sklearn-lda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_exp sklearn_lda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sklearn.LatentDirichletAllocation\n",
    "\n",
    "> Run LDA Model using 'sklearn'\n",
    "\n",
    "Run LDA Model using 'sklearn'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def make_df(csv_name, column = 'Content', output_column = 'text'):\n",
    "    '''Use jieba, create data frame.'''\n",
    "    df = pd.read_csv(csv_name)\n",
    "    df = df.dropna(subset=[column])\n",
    "    df[output_column] = df[column].apply(lambda x: \" \".join(jieba.cut(x)))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\LIJIAX~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.911 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    }
   ],
   "source": [
    "affirmative = make_df(\"data/affirmative.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Students</th>\n",
       "      <th>Content</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>第1组</td>\n",
       "      <td>正三</td>\n",
       "      <td>慕课将分布于世界各地的最优质的教育资源聚集到一起，让任何有学习愿望的人能够低成本的，通常是免...</td>\n",
       "      <td>慕课 将 分布 于 世界各地 的 最 优质 的 教育资源 聚集 到 一起 ， 让 任何 有 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>第1组</td>\n",
       "      <td>正二</td>\n",
       "      <td>在慕课发展过程中的现阶段，中国最大的慕课平台icourse163的用户人数突破100万,与其...</td>\n",
       "      <td>在 慕课 发展 过程 中 的 现阶段 ， 中国 最大 的 慕课 平台 icourse163 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>第1组</td>\n",
       "      <td>正一</td>\n",
       "      <td>研究发现,在慕课融入的课堂学习中,学习者情感体验丰富,知识技能以及元认知能力得到提升,思想观...</td>\n",
       "      <td>研究 发现 , 在 慕课 融入 的 课堂 学习 中 , 学习者 情感 体验 丰富 , 知识 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>第1组</td>\n",
       "      <td>正三</td>\n",
       "      <td>慕课在保证教育质量的同时，降低提供教育的成本，给社会带来的憧憬。任何人任何时候再任何地方，都...</td>\n",
       "      <td>慕课 在 保证 教育 质量 的 同时 ， 降低 提供 教育 的 成本 ， 给 社会 带来 的...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>第1组</td>\n",
       "      <td>正一</td>\n",
       "      <td>对方反一辩友也说是可能出现的欢快气氛，传统课堂集体聆听教师单方面赐予的知识，这难道不是一种容...</td>\n",
       "      <td>对方 反一 辩友 也 说 是 可能 出现 的 欢快 气氛 ， 传统 课堂 集体 聆听 教师 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Group Students                                            Content  \\\n",
       "0   第1组       正三  慕课将分布于世界各地的最优质的教育资源聚集到一起，让任何有学习愿望的人能够低成本的，通常是免...   \n",
       "1   第1组       正二  在慕课发展过程中的现阶段，中国最大的慕课平台icourse163的用户人数突破100万,与其...   \n",
       "2   第1组       正一  研究发现,在慕课融入的课堂学习中,学习者情感体验丰富,知识技能以及元认知能力得到提升,思想观...   \n",
       "3   第1组       正三  慕课在保证教育质量的同时，降低提供教育的成本，给社会带来的憧憬。任何人任何时候再任何地方，都...   \n",
       "4   第1组       正一  对方反一辩友也说是可能出现的欢快气氛，传统课堂集体聆听教师单方面赐予的知识，这难道不是一种容...   \n",
       "\n",
       "                                                text  \n",
       "0  慕课 将 分布 于 世界各地 的 最 优质 的 教育资源 聚集 到 一起 ， 让 任何 有 ...  \n",
       "1  在 慕课 发展 过程 中 的 现阶段 ， 中国 最大 的 慕课 平台 icourse163 ...  \n",
       "2  研究 发现 , 在 慕课 融入 的 课堂 学习 中 , 学习者 情感 体验 丰富 , 知识 ...  \n",
       "3  慕课 在 保证 教育 质量 的 同时 ， 降低 提供 教育 的 成本 ， 给 社会 带来 的...  \n",
       "4  对方 反一 辩友 也 说 是 可能 出现 的 欢快 气氛 ， 传统 课堂 集体 聆听 教师 ...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affirmative.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative = make_df(\"data/negative.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Students</th>\n",
       "      <th>Content</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>第1组</td>\n",
       "      <td>反一</td>\n",
       "      <td>通俗地说，慕课是大规模的网络开放课程。1.传统课堂中可能出现的欢快的气氛，慕课没有；2.传统...</td>\n",
