Topic modelling for clustering user requests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User requests clustering by Dmitriy Milevich\n",
    "## Into\n",
    "For clusterisation I decided to use Latent Derichlet Allocation approach for topic modelling. There are many other approaches that can be fruitful in this case, e.g. KNN clustering on word embeddings. However, for relatively small size of dataset and demonstration purposes this one will suffice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymystem3 import Mystem\n",
    "from nltk.corpus import stopwords\n",
    "from string import punctuation\n",
    "import gensim\n",
    "from gensim.corpora.dictionary import Dictionary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"dataset.txt\", \"r\")\n",
    "lines = f.readlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we do lemmatisation and removal of words that are not rich in meaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mystem = Mystem() \n",
    "russian_stopwords = stopwords.words(\"russian\")\n",
    "\n",
    "def lemmatize_text(text):\n",
    "    tokens = mystem.lemmatize(text.lower())\n",
    "    tokens = [token for token in tokens if token not in russian_stopwords\\\n",
    "              and token != \" \" \\\n",
    "              and token.strip() not in punctuation]\n",
    "    \n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = []\n",
    "for line in lines:\n",
    "    texts.append(\" \".join(lemmatize_text(line)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of texts: 3450\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of texts: {len(texts)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot clouds of words from the whole given corpus to get some intuition about the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x200 at 0x11B0CFF98>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "long_string = \" \".join(texts)\n",
    "wordcloud = WordCloud(background_color=\"white\", contour_width=6, contour_color='steelblue')\n",
    "wordcloud.generate(long_string)\n",
    "wordcloud.to_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build out LDA model on counts of unigrams and bigrams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, ngram_range = (1,2))\n",
    "tfidf = tfidf_vectorizer.fit_transform(texts)\n",
    "tfidf_feature_names = tfidf_vectorizer.get_feature_names()\n",
    "\n",
    "tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, ngram_range = (1,2))\n",
    "tf = tf_vectorizer.fit_transform(texts)\n",
    "tf_feature_names = tf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot most frequent terms in the dataset to get additional intuition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_10_most_common_words(count_data, count_vectorizer):\n",
    "    words = count_vectorizer.get_feature_names()\n",
    "    total_counts = np.zeros(len(words))\n",
    "    for t in count_data:\n",
    "        total_counts+=t.toarray()[0]\n",
    "    \n",
    "    count_dict = (zip(words, total_counts))\n",
    "    count_dict = sorted(count_dict, key=lambda x:x[1], reverse=True)[0:10]\n",
    "    words = [w[0] for w in count_dict]\n",
    "    counts = [w[1] for w in count_dict]\n",
    "    x_pos = np.arange(len(words)) \n",
    "    \n",
    "    plt.figure(2, figsize=(10, 7))\n",
    "    plt.subplot(title='10 most common terms')\n",
    "    sns.barplot(x_pos, counts)\n",
    "    plt.xticks(x_pos, words, rotation=90) \n",
    "    plt.xlabel('terms')\n",
    "    plt.ylabel('counts')\n",
    "    plt.show()\n",
    "\n",
    "count_data = tf_vectorizer.fit_transform(texts)\n",
    "plot_10_most_common_words(count_data, tf_vectorizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we fit LDA model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import NMF, LatentDirichletAllocation\n",
    "\n",
    "no_topics = 4 # this parameter is determined by the experiments (see sections below)\n",
    "nmf = NMF(n_components=no_topics, random_state=1, alpha=.1, l1_ratio=.5, init='nndsvd').fit(tfidf)\n",
    "lda = LatentDirichletAllocation(n_components=no_topics, learning_method='online', learning_offset=50.,random_state=0).fit(tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's classify out texts to the topic with the highest probability according to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = lda\n",
