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TDA/lections/notebooks/lec5_text2vec_classifier.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3dda6a69",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.datasets import fetch_20newsgroups\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer \n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_auc_score\n",
"from sklearn.pipeline import Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7fd6636b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['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']\n"
]
}
],
"source": [
"import gensim.downloader\n",
"print(list(gensim.downloader.info()['models'].keys()))"
]
},
{
"cell_type": "markdown",
"id": "3f93b5f6",
"metadata": {},
"source": [
"# GloVe"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "be870586",
"metadata": {},
"outputs": [],
"source": [
"glove_model = gensim.downloader.load(\"glove-twitter-25\") # load glove vectors\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "599d6406",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.96419 -0.60978 0.67449 0.35113 0.41317 -0.21241 1.3796\n",
" 0.12854 0.31567 0.66325 0.3391 -0.18934 -3.325 -1.1491\n",
" -0.4129 0.2195 0.8706 -0.50616 -0.12781 -0.066965 0.065761\n",
" 0.43927 0.1758 -0.56058 0.13529 ]\n"
]
},
{
"data": {
"text/plain": [
"[('dog', 0.9590820074081421),\n",
" ('monkey', 0.920357882976532),\n",
" ('bear', 0.9143136739730835),\n",
" ('pet', 0.9108031392097473),\n",
" ('girl', 0.8880629539489746),\n",
" ('horse', 0.8872726559638977),\n",
" ('kitty', 0.8870542049407959),\n",
" ('puppy', 0.886769711971283),\n",
" ('hot', 0.886525571346283),\n",
" ('lady', 0.8845519423484802)]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(glove_model['cat']) # word embedding for 'cat'\n",
"glove_model.most_similar(\"cat\") # show words that similar to word 'cat'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2db71cfb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.60927683"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"glove_model.similarity('cat', 'bus')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7788acf5",
"metadata": {},
"outputs": [],
"source": [
"categories = ['alt.atheism', 'comp.graphics', 'sci.space'] \n",
"remove = ('headers', 'footers', 'quotes')\n",
"twenty_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42, categories = categories, remove = remove )\n",
"twenty_test = fetch_20newsgroups(subset='test', shuffle=True, random_state=42, categories = categories, remove = remove )\n"
]
},
{
"cell_type": "markdown",
"id": "79dd1ac1",
"metadata": {},
"source": [
"# Векторизуем обучающую выборку\n",
"Получаем матрицу \"Документ-термин\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0565dd1a",
"metadata": {},
"outputs": [],
"source": [
"vectorizer = CountVectorizer(stop_words='english')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a681a1d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1657, 23297)\n"
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"metadata": {},
"output_type": "execute_result"
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"source": [
"train_data = vectorizer.fit_transform(twenty_train['data'])\n",
"CV_data=pd.DataFrame(train_data.toarray(), columns=vectorizer.get_feature_names_out())\n",
"print(CV_data.shape)\n",
"CV_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b20aef46",
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
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"Index(['00', '000', '0000', '00000', '000000', '000005102000', '000062david42',\n",
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],
"source": [
"# Создадим список слов, присутствующих в словаре.\n",
"words_vocab=CV_data.columns\n",
"print(words_vocab[0:10])"
]
},
{
"cell_type": "markdown",
"id": "d1893e86",
"metadata": {},
"source": [
"## Векторизуем с помощью GloVe\n",
"\n",
"Нужно для каждого документа сложить glove-вектора слов, из которых он состоит.\n",
"В результате получим вектор документа как сумму векторов слов, из него состоящих"
