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\", \"Topic1\", 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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_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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+ "\n",
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+ "<script type=\"text/javascript\">\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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+ " !function(LDAvis){\n",
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+ " 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",
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+ " LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
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+ "execution_count": 24,
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+ "source": [
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+ "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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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",
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- "\n",
- "\n",
- "<div id=\"ldavis_el119801123884820565783178316\"></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",
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- " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
- " document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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- " // already loaded: just create the visualization\n",
- " !function(LDAvis){\n",
- " new LDAvis(\"#\" + \"ldavis_el119801123884820565783178316\", ldavis_el119801123884820565783178316_data);\n",
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- "}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",
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- " // 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",
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- ],
- "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": [
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- "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",
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- "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",
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- " 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",
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- "execution_count": 24,
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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)"
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- {
- "cell_type": "code",
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- "/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",
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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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- "language": "python",
- "name": "python3"
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- "language_info": {
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- "name": "ipython",
- "version": 3
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- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
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- "nbformat": 4,
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