3. Topic Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Тематическое моделирование\n",
    "\n",
    "**Тематическое моделирование** — технология статистического анализа текстов для автоматического выявления тематики в больших коллекциях документов\n",
    "\n",
    "**Тематическая модель** (англ. topic model) — модель коллекции текстовых документов, которая определяет, к каким темам относится каждый документ и какие слова (термины, термы) образуют каждую тему. Для этого не требуется никакой ручной разметки текстов, обучение модели происходит без учителя.\n",
    "\n",
    "Чем-то похоже на кластеризацию, но тематическое моделирование в этом плане является «мягким» и допускает, чтобы документ относился к нескольким кластерам-темам. Тематическое моделирование не претендует на понимание смысла текста, однако оно способно отвечать на вопросы «о чём этот текст» или «какие общие темы имеет эта пара текстов».\n",
    "\n",
    "Тематическая модель формирует сжатое векторное представление текста, которое помогает классифицировать, рубрицировать, аннотировать, сегментировать тексты. В отличие от известных векторных представлений семейства x2vec (word2vec, paragraph2vec, graph2vec и т.д.), в тематических векторах каждая координата соответствует теме и имеет содержательную интерпретацию. Модель привязывает к каждой теме список ключевых слов или фраз, который описывает семантику этой темы. \n",
    "\n",
    "Тему нельзя строго определить ни семантически, ни эпистемологически. Темы выявляются исключительно с помощью автоматического подсчета правдоподобия совестной встречаемости слов. Слово может быть отнесено к нескольким темам, но с разной вероятностью, но слова-соседи для каждой темы у него будут разными. Высокочастотные служебные слова будут иметь примерно одинаковую вероятность для всех тем.\n",
    "\n",
    "\n",
    "### Зачем это нужно?\n",
    "\n",
    "* разведочный информационный поиск (exploratory search) в электронных библиотеках;\n",
    "* поиск по смыслу, а не по ключевым словам;\n",
    "* обнаружение и отслеживание событий в новостных потоках;\n",
    "* выявление тематических сообществ в социальных сетях;\n",
    "* построение профилей интересов пользователей в рекомендательных системах;\n",
    "* категоризация интентов в системах разговорного интеллекта;\n",
    "* поиск мотивов в нуклеотидных и аминокислотных последовательностях;\n",
    "* аннотирование изображений;\n",
    "* поиск аномального поведения объектов в видеопотоке;\n",
    "* выявление паттернов поведения клиетов по транзакционным данным. \n",
    "\n",
    "### Построение тематической модели документа \n",
    "\n",
    "* $p(w|t)$ — матрица искомых условных распределений слов по темам \n",
    "* $p(t|d)$— матрица искомых условных распределений тем по документам\n",
    "* $d$ — документ\n",
    "* $w$ — слово\n",
    "* $d, w$ — наблюдаемые переменные \n",
    "* $t$ — тема (скрытая переменная)\n",
    "\n",
    "<img src='https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/%D0%A2%D0%B5%D0%BC%D0%B0%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B0%D1%8F_%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C.png/1280px-%D0%A2%D0%B5%D0%BC%D0%B0%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B0%D1%8F_%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C.png' width='600' align='left'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Виды тематических моделей\n",
    "\n",
    "* LSA (LSI)\n",
    "* pLSA (pLSI)\n",
    "* LDA\n",
    "* ARTM\n",
    "* ...\n",
    "\n",
    "### Подробнее о тематических моделях\n",
    "\n",
    "* [Кратко, но с формулами](http://www.machinelearning.ru/wiki/index.php?title=%D0%A2%D0%B5%D0%BC%D0%B0%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B5_%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5)\n",
    "* [Подробный обзор тематических моделей от К.В. Воронцова](http://www.machinelearning.ru/wiki/images/d/d5/Voron17survey-artm.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSA (LSI)\n",
    "\n",
    "**Латентно-семантический анализ**, **LSA** *(latent semantic analysis)*, он же **LSI** *(latent semantic indexing)* — самая ранняя модель, предложенная еще в конце 80-х гг. Модель называется латентной, т.к. предполагает введение скрытого (латентного) параметра — темы.\n",
    "\n",
