topic_modeling_Gensim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "spacy.load('en')\n",
    "from spacy.lang.en import English\n",
    "parser = English()\n",
    "\n",
    "def tokenize(text):\n",
    "    lda_tokens = []\n",
    "    tokens = parser(text)\n",
    "    for token in tokens:\n",
    "        if token.orth_.isspace():\n",
    "            continue\n",
    "        elif token.like_url:\n",
    "            lda_tokens.append('URL')\n",
    "        elif token.orth_.startswith('@'):\n",
    "            lda_tokens.append('SCREEN_NAME')\n",
    "        else:\n",
    "            lda_tokens.append(token.lower_)\n",
    "    return lda_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /Users/sli/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet as wn\n",
    "def get_lemma(word):\n",
    "    lemma = wn.morphy(word)\n",
    "    if lemma is None:\n",
    "        return word\n",
    "    else:\n",
    "        return lemma\n",
    "    \n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "def get_lemma2(word):\n",
    "    return WordNetLemmatizer().lemmatize(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dogs dog dog\n",
      "ran run ran\n",
      "discouraged discourage discouraged\n"
     ]
    }
   ],
   "source": [
    "for w in ['dogs', 'ran', 'discouraged']:\n",
    "    print(w, get_lemma(w), get_lemma2(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /Users/sli/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "en_stop = set(nltk.corpus.stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_text_for_lda(text):\n",
    "    tokens = tokenize(text)\n",
    "    tokens = [token for token in tokens if len(token) > 4]\n",
    "    tokens = [token for token in tokens if token not in en_stop]\n",
    "    tokens = [get_lemma(token) for token in tokens]\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rigorous', 'integration', 'piece', 'linear', 'continuous', 'system']\n",
      "['shape', 'transformation', 'polyhedral', 'object']\n",
      "['search', 'challenge', 'opportunity']\n",
      "['programming']\n",
      "['efficient', 'resource', 'allocation', 'flexible', 'channel', 'cooperation', 'ofdma', 'cognitive', 'radio', 'network']\n",
      "['connectivity', 'large', 'scale', 'cognitive', 'radio', 'network']\n",
      "['hardware', 'accelerate', 'shaders', 'using']\n",
      "['domain', 'circuit', 'model', 'chemical', 'reaction']\n",
      "['bin', 'algorithm', 'accurate', 'computer', 'aid', 'device', 'modeling']\n",
      "['output', 'perturbation', 'query', 'relaxation']\n",
      "['parallel', 'processor', 'architecture', 'graphics', 'arithmetic', 'operations']\n",
      "['efficient', 'management', 'multiversion', 'document', 'object', 'reference']\n",
      "['exact', 'regenerate', 'code', 'byzantine', 'fault', 'tolerance', 'distribute', 'storage']\n",
      "['offset', 'cancellation', 'crossing', 'base', 'circuit']\n",
      "['image', 'sensor', 'spike', 'pixel', 'retinal', 'stimulation']\n",
      "['hardness', 'approximation', 'survivable', 'multi', 'level', 'problem']\n",
      "['issue', 'distribute', 'database', 'management', 'system', 'technical', 'overview']\n",
      "['uniform', 'recursive', 'subdivision', 'surface']\n",
      "['stack', 'memory', 'design', 'instruction', 'folding', 'processor']\n",
      "['framework', 'optimal', 'battery', 'management', 'wireless', 'node']\n",
      "['selection', 'reduce', 'encoding', 'complexity', 'h.264/avc']\n",
      "['speed', 'class', 'current', 'circuit']\n",
      "['design', 'gallery', 'general', 'approach', 'setting', 'parameter', 'computer', 'graphics', 'animation']\n",
      "['simple', 'realistic', 'generation']\n",
      "['toward', 'practical', 'constraint', 'database']\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "text_data = []\n",
    "with open('dataset.csv') as f:\n",
    "    for line in f:\n",
    "        tokens = prepare_text_for_lda(line)\n",
    "        text_data.append(tokens)\n",
    "        if random.random() > .99:\n",
    "            print(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim import corpora\n",
    "dictionary = corpora.Dictionary(text_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [dictionary.doc2bow(text) for text in text_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(corpus, open('corpus.pkl', 'wb'))\n",
    "dictionary.save('dictionary.gensim')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try 5 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "NUM_TOPICS = 5\n",
    "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\n",
    "ldamodel.save('model5.gensim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '0.034*\"processor\" + 0.019*\"database\" + 0.019*\"issue\" + 0.019*\"overview\"')\n",
      "(1, '0.051*\"computer\" + 0.028*\"design\" + 0.028*\"graphics\" + 0.028*\"gallery\"')\n",
      "(2, '0.050*\"management\" + 0.027*\"object\" + 0.027*\"circuit\" + 0.027*\"efficient\"')\n",
      "(3, '0.019*\"cognitive\" + 0.019*\"radio\" + 0.019*\"network\" + 0.019*\"distribute\"')\n",
      "(4, '0.029*\"circuit\" + 0.029*\"system\" + 0.029*\"rigorous\" + 0.029*\"integration\"')\n"
     ]
    }
   ],
   "source": [
    "topics = ldamodel.print_topics(num_words=4)\n",
    "for topic in topics:\n",
    "    print(topic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(38, 1), (117, 1)]\n",
      "[(0, 0.06669136), (1, 0.40170625), (2, 0.06670282), (3, 0.39819494), (4, 0.066704586)]\n"
     ]
    }
   ],
   "source": [
    "new_doc = 'Practical Bayesian Optimization of Machine Learning Algorithms'\n",
    "new_doc = prepare_text_for_lda(new_doc)\n",