]
},
{
"cell_type": "markdown",
"id": "bc36b98d",
"metadata": {},
"source": [
"### Посмотрим на примере как будет работать векторизация"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0d6af65a",
"metadata": {},
"outputs": [
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],
"source": [
"text_data = ['Hello world I love python', 'This is a great computer game! 00 000 zyxel']\n",
"# Векторизуем с помощью обученного CountVectorizer\n",
"X = vectorizer.transform(text_data)\n",
"CV_text_data=pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())\n",
"CV_text_data\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "11dda58a",
"metadata": {},
"outputs": [
{
"name": "stdout",
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"text": [
"hello : [-0.77069 0.12827 0.33137 0.0050893 -0.47605 -0.50116\n",
" 1.858 1.0624 -0.56511 0.13328 -0.41918 -0.14195\n",
" -2.8555 -0.57131 -0.13418 -0.44922 0.48591 -0.6479\n",
" -0.84238 0.61669 -0.19824 -0.57967 -0.65885 0.43928\n",
" -0.50473 ]\n",
"love : [-0.62645 -0.082389 0.070538 0.5782 -0.87199 -0.14816 2.2315\n",
" 0.98573 -1.3154 -0.34921 -0.8847 0.14585 -4.97 -0.73369\n",
" -0.94359 0.035859 -0.026733 -0.77538 -0.30014 0.48853 -0.16678\n",
" -0.016651 -0.53164 0.64236 -0.10922 ]\n",
"python : [-0.25645 -0.22323 0.025901 0.22901 0.49028 -0.060829 0.24563\n",
" -0.84854 1.5882 -0.7274 0.60603 0.25205 -1.8064 -0.95526\n",
" 0.44867 0.013614 0.60856 0.65423 0.82506 0.99459 -0.29403\n",
" -0.27013 -0.348 -0.7293 0.2201 ]\n",
"world : [ 0.10301 0.095666 -0.14789 -0.22383 -0.14775 -0.11599 1.8513\n",
" 0.24886 -0.41877 -0.20384 -0.08509 0.33246 -4.6946 0.84096\n",
" -0.46666 -0.031128 -0.19539 -0.037349 0.58949 0.13941 -0.57667\n",
" -0.44426 -0.43085 -0.52875 0.25855 ]\n",
"Hello world I love python : [ -1.55058002 -0.081683 0.27991899 0.58846928 -1.00551002\n",
" -0.82613902 6.18642995 1.44844997 -0.71108004 -1.14717001\n",
" -0.78294002 0.58841 -14.32649982 -1.41929996 -1.09575997\n",
" -0.430875 0.87234702 -0.806399 0.27203003 2.23921998\n",
" -1.23571999 -1.31071102 -1.96934 -0.17641005 -0.1353 ]\n",
"computer : [ 0.64005 -0.019514 0.70148 -0.66123 1.1723 -0.58859 0.25917\n",
" -0.81541 1.1708 1.1413 -0.15405 -0.11369 -3.8414 -0.87233\n",
" 0.47489 1.1541 0.97678 1.1107 -0.14572 -0.52013 -0.52234\n",
" -0.92349 0.34651 0.061939 -0.57375 ]\n",
"game : [ 1.146 0.3291 0.26878 -1.3945 -0.30044 0.77901 1.3537\n",
" 0.37393 0.50478 -0.44266 -0.048706 0.51396 -4.3136 0.39805\n",
" 1.197 0.10287 -0.17618 -1.2881 -0.59801 0.26131 -1.2619\n",
" 0.39202 0.59309 -0.55232 0.005087]\n",
"great : [-8.4229e-01 3.6512e-01 -3.8841e-01 -4.6118e-01 2.4301e-01 3.2412e-01\n",
" 1.9009e+00 -2.2630e-01 -3.1335e-01 -1.0970e+00 -4.1494e-03 6.2074e-01\n",
" -5.0964e+00 6.7418e-01 5.0080e-01 -6.2119e-01 5.1765e-01 -4.4122e-01\n",
" -1.4364e-01 1.9130e-01 -7.4608e-01 -2.5903e-01 -7.8010e-01 1.1030e-01\n",
" -2.7928e-01]\n",
"zyxel : [ 0.79234 0.067376 -0.22639 -2.2272 0.30057 -0.85676 -1.7268\n",
" -0.78626 1.2042 -0.92348 -0.83987 -0.74233 0.29689 -1.208\n",
" 0.98706 -1.1624 0.61415 -0.27825 0.27813 1.5838 -0.63593\n",
" -0.10225 1.7102 -0.95599 -1.3867 ]\n",
"This is a great computer game! 00 000 zyxel : [ 1.73610002 0.74208201 0.35545996 -4.74411008 1.41543998\n",
" -0.34222007 1.78697008 -1.45404002 2.56643 -1.32184002\n",
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]
},
{
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"text": [
"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.\n",
" glove_data=glove_data.append(pd.DataFrame([one_doc]))\n"
]
}
],
"source": [
"# Создадим датафрейм, в который будем сохранять вектор документа\n",
"glove_data=pd.DataFrame()\n",
"\n",
"# Пробегаем по каждой строке датафрейма (по каждому документу)\n",
"for i in range(CV_text_data.shape[0]):\n",
" \n",
" # Вектор одного документа с размерностью glove-модели:\n",
" one_doc = np.zeros(25) \n",
" \n",
" # Пробегаемся по каждому документу, смотрим, какие слова документа присутствуют в нашем словаре\n",
