cs Word2vec 알고리즘 리뷰 3 : 네이버 영화 리뷰 데이터를 이용한 실습
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개발/NLP(Natural Language Processing)

Word2vec 알고리즘 리뷰 3 : 네이버 영화 리뷰 데이터를 이용한 실습

by 시몬쯔 2020. 5. 30.
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네이버 영화 리뷰 dataset 출처 :  https://github.com/e9t/nsmc/

 

저번 포스팅에서 대략적인 Word2Vec 이론을 다뤘으니 실습을 해보도록 하자.

 

 

 

In [ ]:
# import matplotlib as mpl
# import matplotlib.pyplot as plt

# %config InlineBackend.figure_format = 'retina'

# !apt -qq -y install fonts-nanum

# import matplotlib.font_manager as fm
# fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'
# plt.rc('font', family='NanumBarunGothic')
# mpl.font_manager._rebuild()
In [70]:
# 그래프를 노트북 안에 그리기 위해 설정
%matplotlib inline

# 필요한 패키지와 라이브러리를 가져옴
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm

# 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처
mpl.rcParams['axes.unicode_minus'] = False
In [73]:
font_list = fm.findSystemFonts(fontpaths=None, fontext='ttf')

# ttf 폰트 전체갯수
print(len(font_list))
 
834
In [74]:
# 시스템 폰트에서 읽어온 리스트에서 상위 10개만 출력
font_list[:10]
Out[74]:
['C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF',
 'C:\\WINDOWS\\Fonts\\corbell.ttf',
 'C:\\WINDOWS\\Fonts\\LTYPEO.TTF',
 'C:\\WINDOWS\\Fonts\\OUTLOOK.TTF',
 'C:\\WINDOWS\\Fonts\\STXINWEI.TTF',
 'C:\\Windows\\Fonts\\PAPYRUS.TTF',
 'C:\\Windows\\Fonts\\pala.ttf',
 'C:\\WINDOWS\\Fonts\\HANDotumExt.ttf',
 'C:\\Windows\\Fonts\\HANBatangB.ttf',
 'C:\\Windows\\Fonts\\BOD_BLAR.TTF']
In [75]:
[(f.name, f.fname) for f in fm.fontManager.ttflist if 'Nanum' in f.name]
Out[75]:
[('NanumGothic', 'C:\\Windows\\Fonts\\\x7f\x7f\x7f\x7fBOLD.TTF'),
 ('NanumGothic', 'C:\\Windows\\Fonts\\\x7f\x7f\x7f\x7f.TTF'),
 ('NanumGothic', 'C:\\WINDOWS\\Fonts\\\x7f\x7f\x7f\x7fEXTRABOLD.TTF')]
In [76]:
path = 'NanumGothic.ttf'
font_name = fm.FontProperties(fname=path, size=50).get_name()
print(font_name)
plt.rc('font', family=font_name)
 
NanumGothic
In [77]:
import matplotlib.pyplot as plt
In [79]:
import pandas as pd
In [80]:
import urllib.request
%matplotlib inline
import matplotlib.pyplot as plt
import re
from konlpy.tag import Okt
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences
In [81]:
train_data = pd.read_table('./nsmc/ratings_train.txt')
test_data = pd.read_table('./nsmc/ratings_test.txt')
In [82]:
train_data.head()
Out[82]:
  id document label
0 9976970 아 더빙.. 진짜 짜증나네요 목소리 0
1 3819312 흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나 1
2 10265843 너무재밓었다그래서보는것을추천한다 0
3 9045019 교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정 0
4 6483659 사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ... 1
In [83]:
test_data.head()
Out[83]:
  id document label
0 6270596 굳 ㅋ 1
1 9274899 GDNTOPCLASSINTHECLUB 0
2 8544678 뭐야 이 평점들은.... 나쁘진 않지만 10점 짜리는 더더욱 아니잖아 0
3 6825595 지루하지는 않은데 완전 막장임... 돈주고 보기에는.... 0
4 6723715 3D만 아니었어도 별 다섯 개 줬을텐데.. 왜 3D로 나와서 제 심기를 불편하게 하죠?? 0
In [84]:
print(len(train_data))
 
150000
 

print(len(test_data))

