repository cleanup

+import re, pickle
+from selenium import webdriver
+from singer import *
+
+WAIT_TIME = 5
+A, B = 700, 900
+
+with open('singer_name.pickle', 'rb') as f:
+    singer_name = pickle.load(f)
+
+def GetMelonData():
+    singers = []
+    driver = webdriver.Chrome('chromedriver.exe')
+    driver.implicitly_wait(WAIT_TIME)
+
+    for name in singer_name[A:B]:
+        singer = Singer()
+        singer.name = name
+        name = name.replace('#', '%23')
+        name = name.replace('&', '%26') 
+        url = 'https://www.melon.com/search/total/index.htm?q='+ name + '&section=&searchGnbYn=Y&kkoSpl=Y&kkoDpType=&linkOrText=T&ipath=srch_form'
+        driver.get(url)
+        driver.implicitly_wait(WAIT_TIME)
+        tmp = driver.find_elements_by_css_selector('#conts > div.section_atist > div > div.atist_dtl_info > dl > dd:nth-child(4)')[0].text
+        if len(tmp) > 3:
+            singer.sex, singer.group = tmp.split(',')
+        else:
+            singer.sex, singer.group = '.', '.'
+        singer.group.strip()
+        singer.fan = int(driver.find_elements_by_css_selector('#conts > div.section_atist > div > div.atist_dtl_info > div > span > span')[0].text.replace(',', ''))
+        singers.append(singer)
+
+    return singers
+
+with open('singer.pickle', 'rb') as f:
+    before = pickle.load(f)
+print(len(before))
+
+data = GetMelonData()
+
+
+with open('singer.pickle', 'wb') as f:
+    pickle.dump(before + data, f)
+
+print("Done")
\ No newline at end of file

@@ -3,12 +3,11 @@ from selenium import webdriver
 from song import *
 from song import *
 
 
 WAIT_TIME = 5
 WAIT_TIME = 5
-YEAR = '2021년'
-#MONTH = ['01월', '02월']
-#MONTH = ['03월', '04월']
-MONTH = ['05월', '06월']
+YEAR = '2020년'
+#MONTH = ['01월', '02월', '03월']
+#MONTH = ['04월', '05월', '06월']
 #MONTH = ['07월', '08월', '09월']
 #MONTH = ['07월', '08월', '09월']
-#MONTH = ['10월', '11월', '12월']
+MONTH = ['10월', '11월', '12월']
 
 
 def GetMelonData():
 def GetMelonData():
 
 

+import pickle
+from song import *
+
+data = []
+for filename in range(1112, 2122, 202):
+    with open('data/' + str(filename)+'.pickle', 'rb') as f:
+        tmp = pickle.load(f)
+    data.extend(tmp)
+
+singers = set()
+for d in data:
+    singers.add(d.singer)
+
+print("size : ", len(singers))
+print(list(singers))
+#with open('singer_name.pickle', 'wb') as f:
+#    pickle.dump(list(singers), f)
\ No newline at end of file

+import csv, pickle
+from singer import *
+
+
+data = []
+with open('./data/singer.pickle', 'rb') as f:
+    data = pickle.load(f)
+
+f = open('./data/singer.csv', 'w', newline='', encoding='UTF-8')
+wr = csv.writer(f)
+
+for singer in data:
+    wr.writerow(singer.getRow())
+
+f.close()
\ No newline at end of file

-import re, csv, pickle
+import re, csv, pickle, nltk
 from song import *
 from song import *
 from PyKomoran import *
 from PyKomoran import *
 from textrank import KeywordSummarizer
 from textrank import KeywordSummarizer
+#nltk.download('averaged_perceptron_tagger')
 
 
 def komoran_tokenize(sent):
 def komoran_tokenize(sent):
     words = sent.split()
     words = sent.split()
-    words = [w for w in words if ('/NNP' in w or '/NNG' in w or '/SL' in w)]
+    for i in range(len(words)):
+        if words[i].endswith('/SL') and len(words[i]) > 4:
+            words[i] = words[i][:-3]
+            words[i] = '/'.join(nltk.pos_tag(nltk.word_tokenize(words[i]))[0])
+            if words[i].endswith('/NN'):
+                words[i] += 'P'
+    words = [w for w in words if '/NNP' in w or '/NNG' in w  or '/FW' in w or '/JJ' in w]
+
     return words
     return words
 
 
 data = []
 data = []
 for filename in range(1112, 2122, 202):
 for filename in range(1112, 2122, 202):
-    with open(str(filename)+'.pickle', 'rb') as f:
+    with open('data/'+str(filename)+'.pickle', 'rb') as f:
         tmp = pickle.load(f)
         tmp = pickle.load(f)
     data.extend(tmp)
     data.extend(tmp)
 
 
-f = open('data.csv', 'w', newline='', encoding='UTF-8')
+f = open('dataaaa.csv', 'w', newline='', encoding='UTF-8')
 wr = csv.writer(f)
 wr = csv.writer(f)
 komoran = Komoran('STABLE')
 komoran = Komoran('STABLE')
 
 
@@ -36,7 +44,7 @@ for i in range(len(data)):
             window = -1,
             window = -1,
             verbose = False
             verbose = False
         )
         )
-        if len(sents) != 0:
+        if len(sents) > 5:
             keywords = keyword_extractor.summarize(sents, topk=5)
             keywords = keyword_extractor.summarize(sents, topk=5)
             data[i].keywords = list(map(lambda x : x[0][:x[0].find('/')], keywords))
             data[i].keywords = list(map(lambda x : x[0][:x[0].find('/')], keywords))
 
 

+import csv
+
+songs = []
+f = open('song.csv', 'r', encoding='utf-8')
+rdr = csv.reader(f)
+for line in rdr:
+    songs.append(line)
+
+
+f = open('data_edge.csv', 'w', newline='', encoding='UTF-8')
+wr = csv.writer(f)
+
+
+for i in range(len(songs)):
+    for j in range(i + 1, len(songs)):
+        if songs[i][4] == songs[j][4]:
+            wr.writerow([i, j])
+        elif songs[i][0] == songs[j][0] and songs[i][1] == songs[j][1] and int(songs[j][2]) - int(songs[i][2]) <= 5:
+            wr.writerow([i, j])
\ No newline at end of file

+import csv
+
+group = {'솔로' : 1, '그룹' : 2}
+sex = {'남성' : 1, '여성' : 2, '혼성' : 3}
+genre = dict()
+genre_idx = 1
+
+songs = []
+f = open('song.csv', 'r', encoding='utf-8')
+rdr = csv.reader(f)
+for line in rdr:
+    songs.append(line)
+
+singers = []
+f = open('singer.csv', 'r', encoding='utf-8')
+rdr = csv.reader(f)
+for line in rdr:
+    singers.append(line)
+
+singers_dict = {}
+for data in singers:
+    singers_dict[data[0]] = [sex[data[1]], group[data[2]], int(data[3])]
+
+
+f_x = open('data_x.csv', 'w', newline='', encoding='UTF-8')
+wr_x = csv.writer(f_x)
+
+f_y = open('data_y.csv', 'w', newline='', encoding='UTF-8')
+wr_y = csv.writer(f_y)
+
+for data in songs:
+    tmp = [data[0], data[1], data[8]]
+    date = data[7].split('.')
+    tmp.append((int(data[0]) - int(date[0])) * 12 + int(data[1]) - int(date[1]))
+    g = data[6].split(',')[0]
+    if genre.get(g, 0) != 0:
+        tmp.append(genre[g])
+    else:
+        genre[g] = genre_idx
+        tmp.append(genre_idx)
+        genre_idx += 1
+    tmp.extend(singers_dict[data[4]])
+
+    wr_x.writerow(tmp)
+    wr_y.writerow([data[2]])
\ No newline at end of file

+
+class Singer:
+    def __init__(self):
+        self.name = ''
+        self.sex = ''
+        self.group = ''
+        self.fan = 0
+
+    def getRow(self):
+        return [self.name, self.sex, self.group, self.fan]
\ No newline at end of file

