TDA/lections/notebooks/lec5_text2vec_classifier.ipynb

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer 
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_auc_score
from sklearn.pipeline import Pipeline

import gensim.downloader
print(list(gensim.downloader.info()['models'].keys()))

['fasttext-wiki-news-subwords-300', 'conceptnet-numberbatch-17-06-300', 'word2vec-ruscorpora-300', 'word2vec-google-news-300', 'glove-wiki-gigaword-50', 'glove-wiki-gigaword-100', 'glove-wiki-gigaword-200', 'glove-wiki-gigaword-300', 'glove-twitter-25', 'glove-twitter-50', 'glove-twitter-100', 'glove-twitter-200', '__testing_word2vec-matrix-synopsis']

GloVe

glove_model = gensim.downloader.load("glove-twitter-25")  # load glove vectors

print(glove_model['cat']) # word embedding for 'cat'
glove_model.most_similar("cat")  # show words that similar to word 'cat'

[-0.96419  -0.60978   0.67449   0.35113   0.41317  -0.21241   1.3796
  0.12854   0.31567   0.66325   0.3391   -0.18934  -3.325    -1.1491
 -0.4129    0.2195    0.8706   -0.50616  -0.12781  -0.066965  0.065761
  0.43927   0.1758   -0.56058   0.13529 ]

[('dog', 0.9590820074081421),
 ('monkey', 0.920357882976532),
 ('bear', 0.9143136739730835),
 ('pet', 0.9108031392097473),
 ('girl', 0.8880629539489746),
 ('horse', 0.8872726559638977),
 ('kitty', 0.8870542049407959),
 ('puppy', 0.886769711971283),
 ('hot', 0.886525571346283),
 ('lady', 0.8845519423484802)]

glove_model.similarity('cat', 'bus')

0.60927683

categories = ['alt.atheism', 'comp.graphics', 'sci.space'] 
remove = ('headers', 'footers', 'quotes')
twenty_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42, categories = categories, remove = remove )
twenty_test = fetch_20newsgroups(subset='test', shuffle=True, random_state=42, categories = categories, remove = remove )

Векторизуем обучающую выборку

Получаем матрицу "Документ-термин"

vectorizer = CountVectorizer(stop_words='english')

train_data = vectorizer.fit_transform(twenty_train['data'])
CV_data=pd.DataFrame(train_data.toarray(), columns=vectorizer.get_feature_names_out())
print(CV_data.shape)
CV_data.head()

(1657, 23297)

	00	000	0000	00000	000000	000005102000	000062david42	000100255pixel	00041032	0004136	...	zurbrin	zurich	zus	zvi	zwaartepunten	zwak	zwakke	zware	zwarte	zyxel
0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

5 rows × 23297 columns

# Создадим список слов, присутствующих в словаре.
words_vocab=CV_data.columns
print(words_vocab[0:10])

Index(['00', '000', '0000', '00000', '000000', '000005102000', '000062david42',
       '000100255pixel', '00041032', '0004136'],
      dtype='object')

Векторизуем с помощью GloVe

Нужно для каждого документа сложить glove-вектора слов, из которых он состоит. В результате получим вектор документа как сумму векторов слов, из него состоящих

Посмотрим на примере как будет работать векторизация

text_data = ['Hello world I love python', 'This is a great computer game! 00 000 zyxel']
# Векторизуем с помощью обученного CountVectorizer
X = vectorizer.transform(text_data)
CV_text_data=pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
CV_text_data

	00	000	0000	00000	000000	000005102000	000062david42	000100255pixel	00041032	0004136	...	zurbrin	zurich	zus	zvi	zwaartepunten	zwak	zwakke	zware	zwarte	zyxel
0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	1	1	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1

2 rows × 23297 columns

# Создадим датафрейм, в который будем сохранять вектор документа
glove_data=pd.DataFrame()

# Пробегаем по каждой строке датафрейма (по каждому документу)
for i in range(CV_text_data.shape[0]):
    
    # Вектор одного документа с размерностью glove-модели:
    one_doc = np.zeros(25) 
    
    # Пробегаемся по каждому документу, смотрим, какие слова документа присутствуют в нашем словаре
    # Суммируем glove-вектора каждого известного слова в one_doc
    for word in words_vocab[CV_text_data.iloc[i,:] >= 1]:
        if word in glove_model.key_to_index.keys(): 
            print(word, ': ', glove_model[word])
            one_doc += glove_model[word]
    print(text_data[i], ': ', one_doc)
    glove_data=glove_data.append(pd.DataFrame([one_doc]))    

