDictToDataFrame(test_data, featureNum=feature_cnt)
train_data['pr_number'] = list(df.loc[df['label'] == False]['pr_number'])
test_data['pr_number'] = list(df.loc[df['label'] == True]['pr_number'])
train_data['pr_created_at'] = list(df.loc[df['label'] == False]['pr_created_at'])
test_data['pr_created_at'] = list(df.loc[df['label'] == True]['pr_created_at'])
"""问题转化为多标签问题
train_data_y [{pull_number:[r1, r2, ...]}, ... ,{}]
"""
train_data_y = {}
for pull_number in df.loc[df['label'] == False]['pr_number']:
reviewers = list(tagDict[pull_number].drop_duplicates(['review_user_login'])['review_user_login'])
train_data_y[pull_number] = reviewers
test_data_y = {}
for pull_number in df.loc[df['label'] == True]['pr_number']:
reviewers = list(tagDict[pull_number].drop_duplicates(['review_user_login'])['review_user_login'])
test_data_y[pull_number] = reviewers
"""train_data ,test_data 最后一列是pr number test_data_y 的形式是dict"""
return train_data, train_data_y, test_data, test_data_y, convertDict
def preProcess(df, dates):
"""参数说明
df：读取的dataframe对象
dates:四元组，后两位作为测试的年月 (,,year,month)
"""
"""注意： 输入文件中已经带有列名了"""
"""空comment的review包含na信息，但作为结果集是有用的，所以只对训练集去掉na"""
# """处理NAN"""
# df.dropna(how='any', inplace=True)
# df.reset_index(drop=True, inplace=True)
df['pr_title'].fillna(value='', inplace=True)
df['pr_body'].fillna(value='', inplace=True)
"""对df添加一列标识训练集和测试集"""
df['label'] = df['pr_created_at'].apply(
lambda x: (time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_year == dates[2] and
time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_mon == dates[3]))
"""对reviewer名字数字化处理 存储人名映射字典做返回"""
convertDict = DataProcessUtils.changeStringToNumber(df, ['pr_author', 'reviewer'])
"""用于收集所有文本向量分词"""
stopwords = SplitWordHelper().getEnglishStopList() # 获取通用英语停用词
"""问题:lsi的过程不能在整个数据集上面做，不然会导致pr的文本重复问题"""
df_pr = df.copy(deep=True)
df_pr.drop_duplicates(subset=['pull_number'], keep='first', inplace=True)
df_pr.reset_index(drop=True, inplace=True)
# 用于记录pr中文字的数量，对于pr少于10个word的pr.直接去掉
df_pr_word_count = []
textList = []
for row in df_pr.itertuples(index=False, name='Pandas'):
tempList = []
"""获取pull request的标题"""
pr_title = getattr(row, 'pr_title')
pr_title_word_list = [x for x in FleshReadableUtils.word_list(pr_title) if x not in stopwords]
"""初步尝试提取词干效果反而下降了 。。。。"""
"""对单词做提取词干"""
pr_title_word_list = nltkFunction.stemList(pr_title_word_list)
tempList.extend(pr_title_word_list)
"""pull request的body"""
pr_body = getattr(row, 'pr_body')
pr_body_word_list = [x for x in FleshReadableUtils.word_list(pr_body) if x not in stopwords]
"""对单词做提取词干"""
pr_body_word_list = nltkFunction.stemList(pr_body_word_list)
tempList.extend(pr_body_word_list)
if tempList.__len__() >= 10 or getattr(row, 'label'):
textList.append(tempList)
if getattr(row, 'label'):
df_pr_word_count.append(10) # 以便过后面的过滤
else:
df_pr_word_count.append(tempList.__len__())
"""去除无用的训练pr"""
df_pr['count'] = df_pr_word_count
df_pr = df_pr.loc[df_pr['count'] >= 10].copy(deep=True)
df_pr.reset_index(drop=True, inplace=True)