@@ -25,7 +25,7 @@ class Song:
         self.title.strip()
         self.title.strip()
         self.album = re.sub(r"[?'/\"*<>:]", "", self.album)
         self.album = re.sub(r"[?'/\"*<>:]", "", self.album)
         try:
         try:
-            return [self.year, self.month, self.rank, self.title, self.singer, self.album, self.genre, self.date, self.likes, self.keywords]
+            return [self.year, self.month, self.rank, self.title, self.singer, self.album, self.genre, self.date, self.likes, self.keywords, self.lyrics]
         except:
         except:
             return [self.year, self.month, self.rank, self.title, self.singer, self.album, self.genre, self.date, self.likes, []]
             return [self.year, self.month, self.rank, self.title, self.singer, self.album, self.genre, self.date, self.likes, []]
 
 

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+100

+갓츄 (GOT U),남성,그룹,1937
+2PM,남성,그룹,22241
+엠투엠 (M To M),남성,솔로,4007
+효민,여성,솔로,4177
+코요태,혼성,그룹,10065
+최준영,남성,솔로,92
+PRISTIN (프리스틴),여성,그룹,12458
+써니힐,여성,그룹,14961
+김보아,여성,솔로,909
+양요섭,남성,솔로,51578
+간종욱,남성,솔로,405
+티아라 N4,여성,그룹,1805
+조정석,남성,솔로,14406
+신화,남성,그룹,42422
+긱스 (Geeks),남성,그룹,29865
+스윗 콧소로우 (정준하 & 스윗소로우),남성,그룹,271
+이영지,여성,솔로,17954
+JYJ,남성,그룹,29818
+원더걸스,여성,그룹,33950
+진민호,남성,솔로,2352
+H-유진,남성,솔로,391
+정엽,남성,솔로,8815
+장미여관,남성,그룹,6182
+GRAY (그레이),남성,솔로,77885
+Jvcki Wai,여성,솔로,40492
+언터쳐블,남성,그룹,4142
+예성 (YESUNG),남성,솔로,18932
+반하나,여성,솔로,10418
+하진 (HAJIN),여성,솔로,2909
+하하,남성,솔로,5037
+AKMU (악동뮤지션),혼성,그룹,221188
+비룡,남성,솔로,2494
+정키,남성,솔로,38262
+제국의아이들 (ZE:A),남성,그룹,4972
+클로버,혼성,그룹,2210
+하이라이트 (Highlight),남성,그룹,82189
+서영은,여성,솔로,8630
+"댄싱게놈 (유재석, JYP)",남성,그룹,2052
+키썸 (Kisum),여성,솔로,32158
+종현 (JONGHYUN),남성,솔로,96568
+이진아,여성,솔로,31367
+"상주나 (정준하, 윤상)",남성,그룹,1033
+넬 (NELL),남성,그룹,64050
+강다니엘,남성,솔로,69431
+존박,남성,솔로,27126
+더 씨야,여성,그룹,1562
+첸 (CHEN),남성,솔로,144103
+정세운,남성,솔로,42294
+경서예지,여성,그룹,3008
+JK 김동욱,남성,솔로,3668
+박경,남성,솔로,34343
+박우진 (AB6IX),남성,솔로,37515
+알리 (ALi),여성,솔로,20990
+H.O.T.,남성,그룹,18856
+크레파스,여성,그룹,2731
+디유닛,여성,그룹,571
+히키 (Hickee),여성,솔로,1817
+소진 (Sojin),여성,솔로,7028
+샤넌,여성,솔로,7751
+박정아,여성,솔로,475
+제시 (Jessi),여성,솔로,28985
+왁스,여성,솔로,6649
+길,남성,솔로,8345
+Of 비스트,남성,그룹,5142
+이하이,여성,솔로,69402
+리쌍,남성,그룹,48471
+김성규,남성,솔로,57882
+MOBB (MINO & BOBBY),남성,그룹,13549
+플로우식 (Flowsik),남성,솔로,6030
+빅스타,남성,그룹,1184
+블루 (BLOO),남성,솔로,31790
+우먼파워,여성,그룹,69
+하하 X MINO,남성,그룹,2599
+현빈,남성,솔로,2177
+가희,여성,솔로,570
+오윤혜,여성,솔로,1281
+하은요셉,남성,그룹,1335
+DK,남성,솔로,5195
+딘딘,남성,솔로,10379
+솔지하니 (EXID),여성,그룹,2450
+김호중,남성,솔로,40513
+The One (더원),남성,솔로,11446
+송하예,여성,솔로,14066
+에일리(AILEE),여성,솔로,116827
+이상순,남성,솔로,2520
+Osshun Gum,남성,솔로,14904
+옥주현,여성,솔로,4453
+박정현,여성,솔로,33873
+파리돼지앵 (정형돈 & 정재형),남성,그룹,259
+승리,남성,솔로,32198
+이석훈,남성,솔로,19669
+이예준,여성,솔로,13509
+강승윤,남성,솔로,41790
+조성모,남성,솔로,9195
+현아,여성,솔로,39887
+레드벨벳-아이린&슬기,여성,그룹,14038
+PRODUCE 101,남성,그룹,30211
+이적,남성,솔로,48734
+정준하,남성,솔로,1662
+X1 (엑스원),남성,그룹,39988
+지오 (G.O),남성,솔로,1434
+틴탑,남성,그룹,19077
+노홍철,남성,솔로,1164
+유재석,남성,솔로,11848
+뉴이스트 W,남성,그룹,69985
+영탁,남성,솔로,28564
+박혜경,여성,솔로,5166
+미란이 (Mirani),여성,솔로,13728
+SHINee (샤이니),남성,그룹,127149
+비투비,남성,그룹,198816
+T.O.P,남성,솔로,33303
+테이크 (TAKE),남성,그룹,4508
+버나드 박,남성,솔로,8567
+국.슈 (국프의 핫이슈),여성,그룹,843
+펀치넬로 (punchnello),남성,솔로,19036
+IZ*ONE (아이즈원),여성,그룹,85020
+ASH ISLAND,남성,솔로,67895
+제이큐티,여성,그룹,175
+럼블피쉬,여성,솔로,7700
+MJ (써니사이드),남성,솔로,7706
+Woodie Gochild,남성,솔로,8117
+간미연,여성,솔로,996
+방탄소년단,남성,그룹,766605
+지연,여성,솔로,7292
+백현 (BAEKHYUN),남성,솔로,238751
+한동근,남성,솔로,28897
+전효성,여성,솔로,7237
+레디 (Reddy),남성,솔로,17346
+엄정화,여성,솔로,7790
+허영생,남성,솔로,3020
+JJ Project,남성,그룹,27164
+예지,여성,솔로,10990
+보아 (BoA),여성,솔로,46019
+휘인 (Whee In),여성,솔로,47412
+박보람,여성,솔로,21023
+FIX,남성,그룹,154
+BLACKPINK,여성,그룹,204360
+뉴이스트,남성,그룹,95855
+JIN (러블리즈),여성,솔로,6599
+황치열,남성,솔로,56699
+SE7EN,남성,솔로,2977
+f(x),여성,그룹,68903
+Ja Mezz,남성,솔로,8788
+씨스타19,여성,그룹,4426
+Justin Bieber,남성,솔로,65540
+김영근,남성,솔로,5621
+차지연,여성,솔로,3394
+"15& (박지민, 백예린)",여성,그룹,15890
+장현승,남성,솔로,12067
+마이네임,남성,그룹,2146
+강균성 (노을),남성,솔로,2287
+핫펠트 (HA:TFELT),여성,솔로,15456
+EXO-CBX (첸백시),남성,그룹,153892
+숀 (SHAUN),남성,솔로,20502
+인피니트,남성,그룹,108268
+전미도,여성,솔로,8483
+염따,남성,솔로,27203
+장재인,여성,솔로,14106
+Lasse Lindh,남성,솔로,15234
+청하,여성,솔로,97421
+pH-1,남성,솔로,61331
+우디 (Woody),남성,솔로,4870
+한해,남성,솔로,16936
+유승우,남성,솔로,25283
+적재,남성,솔로,36548
+로꼬,남성,솔로,121027
+EXID,여성,그룹,58038
+머쉬베놈 (MUSHVENOM),남성,솔로,23230
+국카스텐,남성,그룹,29923
+쿨 (COOL),혼성,그룹,16138
+소찬휘,여성,솔로,3939
+VIXX (빅스),남성,그룹,70799
+씨스타,여성,그룹,70571
+전인혁 (야다),남성,솔로,471
+서인국,남성,솔로,27691
+신지,여성,솔로,2878
+노블레스,남성,솔로,1990
+김형준,남성,솔로,1620