       "      <td>通俗 地说 ， 慕课 是 大规模 的 网络 开放 课程 。 1 . 传统 课堂 中 可能 出...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>第1组</td>\n",
       "      <td>反一</td>\n",
       "      <td>通俗地说，慕课是大规模的网络开放课程。我方观点为，慕课不能代替传统课堂1.传统课堂中可能出现...</td>\n",
       "      <td>通俗 地说 ， 慕课 是 大规模 的 网络 开放 课程 。 我方 观点 为 ， 慕课 不能 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>第1组</td>\n",
       "      <td>反一</td>\n",
       "      <td>正一观点中有这样一句话“相比在线交流,学习者偏好面对面的交流讨论”这不正是传统课堂所能给予的吗</td>\n",
       "      <td>正一 观点 中有 这样 一句 话 “ 相比 在线 交流 , 学习者 偏好 面对面 的 交流 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>第1组</td>\n",
       "      <td>反一</td>\n",
       "      <td>正二给出的数字中，说明不了慕课的替代性，毕竟中国学生庞大的基数在这。</td>\n",
       "      <td>正二 给出 的 数字 中 ， 说明 不了 慕课 的 替代性 ， 毕竟 中国 学生 庞大 的 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>第1组</td>\n",
       "      <td>反一</td>\n",
       "      <td>正一说的独立思考问题，传统课堂的弹性较大，课堂设计掌握在老师学生手中，可以做到因材施教，针对...</td>\n",
       "      <td>正一说 的 独立思考 问题 ， 传统 课堂 的 弹性 较大 ， 课堂 设计 掌握 在 老师 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Group Students                                            Content  \\\n",
       "0   第1组       反一  通俗地说，慕课是大规模的网络开放课程。1.传统课堂中可能出现的欢快的气氛，慕课没有；2.传统...   \n",
       "1   第1组       反一  通俗地说，慕课是大规模的网络开放课程。我方观点为，慕课不能代替传统课堂1.传统课堂中可能出现...   \n",
       "2   第1组       反一    正一观点中有这样一句话“相比在线交流,学习者偏好面对面的交流讨论”这不正是传统课堂所能给予的吗   \n",
       "3   第1组       反一                 正二给出的数字中，说明不了慕课的替代性，毕竟中国学生庞大的基数在这。   \n",
       "4   第1组       反一  正一说的独立思考问题，传统课堂的弹性较大，课堂设计掌握在老师学生手中，可以做到因材施教，针对...   \n",
       "\n",
       "                                                text  \n",
       "0  通俗 地说 ， 慕课 是 大规模 的 网络 开放 课程 。 1 . 传统 课堂 中 可能 出...  \n",
       "1  通俗 地说 ， 慕课 是 大规模 的 网络 开放 课程 。 我方 观点 为 ， 慕课 不能 ...  \n",
       "2  正一 观点 中有 这样 一句 话 “ 相比 在线 交流 , 学习者 偏好 面对面 的 交流 ...  \n",
       "3  正二 给出 的 数字 中 ， 说明 不了 慕课 的 替代性 ， 毕竟 中国 学生 庞大 的 ...  \n",
       "4  正一说 的 独立思考 问题 ， 传统 课堂 的 弹性 较大 ， 课堂 设计 掌握 在 老师 ...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import pyLDAvis \n",
    "import pyLDAvis.sklearn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import jieba\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "def chinese_word_cut(mytext):\n",
    "    return \" \".join(jieba.cut(mytext))\n",
    "\n",
    "def print_top_words(model, feature_names, n_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic #%d:\" % topic_idx)\n",
    "        print(\" \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-n_top_words - 1:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def get_custom_stopwords(stop_words_file, encoding = 'utf-8'):\n",
    "    with open(stop_words_file, encoding = encoding) as f:\n",
    "        stopwords = f.read()\n",
    "    stopwords_list = stopwords.split('\\n')\n",
    "    custom_stopwords_list = [i for i in stopwords_list]\n",
    "    return custom_stopwords_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = get_custom_stopwords(\"data/stopwords.txt\", encoding='utf-8') # HIT停用词词典\n",
    "max_df = 0.9 # 在超过这一比例的文档中出现的关键词（过于平凡），去除掉。\n",
    "min_df = 5 # 在低于这一数量的文档中出现的关键词（过于独特），去除掉。\n",
    "n_features = 1000 # 最大提取特征数量\n",
    "n_top_words = 20 # 显示主题下关键词的时候，显示多少个\n",
    "col_content = \"text\" # 说明其中的文本信息所在列名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def lda_on_chinese_articles_with_param(df, n_topics, \n",
    "                            col_content, \n",
    "                            stopwords, \n",
    "                            n_features, \n",
    "                            max_df, \n",
    "                            min_df,\n",
    "                            n_top_words):\n",