df_pr.drop(['count'], inplace=True, axis=1)
"""保存只有pr的列表"""
prList = list(df_pr['pull_number'])
"""对已经有的本文特征向量和标签做训练集和测试集的拆分"""
trainData_index = df_pr.loc[df_pr['label'] == False].index
testData_index = df_pr.loc[df_pr['label'] == True].index
trainDataTextList = [textList[x] for x in trainData_index]
testDataTextList = [textList[x] for x in testData_index]
print(textList.__len__())
"""对分词列表建立字典 并提取特征数"""
dictionary = corpora.Dictionary(trainDataTextList)
print('词典：', dictionary)
"""感觉有问题，tfidf模型不应该是在全数据集上面计算，而是在训练集上面计算，而测试集的向量就是
单纯的带入模型的计算结果"""
"""根据词典建立语料库"""
corpus = [dictionary.doc2bow(text) for text in trainDataTextList]
# print('语料库:', corpus)
"""语料库训练TF-IDF模型"""
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
topic_num = 10
lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=topic_num)
topic_list = lsi.print_topics()
print("{0}个主题的单词分布为：\n".format(topic_num))
for topic in topic_list:
print(topic)
"""再次遍历数据，形成向量，向量是稀疏矩阵的形式"""
wordVectors = []
for i in range(0, trainDataTextList.__len__()):
wordVectors.append(dict(lsi[dictionary.doc2bow(trainDataTextList[i])]))
for i in range(0, testDataTextList.__len__()):
wordVectors.append(dict(lsi[dictionary.doc2bow(testDataTextList[i])]))
"""训练集"""
train_data = [wordVectors[x] for x in trainData_index]
"""测试集"""
test_data = [wordVectors[x] for x in testData_index]
"""填充为向量"""
train_v_data = DataProcessUtils.convertFeatureDictToDataFrame(train_data, featureNum=topic_num)
test_v_data = DataProcessUtils.convertFeatureDictToDataFrame(test_data, featureNum=topic_num)
lsi_data = pandas.concat([train_v_data, test_v_data], axis=0) # 0 轴合并
lsi_data['pull_number'] = prList
lsi_data.reset_index(inplace=True, drop=True)
train_data = df.loc[df['label'] == False]
train_data.reset_index(drop=True, inplace=True)
test_data = df.loc[df['label'] == True]
test_data.reset_index(drop=True, inplace=True)
train_data = train_data.merge(lsi_data, on="pull_number")
train_data.drop(columns=['label'], inplace=True)
test_data = test_data.merge(lsi_data, on="pull_number")
test_data.drop(columns=['label'], inplace=True)
"""8ii处理NAN"""
train_data.dropna(how='any', inplace=True)
train_data.reset_index(drop=True, inplace=True)
train_data.fillna(value='', inplace=True)
"""先对tag做拆分"""
trainDict = dict(list(train_data.groupby('pull_number')))
testDict = dict(list(test_data.groupby('pull_number')))
test_data_y = {}
for pull_number in test_data.drop_duplicates(['pull_number'])['pull_number']:
reviewers = list(testDict[pull_number].drop_duplicates(['reviewer'])['reviewer'])
test_data_y[pull_number] = reviewers
train_data_y = {}
for pull_number in train_data.drop_duplicates(['pull_number'])['pull_number']:
reviewers = list(trainDict[pull_number].drop_duplicates(['reviewer'])['reviewer'])
train_data_y[pull_number] = reviewers
return train_data, train_data_y, test_data, test_data_y, convertDict
def preProcess(df, dates):
"""参数说明
df：读取的dataframe对象
dates:四元组，后两位作为测试的年月 (,,year,month)
"""
"""注意： 输入文件中已经带有列名了"""
"""空comment的review包含na信息，但作为结果集是有用的，所以只对训练集去掉na"""
