python TextMining
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01.Crawling
url_request
1 # -*- coding: utf-8 -*- 2 """ 3 Created on Sun Feb 17 11:08:44 2019 4 5 @author: 502-03 6 1.anaconda Prompt?? 7 2.python -m pip install --upgrade pip 8 3.pip install beautifulsoup4 9 """ 10 11 import urllib.request #url ?? 12 from bs4 import BeautifulSoup #html ?? 13 14 url="http://www.naver.com/index.html" 15 16 #1.???? ?? ?? 17 rst=urllib.request.urlopen(url) 18 print(rst) #<http.client.HTTPResponse object at 0x000000000E21F940> 19 20 data_src=rst.read() #source read 21 print(data_src) 22 """ 23 b‘<!doctype html> <html lang="ko"> <head> 24 .... 25 """ 26 27 #2.html ??:src->html?? 28 html=data_src.decode(‘utf-8‘) #??? 29 soup=BeautifulSoup(html,‘html.parser‘) 30 print(soup) 31 """ 32 </script> 33 <title>NAVER</title> 34 </meta></meta></meta></meta></meta></head> 35 <style> 36 """ 37 38 #3.tag?? ?? 39 link=soup.find(‘a‘) #??? ??? 40 print(link) 41 """ 42 <a href="#news_cast" onclick="document.getElementById(‘news_cast2‘).tabIndex = -1; 43 document.getElementById(‘news_cast2‘).focus();return false;"> 44 <span>???? ????</span></a> 45 """ 46 print(‘a tag ??‘,link.string)#a tag ?? ???? ???? 47 48 links=soup.find_all(‘a‘)#?? ???? 49 print(‘a tag size‘,len(links))#a tag size 335 50 links_data=[] #? ??? 51 for a in links:#339 52 print(a.string) 53 links_data.append(a.string) 54 55 print("links_data",len(links_data))#links_data 341 56 print(links_data) 57 """ 58 [‘???? ????‘, ‘?????? ????‘, ‘????? ????‘, ‘????? ????‘, ‘??? ????‘, 59 .... 60 ‘??? ??‘, ‘????‘, ‘NAVER Corp.‘] 61 """
selector
1 # -*- coding: utf-8 -*- 2 """ 3 - ???(selector) ?? ?? ?? 4 -> ? ?? ???(css) 5 -> id(#), class(.) 6 -> select_one(‘???‘) : ?? ?? ?? 7 -> select(‘???‘) : ?? ? ?? ?? 8 """ 9 10 from bs4 import BeautifulSoup #html ?? 11 12 #1.html?? ???? 13 file=open("../data/selector.html",mode=‘r‘,encoding=‘utf-8‘) 14 data_src=file.read() 15 print(data_src) 16 """ 17 <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> 18 <html> 19 <head> 20 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> 21 <title>id/class ???, ? ???</title> 22 <style type="text/css"> 23 """ 24 25 #2.html?? 26 html=BeautifulSoup(data_src,‘html.parser‘) 27 print(html) 28 """ 29 30 <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> 31 32 <html> 33 <head> 34 <meta content="text/html; charset=utf-8" http-equiv="Content-Type"/> 35 <title>id/class ???, ? ???</title> 36 <style type="text/css"> 37 """ 38 39 #3.selector ?? ?? 40 41 #1) id???: selector("tab#id?")) 42 table=html.select_one(‘table#tab‘) 43 print(table) 44 """ 45 <table border="1" id="tab"> 46 <tr> <!-- 1? --> 47 <!