    "new_doc_bow = dictionary.doc2bow(new_doc)\n",
    "print(new_doc_bow)\n",
    "print(ldamodel.get_document_topics(new_doc_bow))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '0.029*\"processor\" + 0.016*\"management\" + 0.016*\"aid\" + 0.016*\"algorithm\"')\n",
      "(1, '0.026*\"radio\" + 0.026*\"network\" + 0.026*\"cognitive\" + 0.026*\"efficient\"')\n",
      "(2, '0.029*\"circuit\" + 0.029*\"distribute\" + 0.016*\"database\" + 0.016*\"management\"')\n"
     ]
    }
   ],
   "source": [
    "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = 3, id2word=dictionary, passes=15)\n",
    "ldamodel.save('model3.gensim')\n",
    "topics = ldamodel.print_topics(num_words=4)\n",
    "for topic in topics:\n",
    "    print(topic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '0.055*\"database\" + 0.055*\"system\" + 0.029*\"technical\" + 0.029*\"recursive\"')\n",
      "(1, '0.038*\"distribute\" + 0.038*\"graphics\" + 0.038*\"regenerate\" + 0.038*\"exact\"')\n",
      "(2, '0.055*\"management\" + 0.029*\"multiversion\" + 0.029*\"reference\" + 0.029*\"document\"')\n",
      "(3, '0.046*\"circuit\" + 0.046*\"object\" + 0.046*\"generation\" + 0.046*\"transformation\"')\n",
      "(4, '0.008*\"programming\" + 0.008*\"circuit\" + 0.008*\"network\" + 0.008*\"surface\"')\n",
      "(5, '0.061*\"radio\" + 0.061*\"cognitive\" + 0.061*\"network\" + 0.061*\"connectivity\"')\n",
      "(6, '0.085*\"programming\" + 0.008*\"circuit\" + 0.008*\"subdivision\" + 0.008*\"management\"')\n",
      "(7, '0.041*\"circuit\" + 0.041*\"design\" + 0.041*\"processor\" + 0.041*\"instruction\"')\n",
      "(8, '0.055*\"computer\" + 0.029*\"efficient\" + 0.029*\"channel\" + 0.029*\"cooperation\"')\n",
      "(9, '0.061*\"stimulation\" + 0.061*\"sensor\" + 0.061*\"retinal\" + 0.061*\"pixel\"')\n"
     ]
    }
   ],
   "source": [
    "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = 10, id2word=dictionary, passes=15)\n",
    "ldamodel.save('model10.gensim')\n",
    "topics = ldamodel.print_topics(num_words=4)\n",
    "for topic in topics:\n",
    "    print(topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = gensim.corpora.Dictionary.load('dictionary.gensim')\n",
    "corpus = pickle.load(open('corpus.pkl', 'rb'))\n",
    "lda = gensim.models.ldamodel.LdaModel.load('model5.gensim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sli/anaconda3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
    {
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       "\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_el119801123884820565783178316\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"system\", \"technical\", \"tolerance\", \"toward\", \"transformation\", \"uniform\", \"using\", \"wireless\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3, 4, 5]};\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_el119801123884820565783178316\", ldavis_el119801123884820565783178316_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_el119801123884820565783178316\", ldavis_el119801123884820565783178316_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_el119801123884820565783178316\", ldavis_el119801123884820565783178316_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim\n",
    "lda_display = pyLDAvis.gensim.prepare(lda, corpus, dictionary, sort_topics=False)\n",
    "pyLDAvis.display(lda_display)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sli/anaconda3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
    {
     "data": {
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       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
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       "\n",
       "<div id=\"ldavis_el119801123896507369919561150\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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       "\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",
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       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el119801123896507369919561150\", ldavis_el119801123896507369919561150_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_el119801123896507369919561150\", ldavis_el119801123896507369919561150_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_el119801123896507369919561150\", ldavis_el119801123896507369919561150_data);\n",
       "            })\n",
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       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda3 = gensim.models.ldamodel.LdaModel.load('model3.gensim')\n",
    "lda_display3 = pyLDAvis.gensim.prepare(lda3, corpus, dictionary, sort_topics=False)\n",
    "pyLDAvis.display(lda_display3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sli/anaconda3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
    {
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       "\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_el119801123903365847082471480\", ldavis_el119801123903365847082471480_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_el119801123903365847082471480\", ldavis_el119801123903365847082471480_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_el119801123903365847082471480\", ldavis_el119801123903365847082471480_data);\n",
       "            })\n",
       "         });\n",
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       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda10 = gensim.models.ldamodel.LdaModel.load('model10.gensim')\n",
    "lda_display10 = pyLDAvis.gensim.prepare(lda10, corpus, dictionary, sort_topics=False)\n",
    "pyLDAvis.display(lda_display10)"
   ]
  }
 ],
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