" # Суммируем glove-вектора каждого известного слова в one_doc\n",
" for word in words_vocab[CV_text_data.iloc[i,:] >= 1]:\n",
" if word in glove_model.key_to_index.keys(): \n",
" print(word, ': ', glove_model[word])\n",
" one_doc += glove_model[word]\n",
" print(text_data[i], ': ', one_doc)\n",
" glove_data=glove_data.append(pd.DataFrame([one_doc])) \n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ff68d8dc",
"metadata": {},
"outputs": [],
"source": [
"def text2vec(text_data):\n",
" \n",
" # Векторизуем с помощью обученного CountVectorizer\n",
" X = vectorizer.transform(text_data)\n",
" CV_text_data=pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())\n",
" CV_text_data\n",
" # Создадим датафрейм, в который будем сохранять вектор документа\n",
" glove_data=pd.DataFrame()\n",
"\n",
" # Пробегаем по каждой строке (по каждому документу)\n",
" for i in range(CV_text_data.shape[0]):\n",
"\n",
" # Вектор одного документа с размерностью glove-модели:\n",
" one_doc = np.zeros(25) \n",
"\n",
" # Пробегаемся по каждому документу, смотрим, какие слова документа присутствуют в нашем словаре\n",
" # Суммируем glove-вектора каждого известного слова в one_doc\n",
" for word in words_vocab[CV_text_data.iloc[i,:] >= 1]:\n",
" if word in glove_model.key_to_index.keys(): \n",
" #print(word, ': ', glove_model[word])\n",
" one_doc += glove_model[word]\n",
" #print(text_data[i], ': ', one_doc)\n",
" glove_data = pd.concat([glove_data, pd.DataFrame([one_doc])], axis = 0)\n",
" #print('glove_data: ', glove_data)\n",
" return glove_data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b778776c",
"metadata": {},
"outputs": [
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{
"cell_type": "code",
"execution_count": 15,
"id": "1bdb459e",
"metadata": {},
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},
"execution_count": 15,
"metadata": {},
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}
],
"source": [
"train_data_glove = text2vec(twenty_train['data']);\n",
"train_data_glove"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "5ac20e79",
"metadata": {},
"outputs": [],
"source": [
"clf = KNeighborsClassifier(n_neighbors = 5)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "08164a25",
"metadata": {},
"outputs": [
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],
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"KNeighborsClassifier()"
]
},
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],
"source": [
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]
},
{
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"execution_count": 19,
"id": "e459faaf",
"metadata": {},
"outputs": [],
"source": [
"test_data_glove = text2vec(twenty_test['data']);"
]
},
{
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"execution_count": 20,
"id": "d8144e75",
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".. ... ... \n",
"0 1.340930 -7.325196 \n",
"0 -6.713165 -3.115454 \n",
"0 -2.609774 -36.597559 \n",
"0 0.447919 -0.469250 \n",
"0 -0.815266 -17.024052 \n",
"\n",
"[1102 rows x 25 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data_glove"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a69830f0",
"metadata": {},
"outputs": [],
"source": [
"predict = clf.predict(test_data_glove )"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "9ac5cf20",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[225 35 59]\n",
" [ 26 313 50]\n",
" [ 56 98 240]]\n",
" precision recall f1-score support\n",
"\n",
" 0 0.73 0.71 0.72 319\n",
" 1 0.70 0.80 0.75 389\n",
" 2 0.69 0.61 0.65 394\n",
"\n",
" accuracy 0.71 1102\n",
" macro avg 0.71 0.71 0.70 1102\n",
"weighted avg 0.71 0.71 0.70 1102\n",
"\n"
]
}
],
"source": [
"print (confusion_matrix(twenty_test['target'], predict))\n",
"print(classification_report(twenty_test['target'], predict))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8cce5a9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b9bff90",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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