    "Ниже — анимация процесса обнаружения тематик в матрице «терм-документ». Каждый столбец матрицы соответствует документу, каждая строка — слову. Ячейки матрицы содержат веса слов в документах (например, значения TF-IDF), более тёмные оттенки соответствуют более высокому весу. Алгоритм LSA группирует как документы, которые используют похожие слова, так и слова, которые встречаются в похожем наборе документов. Полученные кластеры в матрице используются для обнаружения латентных (скрытых) компонентов в исходных данных, соответствующих определённым тематикам.\n",
    "\n",
    "![](img/lsa.gif)\n",
    "\n",
    "Наиболее распространенный вариант LSA основан на использовании сингулярного разложения матрицы (см. [тетрадку про снижение размерности](https://github.com/ancatmara/data-science-nlp/blob/master/3.%20DimReduction.ipynb)). С помощью SVD-разложения любая матрица раскладывается во множество ортогональных матриц, линейная комбинация которых является достаточно точным приближением к исходной матрице. Этим и объясняется название этого алгоритма в `sklearn` — `TruncaredSVD`.\n",
    "\n",
    "Вспомним формулу сингулярного разложения: \n",
    "\n",
    "$A = U\\Sigma V^{T}$\n",
    "\n",
    "Особенность такого разложения в том, что если в матрице $\\Sigma$ оставить только $k$ наибольших сингулярных значений, а в матрицах $U$ и $V$ — только соответствующие этим значениям столбцы, то произведение получившихся матриц $\\Sigma$, $U$ и $V$ будет наилучшим приближением исходной матрицы $A$ к матрице $\\hat{A}$ ранга $k$.\n",
    "\n",
    "$\\hat{A}\\approx A = U\\Sigma V^{T}$\n",
    "\n",
    "Основная идея LSA в том, что если в качестве матрицы $A$ использовать матрицу «терм-документ», то матрица  $\\hat{A}$ отражает основную структуру различных зависимостей, присутствующих в исходной матрице. Эту структуру определяют веса термов. Таким образом, каждый терм и документ представляются при помощи векторов в общем пространстве размерности $k$ (т.н. пространстве гипотез). Близость между любой комбинацией термов и/или документов легко вычисляется при помощи скалярного произведения векторов.\n",
    "\n",
    "[Подробнее об алгоритме](https://en.wikipedia.org/wiki/Latent_semantic_analysis) (по-английски).\n",
    "\n",
    "\n",
    "## pLSA (pLSI)\n",
    "\n",
    "**pLSA** *(probabilistic latent semantic analysis)*, она же **pLSI** *(probabilistic latent semantic indexing)* — вероятностный латентно-семантический анализ (индексирование). Модель предложена в 1999 г. Томасом Хоффманом, [вот оригинальная статья](https://web.archive.org/web/20101214074049/http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR99.pdf). \n",
    "\n",
    "Зачем понадобилось модифицировать LSA? Проблема этого метода в том, что он предполагает, что слова и документы имеют нормальное распределение, но в реальности это не так. Поэтому на практике чаще используется pLSA, основанный на мультиномиальном распределении. Если LSA — это чистая линейная алгебра, то pLSA имеет еще и статистические основания.\n",
    "\n",
    "Согласно pLSA, совместная встречаемость терма и документа $(w, d)$ моделируется как сочетание независимых мультиномиальных распределений, где $c$ — тема. Количество тем — это гиперпараметр, который выбирается до начала анализа. \n",
    "\n",
    "$$ P(w, d)=\\sum_{c} P(c) P(d | c) P(w | c)=P(d) \\sum_{c} P(c | d) P(w | c)$$\n",
    "\n",
    "Ниже — схема алгоритма в т.н. *plate notation*. Прямоугольники на схеме — повторяющиеся элементы, внешний прямоугольник — документы, внутренний — позиции повторяющихся слов в данном документе. Т.е. такая схема показывает один документ, но справедлива для каждого.\n",
    "\n",
    "<img src=\"img/plsi.png\" width=\"400\" align=\"left\" style=\"padding-right: 50px; \">\n",
    "\n",
    "\n",
    "* $d$ — индекс документа\n",
    "* $c$ — тема, к которой принадлежит слово, полученная из распределения тем по документам $P(c|d)$\n",
    "* $w$ — слово, полученное из распределения слов по теме, к которой оно принадлежит $P(w|c)$\n",
    "* $d$ и $w$ — наблюдаемые переменные\n",
    "* $c$ — скрытая переменная.\n",
    "\n",
    "\n",
    "[Подробнее о алгоритме](http://www.machinelearning.ru/wiki/index.php?title=%D0%92%D0%B5%D1%80%D0%BE%D1%8F%D1%82%D0%BD%D0%BE%D1%81%D1%82%D0%BD%D1%8B%D0%B9_%D0%BB%D0%B0%D1%82%D0%B5%D0%BD%D1%82%D0%BD%D1%8B%D0%B9_%D1%81%D0%B5%D0%BC%D0%B0%D0%BD%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B9_%D0%B0%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7) (осторожно, много формул!)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## LDA\n",
    "\n",
    "**LDA** *(Latent Dirichlet Allocation)*, латентное размещение Дирихле — генеративная вероятностная модель корпуса текстов. Самая распространенная модель, разработана в 2002-2003 гг. ([вот оригинальная статья](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) David Blei, Andrew Ng, Michael I. Jordan). Это следующий шаг в развитии тематических моделей, где устранены недостатки pLSA (легко переобучается; при добавлении документа в коллекцию надо все пересчитывать заново). *Сейчас эта тематическая модель используется шире всего.*\n",