-- ?? ? : th --> 48 <th id="id"> ?? </th> 49 ... 50 <td> [email protected] </td> 51 </tr> 52 </table> 53 """ 54 55 #2)id ??? >?? ?? 56 th=html.select_one(‘table#tab > tr > th‘) 57 print(th) #<th id="id"> ?? </th> 58 59 ths=html.select(‘table#tab > tr > th‘) 60 print(ths) 61 """ 62 [<th id="id"> ?? </th>, <th id="name"> ?? </th>, <th id="major"> ?? </th>, <th id="email"> ??? </th>] 63 """ 64 65 print(len(ths)) #4 66 67 for th in ths:#<th id="id"> ?? </th> 68 print(th.string) 69 """ 70 ?? 71 ?? 72 ?? 73 ??? 74 """ 75 76 #2) class ?? :select(‘tag.class?‘) 77 # -5?->?? ? 78 79 # (1) ??? ?? 80 trs=html.select(‘table#tab > tr.odd‘) 81 print(trs) 82 """ 83 [<tr class="odd"> <!-- 3?(??) --> 84 <td> 201602 </td> 85 <td> ??? </td> 86 <td> ???? </td> 87 <td> [email protected] </td> 88 </tr>, <tr class="odd"> <!-- 5? --> 89 <td> 201604 </td> 90 <td> ??? </td> 91 <td> ???? </td> 92 <td> [email protected] </td> 93 </tr>] 94 """ 95 tds_data=[] 96 for tr in trs:# 2? 97 tds=tr.find_all(‘td‘) 98 for td in tds: 99 print(td.string) 100 tds_data.append(td.string) 101 """ 102 201602 103 ??? 104 ???? 105 [email protected] 106 201604 107 ??? 108 ???? 109 [email protected] 110 """ 111 print(len(tds_data)) #8 112 113 #(2) ?? ?? :tag[??=?] 114 trs=html.select(‘tr[class=odd]‘) 115 print(trs) 116 """ 117 [<tr class="odd"> <!-- 3?(??) --> 118 <td> 201602 </td> 119 <td> ??? </td> 120 <td> ???? </td> 121 <td> [email protected] </td> 122 </tr>, <tr class="odd"> <!-- 5? --> 123 <td> 201604 </td> 124 <td> ??? </td> 125 <td> ???? </td> 126 <td> [email protected] </td> 127 </tr>] 128 """
newsCrawling
1 # -*- coding: utf-8 -*- 2 """ 3 news Crawling 4 url=‘http://media.daum.net‘ 5 """ 6 7 import requests #url ?? 8 from bs4 import BeautifulSoup #html ?? 9 10 url=‘http://media.daum.net‘ 11 12 #1.url ?? 13 src=requests.get(url) 14 print(src) #<Response [200]> 15 16 data_src=src.text # source text 17 18 #2.html ?? 19 html=BeautifulSoup(data_src,‘html.parser‘) 20 print(html) 21 22 #3.select(tag[??=?]) 23 # <strong class="tit_g"><a href="http://v.media.daum.net/v/20190217083008573" class="link_txt">美, ??? ? ? ??? ?? ?? ???..韓 ?? ???</a><span class="txt_view">???</span></strong> 24 links=html.select("a[class=link_txt]") 25 print(len(links)) #102 26 27 creawling_data=[]#???? 28 cnt =0; 29 for link in links: 30 cnt+=1 31 cont=str(link.string) #?? ?? 32 #print(cont) 33 print(cnt,‘-‘,cont.strip()) # ??? ,?? ,??? ?? 34 creawling_data.append(cont.split()) 35 36 """ 37 1 - ??? "???? ???..中 ????? ??? ?" 38 2 - "?? ????..????? ??? ? ??" 39 3 - ???? ???? ??? ??..??? ? ???? 40 4 - ?·? ???? ‘??‘ ?? ???..???? ???... 41 5 - 中??? ???? ???.."?? ??? ?? ????... 42 """ 43 44 print(len(creawling_data))#102 45 46 #?? ->text file save 47 file=open(‘../data/crawling_data.txt‘, 48 mode=‘w‘,encoding=‘utf-8‘) 49 50 #list -> str ?? 51 file.write(str(creawling_data)) 52 file.close() 53 print("file save commit")