hello :  [-0.77069    0.12827    0.33137    0.0050893 -0.47605   -0.50116
  1.858      1.0624    -0.56511    0.13328   -0.41918   -0.14195
 -2.8555    -0.57131   -0.13418   -0.44922    0.48591   -0.6479
 -0.84238    0.61669   -0.19824   -0.57967   -0.65885    0.43928
 -0.50473  ]
love :  [-0.62645  -0.082389  0.070538  0.5782   -0.87199  -0.14816   2.2315
  0.98573  -1.3154   -0.34921  -0.8847    0.14585  -4.97     -0.73369
 -0.94359   0.035859 -0.026733 -0.77538  -0.30014   0.48853  -0.16678
 -0.016651 -0.53164   0.64236  -0.10922 ]
python :  [-0.25645  -0.22323   0.025901  0.22901   0.49028  -0.060829  0.24563
 -0.84854   1.5882   -0.7274    0.60603   0.25205  -1.8064   -0.95526
  0.44867   0.013614  0.60856   0.65423   0.82506   0.99459  -0.29403
 -0.27013  -0.348    -0.7293    0.2201  ]
world :  [ 0.10301   0.095666 -0.14789  -0.22383  -0.14775  -0.11599   1.8513
  0.24886  -0.41877  -0.20384  -0.08509   0.33246  -4.6946    0.84096
 -0.46666  -0.031128 -0.19539  -0.037349  0.58949   0.13941  -0.57667
 -0.44426  -0.43085  -0.52875   0.25855 ]
Hello world I love python :  [ -1.55058002  -0.081683     0.27991899   0.58846928  -1.00551002
  -0.82613902   6.18642995   1.44844997  -0.71108004  -1.14717001
  -0.78294002   0.58841    -14.32649982  -1.41929996  -1.09575997
  -0.430875     0.87234702  -0.806399     0.27203003   2.23921998
  -1.23571999  -1.31071102  -1.96934     -0.17641005  -0.1353    ]
computer :  [ 0.64005  -0.019514  0.70148  -0.66123   1.1723   -0.58859   0.25917
 -0.81541   1.1708    1.1413   -0.15405  -0.11369  -3.8414   -0.87233
  0.47489   1.1541    0.97678   1.1107   -0.14572  -0.52013  -0.52234
 -0.92349   0.34651   0.061939 -0.57375 ]
game :  [ 1.146     0.3291    0.26878  -1.3945   -0.30044   0.77901   1.3537
  0.37393   0.50478  -0.44266  -0.048706  0.51396  -4.3136    0.39805
  1.197     0.10287  -0.17618  -1.2881   -0.59801   0.26131  -1.2619
  0.39202   0.59309  -0.55232   0.005087]
great :  [-8.4229e-01  3.6512e-01 -3.8841e-01 -4.6118e-01  2.4301e-01  3.2412e-01
  1.9009e+00 -2.2630e-01 -3.1335e-01 -1.0970e+00 -4.1494e-03  6.2074e-01
 -5.0964e+00  6.7418e-01  5.0080e-01 -6.2119e-01  5.1765e-01 -4.4122e-01
 -1.4364e-01  1.9130e-01 -7.4608e-01 -2.5903e-01 -7.8010e-01  1.1030e-01
 -2.7928e-01]
zyxel :  [ 0.79234   0.067376 -0.22639  -2.2272    0.30057  -0.85676  -1.7268
 -0.78626   1.2042   -0.92348  -0.83987  -0.74233   0.29689  -1.208
  0.98706  -1.1624    0.61415  -0.27825   0.27813   1.5838   -0.63593
 -0.10225   1.7102   -0.95599  -1.3867  ]
This is a great computer game! 00 000 zyxel :  [  1.73610002   0.74208201   0.35545996  -4.74411008   1.41543998
  -0.34222007   1.78697008  -1.45404002   2.56643     -1.32184002
  -1.04677537   0.27867999 -12.95450976  -1.00809997   3.15975004
  -0.52662008   1.93239999  -0.89686999  -0.60924001   1.51628
  -3.16624993  -0.89275002   1.86969995  -1.33607102  -2.23464306]

C:\Users\Андрей\AppData\Local\Temp\ipykernel_8524\2010506005.py:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  glove_data=glove_data.append(pd.DataFrame([one_doc]))

def text2vec(text_data):
    