    "\n",
    "В LDA каждый документ может рассматриваться как набор случайных скрытых тем, где каждая тема определяется распределением слов, при этом каждое слово в конкретном документе можно отнести к одной из его тем. Подобный подход схож с pLSA с той разницей, что в LDA качестве априорного распределения для тем используется распределение Дирихле. Это улучшает качество получающихся тем. \n",
    " \n",
    "<img src=\"img/LDA.png\" width=\"400\" align=\"left\" style=\"padding-right: 50px; \">\n",
    "\n",
    "* $M$ — количество документов\n",
    "* $N$ — количество слов в документе\n",
    "* $\\alpha$ — параметр априорного распределения Дирихле на подокументном распределении тем\n",
    "* $\\beta$ — параметр априорного распределения Дирихле на потемном распределении слов \n",
    "* $\\theta _{i}$  — распределение тем для документа i\n",
    "* $\\varphi _{k}$ — распределение слов для темы k\n",
    "* $ z_{ij}$  — тема, к которой принадлежит j-тое слово в документе i\n",
    "* $w_{ij}$ — конкретное слово\n",
    "\n",
    "<br>\n",
    "\n",
    "То, что $W$ закрашено серым, означает, что единственные наблюдаемые переменные — $w_{ij}$ и $z_{ij}$ , а остальные являются скрытыми.\n",
    "\n",
    "Подробнее о алгоритме [здесь](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BigARTM\n",
    "\n",
    "*ARTM — Additive Regularization for Topic Modeling*\n",
    "\n",
    "**BigARTM** — библиотека с открытым кодом для тематического моделирования больших коллекций текстовых документов и массивов транзакционных данных. Эффективная потоковая параллельная реализация вероятностного тематического моделирования на основе аддитивной регуляризации.\n",
    "\n",
    "> Аддитивная регуляризация (ARTM) позволяет задать сразу несколько критериев-регуляризаторов. Например, чтобы построить тематическую модель новостного потока, необходимо учесть несколько верхних уровней уже имеющегося рубрикатора, научить модель учитывать время документов, разделять темы на подтемы и создавать новые темы по необходимости. ARTM позволяет складывать регуляризаторы от разных моделей, создавая комбинации моделей с заданными свойствами под конкретные приложения. Это приводит к модульной технологии тематического моделирования с высокой степенью повторного использования кода.\n",
    "\n",
    "Зачем нужно что-то еще, если есть LDA? Проблема в том, что задача тематического моделирования имеет очень много (бесконечно много) решений, и LDA выбирает одно из них, не предоставляя никаких средств для выбора лучшего решения под конкретную задачу. \n",
    "\n",
    "<img src= \"img/artm.jpg\" width=\"300\" align=\"right\" style=\"padding-right: 50px;\">\n",
    "\n",
    "BigARTM реализует несколько механизмов, которые снимают многие ограничения простых моделей типа PLSA или LDA и расширяют спектр приложений тематического моделирования.\n",
    "\n",
    "* Regularization. Регуляризаторы, которые можно комбинировать в любых сочетаниях.\n",
    "* Modality. Модальности, которыми можно описывать нетекстовые объекты внутри документов.\n",
    "* Hierarchy. Тематические иерархии, в которых темы разделяются на подтемы.\n",
    "* Intratext. Обработка текста как последовательности тематических векторов слов.\n",
    "* Co-occurrence. Использование данных о совместной встречаемости слов.\n",
    "* Hypergraph. Тематизация сложно структурированных транзакционных данных. \n",
    "\n",
    "#### Подробнее\n",
    "\n",
    "* [Кратко и понятно](http://www.machinelearning.ru/wiki/index.php?title=BigARTM), цитата была отсюда\n",
    "* [Сайт проекта BigARTM](http://bigartm.org/)\n",
    "* [Документация BigARTM](http://docs.bigartm.org/en/stable/index.html)\n",
    "* [Репозиторий](https://github.com/bigartm/bigartm)\n",
    "* [Тьюториалы от авторов библиотеки](https://github.com/bigartm/bigartm-book)\n",
    "* [В обзоре Воронцова](http://www.machinelearning.ru/wiki/images/d/d5/Voron17survey-artm.pdf) тоже об этом немало, стр. 55-65\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практическая часть"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "from sklearn.metrics import *\n",
    "from sklearn.pipeline import *\n",
    "from sklearn.feature_extraction.text import *\n",
    "from sklearn.decomposition import TruncatedSVD, PCA\n",
