LDA Visualization.ipynb

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     "start_time": "2022-12-16T16:22:32.511633Z"
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   "outputs": [
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     "text": [
      "c:\\Users\\vitor\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\past\\builtins\\misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib and slated for removal in Python 3.12; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "C:\\Users\\vitor\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\backports.py:22: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives\n",
      "  import distutils  # noqa\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import ldamodel, TfidfModel\n",
    "\n",
    "import pyLDAvis\n",
    "pyLDAvis.enable_notebook()\n",
    "import pyLDAvis.gensim_models\n",
    "\n",
    "from utils import load_bow\n",
    "\n",
    "from itertools import chain\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 164756/164756 [00:10<00:00, 15580.46it/s]\n"
     ]
    }
   ],
   "source": [
    "database = '25_venues'\n",
    "texts = load_bow(database)\n",
    "id2word = Dictionary(texts)\n",
    "corpus = [id2word.doc2bow(text) for text in tqdm(texts)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 164756/164756 [00:11<00:00, 14767.23it/s]\n"
     ]
    }
   ],
   "source": [
    "model_name = '55_symmetric_auto_0.5_0.0075'\n",
    "df = float(model_name.split('_')[3])\n",
    "tfidf = float(model_name.split('_')[4])\n",
    "\n",
    "clean_id2word = Dictionary(texts)\n",
    "clean_id2word.filter_extremes(no_above=df)\n",
    "tfidf_model = TfidfModel(corpus, id2word=clean_id2word)\n",
    "vocab_tfidf = list(chain.from_iterable(tfidf_model[corpus]))\n",
    "clean_id2word.filter_tokens(bad_ids=[tokenid for tokenid, tfidf_value in vocab_tfidf if tfidf_value < tfidf])\n",
    "clean_corpus = [clean_id2word.doc2bow(text) for text in tqdm(texts)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-16T16:24:13.757237Z",
     "start_time": "2022-12-16T16:22:56.774730Z"
    },
    "scrolled": false
   },
   "outputs": [
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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "34     0.065522  0.095375       1        1  5.104246\n",
       "15     0.327389 -0.082725       2        1  3.665669\n",
       "22     0.130727  0.062465       3        1  3.625790\n",
       "3      0.233426  0.071369       4        1  3.557308\n",
       "32     0.231908  0.170105       5        1  3.474747\n",
       "28    -0.052897 -0.062337       6        1  3.423064\n",
       "53     0.069851 -0.133790       7        1  3.215458\n",
       "44     0.081458 -0.220139       8        1  3.027824\n",
       "26    -0.081429  0.034321       9        1  2.978899\n",
       "21     0.022804 -0.024891      10        1  2.898737\n",
       "0     -0.155036 -0.079181      11        1  2.735120\n",
       "46    -0.081596  0.104390      12        1  2.709249\n",
       "23     0.003111  0.214139      13        1  2.658003\n",
       "9     -0.053985 -0.136156      14        1  2.552226\n",
       "51     0.205639 -0.013521      15        1  2.452790\n",
       "27     0.295942 -0.001300      16        1  2.347799\n",
       "1      0.044100  0.155603      17        1  2.341313\n",
       "14     0.275257  0.297784      18        1  2.207914\n",
       "42    -0.072881  0.232667      19        1  2.206053\n",
       "13     0.131807  0.289667      20        1  2.071713\n",
       "33    -0.283091  0.133103      21        1  1.941359\n",
       "35    -0.317127  0.030865      22        1  1.847115\n",
       "30    -0.155860  0.175813      23        1  1.845401\n",
       "5      0.320018  0.127061      24        1  1.811585\n",
       "8      0.087860 -0.343971      25        1  1.745852\n",
       "40     0.254364 -0.158704      26        1  1.739538\n",
       "50    -0.222703  0.183523      27        1  1.732652\n",
       "17     0.159204  0.196985      28        1  1.682509\n",
       "29    -0.216420  0.018919      29        1  1.474047\n",
       "41     0.011101 -0.281014      30        1  1.352785\n",