    "    articles_cutted = df[col_content].apply(chinese_word_cut)\n",
    "    vect = CountVectorizer(max_df = max_df, \n",
    "                       min_df = min_df, \n",
    "                       token_pattern=u'(?u)\\\\b[^\\\\d\\\\W]\\\\w+\\\\b', \n",
    "                       stop_words=frozenset(stopwords))\n",
    "    tf = vect.fit_transform(articles_cutted)\n",
    "    lda = LatentDirichletAllocation(n_components=n_topics, max_iter=50,\n",
    "                                    learning_method='online',\n",
    "                                    learning_offset=50,\n",
    "                                    random_state=0)\n",
    "    lda.fit(tf)\n",
    "    print_top_words(lda, vect.get_feature_names(), n_top_words)\n",
    "    return lda, tf, vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def lda_on_chinese_articles(df, n_topics):\n",
    "    return lda_on_chinese_articles_with_param(df, n_topics, \n",
    "                            col_content = col_content, \n",
    "                            stopwords = stopwords, \n",
    "                            n_features = n_features, \n",
    "                            max_df = max_df, \n",
    "                            min_df = min_df,\n",
    "                            n_top_words = n_top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\install\\miniconda\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['lex', '①①', '①②', '①③', '①④', '①⑤', '①⑥', '①⑦', '①⑧', '①⑨', '①ａ', '①ｂ', '①ｃ', '①ｄ', '①ｅ', '①ｆ', '①ｇ', '①ｈ', '①ｉ', '①ｏ', '②①', '②②', '②③', '②④', '②⑤', '②⑥', '②⑦', '②⑧', '②⑩', '②ａ', '②ｂ', '②ｄ', '②ｅ', '②ｆ', '②ｇ', '②ｈ', '②ｉ', '②ｊ', '③①', '③⑩', '③ａ', '③ｂ', '③ｃ', '③ｄ', '③ｅ', '③ｆ', '③ｇ', '③ｈ', '④ａ', '④ｂ', '④ｃ', '④ｄ', '④ｅ', '⑤ａ', '⑤ｂ', '⑤ｄ', '⑤ｅ', '⑤ｆ', 'ｌｉ', 'ｎｇ', 'ｚｘｆｉｔｌ'] not in stop_words.\n",
      "  'stop_words.' % sorted(inconsistent))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "学习 学生 交流 课程 学习者 视频 时间 网络 讨论 进行 教师 课堂 自主 知识 教学 兴趣 实现 促进 合作 高等教育\n",
      "Topic #1:\n",
      "教育 发展 传统 大学 已经 取代 我国 不能 社会 课堂 问题 不是 认为 时代 现在 目前 技术 国家 高等教育 高校\n",
      "Topic #2:\n",
      "学生 课堂 传统 学习 教师 老师 教学 进行 方式 过程 视频 知识 课程 需要 一种 不是 问题 时间 没有 模式\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\install\\miniconda\\lib\\site-packages\\pyLDAvis\\_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
     "data": {
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\"\\u8fc7\\u7a0b\", \"\\u8fd9\\u79cd\", \"\\u8fd9\\u79cd\", \"\\u8fd9\\u79cd\", \"\\u8fdb\\u5165\", \"\\u8fdb\\u6b65\", \"\\u8fdb\\u884c\", \"\\u8fdb\\u884c\", \"\\u8fdb\\u884c\", \"\\u9009\\u62e9\", \"\\u9009\\u62e9\", \"\\u901f\\u5ea6\", \"\\u95ee\\u9898\", \"\\u95ee\\u9898\", \"\\u95ee\\u9898\", \"\\u9636\\u6bb5\", \"\\u9636\\u6bb5\", \"\\u9700\\u8981\", \"\\u9700\\u8981\", \"\\u9700\\u8981\", \"\\u97e9\\u6b20\", \"\\u97e9\\u6b20\", \"\\u9ad8\\u6548\", \"\\u9ad8\\u6821\", \"\\u9ad8\\u6821\", \"\\u9ad8\\u6821\", \"\\u9ad8\\u7b49\\u6559\\u80b2\", \"\\u9ad8\\u7b49\\u6559\\u80b2\", \"\\u9ad8\\u7b49\\u6559\\u80b2\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 2]};\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_el1619637200950420452048\", ldavis_el1619637200950420452048_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_el1619637200950420452048\", ldavis_el1619637200950420452048_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_el1619637200950420452048\", ldavis_el1619637200950420452048_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "2     -0.057797 -0.125530       1        1  49.785762\n",
       "0     -0.158830  0.091751       2        1  25.893650\n",