In [85]:
train_data['document'].nunique(), train_data['label'].nunique() 
#중복을 제외하 갯수를 체크해보니 15만개에서 146182개가 되었다.
#label은 0또는1이니 2가 된다
Out[85]:
(146182, 2)
In [86]:
train_data.drop_duplicates(subset=['document'], inplace=True) # document 열에서 중복인 내용이 있다면 중복 제거
In [87]:
print(len(train_data))
 
146183
In [88]:
train_data['label'].value_counts().plot(kind = 'bar')
Out[88]:
<matplotlib.axes._subplots.AxesSubplot at 0x22209262a90>
 
In [89]:
print(train_data.groupby('label').size().reset_index(name = 'count'))
 
   label  count
0      0  73342
1      1  72841
In [90]:
print(train_data.isnull().values.any())
 
True
In [91]:
print(train_data.isnull().sum())
 
id          0
document    1
label       0
dtype: int64
In [92]:
train_data.loc[train_data.document.isnull()]
Out[92]:
  id document label
25857 2172111 NaN 1
In [93]:
train_data = train_data.dropna(how = 'any')
print(train_data.isnull().values.any())
 
False
In [94]:
print(len(train_data))
 
146182
 

한글과 공백을 제외한 문자들을 제거해보자

In [95]:
train_data['document'] = train_data['document'].str.replace("[^ㄱ-하-ㅣ가힣]"," ")
In [96]:
train_data.head()
Out[96]:
  id document label
0 9976970 아 더빙 진짜 짜증나네요 목소리 0
1 3819312 포스터보고 초딩영 줄 오버연기조차 가볍지 않구나 1
2 10265843 너무재밓었다그래서보는것을추천 다 0
3 9045019 교도소 이야기구먼 솔직 재미는 없다 평점 조정 0
4 6483659 사이몬페그의 익살스런 연기가 돋보였던 영 스파이더맨에서 늙어보이기만 던 커스틴 ... 1
In [97]:
train_data.isnull().values.any()
Out[97]:
False
In [98]:
train_data['document'].replace('',np.nan,inplace=True)
print(train_data.isnull().sum())
 
id          0
document    0
label       0
dtype: int64
In [99]:
train_data.loc[train_data.document.isnull()]
Out[99]:
  id document label
In [100]:
train_data = train_data.dropna(how='any')
In [101]:
print(len(train_data))
 
146182
 

Test data에도 똑같이 해줌

In [102]:
test_data.drop_duplicates(subset = ['document'], inplace=True) # document 열에서 중복인 내용이 있다면 중복 제거
test_data['document'] = test_data['document'].str.replace("[^ㄱ-ㅎㅏ-ㅣ가-힣 ]","") # 정규 표현식 수행
test_data['document'].replace('', np.nan, inplace=True) # 공백은 Null 값으로 변경
test_data = test_data.dropna(how='any') # Null 값 제거
print('전처리 후 테스트용 샘플의 개수 :',len(test_data))
 
전처리 후 테스트용 샘플의 개수 : 48995
 

토큰화

In [103]:
stopwords = ['의','가','이','은','들','는','좀','잘','걍','과','도','를','으로','자','에','와','한','하다']
In [104]:
#불용어 정의한다.(조사나 접속사 등)
In [105]:
okt = Okt()
In [106]:
okt.morphs('와 이런 것도 영화라고 차라리 뮤직비디오를 만드는 게 나을 뻔', stem = True)
Out[106]:
['오다', '이렇다', '것', '도', '영화', '라고', '차라리', '뮤직비디오', '를', '만들다', '게', '나다', '뻔']
 

okt를 이용하여 train_data의 document에 있는 불용어들을 제거하자.