# """处理NAN"""
# df.dropna(how='any', inplace=True)
# df.reset_index(drop=True, inplace=True)
df['pr_title'].fillna(value='', inplace=True)
df['pr_body'].fillna(value='', inplace=True)
"""对df添加一列标识训练集和测试集"""
df['label'] = df['pr_created_at'].apply(
lambda x: (time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_year == dates[2] and
time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_mon == dates[3]))
"""对reviewer名字数字化处理 存储人名映射字典做返回"""
convertDict = DataProcessUtils.changeStringToNumber(df, ['pr_author', 'reviewer'])
"""用于收集所有文本向量分词"""
stopwords = SplitWordHelper().getEnglishStopList() # 获取通用英语停用词
textList = []
for row in df.itertuples(index=False, name='Pandas'):
tempList = []
"""获取pull request的标题"""
pr_title = getattr(row, 'pr_title')
pr_title_word_list = [x for x in FleshReadableUtils.word_list(pr_title) if x not in stopwords]
"""初步尝试提取词干效果反而下降了 。。。。"""
"""对单词做提取词干"""
pr_title_word_list = nltkFunction.stemList(pr_title_word_list)
tempList.extend(pr_title_word_list)
"""pull request的body"""
pr_body = getattr(row, 'pr_body')
pr_body_word_list = [x for x in FleshReadableUtils.word_list(pr_body) if x not in stopwords]
"""对单词做提取词干"""
pr_body_word_list = nltkFunction.stemList(pr_body_word_list)
tempList.extend(pr_body_word_list)
textList.append(tempList)
print(textList.__len__())
"""对分词列表建立字典 并提取特征数"""
dictionary = corpora.Dictionary(textList)
print('词典：', dictionary)
feature_cnt = len(dictionary.token2id)
print("词典特征数：", feature_cnt)
"""根据词典建立语料库"""
corpus = [dictionary.doc2bow(text) for text in textList]
# print('语料库:', corpus)
"""语料库训练TF-IDF模型"""
tfidf = models.TfidfModel(corpus)
"""再次遍历数据，形成向量，向量是稀疏矩阵的形式"""
wordVectors = []
for i in range(0, df.shape[0]):
wordVectors.append(dict(tfidf[dictionary.doc2bow(textList[i])]))
"""对已经有的本文特征向量和标签做训练集和测试集的拆分"""
trainData_index = df.loc[df['label'] == False].index
testData_index = df.loc[df['label'] == True].index
"""训练集"""
train_data = [wordVectors[x] for x in trainData_index]
"""测试集"""
test_data = [wordVectors[x] for x in testData_index]
"""填充为向量"""
train_v_data = DataProcessUtils.convertFeatureDictToDataFrame(train_data, featureNum=feature_cnt)
test_v_data = DataProcessUtils.convertFeatureDictToDataFrame(test_data, featureNum=feature_cnt)
train_data = df.loc[df['label'] == False]
train_data.reset_index(drop=True, inplace=True)
test_data = df.loc[df['label'] == True]
test_data.reset_index(drop=True, inplace=True)
train_data = train_data.join(train_v_data)
train_data.drop(columns=['label'], inplace=True)
test_data = test_data.join(test_v_data)
test_data.drop(columns=['label'], inplace=True)
"""8ii处理NAN"""
train_data.dropna(how='any', inplace=True)
train_data.reset_index(drop=True, inplace=True)
train_data.fillna(value='', inplace=True)
"""先对tag做拆分"""
trainDict = dict(list(train_data.groupby('pull_number')))
testDict = dict(list(test_data.groupby('pull_number')))
"""过滤掉评论时间在数据集时间范围内之后的数据"""
end_time = str(dates[2]) + "-" + str(dates[3]) + "-" + "01 00:00:00"
train_data = train_data[train_data['commented_at'] < end_time]
