From 831cfcb8e5b718a4e40876698ba6ce523b90ebb2 Mon Sep 17 00:00:00 2001
From: Li <ts-yundi.li@ASRHQ474.local>
Date: Mon, 21 May 2018 14:53:56 +0800
Subject: [PATCH 1/2] Fix logic error in topic_modelling_gensim notebook

---
 .../topic_modeling_Gensim-checkpoint.ipynb    | 602 ++++++++++++++++++
 topic_modeling_Gensim.ipynb                   |   6 +-
 2 files changed, 605 insertions(+), 3 deletions(-)
 create mode 100644 .ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb

diff --git a/.ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb b/.ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb
new file mode 100644
index 0000000..44bf54b
--- /dev/null
+++ b/.ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb
@@ -0,0 +1,602 @@
+{
+ "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"
+     ]
+    },
+    {
+     "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_el119801123884820565783178316\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el119801123884820565783178316_data = {\"mdsDat\": {\"Freq\": [28.279172897338867, 15.257948875427246, 15.415287017822266, 27.0604190826416, 13.987177848815918], \"cluster\": [1, 1, 1, 1, 1], \"topics\": [1, 2, 3, 4, 5], \"x\": [0.07881159652641653, 0.03101622041425195, -0.005929750579331484, -0.12715680794362827, 0.02325874158229131], \"y\": [0.10141595348805849, -0.058737799347913365, -0.038541183297393755, 0.04190555806568607, -0.04604252890843755]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", 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\"system\", \"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": {
+      "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_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",
+       "  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_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",
+       "         });\n",
+       "}\n",
+       "</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"
+     ]
+    },
+    {
+     "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_el119801123903365847082471480\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
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-5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777, -5.187399864196777]}, \"token.table\": {\"Topic\": [3, 9, 9, 9, 9, 9, 9, 3, 2, 2, 8, 3, 9, 2, 8, 2, 9, 4, 3, 4, 8, 3, 2, 9, 1, 9, 1, 1, 9, 8, 3, 1, 8, 9, 9, 1, 2, 3, 4, 3, 9, 1, 2, 2, 9, 8, 3, 9, 9, 4, 2, 9, 1, 3, 3, 8, 1, 1, 3, 1, 1, 3, 8, 4, 9, 3, 3, 9, 3, 3, 4, 9, 8, 2, 2, 3, 8, 1, 2, 9, 8, 1, 4, 1, 3, 2, 8, 8, 9, 4, 4, 1, 1, 3, 2, 8, 9, 1, 2, 1, 9, 3, 4, 4, 3, 8, 2, 1, 1, 3, 1, 1, 2, 1, 4, 1, 3, 3], \"Freq\": [0.9079827070236206, 0.8767423033714294, 0.8767423033714294, 0.8767423033714294, 0.8767421245574951, 0.8767421841621399, 0.8767421841621399, 0.9079827070236206, 0.9576812982559204, 0.9576812982559204, 0.9874979257583618, 0.9079827070236206, 0.8767423033714294, 0.9576812982559204, 0.9874979257583618, 0.9576812982559204, 0.8767421245574951, 1.0420916080474854, 0.46913942694664, 0.46913942694664, 0.46913942694664, 0.9079827070236206, 0.9576812982559204, 0.6480847001075745, 0.9078534841537476, 0.5524408221244812, 0.9078534841537476, 0.9078534841537476, 0.8767421245574951, 0.9874979257583618, 0.9079827070236206, 0.5773754715919495, 0.5944515466690063, 0.5944515466690063, 0.8767421841621399, 0.5971344709396362, 0.5971344709396362, 0.9079827070236206, 1.0420916080474854, 0.5646829009056091, 0.5646829009056091, 0.9078534841537476, 0.9576812982559204, 0.9576812982559204, 0.8767421245574951, 0.9874979257583618, 0.9079827070236206, 0.8767421841621399, 0.8767421841621399, 1.0420916080474854, 0.5835151672363281, 0.5835151672363281, 0.9078536033630371, 0.9079827070236206, 0.9079827070236206, 0.9874979257583618, 0.9078536033630371, 0.9078534841537476, 0.9079827070236206, 0.9078536033630371, 0.4233453571796417, 0.4233453571796417, 0.9874979257583618, 1.0420916080474854, 0.8767423033714294, 0.9079827070236206, 0.9079827070236206, 0.6480847001075745, 0.9079827070236206, 0.628959596157074, 0.628959596157074, 0.8767421245574951, 0.9874979257583618, 0.9576812982559204, 0.9576811790466309, 0.9079827070236206, 0.9874979257583618, 0.9078536033630371, 0.9576812982559204, 0.8767421841621399, 0.9874979257583618, 0.9078536033630371, 1.0420916080474854, 0.9078534841537476, 0.9079827070236206, 0.6305862665176392, 0.6305862665176392, 0.9874979257583618, 0.6480847001075745, 1.0420916080474854, 1.0420916080474854, 0.9078534841537476, 0.9078536033630371, 0.9079827070236206, 0.9576812982559204, 0.9874979257583618, 0.8767421245574951, 0.9078536033630371, 0.9576811790466309, 0.9078534841537476, 0.8767421841621399, 0.9079827070236206, 1.0420916080474854, 1.0420916080474854, 0.9079827070236206, 0.9874979257583618, 0.9576812982559204, 0.9078534841537476, 0.9078534841537476, 0.9079827070236206, 0.5773754715919495, 0.9078536033630371, 0.9576812982559204, 0.9078534841537476, 1.0420916080474854, 0.9078534841537476, 0.9079827070236206, 0.9079827070236206], \"Term\": [\"accelerate\", \"accurate\", \"aid\", \"algorithm\", \"allocation\", \"animation\", \"approach\", \"approximation\", \"architecture\", \"arithmetic\", \"base\", \"battery\", \"bin\", \"byzantine\", \"cancellation\", \"challenge\", \"channel\", \"chemical\", \"circuit\", \"circuit\", \"circuit\", \"class\", \"code\", \"cognitive\", \"complexity\", \"computer\", \"constraint\", \"continuous\", \"cooperation\", \"crossing\", \"current\", \"database\", \"design\", \"design\", \"device\", \"distribute\", \"distribute\", \"document\", \"domain\", \"efficient\", \"efficient\", \"encoding\", \"exact\", \"fault\", \"flexible\", \"folding\", \"framework\", \"gallery\", \"general\", \"generation\", \"graphics\", \"graphics\", \"h.264/avc\", \"hardness\", \"hardware\", \"instruction\", \"integration\", \"issue\", \"level\", \"linear\", \"management\", \"management\", \"memory\", \"model\", \"modeling\", \"multi\", \"multiversion\", \"network\", \"node\", \"object\", \"object\", \"ofdma\", \"offset\", \"operations\", \"opportunity\", \"optimal\", \"output\", \"overview\", \"parallel\", \"parameter\", \"perturbation\", \"piece\", \"polyhedral\", \"practical\", \"problem\", \"processor\", \"processor\", \"query\", \"radio\", \"reaction\", \"realistic\", \"recursive\", \"reduce\", \"reference\", \"regenerate\", \"relaxation\", \"resource\", \"rigorous\", \"search\", \"selection\", \"setting\", \"shaders\", \"shape\", \"simple\", \"speed\", \"stack\", \"storage\", \"subdivision\", \"surface\", \"survivable\", \"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, 6, 7, 8, 9, 10]};\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_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",
+       "}\n",
+       "</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)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/topic_modeling_Gensim.ipynb b/topic_modeling_Gensim.ipynb
index afeba57..44bf54b 100644
--- a/topic_modeling_Gensim.ipynb
+++ b/topic_modeling_Gensim.ipynb
@@ -170,9 +170,9 @@
     "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)\n",
-    "            text_data.append(tokens)"
+    "            print(tokens)"
    ]
   },
   {
@@ -594,7 +594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,