In [107]:
X_train = []
from tqdm import tqdm
for sentence in tqdm(train_data['document']):
    temp_X = []
    temp_X = okt.morphs(sentence, stem = True)
    temp_X = [word for word in temp_X if not word in stopwords]
    X_train.append(temp_X)
 
100%|██████████████████████████████████████████████████████████████████████| 146182/146182 [05:16<00:00, 462.51it/s]
In [108]:
X_test = []
for sentence in tqdm(test_data['document']):
    temp_X = []
    temp_X = okt.morphs(sentence, stem=True) # 토큰화
    temp_X = [word for word in temp_X if not word in stopwords] # 불용어 제거
    X_test.append(temp_X)
 
100%|████████████████████████████████████████████████████████████████████████| 48995/48995 [02:26<00:00, 333.85it/s]
In [110]:
print(X_train[:5])
 
[['아', '더빙', '진짜', '짜증나다', '목소리'], ['포스터', '보고', '초딩', '영', '줄', '오버', '연기', '조차', '가볍다', '않다'], ['너', '무재', '밓었', '다그', '래서', '보다', '추천', '다'], ['교도소', '이야기', '구먼', '솔직', '재미', '없다', '평점', '조정'], ['사이', '몬페', '그', '익살스럽다', '연기', '돋보이다', '영', '스파이더맨', '에서', '늙다', '보이다', '던', '커스틴', '던스트', '너무나도', '이쁘다', '보이다']]
In [111]:
print(X_test[:5])
 
[['굳다', 'ㅋ'], ['뭐', '야', '평점', '나쁘다', '않다', '점', '짜다', '리', '더', '더욱', '아니다'], ['지루하다', '않다', '완전', '막장', '임', '돈', '주다', '보기', '에는'], ['만', '아니다', '별', '다섯', '개', '주다', '왜', '로', '나오다', '제', '심기', '불편하다'], ['음악', '주가', '되다', '최고', '음악', '영화']]
In [113]:
from gensim.models import Word2Vec
In [114]:
model = Word2Vec(X_train,size=300,window=3, min_count = 5,workers=1)
In [115]:
word_vectors = model.wv
In [117]:
vocabs = word_vectors.vocab.keys()
word_vectors_list = [word_vectors[v] for v in vocabs]
In [118]:
print(word_vectors_list[:5])
 
