topic_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the models for model visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import LdaModel, Phrases\n",
    "from gensim.corpora import Dictionary\n",
    "\n",
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "import load_lee_background_corpus as load_texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the previously saved models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/home/Desenvolvimento/anaconda3/lib/python3.6/site-packages/gensim/test/test_data\n",
      "Returning 300 training texts\n"
     ]
    }
   ],
   "source": [
    "dictionary = Dictionary.load('dicionario_1')\n",
    "phraser = Phrases.load('frases_1')\n",
    "model = LdaModel.load('modelo_1')\n",
    "\n",
    "train_texts = load_texts.get_train_texts()\n",
    "corpus = [dictionary.doc2bow(text) for text in train_texts]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the model's topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.top_topics(texts=train_texts, dictionary=dictionary, coherence='c_v')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User pyLDAvis to visualize the topics\n",
    "\n",
    "The red bars represent the frequency of a term in a given topic, (proportional to p(term | topic)), and the blue bars represent a term's frequency across the entire corpus, (proportional to p(term)). Change the value of λ to adjust the term rankings -- small values of λ (near 0) highlight potentially rare, but exclusive terms for the selected topic, and large values of λ (near 1) highlight frequent, but not necessarily exclusive, terms for the selected topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "3      38.045198        1       1 -0.028247  0.053858\n",
       "1      23.525069        1       2 -0.030173 -0.054605\n",
       "6       7.472182        1       3  0.101784 -0.007059\n",
       "0       6.142968        1       4 -0.062306 -0.013183\n",
       "2       6.080530        1       5  0.165058  0.028814\n",
       "4       5.912636        1       6  0.026319  0.118252\n",
       "7       5.675974        1       7  0.112477 -0.085653\n",
       "5       3.784327        1       8 -0.113875  0.037645\n",
       "9       2.536251        1       9 -0.074964 -0.079454\n",
       "8       0.824867        1      10 -0.096073  0.001384, topic_info=     Category       Freq                    Term      Total  loglift  logprob\n",
       "term                                                                         \n",
       "1661  Default  16.000000                   image  16.000000  30.0000  30.0000\n",
       "836   Default  32.000000                  hidden  32.000000  29.0000  29.0000\n",
       "10    Default  19.000000                  neuron  19.000000  28.0000  28.0000\n",
       "6799  Default  18.000000                  policy  18.000000  27.0000  27.0000\n",
       "2662  Default  26.000000                  action  26.000000  26.0000  26.0000\n",
       "1142  Default  13.000000                    cell  13.000000  25.0000  25.0000\n",
       "915   Default  20.000000                    node  20.000000  24.0000  24.0000\n",
       "2040  Default  29.000000             recognition  29.000000  23.0000  23.0000\n",
       "2897  Default  15.000000           reinforcement  15.000000  22.0000  22.0000\n",
       "829   Default   7.000000                    tree   7.000000  21.0000  21.0000\n",
       "837   Default  14.000000              classifier  14.000000  20.0000  20.0000\n",
       "388   Default  18.000000                   class  18.000000  19.0000  19.0000\n",
       "1408  Default  19.000000                  signal  19.000000  18.0000  18.0000\n",
       "681   Default  31.000000                   layer  31.000000  17.0000  17.0000\n",
       "1940  Default  16.000000                sequence  16.000000  16.0000  16.0000\n",
       "410   Default   8.000000                  object   8.000000  15.0000  15.0000\n",
       "2663  Default   8.000000                   spike   8.000000  14.0000  14.0000\n",
       "686   Default  27.000000                 control  27.000000  13.0000  13.0000\n",
       "389   Default  13.000000                  memory  13.000000  12.0000  12.0000\n",
       "654   Default   6.000000                 circuit   6.000000  11.0000  11.0000\n",
       "7129  Default  10.000000  reinforcement_learning  10.000000  10.0000  10.0000\n",
       "18    Default  25.000000                     net  25.000000   9.0000   9.0000\n",
       "3984  Default  22.000000                  speech  22.000000   8.0000   8.0000\n",
       "856   Default  15.000000          classification  15.000000   7.0000   7.0000\n",
       "1026  Default  10.000000                 machine  10.000000   6.0000   6.0000\n",
       "3141  Default   6.000000                  markov   6.000000   5.0000   5.0000\n",
       "34    Default   9.000000                   field   9.000000   4.0000   4.0000\n",
       "444   Default   9.000000                gradient   9.000000   3.0000   3.0000\n",
       "2302  Default   6.000000                  visual   6.000000   2.0000   2.0000\n",
       "1049  Default  15.000000             hidden_unit  15.000000   1.0000   1.0000\n",
       "...       ...        ...                     ...        ...      ...      ...\n",
       "6032  Topic10   0.078963               graphical   0.381528   3.2225  -6.7090\n",
       "2891  Topic10   0.323973              likelihood   2.905439   2.6040  -5.2973\n",
       "829   Topic10   0.502076                    tree   7.746584   2.0614  -4.8592\n",
       "3458  Topic10   0.117407               automaton   0.805295   2.8721  -6.3123\n",
       "5634  Topic10   0.164581               posterior   1.411085   2.6490  -5.9745\n",