    "predictions = model.transform(tf_vectorizer.transform(texts))\n",
    "pred_labels = [pred.argmax() for pred in predictions]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot corresponding sentenses for each topic and get some intuition about interpretation. E.g. following topic has something to do with issues about sim cards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>label</th>\n",
       "      <th>original</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.874340</td>\n",
       "      <td>0.042007</td>\n",
       "      <td>0.041677</td>\n",
       "      <td>0.041976</td>\n",
       "      <td>0</td>\n",
       "      <td>не могли бы вы мне пин напомнить?\\n</td>\n",
       "      <td>мочь пин напоминать</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.904286</td>\n",
       "      <td>0.032695</td>\n",
       "      <td>0.031434</td>\n",
       "      <td>0.031585</td>\n",
       "      <td>0</td>\n",
       "      <td>да, спасибо! а какой пин для настроек интернет...</td>\n",
       "      <td>спасибо пин настройка интернет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.655757</td>\n",
       "      <td>0.050013</td>\n",
       "      <td>0.244035</td>\n",
       "      <td>0.050194</td>\n",
       "      <td>0</td>\n",
       "      <td>а откуда брать пин для телефона\\n</td>\n",
       "      <td>откуда брать пин телефон</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.906111</td>\n",
       "      <td>0.031253</td>\n",
       "      <td>0.031254</td>\n",
       "      <td>0.031383</td>\n",
       "      <td>0</td>\n",
       "      <td>у меня проблема с сим-картой, что делать??\\n</td>\n",
       "      <td>проблема сей карта делать ??</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.890318</td>\n",
       "      <td>0.036631</td>\n",
       "      <td>0.036093</td>\n",
       "      <td>0.036958</td>\n",
       "      <td>0</td>\n",
       "      <td>Как понять что с симкартой? НЕ работает ничего\\n</td>\n",
       "      <td>понимать симкарта работать ничто</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3  label  \\\n",
       "2   0.874340  0.042007  0.041677  0.041976      0   \n",
       "3   0.904286  0.032695  0.031434  0.031585      0   \n",
       "4   0.655757  0.050013  0.244035  0.050194      0   \n",
       "11  0.906111  0.031253  0.031254  0.031383      0   \n",
       "12  0.890318  0.036631  0.036093  0.036958      0   \n",
       "\n",
       "                                             original  \\\n",
       "2                 не могли бы вы мне пин напомнить?\\n   \n",
       "3   да, спасибо! а какой пин для настроек интернет...   \n",
       "4                   а откуда брать пин для телефона\\n   \n",
       "11       у меня проблема с сим-картой, что делать??\\n   \n",
       "12   Как понять что с симкартой? НЕ работает ничего\\n   \n",
       "\n",
       "                                text  \n",
       "2                мочь пин напоминать  \n",
       "3     спасибо пин настройка интернет  \n",
       "4           откуда брать пин телефон  \n",
       "11      проблема сей карта делать ??  \n",
       "12  понимать симкарта работать ничто  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(predictions)\n",
    "data['label'] = pred_labels\n",
    "data['original'] = lines\n",
    "data['text'] = texts\n",
    "data[data['label']==0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we visualise classiffication counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.XTick at 0x1188e82b0>,\n",
       " <matplotlib.axis.XTick at 0x1188e8048>,\n",
       " <matplotlib.axis.XTick at 0x1188e8ac8>,\n",
       " <matplotlib.axis.XTick at 0x114d0ba20>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "label_counts = [pred_labels.count(i) for i in range(no_topics)]\n",
    "ax.bar(range(no_topics), label_counts)\n",
    "ax.set_xticks(range(no_topics))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use pyLDAvis tool for visualisation of our topic modelling results. There we can see the words with the highest frequency for each topic and investigate intertopic distance. This will give us intuition for the final number of topics that we should use a model parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.83 s, sys: 104 ms, total: 3.94 s\n",