02.NLP
jpype_test
1 # -*- coding: utf-8 -*- 2 """ 3 java ???? ???? 4 """ 5 import jpype 6 7 path=jpype.getDefaultJVMPath() 8 print(path)#C:Program FilesJavajdk1.8.0_181jreinserverjvm.dll
konlpy
1 # -*- coding: utf-8 -*- 2 """ 3 konlpy??? ?? 4 pip install konlpy 5 """ 6 7 from konlpy.tag import Kkma 8 9 #Kkma object 10 kkma=Kkma() 11 12 #?? -> ?? ?? 13 para="??? ??? ?????.?? ??? ?? age? 28? ???." 14 15 ex_sent=kkma.sentences(para) 16 print(ex_sent) 17 """ 18 [‘??? ??? ?????.‘, ‘?? ??? ?? age? 28? ???.‘] 19 """ 20 21 #?? -> ?? ?? 22 ex_nouns=kkma.nouns(para) 23 print(ex_nouns) 24 """ 25 [‘???‘, ‘??‘, ‘??‘, ‘?‘, ‘???‘, ‘28‘, ‘28?‘, ‘?‘] 26 """ 27 28 #?? -> ??? ?? 29 ex_pos=kkma.pos(para) 30 print(ex_pos) #[(text,text class)] 31 """ 32 [(‘???‘, ‘NNG‘), (‘??‘, ‘NNG‘), (‘?‘, ‘JKO‘), (‘??‘, ‘NNG‘), (‘?‘, ‘VCP‘), 33 (‘???‘, ‘EFN‘), (‘.‘, ‘SF‘), (‘?‘, ‘NP‘), (‘?‘, ‘JX‘), (‘???‘, ‘NNG‘), 34 (‘?‘, ‘VCP‘), (‘?‘, ‘ECE‘), (‘age‘, ‘OL‘), (‘?‘, ‘JX‘), (‘28‘, ‘NR‘), 35 (‘?‘, ‘NNM‘), (‘?‘, ‘VCP‘), (‘???‘, ‘EFN‘), (‘.‘, ‘SF‘)] 36 """ 37 ‘‘‘ 38 ??? : ??? ??? ?? ??? ???? ??? ?? ?? 39 NNG ?? ?? NNP ?? ?? NNB ?? ?? NR ?? NP ??? VV ?? 40 VA ??? VX ?? ?? VCP ?? ??? VCN ?? ??? MM ??? 41 MAG ?? ?? MAJ ?? ?? IC ??? JKS ?? ?? JKC ?? ?? 42 JKG ??? ?? JKO ??? ?? JKB ??? ?? JKV ?? ?? 43 JKQ ??? ?? JC ?? ?? JX ??? EP ????? EF ?? ?? 44 EC ?? ?? ETN ??? ?? ?? ETM ??? ?? ?? XPN ?? ??? 45 XSN ???? ??? XSV ?? ?? ??? XSA ??? ?? ??? XR ?? 46 SF ???, ???, ??? SE ??? SS ???,???,?? 47 SP ??,????,??,?? SO ???(??,??,??) 48 SW ???? (??????,????) SH ?? SL ??? SN ?? 49 NF ?????? NV ?????? NA ????? 50 ‘‘‘ 51 52 #NNG:?? ?? NNP:?? ?? NP:??? 53 ex_pos2=[] #?? ?? 54 for (text,text_class) in ex_pos:#(text,text class) 55 if text_class==‘NNG‘ or text_class==‘NNP‘ or text_class==‘NP‘: 56 ex_pos2.append(text) 57 58 print(ex_pos2)#[‘???‘, ‘??‘, ‘??‘, ‘?‘, ‘???‘]
03.WordCloud
ex_nouns
1 # -*- coding: utf-8 -*- 2 """ 3 1.test file ?? 4 2.?? ?? : Kkma 5 3.??? :?? ?? ?? ,?? ?? 6 4.word cloud??? 7 """ 8 from konlpy.tag import Kkma 9 10 #object 11 kkma=Kkma() 12 13 #1.text file?? 14 file=open("../data/text_data.txt",mode=‘r‘,encoding="utf-8") 15 docs=file.read() #text?? ?? 16 file.close() 17 print(docs) 18 """ 19 ??? ??? ?????. ?? ??? ??? ?????. 20 ??? ??? ?? ??? ???. Text mining ??? 2000? ??? ??? ????. 21 """ 22 23 #1).doc -> sentence 24 ex_sent=kkma.sentences(docs) 25 print(len(ex_sent))#4 26 print(ex_sent) 27 """ 28 [‘??? ??? ?????.