    "from sklearn.preprocessing import Normalizer, LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Данные\n",
    "\n",
    "В качестве примера возьмем статьи из газеты «Полярный круг» (г. Салехард, Якутия) за 2014-2017 гг. Каждая статья — отдельный документ. Тексты заранее лемматизированы, осталось убрать из них пунктуацию и стоп-слова. Затем представим наш корпус в виде массива документов, где каждый документ, в свою очередь, будет массивом токенов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ведь',\n",
       " 'весь',\n",
       " 'весьма',\n",
       " 'видеть',\n",
       " 'видит',\n",
       " 'вместе',\n",
       " 'во',\n",
       " 'восемь',\n",
       " 'вот',\n",
       " 'впрочем']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./data/rus_stopwords.txt', 'r', encoding='utf-8') as f:\n",
    "    sw = f.read().split('\\n')\n",
    "    \n",
    "sw[40:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['лев',\n",
       " 'толстой',\n",
       " 'анна',\n",
       " 'каренина',\n",
       " 'писать',\n",
       " 'счастливый',\n",
       " 'семья',\n",
       " 'похожий',\n",
       " 'друг',\n",
       " 'друг']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./data/polkrug_lem.txt', 'r', encoding='utf-8') as f:\n",
    "    text = f.read()\n",
    "    \n",
    "texts = text.split('\\n\\n')\n",
    "\n",
    "punct = '[!\"#$%&()*+,./:;<=>?@[\\]^_`{|}~„“«»†*/\\—–‘’]'\n",
    "\n",
    "texts = [re.sub('\\n', ' ', text) for text in texts]\n",
    "texts = [re.sub(punct, '', text) for text in texts]\n",
    "\n",
    "tokenized_texts = []\n",
    "for text in texts:\n",
    "    text = [w for w in text.split() if w not in sw]\n",
    "    tokenized_texts.append(text)\n",
    "    \n",
    "tokenized_texts[0][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3607"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokenized_texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Векторизуем документы\n",
    "\n",
    "Векторизуем тексты с помощью `doc2vec` (подробнее см. в [тетрадке про эмбеддинги](https://github.com/ancatmara/data-science-nlp/blob/master/2.%20Embeddings.ipynb)). Они будут представлены словарем и корпусом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import *\n",
    "from gensim import corpora\n",
    "from gensim import similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making dictionary...\n",
      "Original: Dictionary(43034 unique tokens: ['10', '1964', 'август', 'адрес', 'анна']...)\n",
      "Filtered: Dictionary(12866 unique tokens: ['10', '1964', 'август', 'адрес', 'анна']...)\n",
      "Vectorizing corpus...\n"
     ]
    }
   ],
   "source": [
    "# почему бы не doc2vec?\n",
    "\n",
    "print('Making dictionary...')\n",
    "dictionary = corpora.Dictionary(tokenized_texts)\n",
    "print('Original: {}'.format(dictionary))\n",
    "dictionary.filter_extremes(no_below = 5, no_above = 0.9, keep_n=None)\n",
    "dictionary.save('./data/polkrug.dict')\n",
    "print('Filtered: {}'.format(dictionary))\n",
    "\n",
    "print('Vectorizing corpus...')\n",
    "corpus = [dictionary.doc2bow(text) for text in tokenized_texts]\n",
    "corpora.MmCorpus.serialize('./data/polkrug.model', corpus) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определение близости документов\n",
    "\n",
    "Распределение тем можно использовать для определения близости документов. В нашем случае картинка будет плохо читаема, если мы возьмем все документы, т.к. их в нашем корпусе больше 3000. Поэтому для построения графика выберем 30 случайных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "\n",
    "# tf-idf векторах\n",
    "tfidf = TfidfModel(corpus)\n",
    "corpus_tfidf = tfidf[corpus]\n",
    "\n",
    "# создание случайной выборки\n",
    "sampling_tfidf = random.choices(corpus_tfidf, k=30)\n",
    "\n",
    "# вычисление сходства по косинусной мере на tf-idf векторах\n",
    "index = similarities.MatrixSimilarity(sampling_tfidf)\n",
    "sims = index[sampling_tfidf]\n",
    "\n",
    "plt.figure(figsize = (12,10))\n",
    "sns.heatmap(data=sims, cmap = 'Spectral').set(xticklabels=[], yticklabels=[])\n",
    "plt.title(\"Матрица близости\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSI (LSA)\n",
    "\n",
    "Данная модель реализована в библиотеке `gensim`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsi = lsimodel.LsiModel(corpus, id2word=dictionary, num_topics=20, chunksize=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  [('год', 0.37685261496034844),\n",
       "   ('город', 0.23365046220761712),\n",
       "   ('салехард', 0.2155548991750232),\n",