       "1      0.216627  0.033779       3        1  24.320588, topic_info=    Category        Freq  Term       Total  loglift  logprob\n",
       "222  Default  585.000000    学习  585.000000  30.0000  30.0000\n",
       "376  Default  216.000000    教育  216.000000  29.0000  29.0000\n",
       "228  Default  693.000000    学生  693.000000  28.0000  28.0000\n",
       "160  Default  193.000000    发展  193.000000  27.0000  27.0000\n",
       "62   Default   73.000000    交流   73.000000  26.0000  26.0000\n",
       "224  Default   61.000000   学习者   61.000000  25.0000  25.0000\n",
       "92   Default  402.000000    传统  402.000000  24.0000  24.0000\n",
       "213  Default   73.000000    大学   73.000000  23.0000  23.0000\n",
       "382  Default   97.000000    方式   97.000000  22.0000  22.0000\n",
       "261  Default   70.000000    已经   70.000000  21.0000  21.0000\n",
       "319  Default   38.000000    我国   38.000000  20.0000  20.0000\n",
       "541  Default  153.000000    老师  153.000000  19.0000  19.0000\n",
       "630  Default   94.000000    过程   94.000000  18.0000  18.0000\n",
       "505  Default   39.000000    社会   39.000000  17.0000  17.0000\n",
       "589  Default   54.000000    讨论   54.000000  16.0000  16.0000\n",
       "374  Default  195.000000    教师  195.000000  15.0000  15.0000\n",
       "581  Default  141.000000    视频  141.000000  14.0000  14.0000\n",
       "389  Default  109.000000    时间  109.000000  13.0000  13.0000\n",
       "538  Default   66.000000    网络   66.000000  12.0000  12.0000\n",
       "25   Default   61.000000    不能   61.000000  11.0000  11.0000\n",
       "387  Default   31.000000    时代   31.000000  10.0000  10.0000\n",
       "6    Default   72.000000    一种   72.000000   9.0000   9.0000\n",
       "192  Default   28.000000    国家   28.000000   8.0000   8.0000\n",
       "123  Default   31.000000    兴趣   31.000000   7.0000   7.0000\n",
       "479  Default   33.000000    目前   33.000000   6.0000   6.0000\n",
       "550  Default   47.000000    自主   47.000000   5.0000   5.0000\n",
       "465  Default   38.000000    现在   38.000000   4.0000   4.0000\n",
       "165  Default   87.000000    取代   87.000000   3.0000   3.0000\n",
       "176  Default   27.000000    合作   27.000000   2.0000   2.0000\n",
       "605  Default  417.000000    课堂  417.000000   1.0000   1.0000\n",
       "..       ...         ...   ...         ...      ...      ...\n",
       "660   Topic3   19.619186    阶段   21.521504   1.3213  -5.1793\n",
       "273   Topic3   14.574011    建设   15.947015   1.3238  -5.4766\n",
       "87    Topic3   17.491873    优秀   19.258952   1.3176  -5.2941\n",
       "479   Topic3   29.353645    目前   33.023484   1.2960  -4.7764\n",
       "160   Topic3  162.778108    发展  193.569349   1.2406  -3.0635\n",
       "379   Topic3   14.036819    文静   15.867362   1.2913  -5.5142\n",
       "261   Topic3   52.365656    已经   70.217550   1.1205  -4.1976\n",
       "213   Topic3   52.467481    大学   73.383873   1.0783  -4.1957\n",
       "465   Topic3   30.018153    现在   38.785302   1.1576  -4.7540\n",
       "134   Topic3   18.982336    出现   23.200291   1.2132  -5.2123\n",
       "30    Topic3   16.269575    世界   19.341038   1.2409  -5.3665\n",
       "25    Topic3   37.832990    不能   61.320229   0.9309  -4.5227\n",
       "681   Topic3   26.007129    高校   38.961303   1.0096  -4.8975\n",
       "450   Topic3   15.217937    满足   18.517383   1.2176  -5.4334\n",
       "165   Topic3   41.323477    取代   87.364940   0.6652  -4.4344\n",
       "92    Topic3   96.792568    传统  402.618552  -0.0116  -3.5833\n",