‘, 29 ‘?? ??? ??? ?????.‘, 30 ‘??? ??? ?? ??? ???.‘, 31 ‘Text mining ??? 2000? ??? ??? ????.‘] 32 """ 33 34 for sent in ex_sent: 35 print(sent) 36 """ 37 ??? ??? ?????. 38 ?? ??? ??? ?????. 39 ??? ??? ?? ??? ???. 40 Text mining ??? 2000? ??? ??? ????. 41 """ 42 43 #2).docs -> nount ?? 44 ex_nouns=kkma.nouns(docs) #?? ?? ?? ?? 45 print(len(ex_nouns))#13 46 print(ex_nouns) 47 """ 48 [‘???‘, ‘??‘, ‘?‘, ‘???‘, ‘??‘, ‘???‘, 49 ‘??‘, ‘2000‘, ‘2000?‘, ‘?‘, ‘??‘, ‘??‘, ‘??‘] 50 """ 51 52 from re import match 53 54 #2~3.?? ?? ?? -> ??? ??(??,?? 1? ??) 55 nouns_words =[]#list 56 nouns_count={} #set or dict 57 for sent in ex_sent:#??? 58 for nouns in kkma.nouns(sent):#??? 59 #??? (??,?? 1? ??) 60 if len(str(nouns))>1 and not(match(‘^[0-9]‘,nouns)): 61 nouns_words.append(nouns) 62 #key=word :value:count 63 nouns_count[nouns]=nouns_count.get(nouns,0)+1 64 65 print(len(nouns_words))#15->12 66 """ 67 [‘???‘, ‘??‘, ‘???‘, ‘??‘, ‘??‘, ‘???‘, 68 ‘??‘, ‘???‘, ‘??‘, ‘??‘, ‘??‘, ‘??‘] 69 """ 70 71 print(nouns_count) 72 """ 73 {‘???‘: 1, ‘??‘: 3, ‘???‘: 2, ‘??‘: 1, 74 ‘???‘: 1, ‘??‘: 1, ‘??‘: 1, ‘??‘: 1, ‘??‘: 1} 75 """ 76 77 #4.word cloud??? 78 from collections import Counter 79 80 #1)dict->Counter ?? 81 word_count=Counter(nouns_count) 82 83 #2)top word 84 top5=word_count.most_common(5) 85 print(top5) 86 """ 87 [(‘??‘, 3), (‘???‘, 2), (‘???‘, 1), (‘??‘, 1), (‘???‘, 1)] 88 """ 89 #3)word cloud ??? :package ?? 90 91 92 import pytagcloud 93 ‘‘‘ 94 Anaconda Prompt?? 95 pip install pygame 96 pip install pytagcloud 97 pip install simplejson 98 ‘‘‘ 99 100 # tag? color, size, tag ?? ?? 101 word_count_list = pytagcloud.make_tags(top5, maxsize=80) 102 # maxsize : ?? ???? 103 print(word_count_list) 104 ‘‘‘ 105 [{‘color‘: (91, 34, 34), ‘size‘: 109, ‘tag‘: ‘??‘}, {‘color‘: (95, 159, 59), ‘size‘: 80, ‘tag‘: ‘???‘}, {‘color‘: (194, 214, 193), ‘size‘: 47, ‘tag‘: ‘???‘}] 106 ‘‘‘ 107 108 pytagcloud.create_tag_image(word_count_list, 109 ‘wordcloud.jpg‘, 110 size=(900, 600), 111 fontname=‘korean‘, rectangular=False) 112 ‘‘‘ 113 ?? ?? ?? ??? Error ?? 114 C:Anaconda3Libsite-packagespytagcloudfonts ???? 115 1. fonts.json ?? ?? ?? 116 [ 117 { 118 "name": "korean", 119 "ttf": "malgun.ttf", 120 2. C:WindowsFonts ???? ‘?? ??‘ ?? ??/fonts ?? ???? 121 3. create_tag_image(fontname=‘korean‘) ?? ?? 122 ‘‘‘
news_wordCloud