+팬텀,남성,그룹,10422
+소녀시대 (GIRLS' GENERATION),여성,그룹,103844
+미쓰에이,여성,그룹,21091
+부가킹즈,남성,그룹,824
+원슈타인,남성,솔로,22456
+이효리,여성,솔로,16278
+올티 (Olltii),남성,솔로,16290
+피에스타,여성,그룹,7752
+박명수,남성,솔로,4551
+"황태지 (황광희, 태양, 지드래곤)",남성,그룹,11295
+형돈이와 대준이,남성,그룹,6952
+솔지,여성,솔로,12557
+동방신기 (TVXQ!),남성,그룹,38061
+김하온 (HAON),남성,솔로,53271
+WINNER,남성,그룹,211739
+우원재,남성,솔로,58495
+펀치 (Punch),여성,솔로,48114
+김현정,여성,솔로,4290
+HI SUHYUN,여성,그룹,9787
+폴킴,남성,솔로,149583
+바이브,남성,그룹,38319
+허니 패밀리,남성,그룹,787
+옥상달빛,여성,그룹,36069
+박칼린,여성,솔로,125
+트루디 (Truedy),여성,솔로,3132
+수지 (SUZY),여성,솔로,46762
+임슬옹,남성,솔로,3133
+브라이언,남성,솔로,3179
+정준하 X ZICO,남성,그룹,1413
+한승희,남성,솔로,1702
+주헌 (몬스타엑스),남성,솔로,14015
+Amber Liu (엠버),여성,솔로,9096
+aespa,여성,그룹,18276
+김민규 (Young Kay),남성,솔로,5476
+클래지콰이,혼성,그룹,14523
+빈첸,남성,솔로,57734
+백아연,여성,솔로,38514
+매드클라운,남성,솔로,63307
+김용준,남성,솔로,2740
+이루,남성,솔로,1644
+케이시 (Kassy),여성,솔로,41083
+프라이머리,남성,솔로,67552
+지소울 (GSoul),남성,솔로,23138
+김동률,남성,솔로,86933
+창모 (CHANGMO),남성,솔로,121635
+칸토,남성,솔로,6001
+처진 달팽이 (유재석 & 이적),남성,그룹,1883
+임재현,남성,솔로,10975
+정기고,남성,솔로,21090
+G-DRAGON,남성,솔로,140239
+양세형 X BewhY,남성,그룹,1319
+김세정,여성,솔로,47880
+이수현,여성,솔로,22462
+이문세,남성,솔로,29383
+김지수,남성,솔로,4827
+박정민,남성,솔로,1455
+임재범,남성,솔로,15245
+백예린 (Yerin Baek),여성,솔로,146751
+영준 (브라운 아이드 소울),남성,솔로,4736
+블락비 바스타즈,남성,그룹,30180
+EXO-K,남성,그룹,130969
+저스디스 (JUSTHIS),남성,솔로,42069
+멜로망스,남성,그룹,70465
+Pro C (프로씨),남성,그룹,466
+AOA,여성,그룹,55173
+효린,여성,솔로,22384
+그리 (GREE),남성,솔로,5026
+B.I,남성,솔로,31776
+나인뮤지스,여성,그룹,14844
+"Apink BnN (보미,남주)",여성,그룹,13401
+민경훈,남성,솔로,21246
+킬라그램 (KILLAGRAMZ),남성,솔로,3502
+Pinkrush,여성,그룹,1148
+술제이 (Sool J),남성,솔로,2908
+정진운,남성,솔로,1600
+김장훈,남성,솔로,1962
+크리스티나,여성,솔로,84
+Lil tachi,남성,솔로,9287
+김진표,남성,솔로,4642
+려욱 (RYEOWOOK),남성,솔로,16496
+SUPER JUNIOR (슈퍼주니어),남성,그룹,66047
+보이비,남성,솔로,5372
+김소리,여성,솔로,215
+케이윌,남성,솔로,92944
+문문 (MoonMoon),남성,솔로,24844
+길구봉구,남성,그룹,21150
+미도와 파라솔,혼성,그룹,9299
+강지영,여성,솔로,1383
+김경희,여성,솔로,3939
+MC몽,남성,솔로,60480
+가호 (Gaho),남성,솔로,9195
+선미,여성,솔로,47458
+데프콘,남성,솔로,7136
+권진아,여성,솔로,54621
+BMK,여성,솔로,3232
+스컬,남성,솔로,3984
+슬레이트,남성,그룹,7516
+Zion.T,남성,솔로,138456
+황광희 X 개코,남성,그룹,1621
+이창민,남성,솔로,2102
+철싸 (노홍철 & 싸이),남성,그룹,251
+슈퍼스타K 2,혼성,그룹,23
+곽진언,남성,솔로,24532
+웬디 (WENDY),여성,솔로,40463
+M&N (미료&나르샤),여성,그룹,720
+나몰라패밀리JW,남성,솔로,263
+신지수,여성,솔로,2631
+윤현상,남성,솔로,21632
+유영진,남성,솔로,1961
+엠씨더맥스 (M.C the MAX),남성,그룹,116274
+박화요비,여성,솔로,15268
+박진영,남성,솔로,16331
+소울다이브,남성,그룹,1881
+Justin Timberlake,남성,솔로,11510
+김광석,남성,솔로,40990
+스피드,남성,그룹,1124
+혁오 (HYUKOH),남성,그룹,120290
+Make Some Noise,여성,그룹,667
+하리,여성,솔로,1150
+아이비,여성,솔로,2945
+이해리 (다비치),여성,솔로,17327
+레이디 제인,여성,솔로,922
+은정 (티아라),여성,솔로,3015
+에이트,혼성,그룹,5005
+환희,남성,솔로,12094
+소향,여성,솔로,17020
+윤종신,남성,솔로,81080
+"Homme (창민, 이현)",남성,그룹,5753
+Illson (더블케이),남성,솔로,7547
+김민석 (멜로망스),남성,솔로,9432
+딕펑스 (DICKPUNKS),남성,그룹,18159
+이천원,남성,그룹,1152
+홍진영,여성,솔로,25068
+V.One,남성,솔로,1563
+지나,여성,솔로,4644
+쇼콜라,여성,그룹,328
+거미,여성,솔로,62223
+환불원정대,여성,그룹,16313
+방용국,남성,솔로,7405
+세븐틴,남성,그룹,200553
+2NE1,여성,그룹,55107
+애프터스쿨,여성,그룹,4923
+김경록 (V.O.S),남성,솔로,1132
+Kei (러블리즈),여성,솔로,14323
+JAMIE (제이미),여성,솔로,18039
+송혜교,여성,솔로,402
+베이지,여성,솔로,2880
+릴보이 (lIlBOI),남성,솔로,24257
+김그림,여성,솔로,3282
+유주 (여자친구),여성,솔로,22619
+먼데이 키즈 (Monday Kiz),남성,솔로,38659
+서은광 (비투비),남성,솔로,51271
+영지,여성,솔로,2603
+마리오,남성,솔로,562
+Junoflo (주노플로),남성,솔로,4896
+규현 (KYUHYUN),남성,솔로,64084
+조관우,남성,솔로,1712
+김보경 (NEON),여성,솔로,12423
+돈 스파이크,남성,솔로,356
+개코,남성,솔로,25958
+DAY6 (데이식스),남성,그룹,107630
+버스커 버스커,남성,그룹,68865
+맥케이 (McKay),남성,솔로,4705
+한경일,남성,솔로,2242
+울랄라세션,남성,그룹,10044
+남우현,남성,솔로,38987
+라디 (Ra. D),남성,솔로,21101
+Wanna One (워너원) - 트리플포지션,남성,그룹,24145
+FTISLAND (FT아일랜드),남성,그룹,28195
+레게 강 같은 평화,남성,그룹,5674
+송가인,여성,솔로,21967
+이기찬,남성,솔로,3998
+장범준,남성,솔로,101561
+F-ve Dolls,여성,그룹,846
+스윙스,남성,솔로,66704
+붐,남성,솔로,468
+유아 (오마이걸),여성,솔로,15646
+견우 (Kyunwoo),남성,솔로,1183
+이수 (엠씨더맥스),남성,솔로,25798
+이영현,여성,솔로,5430
+기리보이,남성,솔로,163112
+솔라 (마마무),여성,솔로,46603
+톱밥,남성,솔로,589
+마크툽 (MAKTUB),남성,솔로,28911
+J.Lee,남성,솔로,7838
+이승환,남성,솔로,27070
+정용화 (CNBLUE),남성,솔로,15171
+Jessica (제시카),여성,솔로,16782
+홍대광,남성,솔로,18614
+It's,남성,그룹,1887
+그루비룸 (GroovyRoom),남성,그룹,43203
+이루마,남성,솔로,22765
+장희영,여성,솔로,2045
+HUS (허밍어반스테레오),남성,솔로,10432
+투개월,혼성,그룹,3591
+한선화,여성,솔로,1113
+라니아,여성,그룹,1303
+신현희와김루트,혼성,그룹,12186
+KUSH (쿠시),남성,솔로,613
+블락비 (Block B),남성,그룹,120747
+브라운 아이드 소울,남성,그룹,49706
+다이나믹 듀오,남성,그룹,84759
+일렉트로보이즈,남성,그룹,1273
+어반자카파,혼성,그룹,102612
+볼빨간사춘기,여성,솔로,259093
+박명수 X 딘딘,남성,그룹,849
+태용 (TAEYONG),남성,솔로,18361
+The Apple,혼성,그룹,12
+산들,남성,솔로,27224
+란 (RAN),여성,솔로,2471
+쿠기 (Coogie),남성,솔로,24515
+수호 (SUHO),남성,솔로,2575
+슬기 (SEULGI),여성,솔로,35369
+스피카,여성,그룹,5867
+B1A4,남성,그룹,51462
+윤하 (YOUNHA),여성,솔로,95202
