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feat: add issue comment TFIDF similarity metrics and issue comment Ja…
…ccard similarity Signed-off-by: bifenglin <515186469@qq.com>
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import string | ||
from tqdm import tqdm | ||
import pandas as pd | ||
import db.clickhouse as clickhouse | ||
import numpy as np | ||
from nltk.corpus import stopwords | ||
from sklearn.feature_extraction.text import CountVectorizer | ||
from scipy.linalg import norm | ||
from sklearn.feature_extraction.text import TfidfVectorizer | ||
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def getSelectedActors(config): | ||
""" | ||
TODO: get Selected Acotrs | ||
""" | ||
sql = 'SELECT DISTINCT(actor_id) FROM opensource.gh_events') | ||
ids = clickhouse.query(sql) | ||
return ids | ||
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def get_jaccard_similarity(clean_list): | ||
total = 0.0 | ||
num = 0.0 | ||
for i in clean_list: | ||
for j in clean_list: | ||
if i != j: | ||
num += 1 | ||
total += jaccard_similarity(i, j) | ||
if num == 0: | ||
return 0 | ||
return total/num | ||
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def get_tfidf_similarity(clean_list): | ||
total = 0.0 | ||
num = 0.0 | ||
for i in clean_list: | ||
for j in clean_list: | ||
if i != j: | ||
num += 1 | ||
total += tfidf_similarity(i, j) | ||
if num == 0: | ||
return 0 | ||
return total/num | ||
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def tfidf_similarity(s1, s2): | ||
def add_space(s): | ||
return ' '.join(s) | ||
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s1, s2 = add_space(s1), add_space(s2) | ||
cv = TfidfVectorizer(tokenizer=lambda s: s.split()) | ||
corpus = [s1, s2] | ||
vectors = cv.fit_transform(corpus).toarray() | ||
return np.dot(vectors[0], vectors[1]) / (norm(vectors[0]) * norm(vectors[1])) | ||
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def jaccard_similarity(s1, s2): | ||
def add_space(s): | ||
return ' '.join(s) | ||
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s1, s2 = add_space(s1), add_space(s2) | ||
cv = CountVectorizer(tokenizer=lambda s: s.split()) | ||
corpus = [s1, s2] | ||
vectors = cv.fit_transform(corpus).toarray() | ||
numerator = np.sum(np.min(vectors, axis=0)) | ||
denominator = np.sum(np.max(vectors, axis=0)) | ||
return 1.0 * numerator / denominator | ||
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# turn a doc into clean tokens | ||
def clean_doc(doc): | ||
# split into tokens by white space | ||
tokens = doc.split() | ||
# remove punctuation from each token | ||
table = str.maketrans('', '', string.punctuation) | ||
tokens = [w.translate(table) for w in tokens] | ||
# remove remaining tokens that are not alphabetic | ||
tokens = [word for word in tokens if word.isalpha()] | ||
# filter out stop words | ||
stop_words = set(stopwords.words('english')) | ||
tokens = [w for w in tokens if not w in stop_words] | ||
# filter out short tokens | ||
tokens = [word for word in tokens if len(word) > 2] | ||
return tokens | ||
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def getRecentComments(config, actors, comments_amount = 100): | ||
""" | ||
Get recent comments per actor. default amount is 100. | ||
""" | ||
logs = pd.DataFrame() | ||
for i, actor in actors: | ||
sql = ''' | ||
SELECT * FROM opensource.gh_events a WHERE a.actor_id = {ACTOR_ID} and a.type = 'IssueCommentEvent' order by created_at desc limit {NUM} | ||
'''.format(ACTOR_ID = actor, NUM = comments_amount) | ||
logs = logs.append(clickhouse.query(sql)) | ||
return logs | ||
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def getIssueCommentJaccardSimilarity(config): | ||
actors = getSelectedActors(config) | ||
logs = getRecentComments(config, actors) | ||
result = [] | ||
grouped = logs.groupby('actor_id') | ||
for actor_id,group in grouped: | ||
string_list = [] | ||
for index in group['issue_comment_body']: | ||
if isinstance(index, str): | ||
string_list.append(index) | ||
clean_list = [] | ||
for index in string_list: | ||
clean_list.append(clean_doc(index)) | ||
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if len(clean_list) < 2: | ||
result.append({'actor_id': actor_id, 'jaccard_similarity': 0}) | ||
continue | ||
jaccard = get_jaccard_similarity(clean_list) | ||
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res_dic = {'actor_id':actor_id, 'jaccard_similarity': jaccard} | ||
result.append(res_dic) | ||
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return result | ||
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def getIssueCommentTFIDFSimilarity(config): | ||
actors = getSelectedActors(config) | ||
logs = getRecent100Comments(config, actors) | ||
result = [] | ||
grouped = logs.groupby('actor_id') | ||
for actor_id,group in grouped: | ||
string_list = [] | ||
for index in group['issue_comment_body']: | ||
if isinstance(index, str): | ||
string_list.append(index) | ||
clean_list = [] | ||
for index in string_list: | ||
clean_list.append(clean_doc(index)) | ||
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if len(clean_list) < 2: | ||
result.append({'actor_id': actor_id, 'tfidf_similarity': 0}) | ||
continue | ||
tfidf = get_tfidf_similarity(clean_list) | ||
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res_dic = {'actor_id':actor_id, 'tfidf_similarity': tfidf} | ||
result.append(res_dic) | ||
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return result |