[array([ 0.35371915,  0.01369796, -0.32485738, -0.07170954, -0.8098252 ,
        0.2438762 ,  0.08732416,  0.63042426,  0.81642616,  0.28534853,
       -0.3122959 , -0.33240107,  0.19078688, -0.01236966,  0.06174204,
        0.41557205, -0.08673642, -1.458105  , -0.32850906, -1.112841  ,
       -0.5430838 ,  0.40445134,  0.10489815,  0.6468941 ,  0.19937538,
       -0.60479933, -0.22478952,  0.7565353 , -0.14293832,  0.23310506,
        0.15129174,  0.6845647 , -0.7044086 ,  0.7915299 , -0.35835764,
       -1.2526863 ,  0.45404494, -0.04736769,  0.5942899 ,  0.59826547,
        0.36945054,  0.47753644, -0.09401665, -0.36907658,  0.23232228,
       -0.46641174, -0.27822164,  0.2882081 ,  0.04361428, -0.45328766,
        0.22835523, -0.36976653,  0.12345321,  0.14125885,  0.63989264,
       -0.19459705,  0.17302327,  0.35701352, -0.7929591 , -0.77486944,
        0.33167386, -0.63483286, -0.7358724 , -0.5367217 , -0.15168835,
       -0.23927116,  0.12286316, -0.5074119 ,  1.3054442 , -0.298176  ,
        0.53662306, -0.38420925,  0.13963717, -0.4172364 , -0.6023217 ,
       -0.737436  ,  0.10716676,  0.3178931 ,  0.58743054,  0.02297241,
       -0.15831982, -0.18929426,  0.20886979, -0.2715996 ,  0.43760517,
       -0.352647  , -0.06899021,  0.5870781 ,  0.15513158, -0.14012474,
        0.78900623,  0.15468648,  0.30054903,  0.4056097 , -0.33693022,
       -0.19352566, -0.40530896, -0.03303072, -0.36479527,  0.12306235,
        0.09257359, -0.18375042,  0.5485907 , -0.05983978,  0.01656177,
       -0.51319855,  0.27850905, -0.25046661,  0.08234145, -0.20900732,
       -0.5142531 , -0.11942197, -0.69790703,  0.34256333,  0.82137275,
        0.9430014 , -0.04666335,  0.39349815,  0.90695465, -0.40254754,
        0.50508046, -0.4258863 , -0.00949783, -0.61609435,  0.0400856 ,
       -0.29342127,  0.4765701 ,  0.14896019, -1.0968763 ,  0.20674203,
       -0.07628355, -0.22713113,  0.38102186, -0.09913569, -0.2546628 ,
        0.7605381 ,  0.00786115, -0.0531752 ,  0.29045078,  0.09086434,
       -0.09674405,  0.445838  ,  0.98627806, -0.15761015,  0.6493283 ,
       -0.09806692, -0.11581327,  0.31925744, -0.34285492,  0.09583668,
        0.2640743 ,  0.38297677, -1.3245939 ,  0.49583235, -0.3515406 ,
        0.5218009 , -0.6748311 , -0.521731  ,  0.09695819,  0.01711136,
        0.43111882,  0.19854143, -0.24784477,  0.75012076,  0.8029055 ,
        1.0587212 ,  0.2252498 , -0.7543822 ,  0.36283895, -0.6082155 ,
       -0.81244385,  0.12543415, -0.13457002,  0.61658704, -0.859349  ,
        0.8003935 ,  0.00818912, -0.26867083, -0.3244661 ,  0.25344384,
       -0.24900945,  0.18456884, -0.6389164 ,  0.3778808 ,  0.7558439 ,
        0.17785466,  0.37517542,  0.25370252,  0.251337  , -0.47009435,
        0.10807495,  0.6289351 , -0.8183315 , -0.77615744,  0.39609525,
       -0.23315974,  0.04918276,  0.879458  ,  0.08264337, -0.01516624,
       -0.19204898, -0.33442628, -0.24506453,  0.6312478 ,  0.28480405,
        0.1000071 ,  0.03199782,  0.76529694, -0.97053623,  0.26944557,
       -0.4845214 ,  0.24615394,  0.49071085, -1.1139286 , -0.09198507,
        1.1445796 , -0.48809683, -0.21912406, -0.31834188, -0.32249808,
       -0.17586514, -0.02084946,  0.36014786,  0.62940025,  0.19775295,
        0.4275967 ,  1.1811138 , -0.55622935, -0.28744274, -0.5984628 ,
       -0.46481133,  0.04407143, -0.44956017,  0.63453496,  0.09470233,
       -0.27863345, -0.20793761,  0.42128265, -0.8122096 ,  0.42522484,
        0.65584326, -0.13752533, -0.03722789, -0.43598282, -0.72522   ,
       -1.2605996 , -0.7767486 ,  0.46211237,  0.4232772 , -0.13487536,
        0.07996047,  0.5894034 ,  0.7337533 ,  0.5783463 , -0.27723   ,
        0.73198324, -0.5529127 ,  0.45494598,  0.85277545, -0.07826032,
       -0.3091203 ,  0.27356094,  0.44235778, -0.60123974,  0.1393649 ,
        0.29696792, -0.38528672,  0.38809997, -0.32254714, -0.6605602 ,
        0.7221839 , -0.01222237,  0.10330763, -0.16048087, -0.3377224 ,
        0.06903428, -0.20879556, -0.49042755, -0.2978255 ,  0.6634165 ,
       -0.0040673 , -0.30612382,  0.25746432,  0.01667893,  0.10528573,
       -0.9395296 , -0.5134512 , -0.17095241,  0.20566861,  0.19690408,
       -0.69362146,  0.46221745, -0.37353134,  0.17481603,  1.1287862 ,
       -0.94452304, -0.14353661, -0.54536766, -0.10933635, -0.8106252 ],
      dtype=float32), array([ 1.89083979e-01,  3.60822499e-01,  7.52017677e-01, -3.48109305e-01,
       -2.02179998e-01,  1.95758149e-01,  1.07987747e-02,  2.16522679e-01,
        7.80965388e-02,  5.46024323e-01,  4.47630286e-01, -2.50288010e-01,
        3.74688029e-01,  3.50882798e-01,  2.24708363e-01, -7.88937032e-01,
       -2.01267555e-01,  5.12138642e-02,  4.48362157e-02, -4.50382046e-02,
       -3.82816911e-01,  4.62848485e-01, -1.72528237e-01, -2.24339798e-01,
        1.38546079e-01,  4.27904785e-01,  5.54159954e-02, -2.41944958e-02,
       -8.99843201e-02, -1.19710870e-01,  5.64997494e-01, -1.27891228e-01,
       -6.04645252e-01,  4.59669173e-01, -4.07027364e-01,  3.06060016e-01,
        9.86440759e-03,  2.85450518e-01,  6.26932979e-02,  5.17451316e-02,
       -1.07931681e-01, -2.93999702e-01, -3.39162529e-01,  2.97916681e-01,
        1.00361094e-01, -4.27598834e-01, -1.60671473e-02,  2.47456923e-01,
       -4.32545543e-01,  6.28253996e-01, -1.87700942e-01,  7.77709335e-02,
        4.08763677e-01,  4.48305935e-01,  1.81456432e-01, -3.58734667e-01,
       -4.65122201e-02,  6.05657697e-01,  3.06532949e-01, -4.28248346e-01,
       -4.96852875e-01,  2.42814030e-02, -7.16932714e-02, -2.23727793e-01,
        4.29408193e-01,  4.77099359e-01,  5.99484265e-01, -1.14740156e-01,
        6.29065335e-02,  1.35380998e-01, -5.70586562e-01,  1.34728923e-01,
        6.01069510e-01,  4.44200873e-01,  1.23525038e-01, -2.21321955e-01,
       -2.08631530e-02, -1.22782357e-01,  7.99565017e-01,  6.71724677e-01,
        2.06830800e-01,  5.25520325e-01, -4.09425832e-02,  6.59353808e-02,
        1.61390305e-01, -4.82085794e-02, -6.60792232e-01,  4.08435129e-02,
        9.48701739e-01, -6.49247617e-02, -1.02402620e-01,  6.12356663e-01,
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       -8.28587115e-02, -6.48742676e-01,  4.97995645e-01, -3.09794247e-01],
      dtype=float32)]
 