       "   ('работа', 0.17158479684939004),\n",
       "   ('день', 0.14835110883068858),\n",
       "   ('человек', 0.14298072611275486),\n",
       "   ('россия', 0.12248783757212399),\n",
       "   ('ребенок', 0.12091673090479456),\n",
       "   ('городской', 0.10904850693247384),\n",
       "   ('округ', 0.10412368839396716)]),\n",
       " (1,\n",
       "  [('год', -0.3707260440151335),\n",
       "   ('война', -0.1943153519055783),\n",
       "   ('салехард', 0.17001024421786756),\n",
       "   ('город', 0.15275502194199414),\n",
       "   ('сторона', 0.13621043183883472),\n",
       "   ('полиция', 0.12744644814555064),\n",
       "   ('победа', -0.12661435463463),\n",
       "   ('работа', 0.11123239848303894),\n",
       "   ('великий', -0.10784913271890878),\n",
       "   ('май', -0.10224170897891326)]),\n",
       " (2,\n",
       "  [('война', -0.19738780953476345),\n",
       "   ('победа', -0.1569303476290113),\n",
       "   ('социальный', -0.15342346075796565),\n",
       "   ('россия', 0.14143704261763446),\n",
       "   ('июнь', 0.138776559682956),\n",
       "   ('улица', 0.1357567207808184),\n",
       "   ('библиотека', 0.12897529227952428),\n",
       "   ('год', -0.12367184324011182),\n",
       "   ('май', -0.11172049445184545),\n",
       "   ('лето', 0.10418202463549453)]),\n",
       " (3,\n",
       "  [('год', 0.5499170364896014),\n",
       "   ('ребенок', -0.17767532314262777),\n",
       "   ('город', -0.15341825369509013),\n",
       "   ('округ', 0.14352440687070422),\n",
       "   ('день', -0.14338172402859375),\n",
       "   ('россия', 0.12404958807811796),\n",
       "   ('север', 0.11296968100053162),\n",
       "   ('обдорск', 0.1121216789547802),\n",
       "   ('дом', -0.11088558926888344),\n",
       "   ('организация', -0.10951517848800323)]),\n",
       " (4,\n",
       "  [('город', 0.2029774482728864),\n",
       "   ('день', 0.14410802081844182),\n",
       "   ('жилой', -0.13475222179909196),\n",
       "   ('услуга', -0.1299244760511472),\n",
       "   ('компания', -0.12067228054457126),\n",
       "   ('помещение', -0.12030873189716053),\n",
       "   ('война', 0.11481281197909422),\n",
       "   ('школа', 0.10287751499213908),\n",
       "   ('гражданин', -0.10251511051801812),\n",
       "   ('округ', -0.09543243816339034)])]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsi.show_topics(num_topics=5, num_words=10, formatted=False) # можно показать не все темы, а только часть"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, как снижение размерности влияет на матрицу близости. \n",
    "\n",
    "**NB!** Это не те же самые тексты, что на предыдущей картинке, просто другие случайные 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampling = random.choices(corpus, k=30)\n",
    "\n",
    "sampling_lsi = lsi[sampling]\n",
    "index = similarities.MatrixSimilarity(lsi[sampling])\n",
    "sims = index[sampling_lsi]\n",
    "sims  = (sims + 1)/2.\n",
    "\n",
    "plt.figure(figsize = (12,10))\n",
    "sns.heatmap(data=sims, cmap = 'Spectral').set(xticklabels=[], yticklabels=[])\n",
    "plt.title(\"Матрица близости\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA\n",
    "\n",
    "Данная модель также реализована в библиотеке `gensim`. Один из ее плюсов в том, что можно дообучать готовую модель (а отличие от LSA и pLSA, где даже при добавлении одного нового документа приходится обучать модель с нуля)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "Wall time: 21.8 s\n"
     ]
    }
   ],
   "source": [
    "print(\"Training model...\")\n",
    "\n",
    "%time lda = ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=20, chunksize=50, update_every=1, passes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(17,\n",
       "  [('-', 0.027139531),\n",
       "   ('год', 0.013591349),\n",
       "   ('зырянин', 0.008487297),\n",
       "   ('александр', 0.008136378),\n",
       "   ('написать', 0.0071233003),\n",
       "   ('жизнь', 0.0069715385),\n",
       "   ('путь', 0.0067909956),\n",
       "   ('работать', 0.006758359),\n",
       "   ('смерть', 0.0066299513),\n",
       "   ('служить', 0.006527992)]),\n",
       " (19,\n",
       "  [('год', 0.05027296),\n",
       "   ('обдорск', 0.02588423),\n",
       "   ('русский', 0.0152272),\n",
       "   ('север', 0.012690327),\n",
       "   ('век', 0.011329643),\n",
       "   ('первый', 0.011120653),\n",
       "   ('северный', 0.011036167),\n",
       "   ('сибирь', 0.010240931),\n",
       "   ('обдорский', 0.010174131),\n",
       "   ('россия', 0.008400448)]),\n",
       " (11,\n",