train_data.reset_index(drop=True, inplace=True)
test_data_y = {}
for pull_number in test_data.drop_duplicates(['pull_number'])['pull_number']:
reviewers = list(testDict[pull_number].drop_duplicates(['reviewer'])['reviewer'])
test_data_y[pull_number] = reviewers
train_data_y = {}
for pull_number in train_data.drop_duplicates(['pull_number'])['pull_number']:
reviewers = list(trainDict[pull_number].drop_duplicates(['reviewer'])['reviewer'])
train_data_y[pull_number] = reviewers
return train_data, train_data_y, test_data, test_data_y, convertDict
def preProcess(df, date, project, isSTD=False, isNOR=False):
"""参数说明
df：读取的dataframe对象
testDate:作为测试的年月 (year,month)
isSTD:对数据是否标准化
isNOR:对数据是否归一化
"""
print("start df shape:", df.shape)
"""过滤NA的数据"""
df.dropna(axis=0, how='any', inplace=True)
print("after fliter na:", df.shape)
"""对df添加一列标识训练集和测试集"""
df['label'] = df['pr_created_at'].apply(
lambda x: (time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_year == date[2] and
time.strptime(x, "%Y-%m-%d %H:%M:%S").tm_mon == date[3]))
df.reset_index(drop=True, inplace=True)
"""对人名字做数字处理"""
"""频率不过的评审者在编号之前就已经过滤了，不用考虑分类不连续的情况"""
"""这里reviewer_user_login 放在 第一个否则会影响candicateNum这个变量在后面的引用"""
convertDict = DataProcessUtils.changeStringToNumber(df, ['review_user_login'])
print(df.shape)
candicateNum = max(df.loc[df['label'] == 0]['review_user_login'])
print("candicate Num:", candicateNum)
"""先对输入数据做精简 只留下感兴趣的数据"""
df = df[['pr_number', 'pr_title', 'pr_body', 'review_user_login', 'label']].copy(deep=True)
print("before filter:", df.shape)
df.drop_duplicates(inplace=True)
print("after filter:", df.shape)
"""先提前统计正确答案"""
tagDict = dict(list(df.groupby('pr_number')))
train_data = df.loc[df['label'] == 0].copy(deep=True)
test_data = df.loc[df['label'] == 1].copy(deep=True)
"""问题转化为多标签问题
train_data_y [{pull_number:[r1, r2, ...]}, ... ,{}]
"""
train_data_y = {}
pull_number_list = train_data.drop_duplicates(['pr_number']).copy(deep=True)['pr_number']
for pull_number in pull_number_list:
reviewers = list(tagDict[pull_number].drop_duplicates(['review_user_login'])['review_user_login'])
train_data_y[pull_number] = reviewers
train_data.drop(columns=['review_user_login'], inplace=True)
train_data.drop_duplicates(inplace=True)
train_data.drop_duplicates(subset=['pr_number'], inplace=True)
"""训练集 结果做出多标签分类通用的模式"""
train_data_y = DataProcessUtils.convertLabelListToDataFrame(train_data_y, pull_number_list, candicateNum)
test_data_y = {}
pull_number_list = test_data.drop_duplicates(['pr_number']).copy(deep=True)['pr_number']
for pull_number in test_data.drop_duplicates(['pr_number'])['pr_number']:
reviewers = list(tagDict[pull_number].drop_duplicates(['review_user_login'])['review_user_login'])
test_data_y[pull_number] = reviewers
test_data.drop(columns=['review_user_login'], inplace=True)
test_data.drop_duplicates(inplace=True)
"""pr_number 经过去重"""
test_data.drop_duplicates(subset=['pr_number'], inplace=True)