       "585   Topic3   32.349961    认为   69.190288   0.6536  -4.6792\n",
       "328   Topic3   28.786277    技术   57.259845   0.7261  -4.7959\n",
       "267   Topic3   22.634469    平台   39.325424   0.8614  -5.0364\n",
       "659   Topic3   35.984792    问题  106.755696   0.3264  -4.5727\n",
       "23    Topic3   34.629066    不是  113.972096   0.2226  -4.6112\n",
       "368   Topic3   23.711259  教学模式   47.537040   0.7183  -4.9899\n",
       "682   Topic3   26.162763  高等教育   60.021366   0.5835  -4.8915\n",
       "2     Topic3   23.867739    一定   55.105412   0.5771  -4.9833\n",
       "605   Topic3   37.443476    课堂  417.495616  -0.9976  -4.5330\n",
       "428   Topic3   25.236943    模式   87.270561   0.1731  -4.9275\n",
       "439   Topic3   25.414306    没有  106.968073  -0.0234  -4.9205\n",
       "89    Topic3   20.981978    优质   41.827379   0.7240  -5.1122\n",
       "608   Topic3   24.783549    课程  170.879329  -0.5169  -4.9457\n",
       "227   Topic3   21.538577    学校   56.903557   0.4423  -5.0860\n",
       "\n",
       "[214 rows x 6 columns], token_table=      Topic      Freq  Term\n",
       "term                       \n",
       "2         1  0.308500    一定\n",
       "2         2  0.254059    一定\n",
       "2         3  0.435529    一定\n",
       "3         1  0.976401   一对一\n",
       "6         1  0.982980    一种\n",
       "6         3  0.013845    一种\n",
       "8         1  0.962507    一遍\n",
       "9         1  0.964587    上课\n",
       "9         2  0.035725    上课\n",
       "11        2  0.953747   不仅仅\n",
       "12        1  0.736703    不会\n",
       "12        2  0.102320    不会\n",
       "12        3  0.143248    不会\n",
       "23        1  0.570315    不是\n",
       "23        2  0.122837    不是\n",
       "23        3  0.307093    不是\n",
       "25        1  0.375080    不能\n",
       "25        3  0.619698    不能\n",
       "30        1  0.103407    世界\n",
       "30        2  0.051704    世界\n",
       "30        3  0.827257    世界\n",
       "37        3  0.945107    中国\n",
       "46        1  0.985634   主动性\n",
       "52        1  0.181471    之间\n",
       "52        2  0.771252    之间\n",
       "56        3  0.931993    事物\n",
       "57        1  0.647165    互动\n",
       "57        2  0.344237    互动\n",
       "62        2  0.993293    交流\n",
       "74        3  0.938994    代表\n",
       "...     ...       ...   ...\n",
       "630       1  0.977680    过程\n",
       "630       3  0.021254    过程\n",
       "636       1  0.648635    这种\n",
       "636       2  0.165609    这种\n",
       "636       3  0.193211    这种\n",
       "639       3  0.943110    进入\n",
       "641       3  0.985027    进步\n",
       "642       1  0.650824    进行\n",
       "642       2  0.275349    进行\n",
       "642       3  0.075095    进行\n",
       "645       1  0.657215    选择\n",
       "645       2  0.328607    选择\n",
       "647       2  0.907891    速度\n",
       "659       1  0.562031    问题\n",
       "659       2  0.103039    问题\n",
       "659       3  0.337219    问题\n",
       "660       1  0.092930    阶段\n",
       "660       3  0.929303    阶段\n",
       "670       1  0.730287    需要\n",
       "670       2  0.123429    需要\n",
       "670       3  0.154286    需要\n",
       "676       1  0.321024    韩欠\n",
       "676       2  0.671233    韩欠\n",
       "680       2  0.965583    高效\n",
       "681       1  0.153999    高校\n",
       "681       2  0.179665    高校\n",
       "681       3  0.667329    高校\n",
       "682       1  0.133286  高等教育\n",
       "682       2  0.416518  高等教育\n",