+나윤권,남성,솔로,17090
+유재환,남성,솔로,6184
+지누션,남성,그룹,5540
+바비 킴,남성,솔로,6148
+샘김 (Sam Kim),남성,솔로,41478
+(여자)아이들,여성,그룹,85181
+유성은,여성,솔로,21000
+찬열 (CHANYEOL),남성,솔로,141811
+신효범,여성,솔로,971
+전소미,여성,솔로,23874
+채동하,남성,솔로,2322
+레이디스 코드,여성,그룹,28777
+아이유,여성,솔로,736641
+GD&TOP,남성,그룹,26213
+제이스,여성,솔로,1466
+알렉스,남성,솔로,1896
+로이킴,남성,솔로,71621
+빅마마,여성,그룹,4846
+크레용팝,여성,그룹,6664
+이비아,여성,솔로,2351
+요조,여성,솔로,12536
+김은영,여성,솔로,19563
+비투비-블루,남성,그룹,41273
+사이먼 도미닉,남성,솔로,72330
+마이티 마우스,남성,그룹,2364
+화사 (Hwa Sa),여성,솔로,60870
+베이비소울 (러블리즈),여성,솔로,7015
+Supreme Team,남성,그룹,21775
+이특 (LEETEUK),남성,솔로,5955
+이정아,여성,솔로,1129
+임창정,남성,솔로,95165
+Eye To Eye,여성,그룹,692
+MAN1AC,남성,솔로,690
+모모랜드 (MOMOLAND),여성,그룹,15692
+김희철,남성,솔로,12438
+Dok2,남성,솔로,71679
+전상근,남성,솔로,11001
+김건모,남성,솔로,17341
+포스트맨(Postmen),남성,그룹,13404
+JOO,여성,솔로,5181
+이재훈 (쿨),남성,솔로,3632
+LC9,남성,그룹,108
+유재석 X Dok2,남성,그룹,1039
+디오 (D.O.),남성,솔로,150413
+에이치코드 (H:CODE),남성,솔로,3896
+정형돈,남성,솔로,2942
+The Quiett,남성,솔로,54090
+김동희,여성,솔로,2714
+미지,여성,그룹,428
+국민의 아들,남성,그룹,16277
+포이트리,남성,그룹,257
+제니 (JENNIE),여성,솔로,35237
+정효빈,여성,솔로,5890
+오렌지 캬라멜,여성,그룹,13206
+애프터스쿨 BLUE,여성,그룹,505
+손승연,여성,솔로,20702
+임한별,남성,솔로,14105
+Tablo,남성,솔로,18056
+김도현,남성,솔로,122
+포맨,남성,그룹,48319
+잔나비,남성,그룹,109211
+레이나 (Raina),여성,솔로,7228
+김연우,남성,솔로,35937
+이현,남성,솔로,3687
+시크릿,여성,그룹,9497
+김남주(에이핑크),여성,솔로,16870
+C-CLOWN,남성,그룹,4308
+Bruno Mars,남성,솔로,93430
+NCT DREAM,남성,그룹,141345
+니엘,남성,솔로,7700
+MC 스나이퍼,남성,솔로,16849
+윤도현,남성,솔로,17989
+주비스,여성,그룹,447
+미스터 파파,남성,그룹,75
+"트로이 (범키, 칸토, 재웅, 창우)",남성,그룹,2078
+싸이 (PSY),남성,솔로,41175
+#Gun,남성,솔로,4191
+레드애플,남성,그룹,1401
+루피 (Loopy),남성,솔로,31745
+공기남,남성,솔로,10840
+조이 (JOY),여성,솔로,43223
+민아 (걸스데이),여성,솔로,9763
+"으뜨거따시 (하하, 자이언티)",남성,그룹,2211
+허성현 (Huh!),남성,솔로,5462
+에릭남 (Eric Nam),남성,솔로,39710
+이승훈,남성,솔로,25277
+모트 (Motte),여성,솔로,18499
+MASTA WU,남성,솔로,2030
+노지훈,남성,솔로,2105
+자우림,혼성,그룹,35545
+태양,남성,솔로,69278
+베이식 (Basick),남성,솔로,11246
+에피톤 프로젝트,남성,솔로,41157
+인순이,여성,솔로,2191
+씨리얼,여성,그룹,236
+V,남성,솔로,163198
+서인영,여성,솔로,5454
+살찐고양이,여성,솔로,733
+오종혁,남성,솔로,1204
+김재환,남성,솔로,58776
+숙희,여성,솔로,5923
+소녀시대-태티서 (Girls' Generation-TTS),여성,그룹,37379
+2AM,남성,그룹,9007
+XIA (준수),남성,솔로,55140
+소녀온탑,여성,솔로,2662
+소울스타 (SOULSTAR),남성,그룹,7027
+인피니트H,남성,그룹,31719
+MINO (송민호),남성,솔로,90680
+타우,남성,솔로,189
+린,여성,솔로,44746
+김경호,남성,솔로,12718
+나얼,남성,솔로,52399
+EXO,남성,그룹,527379
+임세준,남성,솔로,12620
+B.A.P,남성,그룹,24947
+브레이브걸스,여성,그룹,43868
+Apink (에이핑크),여성,그룹,139149
+황인욱,남성,솔로,9285
+Knock,남성,그룹,3981
+YB,남성,그룹,16936
+이장우,남성,솔로,264
+오마이걸 (OH MY GIRL),여성,그룹,119036
+하은,남성,솔로,8998
+JBJ,남성,그룹,26195
+센치한 하하 (하하 & 10cm),남성,그룹,460
+김준수,남성,솔로,55140
+장기하와 얼굴들,남성,그룹,20816
+형용돈죵 (정형돈 & G-DRAGON),남성,그룹,2154
+배다해,여성,솔로,1190
+이보람,여성,솔로,3995
+김완선,여성,솔로,2338
+토이,남성,솔로,50498
+지민,남성,솔로,137545
+AB6IX (에이비식스),남성,그룹,32719
+런치 (LUNCH),여성,솔로,2242
+헤이즈 (Heize),여성,솔로,152376
+가인,여성,솔로,27472
+정승환,남성,솔로,99954
+양파,여성,솔로,9949
+미료,여성,솔로,2772
+NCT 127,남성,그룹,138331
+원티드,남성,그룹,2572
+TWICE (트와이스),여성,그룹,214135
+정준영,남성,솔로,20483
+S.E.S.,여성,그룹,12657
+피기돌스,여성,그룹,213
+송지은,여성,솔로,7592
+유산슬,남성,솔로,11849
+티아라,여성,그룹,20747
+배치기,남성,그룹,16357
+마이키,남성,솔로,201
+이지영 (빅마마),여성,솔로,584
+용감한 녀석들,혼성,그룹,537
+걸스데이,여성,그룹,53837
+DEAN,남성,솔로,159348
+정은지,여성,솔로,63690
+민현 (뉴이스트),남성,솔로,59721
+YDG,남성,솔로,7712
+그냥노창,남성,솔로,36619
+루나 (LUNA),여성,솔로,10981
+아이오아이 (I.O.I),여성,그룹,78096
+엠블랙,남성,그룹,5207
+메이비 (Maybee),여성,솔로,1837
+WIN,남성,그룹,10239
+대성,남성,솔로,25918
+김나영,여성,솔로,58687
+화려강산,여성,그룹,845
+BIGBANG,남성,그룹,261922
+민서,여성,솔로,16877
+레인보우 블랙,여성,그룹,897
+프리스타일,남성,그룹,6028
+SUPER JUNIOR-D&E,남성,그룹,21600
+신지훈,여성,솔로,6833
+HYNN (박혜원),여성,솔로,27557
+Wanna One (워너원) - 남바완,남성,그룹,15711
+양정승,남성,솔로,1801
+씨야,여성,그룹,11960
+블랙넛 (Black Nut),남성,솔로,39992
+별,여성,솔로,7276
+달마시안 (DMTN),남성,그룹,732
+태일 (블락비),남성,솔로,21790
+NS 윤지,여성,솔로,5415
+태사비애,여성,그룹,3449
+조권,남성,솔로,3801
+멜로디데이 (MelodyDay),여성,그룹,6969
+SURAN (수란),여성,솔로,39678
+애즈원,여성,그룹,10773
+2YOON (포미닛 투윤),여성,그룹,971
+희진 (GOOD DAY),여성,솔로,1165
+육지담,여성,솔로,5417
+방예담,남성,솔로,5589
+벤,여성,솔로,82786
+Raiden,남성,솔로,995
+닐로(Nilo),남성,솔로,18389
+박봄 (Park Bom),여성,솔로,16160
+이수영,여성,솔로,18305
+카더가든,남성,솔로,53432
+양홍원,남성,솔로,32848
+SIXC (6 crazy),남성,그룹,1109
+바닷길 (길 & 바다),혼성,그룹,237
+유두래곤,남성,솔로,1963
+신혜성,남성,솔로,17220
+태민 (TAEMIN),남성,솔로,67004
+성훈 (브라운 아이드 소울),남성,솔로,2676
+7 go up,여성,그룹,1515
+MFBTY,혼성,그룹,6581
+이승철,남성,솔로,21612
+제이레빗(J Rabbit),여성,그룹,50616
+플라이 투 더 스카이,남성,그룹,35585
+지코 (ZICO),남성,솔로,190505
+노라조,남성,그룹,11063
+이소라,여성,솔로,37086
+아이언,남성,솔로,18447
+소녀시대-Oh!GG,여성,그룹,7524
+펜타곤,남성,그룹,21542
+여자친구 (GFRIEND),여성,그룹,129614
+신용재 (2F),남성,솔로,32074
+G.A.B (길 & BoA),혼성,그룹,40
+식케이 (Sik-K),남성,솔로,48762
+임영웅,남성,솔로,84053
+이선희,여성,솔로,34825