      "Wall time: 1min 38s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/home/anaconda/envs/fastai/lib/python3.6/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from pyLDAvis import sklearn as sklearn_lda\n",
    "import pickle \n",
    "import pyLDAvis\n",
    "import os\n",
    "LDAvis_prepared = sklearn_lda.prepare(model, tf, tf_vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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_el334947494402485817957757\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "3     -0.077744 -0.016040       1        1  38.428070\n",
       "0      0.320262  0.046642       2        1  26.377691\n",
       "1     -0.074671 -0.228827       3        1  18.471454\n",
       "2     -0.167847  0.198225       4        1  16.722785, topic_info=     Category         Freq              Term        Total  loglift  logprob\n",
       "2258  Default   656.000000           сколько   656.000000  30.0000  30.0000\n",
       "2442  Default   338.000000            стоить   338.000000  29.0000  29.0000\n",
       "1380  Default   329.000000           остаток   329.000000  28.0000  28.0000\n",
       "2292  Default   309.000000    сколько стоить   309.000000  27.0000  27.0000\n",
       "1511  Default   313.000000               пин   313.000000  26.0000  26.0000\n",
       "2519  Default  1105.000000             тариф  1105.000000  25.0000  25.0000\n",
       "1440  Default   358.000000             пакет   358.000000  24.0000  24.0000\n",
       "2199  Default   238.000000             симка   238.000000  23.0000  23.0000\n",
       "624   Default   504.000000            звонок   504.000000  22.0000  22.0000\n",
       "832   Default   320.000000             карта   320.000000  21.0000  21.0000\n",
       "1011  Default   700.000000            минута   700.000000  20.0000  20.0000\n",
       "876   Default   208.000000               код   208.000000  19.0000  19.0000\n",
       "2672  Default   352.000000          узнавать   352.000000  18.0000  18.0000\n",
       "2576  Default   316.000000           телефон   316.000000  17.0000  17.0000\n",
       "1930  Default   201.000000          работать   201.000000  16.0000  16.0000\n",
       "2153  Default   274.000000               сей   274.000000  15.0000  15.0000\n",
       "2156  Default   253.000000         сей карта   253.000000  14.0000  14.0000\n",
       "1519  Default   166.000000           пин код   166.000000  13.0000  13.0000\n",
       "2683  Default   121.000000  узнавать остаток   121.000000  12.0000  12.0000\n",
       "1386  Default   103.000000    остаток минута   103.000000  11.0000  11.0000\n",
       "458   Default   141.000000            делать   141.000000  10.0000  10.0000\n",
       "2483  Default   218.000000             сфера   218.000000   9.0000   9.0000\n",
       "1582  Default   319.000000            плутон   319.000000   8.0000   8.0000\n",
       "1363  Default   179.000000        оставаться   179.000000   7.0000   7.0000\n",
       "1958  Default    87.000000          разговор    87.000000   6.0000   6.0000\n",
       "348   Default   160.000000           входить   160.000000   5.0000   5.0000\n",
       "1760  Default    87.000000        посмотреть    87.000000   4.0000   4.0000\n",
       "2548  Default   229.000000      тариф плутон   229.000000   3.0000   3.0000\n",
       "2551  Default   261.000000      тариф сатурн   261.000000   2.0000   2.0000\n",
       "1388  Default    84.000000     остаток пакет    84.000000   1.0000   1.0000\n",
       "...       ...          ...               ...          ...      ...      ...\n",
       "978    Topic4    13.232671          мегабайт    13.942878   1.7361  -6.0432\n",
       "2641   Topic4    12.651398         тп плутон    13.361856   1.7338  -6.0881\n",
       "283    Topic4    12.638939    включать пакет    13.349917   1.7337  -6.0891\n",
       "820    Topic4    14.476980               как    15.291969   1.7336  -5.9533\n",
       "1502   Topic4    58.204380        переходить    61.552481   1.7325  -4.5619\n",
       "344    Topic4    20.008019            вулкан    21.249732   1.7282  -5.6297\n",
       "1846   Topic4    10.589356    привет сколько    11.288756   1.7244  -6.2660\n",
       "1972   Topic4    10.176149    разговор тариф    10.872961   1.7222  -6.3058\n",