       "  [('вода', 0.020473776),\n",
       "   ('год', 0.015421258),\n",
       "   ('салехард', 0.013129102),\n",
       "   ('сообщать', 0.011019596),\n",
       "   ('дело', 0.010898765),\n",
       "   ('устанавливать', 0.010870391),\n",
       "   ('факт', 0.010450788),\n",
       "   ('находиться', 0.010316352),\n",
       "   ('случай', 0.009459841),\n",
       "   ('вид', 0.009288408)]),\n",
       " (9,\n",
       "  [('округ', 0.018680487),\n",
       "   ('год', 0.016062962),\n",
       "   ('проект', 0.014693652),\n",
       "   ('ямал', 0.012475975),\n",
       "   ('регион', 0.011762369),\n",
       "   ('развитие', 0.008710078),\n",
       "   ('строительство', 0.008446187),\n",
       "   ('учитель', 0.008211333),\n",
       "   ('губернатор', 0.0079709105),\n",
       "   ('янао', 0.007902338)]),\n",
       " (15,\n",
       "  [('николаевич', 0.028375592),\n",
       "   ('научный', 0.022099635),\n",
       "   ('развитие', 0.01757266),\n",
       "   ('регион', 0.015810607),\n",
       "   ('наука', 0.015741413),\n",
       "   ('ученый', 0.015200309),\n",
       "   ('зона', 0.0149419),\n",
       "   ('тобольский', 0.013574065),\n",
       "   ('россия', 0.012261234),\n",
       "   ('страна', 0.011940397)])]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.show_topics(num_topics=5, num_words=10, formatted=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDAVis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDAVis выдает интерактвную визуализацию тем и слов, которые их определяют. Это выглядит вот так:\n",
    "\n",
    "![](./img/ldavis.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 6min 6s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el1549220187043770802731783015\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1549220187043770802731783015_data = {\"mdsDat\": {\"x\": [0.150361687428934, -0.1148884192461433, 0.04648997288607895, 0.1739895126100776, -0.09853021679926258, -0.1995475021053604, 0.24329557153029144, -0.07380921015299433, -0.1813332621687925, 0.04365133325366665, -0.21914450788994197, -0.17320316127796034, 0.13224353075971232, 0.05435312003582515, -0.03735889612488626, 0.14913500192503595, 0.12572879685226337, 0.09216265364062838, -0.07787501805488926, -0.03572098710228285], \"y\": [-0.18559020452547914, 0.04724080413802639, -0.09262798026738195, -0.010937803474628848, -0.16793224620977984, -0.03563159384108073, 0.01052006794000594, -0.08245647529916597, 0.060397575205384524, -0.024903227737799058, 0.12235825683909603, -0.0643634802463283, 0.0549487757857861, 0.04153586014683224, -0.15642148993180469, 0.2112904565107063, 0.11859334342878738, -0.14671686238510126, 0.06122223209663081, 0.23947399182729528], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 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\"\\u0431\\u0438\\u0431\\u043b\\u0438\\u043e\\u0442\\u0435\\u0447\\u043d\\u044b\\u0439\", \"\\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\", \"\\u043c\\u0430\\u0441\\u0441\\u043e\\u0432\\u044b\\u0439\", \"\\u0447\\u0438\\u0442\\u0430\\u0442\\u044c\", \"\\u0433\\u0440\\u0443\\u043f\\u043f\\u0430\", \"\\u0437\\u0430\\u043d\\u0438\\u043c\\u0430\\u0442\\u044c\\u0441\\u044f\", \"\\u0443\\u0445\\u043e\\u0434\", \"24\", \"\\u0445\\u043e\\u0437\\u044f\\u0438\\u043d\", \"\\u043a\\u0443\\u043b\\u044c\\u0442\\u0443\\u0440\\u0430\", \"\\u0441\\u0440\\u0435\\u0434\\u0438\", \"\\u0433\\u043e\\u0434\", \"\\u0441\\u0430\\u043b\\u0435\\u0445\\u0430\\u0440\\u0434\\u0441\\u043a\\u0438\\u0439\", \"\\u043e\\u0431\\u0440\\u0430\\u0437\", \"25\", \"\\u0437\\u0434\\u043e\\u0440\\u043e\\u0432\\u044c\\u0435\", \"\\u0446\\u0435\\u043d\\u0442\\u0440\", \"\\u043c\\u0435\\u0441\\u0442\\u043e\", \"\\u043f\\u0430\\u0432\\u0435\\u043b\", \"\\u0430\\u043f\\u043e\\u0441\\u0442\\u043e\\u043b\", 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\"\\u043a\\u0443\\u0445\\u043d\\u044f\", \"\\u043a\\u0430\\u0444\\u0435\", \"\\u0433\\u0440\\u0435\\u0447\\u0435\\u0441\\u043a\\u0438\\u0439\", \"\\u043a\\u043e\\u043b\\u043e\\u043d\\u0438\\u044f\", \"\\u0435\\u043f\\u0430\\u0440\\u0445\\u0438\\u044f\", \"\\u043c\\u0435\\u0447\\u0435\\u0442\\u044c\", \"\\u0441\\u0442\", \"\\u043f\\u043e\\u0441\\u0442\\u0440\\u0430\\u0434\\u0430\\u0432\\u0448\\u0438\\u0439\", \"\\u043d\\u043e\\u0436\", \"1900\", \"\\u043e\\u043a\\u0440\\u0435\\u0441\\u0442\\u043d\\u043e\\u0441\\u0442\\u044c\", \"\\u043f\\u0435\\u0442\\u0440\", \"\\u0443\\u043a\", \"\\u043d\\u0430\\u043a\\u0430\\u0437\\u0430\\u043d\\u0438\\u0435\", \"\\u0441\\u0443\\u0434\", \"\\u0434\\u0443\\u0445\\u043e\\u0432\\u043d\\u044b\