       "682       3  0.433179  高等教育\n",
       "\n",
       "[289 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda, tf, vect = lda_on_chinese_articles(df = affirmative, n_topics = 3)\n",
    "pyLDAvis.sklearn.prepare(lda, tf, vect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "TypeError: __init__() got an unexpected keyword argument 'n_topics'\n",
    "```\n",
    "\n",
    "一般出现这种问题都是程序中字母写错、漏写之类的问题\n",
    "\n",
    "————————————————\n",
    "\n",
    "版权声明：本文为CSDN博主「zhuimengshaonian66」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。\n",
    "原文链接：https://blog.csdn.net/zhuimengshaonian66/article/details/81700959\n",
    "\n",
    "`n_components` 参数名称修改了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\install\\miniconda\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['lex', '①①', '①②', '①③', '①④', '①⑤', '①⑥', '①⑦', '①⑧', '①⑨', '①ａ', '①ｂ', '①ｃ', '①ｄ', '①ｅ', '①ｆ', '①ｇ', '①ｈ', '①ｉ', '①ｏ', '②①', '②②', '②③', '②④', '②⑤', '②⑥', '②⑦', '②⑧', '②⑩', '②ａ', '②ｂ', '②ｄ', '②ｅ', '②ｆ', '②ｇ', '②ｈ', '②ｉ', '②ｊ', '③①', '③⑩', '③ａ', '③ｂ', '③ｃ', '③ｄ', '③ｅ', '③ｆ', '③ｇ', '③ｈ', '④ａ', '④ｂ', '④ｃ', '④ｄ', '④ｅ', '⑤ａ', '⑤ｂ', '⑤ｄ', '⑤ｅ', '⑤ｆ', 'ｌｉ', 'ｎｇ', 'ｚｘｆｉｔｌ'] not in stop_words.\n",
      "  'stop_words.' % sorted(inconsistent))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "课堂 学生 传统 学习 没有 取代 教师 交流 老师 不能 互动 视频 认为 知识 师生 可能 面对面 合作 进行 不是\n",
      "Topic #1:\n",
      "学习 课程 学生 教育 教学 学习者 教师 发展 知识 问题 需要 无法 我国 不能 存在 进行 没有 认为 平台 方式\n",
      "Topic #2:\n",
      "传统 课堂 学生 现在 教育 进步 发展 高校 效果 韩高英 不是 无法 取代 中国 线下 实际 讨论 大学生 网上 时间\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\install\\miniconda\\lib\\site-packages\\pyLDAvis\\_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
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       "\n",
       "\n",
       "<div id=\"ldavis_el161963691291761653753131\"></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_el161963691291761653753131\", ldavis_el161963691291761653753131_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_el161963691291761653753131\", ldavis_el161963691291761653753131_data);\n",
       "      });\n",
       "    });\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_el161963691291761653753131\", ldavis_el161963691291761653753131_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "1      0.137078  0.085646       1        1  55.111775\n",
       "0      0.017437 -0.145236       2        1  32.684201\n",
       "2     -0.154515  0.059590       3        1  12.204024, topic_info=    Category        Freq  Term       Total  loglift  logprob\n",
       "535  Default  393.000000    课堂  393.000000  30.0000  30.0000\n",
       "69   Default  365.000000    传统  365.000000  29.0000  29.0000\n",
       "537  Default  245.000000    课程  245.000000  28.0000  28.0000\n",
       "196  Default  530.000000    学生  530.000000  27.0000  27.0000\n",
       "409  Default   37.000000    现在   37.000000  26.0000  26.0000\n",
       "391  Default  166.000000    没有  166.000000  25.0000  25.0000\n",
       "132  Default  102.000000    发展  102.000000  24.0000  24.0000\n",
       "137  Default  107.000000    取代  107.000000  23.0000  23.0000\n",
       "602  Default   55.000000    高校   55.000000  22.0000  22.0000\n",
       "326  Default  154.000000    教育  154.000000  21.0000  21.0000\n",
       "48   Default   55.000000    互动   55.000000  20.0000  20.0000\n",
       "50   Default   78.000000    交流   78.000000  19.0000  19.0000\n",
       "336  Default   76.000000    无法   76.000000  18.0000  18.0000\n",
       "596  Default   43.000000   韩高英   43.000000  17.0000  17.0000\n",