# test_data_y = DataProcessUtils.convertLabelListToDataFrame(test_data_y, pull_number_list, candicateNum)
test_data_y = DataProcessUtils.convertLabelListToListArray(test_data_y, pull_number_list)
"""获得pr list"""
prList = list(test_data['pr_number'])
"""先尝试所有信息团在一起"""
df = df[['pr_number', 'pr_title', 'pr_body', 'label']].copy(deep=True)
df.drop_duplicates(inplace=True)
df.reset_index(drop=True, inplace=True)
"""用于收集所有文本向量分词"""
stopwords = SplitWordHelper().getEnglishStopList() # 获取通用英语停用词
textList = []
for row in df.itertuples(index=False, name='Pandas'):
tempList = []
"""获取pull request的标题"""
pr_title = getattr(row, 'pr_title')
pr_title_word_list = [x for x in FleshReadableUtils.word_list(pr_title) if x not in stopwords]
"""初步尝试提取词干效果反而下降了 。。。。"""
"""对单词做提取词干"""
pr_title_word_list = nltkFunction.stemList(pr_title_word_list)
tempList.extend(pr_title_word_list)
"""pull request的body"""
pr_body = getattr(row, 'pr_body')
pr_body_word_list = [x for x in FleshReadableUtils.word_list(pr_body) if x not in stopwords]
"""对单词做提取词干"""
pr_body_word_list = nltkFunction.stemList(pr_body_word_list)
tempList.extend(pr_body_word_list)
textList.append(tempList)
print(textList.__len__())
"""对分词列表建立字典 并提取特征数"""
dictionary = corpora.Dictionary(textList)
print('词典：', dictionary)
feature_cnt = len(dictionary.token2id)
print("词典特征数：", feature_cnt)
"""根据词典建立语料库"""
corpus = [dictionary.doc2bow(text) for text in textList]
# print('语料库:', corpus)
"""语料库训练TF-IDF模型"""
tfidf = models.TfidfModel(corpus)
"""再次遍历数据，形成向量，向量是稀疏矩阵的形式"""
wordVectors = []
for i in range(0, df.shape[0]):
wordVectors.append(dict(tfidf[dictionary.doc2bow(textList[i])]))
"""对已经有的本文特征向量和标签做训练集和测试集的拆分"""
trainData_index = df.loc[df['label'] == False].index
testData_index = df.loc[df['label'] == True].index
"""训练集"""
train_data = [wordVectors[x] for x in trainData_index]
"""测试集"""
test_data = [wordVectors[x] for x in testData_index]
"""填充为向量"""
train_data = DataProcessUtils.convertFeatureDictToDataFrame(train_data, featureNum=feature_cnt)
test_data = DataProcessUtils.convertFeatureDictToDataFrame(test_data, featureNum=feature_cnt)
"""参数规范化"""
if isSTD:
stdsc = StandardScaler()
train_data_std = stdsc.fit_transform(train_data)
test_data_std = stdsc.transform(test_data)
return train_data_std, train_data_y, test_data_std, test_data_y, convertDict, prList
elif isNOR:
maxminsc = MinMaxScaler()
train_data_std = maxminsc.fit_transform(train_data)
test_data_std = maxminsc.transform(test_data)
return train_data_std, train_data_y, test_data_std, test_data_y, convertDict, prList
else:
return train_data, train_data_y, test_data, test_data_y, convertDict, prList
def appendTextualFeatureVector(inputDf, projectName, date, pull_number_name):
"""
用tf-idf模型计算pr的所有title,pr的文本
pr的信息直接从PRDataFile 那里获取
@description: 给df, 在之前的dataframe的基础上面追加 pr路径形成的tf-idf特征向量
@notice: datafrme 必须有pull_number_id，可以重复
@param origin_df: 预先读取好的dataframe
@param projectName: 指定项目名
@param date: 开始年，开始月，结束年，结束月的四元组
@return: df: 添加路径权重后的dataframe，可直接用于机器学习算法
"""
"""对输入df做label存在检测"""
if 'label' not in inputDf.columns:
raise Exception("label not in input dataframe!")