From 29f75a5b948d0081eff0cf0b9fb95fe898bdbab0 Mon Sep 17 00:00:00 2001
From: Li <ts-yundi.li@ASRHQ474.local>
Date: Mon, 21 May 2018 14:58:49 +0800
Subject: [PATCH 2/2] Delete unnecessary checkpoints files

---
 .../topic_modeling_Gensim-checkpoint.ipynb    | 602 ------------------
 1 file changed, 602 deletions(-)
 delete mode 100644 .ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb

diff --git a/.ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb b/.ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb
deleted file mode 100644
index 44bf54b..0000000
--- a/.ipynb_checkpoints/topic_modeling_Gensim-checkpoint.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "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"
-     ]
-    },
-    {
-     "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_el119801123884820565783178316\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "\n",
-       "var ldavis_el119801123884820565783178316_data = {\"mdsDat\": {\"Freq\": [28.279172897338867, 15.257948875427246, 15.415287017822266, 27.0604190826416, 13.987177848815918], \"cluster\": [1, 1, 1, 1, 1], \"topics\": [1, 2, 3, 4, 5], \"x\": [0.07881159652641653, 0.03101622041425195, -0.005929750579331484, -0.12715680794362827, 0.02325874158229131], \"y\": [0.10141595348805849, -0.058737799347913365, -0.038541183297393755, 0.04190555806568607, -0.04604252890843755]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", 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\"system\", \"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": {
-      "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_el119801123896507369919561150\"></div>\n",
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-   "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"
-     ]
-    },
-    {
-     "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_el119801123903365847082471480\"></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",
-       "  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",
-       "}\n",
-       "</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)"
-   ]
-  }
- ],
- "metadata": {
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-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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-    "name": "ipython",
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-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
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- "nbformat": 4,
- "nbformat_minor": 2
-}