       "4266  Topic10   0.154641                  belief   1.293090   2.6740  -6.0368\n",
       "2214  Topic10   0.266555                     log   3.488619   2.2260  -5.4924\n",
       "4088  Topic10   0.158353             conditional   1.437217   2.5921  -6.0131\n",
       "8050  Topic10   0.116513                    hmms   0.823413   2.8422  -6.3199\n",
       "3352  Topic10   0.282106                  expert   4.936857   1.9355  -5.4357\n",
       "4791  Topic10   0.138439           probabilistic   1.328391   2.5364  -6.1475\n",
       "6151  Topic10   0.122408                 tangent   1.061141   2.6380  -6.2706\n",
       "3141  Topic10   0.276900                  markov   6.607107   1.6255  -5.4543\n",
       "3353  Topic10   0.179386                  symbol   2.531714   2.1506  -5.8884\n",
       "1792  Topic10   0.227837               recurrent   5.363515   1.6390  -5.6493\n",
       "915   Topic10   0.385056                    node  20.175559   0.8389  -5.1245\n",
       "1940  Topic10   0.349768                sequence  16.207536   0.9617  -5.2207\n",
       "836   Topic10   0.416944                  hidden  32.245471   0.4495  -5.0450\n",
       "34    Topic10   0.251825                   field   9.302240   1.1884  -5.5492\n",
       "1026  Topic10   0.265997                 machine  10.953753   1.0798  -5.4945\n",
       "4080  Topic10   0.162993                 cluster   2.862187   1.9321  -5.9842\n",
       "1292  Topic10   0.200447              transition   7.621298   1.1596  -5.7774\n",
       "3445  Topic10   0.165299                  string   3.682534   1.6941  -5.9702\n",
       "98    Topic10   0.192604                  matrix   8.682409   0.9893  -5.8173\n",
       "1907  Topic10   0.182752           approximation   7.414884   1.0946  -5.8698\n",
       "3415  Topic10   0.163700                   graph   4.544382   1.4741  -5.9799\n",
       "6370  Topic10   0.150210                  jordan   2.826238   1.8630  -6.0659\n",
       "2405  Topic10   0.155668                gaussian   5.867453   1.1683  -6.0302\n",
       "1504  Topic10   0.150847                    code   4.792323   1.3392  -6.0617\n",
       "997   Topic10   0.151878                 maximum   5.671725   1.1775  -6.0549\n",
       "\n",
       "[753 rows x 6 columns], token_table=      Topic      Freq              Term\n",
       "term                                   \n",
       "2544      6  0.871710                2d\n",
       "2494      6  1.135581                3d\n",
       "2662      2  0.949844            action\n",
       "8239      2  1.020712       action_pair\n",
       "1797      1  0.757892        activation\n",
       "1797      5  0.084210        activation\n",
       "1797      7  0.084210        activation\n",
       "1250      1  0.137896          activity\n",
       "1250      2  0.137896          activity\n",
       "1250      3  0.137896          activity\n",
       "1250      5  0.551584          activity\n",
       "1250      7  0.137896          activity\n",
       "1802      1  0.116495          adaptive\n",
       "1802      2  0.465981          adaptive\n",
       "1802      3  0.116495          adaptive\n",
       "1802      4  0.232991          adaptive\n",
       "1802      7  0.116495          adaptive\n",
       "5031      5  1.789680          afferent\n",
       "1234      2  0.995646             agent\n",
       "2657      3  0.791043         amplitude\n",
       "7         7  0.874780            analog\n",
       "3781      7  1.120332       analog_vlsi\n",
       "1907      2  0.404592     approximation\n",
       "1907      4  0.269728     approximation\n",
       "1907      8  0.134864     approximation\n",
       "1907      9  0.134864     approximation\n",
       "403       1  0.630933      architecture\n",
       "403       2  0.105156      architecture\n",
       "403       3  0.052578      architecture\n",
       "403       4  0.052578      architecture\n",
       "...     ...       ...               ...\n",
       "2861      3  0.315986          velocity\n",
       "2861      5  0.315986          velocity\n",
       "2468      6  1.134320             video\n",
       "1530      1  0.243279              view\n",
       "1530      2  0.243279              view\n",
       "1530      6  0.486559              view\n",
       "2311      6  0.828663            vision\n",
       "2302      5  0.584658            visual\n",
       "2302      6  0.292329            visual\n",
       "7143      1  0.682003           viterbi\n",
       "1807      7  0.997308              vlsi\n",
       "6423      1  0.986452        vocabulary\n",
       "2690      7  0.871926           voltage\n",
       "6432      1  0.943255             vowel\n",
       "7085      1  1.147835            waibel\n",
       "8257      2  0.845359           watkins\n",
       "8268      2  1.485263  watkins_learning\n",
       "2821      3  0.669110          waveform\n",
       "8472      6  1.138534           wavelet\n",
       "5121      4  0.899448      weight_decay\n",
       "3809      7  0.659340            winner\n",
       "3890      7  0.998033       winner_take\n",
       "1218      1  0.979060              word\n",
       "3384      1  0.735854  word_recognition\n",
       "2317      1  0.155443             world\n",
       "2317      2  0.621772             world\n",
       "7760      1  0.813448            writer\n",
       "7996      7  1.714499               wta\n",
       "524       8  0.362477                xi\n",
       "524       9  0.362477                xi\n",
       "\n",
       "[748 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 2, 7, 1, 3, 5, 8, 6, 10, 9])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(model, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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