+치타 (CHEETAH),여성,솔로,19563
+백지영,여성,솔로,34758
+김진호 (SG워너비),남성,솔로,17646
+길미,여성,솔로,3610
+Ab 에비뉴,여성,그룹,359
+트러블메이커,혼성,그룹,7123
+제이세라,여성,솔로,8566
+GG (박명수 & G-Dragon),남성,그룹,1915
+유엔,남성,그룹,2687
+김원주 (2F),남성,솔로,2117
+이루펀트,남성,그룹,10075
+GD X TAEYANG,남성,그룹,26268
+하성운,남성,솔로,50846
+C JAMM,남성,솔로,54481
+양다일,남성,솔로,60902
+써니사이드,남성,그룹,1148
+박완규,남성,솔로,4684
+이설아,여성,솔로,2806
+태연 (TAEYEON),여성,솔로,224854
+버블 시스터즈,여성,그룹,3555
+부활,남성,그룹,13219
+장혜진,여성,솔로,7542
+2LSON,혼성,그룹,16169
+송이한,남성,솔로,3041
+제아파이브,남성,그룹,975
+신예영,여성,솔로,4939
+V.O.S,남성,그룹,14440
+니모,여성,솔로,520
+헬로비너스,여성,그룹,5997
+ITZY (있지),여성,그룹,50746
+신보라,여성,솔로,1851
+나플라 (nafla),남성,솔로,43206
+하우두유둘 (유재석 & 유희열),남성,그룹,553
+윤혁(디셈버),남성,솔로,973
+윤민수(바이브),남성,솔로,14625
+허각,남성,솔로,58778
+버즈,남성,그룹,62804
+러블리즈,여성,그룹,52175
+BewhY (비와이),남성,솔로,88130
+"이유 갓지(GOD G) 않은 이유 (박명수, 아이유)",혼성,그룹,9788
+선재 (snzae),남성,솔로,3879
+테이스티,남성,그룹,738
+김태우,남성,솔로,11725
+비,남성,솔로,16578
+장우혁,남성,솔로,5065
+정인,여성,솔로,22321
+김현중,남성,솔로,2983
+소유 (SOYOU),여성,솔로,29777
+스탠딩 에그,남성,그룹,95091
+오담률 (김농밀),남성,솔로,7202
+유키스,남성,그룹,2447
+거머리 (박명수 & 프라이머리),남성,그룹,209
+김효은,남성,솔로,15990
+가비엔제이,여성,그룹,15122
+Wanna One (워너원),남성,그룹,235209
+용준형,남성,솔로,40777
+린다G,여성,솔로,3501
+HIGH4 (하이포),남성,그룹,2645
+조용필,남성,솔로,9715
+스윗소로우 (SWEET SORROW),남성,그룹,14736
+데프콘,남성,솔로,7136
+레인보우 픽시,여성,그룹,495
+50kg,남성,그룹,106
+god,남성,그룹,56583
+정준일,남성,솔로,61470
+오반 (OVAN),남성,솔로,32666
+윤비 (YunB),남성,솔로,3165
+Sondia,여성,솔로,6968
+나비,여성,솔로,16376
+김종국,남성,솔로,11127
+허니지 (Honey-G),남성,그룹,1453
+뚱스,남성,그룹,353
+타이니지,여성,그룹,884
+S.M. THE BALLAD,혼성,그룹,5822
+최자,남성,솔로,3408
+더 포지션,남성,솔로,3557
+하동균,남성,솔로,19503
+GOT7 (갓세븐),남성,그룹,95026
+개리,남성,솔로,38630
+보이프렌드,남성,그룹,6699
+딥플로우,남성,솔로,9659
+40,남성,솔로,30984
+지선,여성,솔로,2469
+하림,남성,솔로,5826
+015B,남성,그룹,8924
+김필,남성,솔로,46258
+"오대천왕 (정형돈, 밴드 혁오)",남성,그룹,2576
+나다 (NADA),여성,솔로,3148
+은지원,남성,솔로,20916
+스텔라,여성,그룹,5283
+윤두준,남성,솔로,35857
+코드 쿤스트 (CODE KUNST),남성,솔로,33935
+진운,남성,솔로,962
+클래지,남성,솔로,319
+KATIE,여성,솔로,12359
+미스에스,여성,그룹,3753
+제아,여성,솔로,3996
+일 락,남성,솔로,1335
+하현우 (국카스텐),남성,솔로,29828
+유나킴,여성,솔로,2675
+장덕철,남성,그룹,13660
+슬리피,남성,솔로,4257
+KCM,남성,솔로,8106
+에디킴,남성,솔로,31698
+Khundi Panda,남성,솔로,8434
+레인보우,여성,그룹,7235
+범키,남성,솔로,28191
+무한도전,남성,그룹,18954
+인크레더블,남성,솔로,2010
+CHEEZE (치즈),여성,솔로,85794
+아웃사이더,남성,솔로,10898
+소지섭,남성,솔로,2870
+수퍼비,남성,솔로,48833
+San E,남성,솔로,87799
+빈지노 (Beenzino),남성,솔로,131598
+언프리티 랩스타 2,여성,그룹,982
+SHY (손호영),남성,솔로,6081
+언니쓰,여성,그룹,11996
+애프터스쿨 RED,여성,그룹,382
+이승기,남성,솔로,32474
+티파니 (TIFFANY),여성,솔로,24265
+BILL STAX (빌스택스),남성,솔로,29521
+"투하트 (우현, 키)",남성,그룹,20785
+손담비,여성,솔로,2307
+마야,여성,솔로,2153
+윤건,남성,솔로,12004
+장미하관 (노홍철 & 장미여관),남성,그룹,181
+2BIC(투빅),남성,그룹,17264
+브라운아이드걸스,여성,그룹,21169
+M.I.B,남성,그룹,2579
+임도혁,남성,솔로,2435
+순순희,남성,그룹,2236
+드렁큰 타이거,남성,솔로,13764
+Red Velvet (레드벨벳),여성,그룹,256857
+iKON,남성,그룹,124461
+박재범,남성,솔로,110512
+AOA CREAM,여성,그룹,4154
+팔로알토 (Paloalto),남성,솔로,30343
+Bro,남성,솔로,3206
+터보,남성,그룹,10810
+토니 안,남성,솔로,3755
+Rohann (이로한),남성,솔로,15273
+서태지,남성,솔로,19048
+버벌진트,남성,솔로,37147
+이진성 (먼데이 키즈),남성,솔로,3405
+넉살,남성,솔로,25689
+제이워크,남성,그룹,9240
+세훈&찬열,남성,그룹,38371
+4minute,여성,그룹,15839
+디아,여성,솔로,4423
+SG 워너비,남성,그룹,55054
+성시경,남성,솔로,114273
+다비치,여성,그룹,124177
+글램,여성,그룹,467
+에이프릴 (APRIL),여성,그룹,21605
+M시그널,남성,그룹,131
+병살 (정준하 & 김C),남성,그룹,68
+월하소년 (月下少年),남성,솔로,2485
+김재석,남성,솔로,418
+장우영,남성,솔로,5268
+김범수,남성,솔로,37849
+헨리 (HENRY),남성,솔로,29747
+마마무 (Mamamoo),여성,그룹,232414
+ILLIONAIRE RECORDS,남성,그룹,38247
+"싹쓰리 (유두래곤, 린다G, 비룡)",혼성,그룹,39949
+마미손,남성,솔로,14233
+태완 (Taewan),남성,솔로,2796
+강민경 (다비치),여성,솔로,9486
+Wanna One (워너원) - 더힐,남성,그룹,17039
+쥬얼리,여성,그룹,1833
+엔플라잉 (N.Flying),남성,그룹,32149
+BOBBY,남성,솔로,67214
+소피야 (Sophiya),여성,솔로,3640
+앤씨아,여성,솔로,8051
+초아,여성,솔로,11239
+행주,남성,솔로,10424
+뉴챔프,남성,솔로,2328
+Crush,남성,솔로,217148
+박원,남성,솔로,48692
+Lim Kim,여성,솔로,22848
+MFBTY,혼성,그룹,6580
+JJ,여성,솔로,206
+지아,여성,솔로,40805
+백청강,남성,솔로,2344
+김민지,여성,솔로,24
+이우,남성,솔로,5864
+더 네임,남성,솔로,2054
+10CM,남성,솔로,154723
+더 넛츠 (The NuTs),남성,그룹,3064
+경서,여성,솔로,4012
+Wanna One (워너원) - 린온미,남성,그룹,17710
+CNBLUE (씨엔블루),남성,그룹,22147
+수펄스,여성,그룹,92
+CL,여성,솔로,16446
+박효신,남성,솔로,253913
+임정희,여성,솔로,7502
+윤성기,남성,솔로,234
+김조한,남성,솔로,3886
+카라,여성,그룹,13912
+플라워,남성,그룹,6064
+디셈버,남성,그룹,15516
+휘성 (Realslow),남성,솔로,35405
+JUNIEL(서아),여성,솔로,18900
+UV,남성,그룹,4092
+젝스키스,남성,그룹,49258
+이기광,남성,솔로,35847
+변진섭,남성,솔로,4654
+윤아 (YOONA),여성,솔로,20628
+비스트,남성,그룹,128649
+박지윤,여성,솔로,12554
+천상지희 다나&선데이,여성,그룹,295
+루이 (긱스),남성,솔로,8144
+윤미래,여성,솔로,50811
+에픽하이 (EPIK HIGH),남성,그룹,107462
+달샤벳,여성,그룹,5827
+셰인,남성,솔로,280
+노을,남성,그룹,42393
+나몰라패밀리N,남성,그룹,433
+고유진 (플라워),남성,솔로,3009
+Kid Milli,남성,솔로,66776
+주석,남성,솔로,1934
+슈퍼스타K 3,혼성,그룹,250
+세븐티핑거스 (하하 & 장기하와 얼굴들),남성,그룹,102

