feat: add issue comment TFIDF similarity metrics and issue comment Ja…

python/metrics/index.py

@@ -2,6 +2,7 @@
 import activity_openrank
 import attention
 import related_users 
+import issue_comment_similartiy
 
 def getRepoActivity(config):
     return activity_openrank.getRepoActivity(config)
@@ -15,6 +16,10 @@ def getRelatedUsers(config):
     return related_users.getRelatedUsers(config)
 def getAttention(config):
     return attention.getAttention(config)
+def getIssueCommentTFIDFSimilarity(config):
+    return issue_comment_similartiy.getIssueCommentTFIDFSimilarity(config)
+def getIssueCommentJaccardSimilarity(config):
+    return issue_comment_similartiy.getIssueCommentJaccardSimilarity(config)
 
 # chaoss metrics
 def chaossCodeChangeCommits(config): 
@@ -31,3 +36,4 @@ def chaossChangeRequestsDeclined(config):
     return chaoss.chaossChangeRequestsDeclined(config)
 def chaossIssueResolutionDuration(config):
     return chaoss.chaossIssueResolutionDuration(config)
+

python/metrics/issue_comment_similartiy.py

@@ -0,0 +1,141 @@
+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
+
+def getSelectedActors(config):
+    """
+    TODO: get Selected Acotrs
+    """
+    sql = 'SELECT DISTINCT(actor_id) FROM opensource.gh_events')
+    ids = clickhouse.query(sql)
+    return ids
+
+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
+
+
+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
+
+
+def tfidf_similarity(s1, s2):
+    def add_space(s):
+        return ' '.join(s)
+
+    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]))
+
+def jaccard_similarity(s1, s2):
+    def add_space(s):
+        return ' '.join(s)
+
+    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
+
+# 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
+
+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
+
+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))
+
+        if len(clean_list) < 2:
+            result.append({'actor_id': actor_id, 'jaccard_similarity': 0})
+            continue
+        jaccard = get_jaccard_similarity(clean_list)
+
+        res_dic = {'actor_id':actor_id, 'jaccard_similarity': jaccard}
+        result.append(res_dic)
+
+    return result
+
+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))
+
+        if len(clean_list) < 2:
+            result.append({'actor_id': actor_id, 'tfidf_similarity': 0})
+            continue
+        tfidf = get_tfidf_similarity(clean_list)
+
+        res_dic = {'actor_id':actor_id, 'tfidf_similarity': tfidf}
+        result.append(res_dic)
+
+    return result