\u0439\", \"\\u0442\\u043e\\u0431\\u043e\\u043b\\u044c\\u0441\\u043a\\u0438\\u0439\", \"\\u0440\\u0435\\u0436\\u0438\\u043c\", \"\\u043c\\u0435\\u0441\\u0442\\u043d\\u044b\\u0439\", \"\\u0434\\u043e\\u043b\\u0433\\u0438\\u0439\", \"\\u0432\\u0435\\u0440\\u0430\", \"\\u043f\\u043e\\u043b\\u0438\\u0446\\u0435\\u0439\\u0441\\u043a\\u0438\\u0439\", \"\\u043e\\u043a\\u043d\\u043e\", \"\\u043c\\u0430\\u0439\", \"\\u0433\\u043e\\u0434\", \"\\u0436\\u0435\\u043b\\u0430\\u0442\\u044c\", \"\\u0440\\u0430\\u043d\\u043d\\u0438\\u0439\", \"\\u043e\\u0431\\u0449\\u0438\\u0439\", \"\\u0441\\u0430\\u043b\\u0435\\u0445\\u0430\\u0440\\u0434\\u0441\\u043a\\u0438\\u0439\", \"\\u043f\\u043e\\u0437\\u0434\\u043d\\u043e\", \"\\u043d\\u0435\\u0441\\u043e\\u0432\\u0435\\u0440\\u0448\\u0435\\u043d\\u043d\\u043e\\u043b\\u0435\\u0442\\u043d\\u0438\\u0439\", \"\\u0433\\u043e\\u0440\\u043e\\u0434\\u0441\\u043a\\u043e\\u0439\"], \"Total\": [11627.0, 5210.0, 2132.0, 1526.0, 1845.0, 2258.0, 2543.0, 1231.0, 2928.0, 3543.0, 1920.0, 4092.0, 1507.0, 1369.0, 1416.0, 1186.0, 4786.0, 2756.0, 1804.0, 4167.0, 1251.0, 1156.0, 2334.0, 1556.0, 2167.0, 904.0, 1624.0, 1995.0, 1387.0, 1681.0, 748.9213256835938, 637.250244140625, 571.6978149414062, 399.09088134765625, 356.8322448730469, 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674.5311889648438, 11627.8310546875, 1486.780517578125, 2756.48779296875, 4167.4619140625, 1972.99755859375, 1920.611328125, 1272.3353271484375, 1681.25146484375, 1716.7135009765625, 340.15313720703125, 244.79391479492188, 196.92881774902344, 188.0561065673828, 178.04000854492188, 176.60629272460938, 136.15792846679688, 131.16964721679688, 130.65155029296875, 119.53606414794922, 189.7859649658203, 116.864990234375, 112.25818634033203, 109.25127410888672, 108.34178924560547, 107.95943450927734, 104.12056732177734, 102.13240814208984, 100.66401672363281, 99.72736358642578, 99.5846176147461, 98.60778045654297, 361.19573974609375, 95.03356170654297, 92.89247131347656, 128.71920776367188, 92.20236206054688, 90.39340209960938, 84.74833679199219, 88.1700668334961, 89.14936828613281, 361.1431579589844, 248.74720764160156, 267.9436950683594, 443.57525634765625, 526.9613037109375, 293.3731994628906, 502.0135192871094, 729.781494140625, 309.5553283691406, 418.8307800292969, 613.1920776367188, 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87.98987579345703, 98.00721740722656, 86.18766021728516, 83.55901336669922, 83.22098541259766, 80.96712493896484, 79.60121154785156, 78.97660827636719, 132.3916015625, 107.42108154296875, 1171.421142578125, 189.8830108642578, 2258.716552734375, 5210.56982421875, 739.421875, 1200.7266845703125, 157.04713439941406, 1241.0958251953125, 888.0980834960938, 1804.05517578125, 296.6658630371094, 554.2158203125, 539.6604614257812, 4786.14697265625, 492.73016357421875, 412.42108154296875, 545.2896118164062, 945.70947265625, 1972.99755859375, 3543.881103515625, 1439.155029296875, 761.99462890625, 1961.489990234375, 872.06103515625, 916.699951171875, 1716.7135009765625, 479.5549621582031, 1342.1864013671875, 1448.432861328125, 1995.6351318359375, 1277.2838134765625, 879.7933349609375, 1272.3353271484375, 1514.9703369140625, 403.20947265625, 341.2703552246094, 318.6821594238281, 308.3080749511719, 216.20484924316406, 214.88705444335938, 190.4145965576172, 180.0650177001953, 179.82977294921875, 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\"\\u044f\\u043d\\u0430\\u043e\", \"\\u044f\\u043d\\u0430\\u043e\", \"\\u044f\\u043d\\u0430\\u043e\", \"\\u044f\\u043d\\u0430\\u043e\", \"\\u044f\\u043d\\u0430\\u043e\", \"\\u044f\\u0440\\u043a\\u0438\\u0439\", \"\\u044f\\u0440\\u043a\\u0438\\u0439\", \"\\u044f\\u0440\\u043a\\u0438\\u0439\", \"\\u044f\\u0440\\u043a\\u0438\\u0439\", \"\\u044f\\u0440\\u043a\\u0438\\u0439\", \"\\u044f\\u0449\\u0438\\u043a\", \"\\u2116\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [10, 14, 5, 7, 4, 9, 17, 20, 8, 6, 18, 15, 19, 1, 3, 12, 11, 16, 2, 13]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el1549220187043770802731783015\", ldavis_el1549220187043770802731783015_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el1549220187043770802731783015\", ldavis_el1549220187043770802731783015_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el1549220187043770802731783015\", ldavis_el1549220187043770802731783015_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.gensim as gensimvis\n",
    "\n",
    "%time vis_data = gensimvis.prepare(lda, corpus, dictionary)\n",