-__name__ = 'textrank'
-__author__ = 'GyuhoLee'
-__version__ = '0.0.1'
-
-from .summarizer import KeywordSummarizer
-from .summarizer import KeysentenceSummarizer
\ No newline at end of file

-import numpy as np
-from sklearn.preprocessing import normalize
-
-def pagerank(x, df=0.85, max_iter=30, bias=None):
-    """
-    Arguments
-    ---------
-    x : scipy.sparse.csr_matrix
-        shape = (n vertex, n vertex)
-    df : float
-        Damping factor, 0 < df < 1
-    max_iter : int
-        Maximum number of iteration
-    bias : numpy.ndarray or None
-        If None, equal bias
-    Returns
-    -------
-    R : numpy.ndarray
-        PageRank vector. shape = (n vertex, 1)
-    """
-
-    assert 0 < df < 1
-
-    # initialize
-    A = normalize(x, axis=0, norm='l1')
-    R = np.ones(A.shape[0]).reshape(-1,1)
-
-    # check bias
-    if bias is None:
-        bias = (1 - df) * np.ones(A.shape[0]).reshape(-1,1)
-    else:
-        bias = bias.reshape(-1,1)
-        bias = A.shape[0] * bias / bias.sum()
-        assert bias.shape[0] == A.shape[0]
-        bias = (1 - df) * bias
-
-    # iteration
-    for _ in range(max_iter):
-        R = df * (A * R) + bias
-
-    return R
\ No newline at end of file