       "1507   Topic4    30.682606  переходить тариф    32.834073   1.7206  -5.2022\n",
       "2460   Topic4    10.622545    стоить роуминг    11.371754   1.7202  -6.2629\n",
       "1731   Topic4    11.101981    поменять тариф    11.888651   1.7199  -6.2188\n",
       "1493   Topic4    51.151853           переход    59.946235   1.6297  -4.6911\n",
       "2638   Topic4    41.668531                тп    49.835880   1.6094  -4.8961\n",
       "2258   Topic4   390.139352           сколько   656.541923   1.2679  -2.6594\n",
       "277    Topic4    50.745407          включать    70.447082   1.4604  -4.6991\n",
       "2704   Topic4    27.325192              уран    34.606264   1.5522  -5.3181\n",
       "162    Topic4    61.819529           аполлон    96.453556   1.3436  -4.5017\n",
       "1685   Topic4    41.322439         позвонить    61.661600   1.3881  -4.9045\n",
       "1499   Topic4    22.055257     переход тариф    27.116036   1.5818  -5.5323\n",
       "2519   Topic4   219.970400             тариф  1105.817088   0.1736  -3.2324\n",
       "1011   Topic4   154.173063            минута   700.681365   0.2744  -3.5878\n",
       "2044   Topic4    64.604564           роуминг   211.675404   0.6016  -4.4576\n",
       "1582   Topic4    68.573100            плутон   319.172867   0.2506  -4.3980\n",
       "504    Topic4    25.240889            деньги    43.736196   1.2387  -5.3974\n",
       "1559   Topic4    39.033888             плата   119.216814   0.6719  -4.9614\n",
       "2082   Topic4    42.307590            сатурн   345.654838  -0.3121  -4.8809\n",
       "748    Topic4    34.804860          интернет   248.779935  -0.1784  -5.0761\n",
       "624    Topic4    40.420476            звонок   504.737237  -0.7363  -4.9265\n",
       "2750   Topic4    27.978070            хотеть   104.676043   0.4689  -5.2945\n",
       "2421   Topic4    28.943192         стоимость   202.955885  -0.1593  -5.2605\n",
       "\n",
       "[235 rows x 6 columns], token_table=      Topic      Freq               Term\n",
       "term                                    \n",
       "52        1  0.985091                400\n",
       "55        1  0.958220         400 минута\n",
       "98        2  0.852674                pin\n",
       "98        3  0.131181                pin\n",
       "99        2  0.954298            pin код\n",
       "99        3  0.030784            pin код\n",
       "116       4  0.937050               абон\n",
       "117       4  0.981475         абон плата\n",
       "120       1  0.984713            абонент\n",
       "127       2  0.989687        абонентский\n",
       "128       2  0.994239  абонентский плата\n",
       "153       2  0.988227           активный\n",
       "162       1  0.352501            аполлон\n",
       "162       3  0.010368            аполлон\n",
       "162       4  0.642796            аполлон\n",
       "192       1  0.991166          бесплатно\n",
       "197       1  0.997096         бесплатный\n",
       "202       1  0.987685  бесплатный минута\n",
       "217       1  0.144097                ваш\n",
       "217       2  0.848570                ваш\n",
       "227       2  0.974568            вводить\n",
       "261       2  0.989545              взять\n",
       "266       1  0.024267             видеть\n",
       "266       2  0.970699             видеть\n",
       "277       1  0.255511           включать\n",
       "277       2  0.014195           включать\n",
       "277       4  0.723948           включать\n",
       "283       4  0.973789     включать пакет\n",
       "284       4  0.939162     включать тариф\n",
       "292       3  0.973640             внутри\n",
       "...     ...       ...                ...\n",
       "2573      4  0.980030           тарифный\n",
       "2574      4  0.993242      тарифный план\n",
       "2576      1  0.205592            телефон\n",
       "2576      2  0.790739            телефон\n",
       "2576      3  0.003163            телефон\n",
       "2583      1  0.021109     телефон видеть\n",
       "2583      2  0.971034     телефон видеть\n",
       "2638      1  0.020066                 тп\n",
       "2638      3  0.140461                 тп\n",
       "2638      4  0.842766                 тп\n",