1 # -*- coding: utf-8 -*- 2 """ 3 news crawling data file 4 - word cloud ??? 5 """ 6 7 from konlpy.tag import Kkma 8 9 #object ?? 10 kkma =Kkma() 11 12 #1.text file load 13 file=open("../data/crawling_data.txt", 14 encoding=‘utf-8‘) 15 crawling_data=file.read() 16 file.close() 17 print(crawling_data) 18 """ 19 [[‘???‘, ‘"????‘, ‘???..中‘, ‘?????‘, ‘???‘, ‘?"‘], 20 [‘"??‘, ‘????..?????‘, ‘???‘, ‘?‘, ‘??"‘], 21 [‘????‘, ‘????‘, ‘???‘, ‘??..???‘, ‘?‘, ‘????‘], 22 ... 23 [‘???‘, ‘????‘, ‘??‘, ‘???‘, ‘???‘, ‘??‘, ‘??‘, ‘??‘, ‘?????‘]] 24 """ 25 26 #2.docs-> sentences ?? 27 ex_sent=kkma.sentences(crawling_data) 28 print(len(ex_sent)) #9 29 print(ex_sent) 30 31 #3??->?? ?? 32 # 4. text ??? : ??, 1? ?? ?? 33 # 5. word count : dict 34 ex_nouns=[] #list 35 word_count={} #dict 36 for sent in ex_sent:# ??? 37 for nouns in kkma.nouns(sent):# ??? 38 if len(str(nouns))>1 and not(match(‘^[0-9]‘,nouns)): 39 ex_nouns.append(nouns) 40 word_count[nouns]=word_count.get(nouns,0)+1 41 print(len(ex_nouns)) #439 42 print(ex_nouns) 43 print(word_count) 44 45 # 5. Counter ?? : top10 ?? 46 from collections import Counter 47 word_count=Counter(word_count) 48 top10=word_count.most_common(10) 49 print(top10) 50 """ 51 [(‘?‘, 4), (‘???‘, 3), (‘?‘, 3), (‘?‘, 3), (‘?‘, 3), (‘?‘, 3), (‘?‘, 3), (‘???‘, 3), (‘??‘, 3), (‘?‘, 3)] 52 """ 53 54 55 # 6. word cloud ??? 56 import pytagcloud 57 ‘‘‘ 58 Anaconda Prompt?? 59 pip install pygame 60 pip install pytagcloud 61 pip install simplejson 62 ‘‘‘ 63 # tag? color, size, tag ?? ?? 64 word_count_list = pytagcloud.make_tags(top10, maxsize=80) 65 # maxsize : ?? ???? 66 print(word_count_list) 67 ‘‘‘ 68 [{‘color‘: (91, 34, 34), ‘size‘: 109, ‘tag‘: ‘??‘}, {‘color‘: (95, 159, 59), ‘size‘: 80, ‘tag‘: ‘???‘}, {‘color‘: (194, 214, 193), ‘size‘: 47, ‘tag‘: ‘???‘}] 69 ‘‘‘ 70 71 pytagcloud.create_tag_image(word_count_list, 72 ‘news_wordcloud.jpg‘, 73 size=(900, 600), 74 fontname=‘korean‘, rectangular=False) 75 ‘‘‘ 76 ?? ?? ?? ??? Error ?? 77 C:Anaconda3Libsite-packagespytagcloudfonts ???? 78 1. fonts.json ?? ?? ?? 79 [ 80 { 81 "name": "korean", 82 "ttf": "malgun.ttf", 83 2. C:WindowsFonts ???? ‘?? ??‘ ?? ??/fonts ?? ???? 84 3. create_tag_image(fontname=‘korean‘) ?? ?? 85 ‘‘‘
04.SparseMatrix
TfidfVectorizer
1 # -*- coding: utf-8 -*- 2 """ 3 TfidfVectorizer ?? : Tfidf(??? ?? ??) ?? ?? ??? 4 1. ?? ??? : ?? -> ??(word) 5 2. ?? ??(word dict) : {word : ????} 6 3. ?? ??(sparse matrix) : ?? ????? ?? ???(TF, TFiDF) 7 1) TF : ?? ????? -> ??? ??(??? ?? ?? ??.) 8 2) TFiDF : ?? ????? x ?? ?????? ??(?? ???) 9 -> TFiDF = tf(d, t) x log(n/df(t)) 10 """ 11 12 from sklearn.feature_extraction.text import TfidfVectorizer 13 14 #?? 15 sentences = [ 16 "Mr. Green killed Colonel Mustard in the study with the candlestick. Mr. Green is not a very nice fellow.", 17 "Professor Plum has a green plant in his study.", 18 "Miss Scarlett watered Professor Plum‘s green plant while he was away from his office last week." 19 ] 20 21 #1.?? ???:??->??