print("input shape:", inputDf.shape)
print(date)
df = inputDf[[pull_number_name]].copy(deep=True)
df.drop_duplicates(inplace=True)
df.reset_index(drop=True, inplace=True)
df.columns = ['pr_number']
"""读取pullrequestData 文件"""
pull_request_path = projectConfig.getPullRequestPath()
pullRequestData = pandasHelper.readTSVFile(
os.path.join(pull_request_path, f'ALL_{projectName}_data_pullrequest.tsv'),
pandasHelper.INT_READ_FILE_WITH_HEAD, low_memory=False
)
"""pull_number和pr review commit relation做拼接"""
df = pandas.merge(df, pullRequestData, left_on='pr_number', right_on='number')
df = df[['pr_number', 'title', 'body']].copy(deep=True)
df.columns = ['pr_number', 'pr_title', 'pr_body']
df.drop_duplicates(inplace=True)
df.reset_index(drop=True, inplace=True)
df.fillna(value='', inplace=True)
"""用于收集所有文本向量分词"""
stopwords = SplitWordHelper().getEnglishStopList() # 获取通用英语停用词
textList = []
for row in df.itertuples(index=False, name='Pandas'):
tempList = []
"""获取pull request的标题"""
pr_title = row[list(df.columns).index('pr_title')]
pr_title_word_list = [x for x in FleshReadableUtils.word_list(pr_title) if x not in stopwords]
"""初步尝试提取词干效果反而下降了 。。。。"""
"""对单词做提取词干"""
pr_title_word_list = nltkFunction.stemList(pr_title_word_list)
tempList.extend(pr_title_word_list)
"""pull request的body"""
pr_body = row[list(df.columns).index('pr_body')]
pr_body_word_list = [x for x in FleshReadableUtils.word_list(pr_body) if x not in stopwords]
"""对单词做提取词干"""
pr_body_word_list = nltkFunction.stemList(pr_body_word_list)
tempList.extend(pr_body_word_list)
textList.append(tempList)
print(textList.__len__())
"""对分词列表建立字典 并提取特征数"""
dictionary = corpora.Dictionary(textList)
print('词典：', dictionary)
feature_cnt = len(dictionary.token2id)
print("词典特征数：", feature_cnt)
"""根据词典建立语料库"""
corpus = [dictionary.doc2bow(text) for text in textList]
# print('语料库:', corpus)
"""语料库训练TF-IDF模型"""
tfidf = models.TfidfModel(corpus)
"""再次遍历数据，形成向量，向量是稀疏矩阵的形式"""
wordVectors = []
for i in range(0, df.shape[0]):
wordVectors.append(dict(tfidf[dictionary.doc2bow(textList[i])]))
"""填充为向量"""
wordVectors = DataProcessUtils.convertFeatureDictToDataFrame(wordVectors, featureNum=feature_cnt)
"""PCA 做缩减之前需要把pr_path_weight_df 做分割 训练集和测试集分别处理"""
tempData = wordVectors.copy(deep=True)
tempData['pr_number'] = df['pr_number']
labelData = inputDf[['pr_number', 'label']].drop_duplicates().copy(deep=True)
tempData = pandas.merge(tempData, labelData, on='pr_number')
tempData_train = tempData.loc[tempData['label'] == 0].copy(deep=True)
tempData_test = tempData.loc[tempData['label'] == 1].copy(deep=True)
tempData_train.drop(columns=['pr_number', 'label'], inplace=True)
tempData_test.drop(columns=['pr_number', 'label'], inplace=True)
"""PAC 做缩减"""
pca = PCA(n_components=0.95)
tempData_train = pca.fit_transform(tempData_train)
print("after pca :", tempData_train.shape)
print(pca.explained_variance_ratio_)
tempData_train = pandas.DataFrame(tempData_train)
tempData_test = pca.transform(tempData_test)
print("after pca :", tempData_train.shape)
tempData_test = pandas.DataFrame(tempData_test)
tempData = pandas.concat([tempData_train, tempData_test], axis=0)
tempData.reset_index(drop=True, inplace=True)
tempData['pr_number_t'] = df['pr_number'].copy(deep=True)
"""和原来特征做拼接"""
inputDf = pandas.merge(inputDf, tempData, left_on=pull_number_name, right_on='pr_number_t')
inputDf.drop(columns=['pr_number_t'], inplace=True)
return inputDf