임베딩 결과 시각화

 
  1. PCA
In [119]:
from sklearn.decomposition import PCA
In [120]:
pca = PCA(n_components=2)#300차원 벡터를 2차원으로 축소
In [121]:
xys = pca.fit_transform(word_vectors_list)
xs = xys[:,0]
ys = xys[:,1]
In [122]:
print(xys[:5])
 
[[-2.65700024  2.08231725]
 [-1.4255432   0.63475804]
 [-1.54043232  1.2095613 ]
 [-0.9708202   1.72173189]
 [-0.84313219 -1.06927979]]
In [123]:
print(xs[:5])
 
[-2.65700024 -1.4255432  -1.54043232 -0.9708202  -0.84313219]
In [124]:
print(ys[:5])
 
[ 2.08231725  0.63475804  1.2095613   1.72173189 -1.06927979]
In [127]:
def plot_2d_graph(vocabs, xs, ys):
    plt.figure(figsize=(25,15))
    plt.scatter(xs, ys, marker = 'o')
    for i,v in enumerate(vocabs):
        plt.annotate(v, xy=(xs[i], ys[i]))
In [129]:
plot_2d_graph(vocabs, xs, ys)
 
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 30002 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 30002 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20013 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 20013 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20154 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 20154 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 25925 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 25925 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20035 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 20035 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 22899 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 22899 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 28961 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 28961 missing from current font.
  font.load_char(ord(s), flags=flags)
 
 

PCA를 사용한 차원축소는 cluster를 분별하기가 힘들다.-> t_SNE

In [130]:
from sklearn.manifold import TSNE
In [131]:
model2 = TSNE(learning_rate = 100)
transformed = model2.fit_transform(word_vectors_list)

xs2 = transformed[:,0]
ys2 = transformed[:,1]

plt.figure(figsize=(28,21))

plt.scatter(xs2,ys2)

for i,v in enumerate(vocabs):
    plt.annotate(v, xy=(xs2[i],ys2[i]))
    
plt.show()
 
C:\Users\saehee jeon\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 30002 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 30002 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20013 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 20013 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20154 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 20154 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 25925 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 25925 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20035 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 20035 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 22899 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 22899 missing from current font.
  font.load_char(ord(s), flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 28961 missing from current font.
  font.set_text(s, 0.0, flags=flags)
C:\Users\sh\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py:176: RuntimeWarning: Glyph 28961 missing from current font.
  font.load_char(ord(s), flags=flags)
 
In [62]:
print(len(X_test))
print(len(y_test))
 
48650
48306
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