-from collections import Counter
-import math
-import numpy as np
-import scipy as sp
-from scipy.sparse import csr_matrix
-from sklearn.metrics import pairwise_distances
-
-from .utils import scan_vocabulary
-from .utils import tokenize_sents
-
-
-def sent_graph(sents, tokenize=None, min_count=2, min_sim=0.3,
-    similarity=None, vocab_to_idx=None, verbose=False):
-    """
-    Arguments
-    ---------
-    sents : list of str
-        Sentence list
-    tokenize : callable
-        tokenize(sent) return list of str
-    min_count : int
-        Minimum term frequency
-    min_sim : float
-        Minimum similarity between sentences
-    similarity : callable or str
-        similarity(s1, s2) returns float
-        s1 and s2 are list of str.
-        available similarity = [callable, 'cosine', 'textrank']
-    vocab_to_idx : dict
-        Vocabulary to index mapper.
-        If None, this function scan vocabulary first.
-    verbose : Boolean
-        If True, verbose mode on
-    Returns
-    -------
-    sentence similarity graph : scipy.sparse.csr_matrix
-        shape = (n sents, n sents)
-    """
-
-    if vocab_to_idx is None:
-        idx_to_vocab, vocab_to_idx = scan_vocabulary(sents, tokenize, min_count)
-    else:
-        idx_to_vocab = [vocab for vocab, _ in sorted(vocab_to_idx.items(), key=lambda x:x[1])]
-
-    x = vectorize_sents(sents, tokenize, vocab_to_idx)
-    if similarity == 'cosine':
-        x = numpy_cosine_similarity_matrix(x, min_sim, verbose, batch_size=1000)
-    else:
-        x = numpy_textrank_similarity_matrix(x, min_sim, verbose, batch_size=1000)
-    return x
-
-def vectorize_sents(sents, tokenize, vocab_to_idx):
-    rows, cols, data = [], [], []
-    for i, sent in enumerate(sents):
-        counter = Counter(tokenize(sent))
-        for token, count in counter.items():
-            j = vocab_to_idx.get(token, -1)
-            if j == -1:
-                continue
-            rows.append(i)
-            cols.append(j)
-            data.append(count)
-    n_rows = len(sents)
-    n_cols = len(vocab_to_idx)
-    return csr_matrix((data, (rows, cols)), shape=(n_rows, n_cols))
-
-def numpy_cosine_similarity_matrix(x, min_sim=0.3, verbose=True, batch_size=1000):
-    n_rows = x.shape[0]
-    mat = []
-    for bidx in range(math.ceil(n_rows / batch_size)):
-        b = int(bidx * batch_size)
-        e = min(n_rows, int((bidx+1) * batch_size))
-        psim = 1 - pairwise_distances(x[b:e], x, metric='cosine')
-        rows, cols = np.where(psim >= min_sim)
-        data = psim[rows, cols]
-        mat.append(csr_matrix((data, (rows, cols)), shape=(e-b, n_rows)))
-        if verbose:
-            print('\rcalculating cosine sentence similarity {} / {}'.format(b, n_rows), end='')
-    mat = sp.sparse.vstack(mat)
-    if verbose:
-        print('\rcalculating cosine sentence similarity was done with {} sents'.format(n_rows))
-    return mat
-
-def numpy_textrank_similarity_matrix(x, min_sim=0.3, verbose=True, min_length=1, batch_size=1000):
-    n_rows, n_cols = x.shape
-
-    # Boolean matrix
-    rows, cols = x.nonzero()
-    data = np.ones(rows.shape[0])
-    z = csr_matrix((data, (rows, cols)), shape=(n_rows, n_cols))
-
-    # Inverse sentence length
-    size = np.asarray(x.sum(axis=1)).reshape(-1)
-    size[np.where(size <= min_length)] = 10000
-    size = np.log(size)
-
-    mat = []
-    for bidx in range(math.ceil(n_rows / batch_size)):
-
-        # slicing
-        b = int(bidx * batch_size)
-        e = min(n_rows, int((bidx+1) * batch_size))
-
-        # dot product
-        inner = z[b:e,:] * z.transpose()
-
-        # sentence len[i,j] = size[i] + size[j]
-        norm = size[b:e].reshape(-1,1) + size.reshape(1,-1)
-        norm = norm ** (-1)
-        norm[np.where(norm == np.inf)] = 0
-
-        # normalize
-        sim = inner.multiply(norm).tocsr()
-        rows, cols = (sim >= min_sim).nonzero()
-        data = np.asarray(sim[rows, cols]).reshape(-1)
-
-        # append
-        mat.append(csr_matrix((data, (rows, cols)), shape=(e-b, n_rows)))
-
-        if verbose:
-            print('\rcalculating textrank sentence similarity {} / {}'.format(b, n_rows), end='')
-
-    mat = sp.sparse.vstack(mat)
-    if verbose:
-        print('\rcalculating textrank sentence similarity was done with {} sents'.format(n_rows))
-
-    return mat
-
-def graph_with_python_sim(tokens, verbose, similarity, min_sim):
-    if similarity == 'cosine':
-        similarity = cosine_sent_sim
-    elif callable(similarity):
-        similarity = similarity
-    else:
-        similarity = textrank_sent_sim
-
-    rows, cols, data = [], [], []
-    n_sents = len(tokens)
-    for i, tokens_i in enumerate(tokens):
-        if verbose and i % 1000 == 0:
-            print('\rconstructing sentence graph {} / {} ...'.format(i, n_sents), end='')
-        for j, tokens_j in enumerate(tokens):
-            if i >= j:
-                continue
-            sim = similarity(tokens_i, tokens_j)
-            if sim < min_sim:
-                continue
-            rows.append(i)
-            cols.append(j)
-            data.append(sim)
-    if verbose:
-        print('\rconstructing sentence graph was constructed from {} sents'.format(n_sents))
-    return csr_matrix((data, (rows, cols)), shape=(n_sents, n_sents))
-
-def textrank_sent_sim(s1, s2):
-    """
-    Arguments
-    ---------
-    s1, s2 : list of str
-        Tokenized sentences
-    Returns
-    -------
-    Sentence similarity : float
-        Non-negative number
-    """
-    n1 = len(s1)
-    n2 = len(s2)
-    if (n1 <= 1) or (n2 <= 1):
-        return 0
-    common = len(set(s1).intersection(set(s2)))
-    base = math.log(n1) + math.log(n2)
-    return common / base
-
-def cosine_sent_sim(s1, s2):
-    """
-    Arguments
-    ---------
-    s1, s2 : list of str
-        Tokenized sentences
-    Returns
-    -------
-    Sentence similarity : float
-        Non-negative number
-    """
-    if (not s1) or (not s2):
-        return 0
-
-    s1 = Counter(s1)
-    s2 = Counter(s2)
-    norm1 = math.sqrt(sum(v ** 2 for v in s1.values()))
-    norm2 = math.sqrt(sum(v ** 2 for v in s2.values()))
-    prod = 0
-    for k, v in s1.items():
-        prod += v * s2.get(k, 0)
-    return prod / (norm1 * norm2)
\ No newline at end of file

-import numpy as np
-from .rank import pagerank
-from .sentence import sent_graph
-from .word import word_graph
-
-
-class KeywordSummarizer:
-    """
-    Arguments
-    ---------
-    sents : list of str
-        Sentence list
-    tokenize : callable
-        Tokenize function: tokenize(str) = list of str
-    min_count : int
-        Minumum frequency of words will be used to construct sentence graph
-    window : int
-        Word cooccurrence window size. Default is -1.
-        '-1' means there is cooccurrence between two words if the words occur in a sentence
-    min_cooccurrence : int
-        Minimum cooccurrence frequency of two words
-    vocab_to_idx : dict or None
-        Vocabulary to index mapper
-    df : float
-        PageRank damping factor
-    max_iter : int
-        Number of PageRank iterations
-    verbose : Boolean
-        If True, it shows training progress
-    """
-    def __init__(self, sents=None, tokenize=None, min_count=2,
-        window=-1, min_cooccurrence=2, vocab_to_idx=None,
-        df=0.85, max_iter=30, verbose=False):
-
-        self.tokenize = tokenize
-        self.min_count = min_count
-        self.window = window
-        self.min_cooccurrence = min_cooccurrence
-        self.vocab_to_idx = vocab_to_idx
-        self.df = df
-        self.max_iter = max_iter
-        self.verbose = verbose
-
-        if sents is not None:
-            self.train_textrank(sents)
-
-    def train_textrank(self, sents, bias=None):
-        """
-        Arguments
-        ---------
-        sents : list of str
-            Sentence list
-        bias : None or numpy.ndarray
-            PageRank bias term
-        Returns
-        -------
-        None
-        """
-
-        g, self.idx_to_vocab = word_graph(sents,
-            self.tokenize, self.min_count,self.window,
-            self.min_cooccurrence, self.vocab_to_idx, self.verbose)
-        self.R = pagerank(g, self.df, self.max_iter, bias).reshape(-1)
-        if self.verbose:
-            print('trained TextRank. n words = {}'.format(self.R.shape[0]))
-
-    def keywords(self, topk=30):
-        """
-        Arguments
-        ---------
-        topk : int
-            Number of keywords selected from TextRank
-        Returns
-        -------
-        keywords : list of tuple
-            Each tuple stands for (word, rank)
-        """
-        if not hasattr(self, 'R'):
-            raise RuntimeError('Train textrank first or use summarize function')
-        idxs = self.R.argsort()[-topk:]
-        keywords = [(self.idx_to_vocab[idx], self.R[idx]) for idx in reversed(idxs)]
-        return keywords
-
-    def summarize(self, sents, topk=30):
-        """
-        Arguments
-        ---------
-        sents : list of str
-            Sentence list
-        topk : int
-            Number of keywords selected from TextRank
-        Returns
-        -------
-        keywords : list of tuple
-            Each tuple stands for (word, rank)
-        """
-        self.train_textrank(sents)
-        return self.keywords(topk)
-
-
-class KeysentenceSummarizer:
-    """
-    Arguments
-    ---------
-    sents : list of str
-        Sentence list
-    tokenize : callable
-        Tokenize function: tokenize(str) = list of str
-    min_count : int
-        Minumum frequency of words will be used to construct sentence graph
-    min_sim : float
-        Minimum similarity between sentences in sentence graph
-    similarity : str
-        available similarity = ['cosine', 'textrank']
-    vocab_to_idx : dict or None
-        Vocabulary to index mapper
-    df : float
-        PageRank damping factor
-    max_iter : int
-        Number of PageRank iterations
-    verbose : Boolean
-        If True, it shows training progress
-    """
-    def __init__(self, sents=None, tokenize=None, min_count=2,
-        min_sim=0.3, similarity=None, vocab_to_idx=None,
-        df=0.85, max_iter=30, verbose=False):
-
-        self.tokenize = tokenize
-        self.min_count = min_count
-        self.min_sim = min_sim
-        self.similarity = similarity
-        self.vocab_to_idx = vocab_to_idx
-        self.df = df
-        self.max_iter = max_iter
-        self.verbose = verbose
-
-        if sents is not None:
-            self.train_textrank(sents)
-
-    def train_textrank(self, sents, bias=None):
-        """
-        Arguments
-        ---------
-        sents : list of str
-            Sentence list
-        bias : None or numpy.ndarray
-            PageRank bias term
-            Shape must be (n_sents,)
-        Returns
-        -------
-        None
-        """
-        g = sent_graph(sents, self.tokenize, self.min_count,
-            self.min_sim, self.similarity, self.vocab_to_idx, self.verbose)
-        self.R = pagerank(g, self.df, self.max_iter, bias).reshape(-1)
-        if self.verbose:
-            print('trained TextRank. n sentences = {}'.format(self.R.shape[0]))
-
-    def summarize(self, sents, topk=30, bias=None):
-        """
-        Arguments
-        ---------
-        sents : list of str
-            Sentence list
-        topk : int
-            Number of key-sentences to be selected.
-        bias : None or numpy.ndarray
-            PageRank bias term
-            Shape must be (n_sents,)
-        Returns
-        -------
-        keysents : list of tuple
-            Each tuple stands for (sentence index, rank, sentence)
-        Usage
-        -----
-            >>> from textrank import KeysentenceSummarizer
-            >>> summarizer = KeysentenceSummarizer(tokenize = tokenizer, min_sim = 0.5)
-            >>> keysents = summarizer.summarize(texts, topk=30)
-        """
-        n_sents = len(sents)
-        if isinstance(bias, np.ndarray):
-            if bias.shape != (n_sents,):
-                raise ValueError('The shape of bias must be (n_sents,) but {}'.format(bias.shape))
-        elif bias is not None:
-            raise ValueError('The type of bias must be None or numpy.ndarray but the type is {}'.format(type(bias)))
-        self.train_textrank(sents, bias)
-        idxs = self.R.argsort()[-topk:]
-        keysents = [(idx, self.R[idx], sents[idx]) for idx in reversed(idxs)]
-        return keysents
\ No newline at end of file