       "10     0.180748 -0.320774      31        1  1.349795\n",
       "37    -0.256173 -0.074530      32        1  1.325830\n",
       "54     0.172114 -0.208489      33        1  1.296671\n",
       "7      0.031170  0.347206      34        1  1.289465\n",
       "12    -0.072987 -0.315049      35        1  1.273389\n",
       "20    -0.407223  0.034550      36        1  1.267704\n",
       "45    -0.166864 -0.235074      37        1  1.249372\n",
       "47     0.366699  0.204697      38        1  1.180930\n",
       "24    -0.112924  0.397372      39        1  1.145726\n",
       "16     0.277995 -0.269324      40        1  1.112311\n",
       "31    -0.244835 -0.160801      41        1  1.109722\n",
       "43    -0.182070  0.327992      42        1  1.068058\n",
       "2      0.475164  0.060933      43        1  1.057229\n",
       "6     -0.272924 -0.278990      44        1  0.865888\n",
       "39    -0.424856 -0.087000      45        1  0.833900\n",
       "25    -0.293799  0.289514      46        1  0.827471\n",
       "11    -0.143436 -0.325596      47        1  0.796978\n",
       "52    -0.253731 -0.438592      48        1  0.781428\n",
       "36     0.446307 -0.105708      49        1  0.662102\n",
       "49    -0.045278  0.420854      50        1  0.605023\n",
       "38     0.374909 -0.341454      51        1  0.571529\n",
       "4      0.041663 -0.491089      52        1  0.561835\n",
       "48    -0.444727  0.258750      53        1  0.487224\n",
       "18    -0.482991 -0.239444      54        1  0.431893\n",
       "19     0.179587  0.493622      55        1  0.421763, topic_info=             Term           Freq          Total Category  logprob  loglift\n",
       "346         image   96104.000000   96104.000000  Default  20.0000  20.0000\n",
       "16        feature   87245.000000   87245.000000  Default  19.0000  19.0000\n",
       "676       network  107679.000000  107679.000000  Default  18.0000  18.0000\n",
       "50           time   71059.000000   71059.000000  Default  17.0000  17.0000\n",
       "27          model  198570.000000  198570.000000  Default  16.0000  16.0000\n",
       "...           ...            ...            ...      ...      ...      ...\n",
       "628     sensitive    1306.329954    3768.260526  Topic55  -3.8274   4.4091\n",
       "1076    mechanism    1267.424044   15551.659694  Topic55  -3.8577   2.9613\n",
       "23    information    1269.220229   61045.325104  Topic55  -3.8563   1.5953\n",
       "68          datum     934.031937  101635.197579  Topic55  -4.1629   0.7788\n",
       "1330        share     536.540595    5606.896678  Topic55  -4.7173   3.1219\n",
       "\n",
       "[2678 rows x 6 columns], token_table=       Topic      Freq               Term\n",
       "term                                     \n",
       "5953      35  0.133505           abnormal\n",
       "5953      37  0.865791           abnormal\n",
       "9082      43  0.997201             abrupt\n",
       "6284      42  0.990420            abstain\n",
       "5665       7  0.986102  academia_industry\n",
       "...      ...       ...                ...\n",
       "1268      34  0.011282               year\n",
       "1268      35  0.088802               year\n",
       "1268      36  0.001456               year\n",
       "1268      53  0.013102               year\n",
       "10210     49  0.988407       zeroth_order\n",
       "\n",
       "[16228 rows x 3 columns], R=20, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[35, 16, 23, 4, 33, 29, 54, 45, 27, 22, 1, 47, 24, 10, 52, 28, 2, 15, 43, 14, 34, 36, 31, 6, 9, 41, 51, 18, 30, 42, 11, 38, 55, 8, 13, 21, 46, 48, 25, 17, 32, 44, 3, 7, 40, 26, 12, 53, 37, 50, 39, 5, 49, 19, 20])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_model = ldamodel.LdaModel.load(f'models/lda_models/{model_name}')\n",
    "vis = pyLDAvis.gensim_models.prepare(lda_model, clean_corpus, clean_id2word, mds=\"mmds\", R=20)\n",
    "pyLDAvis.save_html(vis, f'models/lda_models_vis/{model_name}.html')\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyLDAvis.save_html(vis, f'models/lda_models_vis/{model_name}.html')"
   ]
  }
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