    # Векторизуем с помощью обученного CountVectorizer
    X = vectorizer.transform(text_data)
    CV_text_data=pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
    CV_text_data
    # Создадим датафрейм, в который будем сохранять вектор документа
    glove_data=pd.DataFrame()

    # Пробегаем по каждой строке (по каждому документу)
    for i in range(CV_text_data.shape[0]):

        # Вектор одного документа с размерностью glove-модели:
        one_doc = np.zeros(25) 

        # Пробегаемся по каждому документу, смотрим, какие слова документа присутствуют в нашем словаре
        # Суммируем glove-вектора каждого известного слова в one_doc
        for word in words_vocab[CV_text_data.iloc[i,:] >= 1]:
            if word in glove_model.key_to_index.keys(): 
                #print(word, ': ', glove_model[word])
                one_doc += glove_model[word]
        #print(text_data[i], ': ', one_doc)
        glove_data = pd.concat([glove_data, pd.DataFrame([one_doc])], axis = 0)
    #print('glove_data: ', glove_data)
    return glove_data

glove_data

	0	1	2	3	4	5	6	7	8	9	...	15	16	17	18	19	20	21	22	23	24
0	-1.55058	-0.081683	0.279919	0.588469	-1.00551	-0.826139	6.18643	1.44845	-0.71108	-1.14717	...	-0.430875	0.872347	-0.806399	0.27203	2.23922	-1.23572	-1.310711	-1.96934	-0.176410	-0.135300
0	1.73610	0.742082	0.355460	-4.744110	1.41544	-0.342220	1.78697	-1.45404	2.56643	-1.32184	...	-0.526620	1.932400	-0.896870	-0.60924	1.51628	-3.16625	-0.892750	1.86970	-1.336071	-2.234643

2 rows × 25 columns

train_data_glove = text2vec(twenty_train['data']);
train_data_glove

	0	1	2	3	4	5	6	7	8	9	...	15	16	17	18	19	20	21	22	23	24
0	-8.521142	2.020376	-10.802921	3.167636	0.252469	15.544048	17.631184	-32.581192	9.696540	-11.103087	...	2.810453	7.900215	0.962129	17.691130	-1.252574	-10.098049	0.500113	1.348694	2.186150	-16.556824
0	6.576228	20.336350	-32.675150	-9.073872	17.515655	-6.488794	59.458419	-75.384298	13.323775	-14.443218	...	21.407738	23.525118	0.325680	19.871444	-27.585188	-4.559155	-7.417482	-16.694553	-0.197711	-58.948193
0	1.329914	3.060870	-1.868484	1.392735	-1.335277	-5.014955	12.859476	-9.978156	-0.869613	-2.031490	...	2.925134	2.872930	2.184486	3.831770	-0.877866	-0.927770	0.700101	-9.855365	-5.419429	-2.279330
0	-4.866150	-0.273176	3.515124	-5.008165	-1.236789	-7.951168	-11.015882	-3.496241	16.024286	-9.388742	...	-0.471141	3.575378	6.193222	0.349430	15.040248	-10.369132	-0.848717	-0.564796	-1.114126	-7.844431
0	-3.115007	-1.805252	-5.419340	-0.393406	-0.406461	-2.724340	7.898330	-15.619113	0.231822	-3.628156	...	5.944151	8.309932	-0.656084	12.178709	-6.118551	-3.286376	3.450946	2.055343	0.463787	-12.644626
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
0	-0.930954	4.974043	-8.147008	-5.147130	3.960455	-1.344022	7.818063	-25.427420	4.624732	-7.218097	...	3.623038	4.453189	2.405320	8.032963	-8.029539	0.838867	-4.757457	-5.755052	-9.496197	-21.542710
0	-0.770690	0.128270	0.331370	0.005089	-0.476050	-0.501160	1.858000	1.062400	-0.565110	0.133280	...	-0.449220	0.485910	-0.647900	-0.842380	0.616690	-0.198240	-0.579670	-0.658850	0.439280	-0.504730
0	1.491177	6.992638	-7.921970	-7.157521	6.641657	-2.958020	12.820770	-18.502946	6.838083	-2.717310	...	-1.344873	4.170405	-0.178030	5.699992	-7.295038	-3.683306	-2.718006	-0.117608	-7.205832	-13.863438
0	2.523770	5.817394	2.184340	-2.996497	-0.267181	-10.059634	6.344402	-2.047127	2.679123	-7.642505	...	-1.230296	1.409746	-3.322040	-5.068259	-0.648718	0.753010	-6.220990	-5.012004	-1.518542	-10.156440
0	-0.118691	11.860546	-2.567264	-10.955913	-4.239322	-9.340552	21.189778	-10.895375	2.659030	-3.848115	...	0.726191	11.634998	-5.447248	1.293007	-7.882002	-2.527453	0.298939	-6.107062	3.365051	-15.641826