-from collections import Counter
-from scipy.sparse import csr_matrix
-import numpy as np
-
-
-def scan_vocabulary(sents, tokenize=None, min_count=2):
-    """
-    Arguments
-    ---------
-    sents : list of str
-        Sentence list
-    tokenize : callable
-        tokenize(str) returns list of str
-    min_count : int
-        Minumum term frequency
-    Returns
-    -------
-    idx_to_vocab : list of str
-        Vocabulary list
-    vocab_to_idx : dict
-        Vocabulary to index mapper.
-    """
-    counter = Counter(w for sent in sents for w in tokenize(sent))
-    counter = {w:c for w,c in counter.items() if c >= min_count}
-    idx_to_vocab = [w for w, _ in sorted(counter.items(), key=lambda x:-x[1])]
-    vocab_to_idx = {vocab:idx for idx, vocab in enumerate(idx_to_vocab)}
-    return idx_to_vocab, vocab_to_idx
-
-def tokenize_sents(sents, tokenize):
-    """
-    Arguments
-    ---------
-    sents : list of str
-        Sentence list
-    tokenize : callable
-        tokenize(sent) returns list of str (word sequence)
-    Returns
-    -------
-    tokenized sentence list : list of list of str
-    """
-    if tokenize is not None:
-        return [tokenize(sent) for sent in sents]
-    else:
-        return sents
-
-def vectorize(tokens, vocab_to_idx):
-    """
-    Arguments
-    ---------
-    tokens : list of list of str
-        Tokenzed sentence list
-    vocab_to_idx : dict
-        Vocabulary to index mapper
-    Returns
-    -------
-    sentence bow : scipy.sparse.csr_matrix
-        shape = (n_sents, n_terms)
-    """
-    rows, cols, data = [], [], []
-    for i, tokens_i in enumerate(tokens):
-        for t, c in Counter(tokens_i).items():
-            j = vocab_to_idx.get(t, -1)
-            if j == -1:
-                continue
-            rows.append(i)
-            cols.append(j)
-            data.append(c)
-    n_sents = len(tokens)
-    n_terms = len(vocab_to_idx)
-    x = csr_matrix((data, (rows, cols)), shape=(n_sents, n_terms))
-    return x
\ No newline at end of file

-from collections import defaultdict
-from scipy.sparse import csr_matrix
-
-from .utils import scan_vocabulary
-from .utils import tokenize_sents
-
-
-def word_graph(sents, tokenize=None, min_count=2, window=2,
-    min_cooccurrence=2, vocab_to_idx=None, verbose=False):
-    """
-    Arguments
-    ---------
-    sents : list of str
-        Sentence list
-    tokenize : callable
-        tokenize(str) returns list of str
-    min_count : int
-        Minumum term frequency
-    window : int
-        Co-occurrence window size
-    min_cooccurrence : int
-        Minimum cooccurrence frequency
-    vocab_to_idx : dict
-        Vocabulary to index mapper.
-        If None, this function scan vocabulary first.
-    verbose : Boolean
-        If True, verbose mode on
-    Returns
-    -------
-    co-occurrence word graph : scipy.sparse.csr_matrix
-    idx_to_vocab : list of str
-        Word list corresponding row and column
-    """
-    if vocab_to_idx is None:
-        idx_to_vocab, vocab_to_idx = scan_vocabulary(sents, tokenize, min_count)
-    else:
-        idx_to_vocab = [vocab for vocab, _ in sorted(vocab_to_idx.items(), key=lambda x:x[1])]
-
-    tokens = tokenize_sents(sents, tokenize)
-    g = cooccurrence(tokens, vocab_to_idx, window, min_cooccurrence, verbose)
-    return g, idx_to_vocab
-
-def cooccurrence(tokens, vocab_to_idx, window=2, min_cooccurrence=2, verbose=False):
-    """
-    Arguments
-    ---------
-    tokens : list of list of str
-        Tokenized sentence list
-    vocab_to_idx : dict
-        Vocabulary to index mapper
-    window : int
-        Co-occurrence window size
-    min_cooccurrence : int
-        Minimum cooccurrence frequency
-    verbose : Boolean
-        If True, verbose mode on
-    Returns
-    -------
-    co-occurrence matrix : scipy.sparse.csr_matrix
-        shape = (n_vocabs, n_vocabs)
-    """
-    counter = defaultdict(int)
-    for s, tokens_i in enumerate(tokens):
-        if verbose and s % 1000 == 0:
-            print('\rword cooccurrence counting {}'.format(s), end='')
-        vocabs = [vocab_to_idx[w] for w in tokens_i if w in vocab_to_idx]
-        n = len(vocabs)
-        for i, v in enumerate(vocabs):
-            if window <= 0:
-                b, e = 0, n
-            else:
-                b = max(0, i - window)
-                e = min(i + window, n)
-            for j in range(b, e):
-                if i == j:
-                    continue
-                counter[(v, vocabs[j])] += 1
-                counter[(vocabs[j], v)] += 1
-    counter = {k:v for k,v in counter.items() if v >= min_cooccurrence}
-    n_vocabs = len(vocab_to_idx)
-    if verbose:
-        print('\rword cooccurrence counting from {} sents was done'.format(s+1))
-    return dict_to_mat(counter, n_vocabs, n_vocabs)
-
-def dict_to_mat(d, n_rows, n_cols):
-    """
-    Arguments
-    ---------
-    d : dict
-        key : (i,j) tuple
-        value : float value
-    Returns
-    -------
-    scipy.sparse.csr_matrix
-    """
-    rows, cols, data = [], [], []
-    for (i, j), v in d.items():
-        rows.append(i)
-        cols.append(j)
-        data.append(v)
-    return csr_matrix((data, (rows, cols)), shape=(n_rows, n_cols))
\ No newline at end of file