(word) 22 tfidf_fit=TfidfVectorizer().fit(sentences) 23 print(tfidf_fit)#object info 24 """ 25 TfidfVectorizer(analyzer=‘word‘, binary=False, decode_error=‘strict‘, 26 dtype=<class ‘numpy.int64‘>, encoding=‘utf-8‘, input=‘content‘, 27 lowercase=True, max_df=1.0, max_features=None, min_df=1, 28 ngram_range=(1, 1), norm=‘l2‘, preprocessor=None, smooth_idf=True, 29 stop_words=None, strip_accents=None, sublinear_tf=False, 30 token_pattern=‘(?u)\b\w\w+\b‘, tokenizer=None, use_idf=True, 31 vocabulary=None) 32 """ 33 34 #2.?? ??(word dict) : {word:????} 35 voca=tfidf_fit.vocabulary_ 36 print(‘word size=‘,len(voca))#word size= 31 37 print(voca)# ‘mr‘: 14, #14? ???? 38 """ 39 {‘mr‘: 14, ‘green‘: 5, ‘killed‘: 11, ‘colonel‘: 2, ‘mustard‘: 15, ‘in‘: 9, 40 ‘the‘: 24, ‘study‘: 23, ‘with‘: 30, ‘candlestick‘: 1, ‘is‘: 10, ‘not‘: 17, 41 ‘very‘: 25, ‘nice‘: 16, ‘fellow‘: 3, ‘professor‘: 21, ‘plum‘: 20, ‘has‘: 6, 42 ‘plant‘: 19, ‘his‘: 8, ‘miss‘: 13, ‘scarlett‘: 22, ‘watered‘: 27, ‘while‘: 29, 43 ‘he‘: 7, ‘was‘: 26, ‘away‘: 0, ‘from‘: 4, ‘office‘: 18, ‘last‘: 12, ‘week‘: 28} 44 """ 45 46 #??? ????:word embedding 47 48 #3.?? ??(sparse matrix) :text:?? DTM(?:D ,?:T) 49 tfidf=TfidfVectorizer()#object 50 sparse_tfidf=tfidf.fit_transform(sentences) 51 print(type(sparse_tfidf))#<class ‘scipy.sparse.csr.csr_matrix‘> 52 print(sparse_tfidf.shape)#DTM=(3?(Doc), 31?(Term)) 53 print("1.scipy.sparse.matrix") 54 print(sparse_tfidf) 55 """ 56 (row:doc,col:term) ??? (weight)=Tfidf 57 (0, 14) 0.4411657657527482:‘mr‘ 58 (0, 5) 0.26055960805891015:‘green‘ 59 (1, 5) 0.2690399207469689 :‘green‘ 60 (1, 8) 0.34643788271971976 61 (2, 5) 0.15978698032384395 62 (2, 21) 0.2057548299742193 63 (2, 20) 0.2057548299742193 64 ... 65 """ 66 67 print("2.numpy sparse.matrix") 68 #scipy->numpy ??? 69 tfidf_arr=sparse_tfidf.toarray() 70 print(tfidf_arr.shape) #(3, 31) 71 print(type(tfidf_arr))#<class ‘numpy.ndarray‘> 72 print(tfidf_arr) 73 """ 74 [[0. 0.22058288 0.22058288 0.22058288 0. 0.26055961 75 0. 0. 0. 0.16775897 0.22058288 0.22058288 76 0. 0. 0.44116577 0.22058288 0.22058288 0.22058288 77 0. 0. 0. 0. 0. 0.16775897 78 0.44116577 0.22058288 0. 0. 0. 0. 79 0.22058288] 80 [0. 0. 0. 0. 0. 0.26903992 81 0.45552418 0. 0.34643788 0.34643788 0. 0. 82 0. 0. 0. 0. 0. 0. 83 0. 0.34643788 0.34643788 0.34643788 0. 0.34643788 84 0. 0. 0. 0. 0. 0. 85 0. ] 86 [0.27054288 0. 0. 0. 0.27054288 0.15978698 87 0. 0.27054288 0.20575483 0. 0. 0. 88 0.27054288 0.27054288 0. 0. 0. 0. 89 0.27054288 0.20575483 0.20575483 0.20575483 0.27054288 0. 90 0. 0. 0.27054288 0.27054288 0.27054288 0.27054288 91 0. ]] 92 """ 93 94 """ 95 1.scipy sparse matrix 96 -> tensorflow model 97 2.numpy sparse matrix 98 -> sklean model 99 """