       "2641      4  0.972919          тп плутон\n",
       "2649      4  0.990093             трафик\n",
       "2656      4  0.971870       трафик тариф\n",
       "2657      2  0.981571          требовать\n",
       "2672      1  0.337882           узнавать\n",
       "2672      2  0.079502           узнавать\n",
       "2672      3  0.579227           узнавать\n",
       "2683      3  0.990221   узнавать остаток\n",
       "2689      1  0.994775   узнавать сколько\n",
       "2704      2  0.202276               уран\n",
       "2704      4  0.780206               уран\n",
       "2750      1  0.286599             хотеть\n",
       "2750      2  0.200619             хотеть\n",
       "2750      3  0.257939             хотеть\n",
       "2750      4  0.267492             хотеть\n",
       "2775      1  0.815242               цена\n",
       "2775      4  0.178334               цена\n",
       "2824      1  0.636336             юпитер\n",
       "2824      3  0.359120             юпитер\n",
       "2844      1  0.964732          ярославль\n",
       "\n",
       "[290 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 1, 2, 3])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "From examining the results we can assume the following interpretations for the clusters:\n",
    "\n",
    "1 - questions about the current conditions of the tarif of the user (price per minute)\n",
    "\n",
    "2 - questions related to sim card and pin code\n",
    "\n",
    "3 - questions about how much balance is currently left (calls, internet)\n",
    "\n",
    "4 - questions about comparison between different tarifs\n",
    "\n",
    "Note that correponding numbers of topics are shifted by 1 compared to our previous investigations\n",
    "\n",
    "Finally we do t-SNE dimensionality reduction to see how well final clusters are separated from each other. High degree of separation corresponds to presumably more meaningful model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 3450 samples in 0.002s...\n",
      "[t-SNE] Computed neighbors for 3450 samples in 0.079s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 3450\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 3450\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 3450\n",
      "[t-SNE] Computed conditional probabilities for sample 3450 / 3450\n",
      "[t-SNE] Mean sigma: 0.000514\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 53.464077\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.329254\n"
     ]
    },
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\",\"dtype\":\"float32\",\"shape\":[3450]},\"y\":{\"__ndarray__\":\"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Clustering of 4 LDA Topics\"},\"id\":\"1099\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1110\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1105\",\"type\":\"LinearScale\"}],\"root_ids\":[\"1098\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"fe83f252-1e71-4897-ae96-25687eb566d9\",\"roots\":{\"1098\":\"76c65380-e9ab-4965-94ee-68ac065676eb\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1098"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from bokeh.plotting import figure, output_file, show\n",
    "from bokeh.models import Label\n",
    "from bokeh.io import output_notebook\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "tsne_model = TSNE(n_components=2, verbose=1, random_state=0, angle=.99, init='pca')\n",
    "tsne_lda = tsne_model.fit_transform(predictions)\n",
    "\n",
    "output_notebook()\n",
    "mycolors = np.array([color for name, color in mcolors.TABLEAU_COLORS.items()])\n",
    "plot = figure(title=\"t-SNE Clustering of {} LDA Topics\".format(no_topics), \n",
    "              plot_width=900, plot_height=700)\n",
    "plot.scatter(x=tsne_lda[:,0], y=tsne_lda[:,1], color=mycolors[pred_labels])\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "Here I prepared a demo of how topic modelling using LDA can be applied for identifying clusters of user requests. The examination of identified clusters is preliminary and requires further accurate tweaking of parameters for precise identification of different user intentions. Other clustering techniques can be applied as well for comparison of identified clusters."
   ]
  }
 ],
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