1657 rows × 25 columns

clf = KNeighborsClassifier(n_neighbors = 5)

clf.fit(train_data_glove, twenty_train['target'])

KNeighborsClassifier()

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KNeighborsClassifier

KNeighborsClassifier()

test_data_glove = text2vec(twenty_test['data']);

test_data_glove

	0	1	2	3	4	5	6	7	8	9	...	15	16	17	18	19	20	21	22	23	24
0	-6.760635	5.063863	-2.779060	3.699120	-2.858086	0.135230	20.811229	-19.425567	7.302950	-5.826012	...	3.833378	6.794452	-0.921720	12.187404	-5.547615	-4.133999	3.588260	-0.497106	-2.542142	-11.362855
0	1.632616	2.512300	-0.745513	-3.081154	2.182067	-1.988816	7.533100	-1.015740	-0.829598	-2.764237	...	0.791851	2.114150	-2.249193	-0.163590	-1.177710	-2.496928	-5.074085	-2.666947	0.662050	-3.590550
0	2.115766	2.142060	-0.445607	-3.229030	1.154580	-2.877278	6.399954	-10.445769	2.230760	-3.299899	...	4.388870	8.515056	-0.766260	3.549431	-1.643443	-0.825730	-2.968016	-0.808924	-0.000160	-7.468189
0	-0.802784	5.199443	4.294071	-7.390966	2.747166	-1.359952	15.032628	-1.601590	1.474406	2.570105	...	3.043432	6.176236	-6.193988	-3.990476	-2.345854	-5.534376	-8.925422	1.553300	0.905790	-12.824533
0	29.926489	65.324993	-25.059592	-64.080130	77.565282	-34.614604	75.643770	-115.600859	90.847175	-42.971146	...	40.956031	50.322156	-19.537098	28.903925	-34.643949	-69.894146	-94.992145	-48.601895	-29.098555	-91.934770
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
0	1.829235	4.513807	2.916520	2.237308	-1.704831	-1.811192	22.196895	-12.858912	-4.054810	-3.130457	...	6.019246	8.949456	-4.682214	-5.648911	-1.026898	3.719006	2.449941	-6.487197	1.340930	-7.325196
0	-0.963815	5.491164	3.567377	-6.048021	-5.059298	-0.977958	15.131499	-0.904470	2.185990	-1.459807	...	0.968499	4.725793	-0.726944	1.328612	-3.144209	1.643127	-1.259245	-0.880740	-6.713165	-3.115454
0	6.801324	15.348126	-17.051718	5.030998	9.332448	-5.716691	56.409175	-56.250411	-4.028209	-11.687558	...	22.884424	12.940570	1.058664	21.879058	-20.897253	2.537755	3.774890	-11.495336	-2.609774	-36.597559
0	1.054090	0.764524	1.958340	-1.085245	-0.441392	-0.421970	6.139770	-0.612219	-2.251460	-0.465165	...	0.377958	1.957450	-1.705220	-0.509700	0.016110	1.461620	1.589069	2.267340	0.447919	-0.469250
0	-18.387286	13.274879	-7.895913	-1.831442	-10.424961	-12.248442	32.153890	-40.169293	13.089525	-21.306493	...	6.497279	8.340729	4.996109	23.442078	-3.701088	-11.671505	9.209790	-10.002501	-0.815266	-17.024052

1102 rows × 25 columns

predict = clf.predict(test_data_glove )

print (confusion_matrix(twenty_test['target'], predict))
print(classification_report(twenty_test['target'], predict))

[[225  35  59]
 [ 26 313  50]
 [ 56  98 240]]
              precision    recall  f1-score   support

           0       0.73      0.71      0.72       319
           1       0.70      0.80      0.75       389
           2       0.69      0.61      0.65       394

    accuracy                           0.71      1102
   macro avg       0.71      0.71      0.70      1102
weighted avg       0.71      0.71      0.70      1102