    "pyLDAvis.display(vis_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Вот тут](https://www.machinelearningplus.com/nlp/topic-modeling-visualization-how-to-present-results-lda-models/#13.-t-SNE-Clustering-Chart) можно почитать про разные способы визуализации для тематического моделирования и посмотреть примеры кода."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t-SNE\n",
    "\n",
    "Подробнее об алгоритме см. в [тетрадке про снижение размерности](https://github.com/ancatmara/data-science-nlp/blob/master/3.%20DimReduction.ipynb). Главный его минус, пожалуй, в том, что красивую картинку он выдает почти всегда, но при этом часто она может быть очень далека от действительности...\n",
    "\n",
    "### Способ №1: yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import yellowbrick\n",
    "from yellowbrick.text import TSNEVisualizer\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "data = [' '.join(text) for text in tokenized_texts]\n",
    "vect = TfidfVectorizer(max_df=0.8, min_df=5, ngram_range=(1,3), analyzer='word', stop_words = sw)\n",
    "X = vect.fit_transform(data)\n",
    "X.shape\n",
    "\n",
    "tsne = TSNEVisualizer()\n",
    "tsne.fit(X)\n",
    "tsne.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно сделать раскрасить проекцию по каким-нибудь атрибутам — например, найти кластеры с помощью KMeans и обозначить их разными цветами."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'KMeans' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-571f976fee33>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"c{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mtsne\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTSNEVisualizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'KMeans' is not defined"
     ]
    }
   ],
   "source": [
    "clf = KMeans(n_clusters=5)\n",
    "clf.fit_transform(X)\n",
    "colors = [\"c{}\".format(c) for c in clf.labels_]\n",
    "\n",
    "tsne = TSNEVisualizer()\n",
    "tsne.fit(X, colors)\n",
    "tsne.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Способ №2: sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import *\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.feature_extraction.text import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduced = PCA(n_components=30, random_state=42).fit_transform(X.todense())\n",
    "X_embedded = TSNE(n_components=2, perplexity=5, verbose=0).fit_transform(X_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_df = pd.DataFrame({'X': X_embedded[:, 0], 'Y': X_embedded[:, 1], 'cluster': colors})\n",
    "sns.FacetGrid(vis_df, hue='cluster', height=10).map(plt.scatter, \"X\", \"Y\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Что еще посмотреть?\n",
    "\n",
    "Также можно извлечь ключевые слова для каждой темы и добавить их на график. [Вот хороший тьюториал](https://shuaiw.github.io/2016/12/22/topic-modeling-and-tsne-visualzation.html) о том, как это сделать. \n",
    "\n",
    "И еще неплохой [тьюториал по тематическому моделированию](https://nlpforhackers.io/topic-modeling/), где показана другая библиотека для визуализации — `bokeh`.\n",
    "\n",
    "### Как оценить результаты?\n",
    "\n",
    "Самый простой способ — попытаться интерпретировать темы «на глаз». Провести автоматическую оценку качества тематического моделирования, особенно внешнюю (extrinsic) сложнее, и здесь до сих пор нет четкого стандарта по метрикам. Вот несколько материалов, где предлагаются варианты решения этой проблемы.\n",
    "\n",
    "* [Лекция Лоры Дитц из университета Маннхайма](http://topicmodels.info/ckling/tmt/part4.pdf)\n",
    "* [Оценка «связности» тем (с примерами кода)](https://datascienceplus.com/evaluation-of-topic-modeling-topic-coherence/)\n",
    "* [Оценка с помощью библиотеки `tmtoolkit`](https://datascience.blog.wzb.eu/2017/11/09/topic-modeling-evaluation-in-python-with-tmtoolkit/)\n",
    "* [Обсуждение на Quora](https://www.quora.com/What-are-good-ways-of-evaluating-the-topics-generated-by-running-LDA-on-a-corpus)\n",
    "\n",
    "## Задание\n",
    "\n",
    "Возьмите любой большой корпус текстов и попробуйте на нем тематическое моделирование. Если нет своих интересных данных, то можно взять [корпус региональной прессы](https://www.dropbox.com/s/46mn8dp3l3jgkx0/regional_papers.rar?dl=0). \n",
    "\n",
    "Сравните разные алгоритмы тематического моделирования. Визуализируйте результаты, раскрасьте проекции (по любым атрибутам), добавьте на график метки документов и/или топ-3 слов для каждой из получившихся тем. Хорошо ли интерпретируются полученные темы? Если нет, то почему? Что сделать, чтобы это улучшить?"
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