       "189  Default  382.000000    学习  382.000000  16.0000  16.0000\n",
       "191  Default   95.000000   学习者   95.000000  15.0000  15.0000\n",
       "564  Default   22.000000    进步   22.000000  14.0000  14.0000\n",
       "313  Default   33.000000    效果   33.000000  13.0000  13.0000\n",
       "223  Default   43.000000    师生   43.000000  12.0000  12.0000\n",
       "480  Default   91.000000    老师   91.000000  11.0000  11.0000\n",
       "475  Default   28.000000    网上   28.000000  10.0000  10.0000\n",
       "17   Default   85.000000    不是   85.000000   9.0000   9.0000\n",
       "208  Default   29.000000    实际   29.000000   8.0000   8.0000\n",
       "28   Default   22.000000    中国   22.000000   7.0000   7.0000\n",
       "264  Default   31.000000    情况   31.000000   6.0000   6.0000\n",
       "518  Default   31.000000    讨论   31.000000   5.0000   5.0000\n",
       "183  Default   23.000000   大学生   23.000000   4.0000   4.0000\n",
       "461  Default   15.000000    线下   15.000000   3.0000   3.0000\n",
       "595  Default   36.000000   面对面   36.000000   2.0000   2.0000\n",
       "338  Default   60.000000    时间   60.000000   1.0000   1.0000\n",
       "..       ...         ...   ...         ...      ...      ...\n",
       "311   Topic3    4.275468    放在    5.303528   1.8879  -5.8414\n",
       "387   Topic3   12.447864    氛围   16.134799   1.8440  -4.7727\n",
       "15    Topic3    4.739040    不好    6.171447   1.8393  -5.7384\n",
       "103   Topic3    4.614922    冲击    6.139037   1.8180  -5.7650\n",
       "146   Topic3    5.118667    同意    6.906186   1.8039  -5.6614\n",
       "29    Topic3    6.913173   中国化    9.520767   1.7834  -5.3608\n",
       "51    Topic3    5.604218   交流会    7.874333   1.7633  -5.5707\n",
       "28    Topic3   15.347509    中国   22.533439   1.7194  -4.5633\n",
       "183   Topic3   14.662490   大学生   23.118634   1.6481  -4.6090\n",
       "313   Topic3   20.397175    效果   33.663786   1.6024  -4.2788\n",
       "475   Topic3   14.562421    网上   28.211891   1.4421  -4.6158\n",
       "208   Topic3   14.845258    实际   29.311865   1.4231  -4.5966\n",
       "596   Topic3   18.636309   韩高英   43.785640   1.2492  -4.3691\n",
       "518   Topic3   14.700385    讨论   31.639051   1.3369  -4.6064\n",
       "602   Topic3   20.661917    高校   55.192832   1.1209  -4.2660\n",
       "69    Topic3   67.865793    传统  365.842464   0.4187  -3.0767\n",
       "535   Topic3   64.894125    课堂  393.521021   0.3010  -3.1215\n",
       "420   Topic3    7.578386    电脑   12.426259   1.6089  -5.2689\n",
       "264   Topic3   12.455764    情况   31.037450   1.1904  -4.7721\n",
       "132   Topic3   21.210795    发展  102.581642   0.5273  -4.2397\n",
       "196   Topic3   45.561604    学生  530.736552  -0.3518  -3.4752\n",
       "17    Topic3   18.518694    不是   85.672735   0.5717  -4.3755\n",
       "326   Topic3   21.985357    教育  154.573813   0.1531  -4.2039\n",
       "336   Topic3   15.874527    无法   76.759756   0.5274  -4.5295\n",
       "364   Topic3   10.331037   杜秋艳   24.835933   1.2263  -4.9591\n",
       "338   Topic3   14.021038    时间   60.600830   0.6397  -4.6537\n",
       "137   Topic3   15.627000    取代  107.465847   0.1752  -4.5452\n",
       "480   Topic3   13.134942    老师   91.787935   0.1592  -4.7190\n",
       "489   Topic3   11.607482    能够   55.194677   0.5442  -4.8426\n",
       "603   Topic3   10.389837  高等教育   46.143248   0.6125  -4.9534\n",
       "\n",
       "[204 rows x 6 columns], token_table=      Topic      Freq  Term\n",
       "term                       \n",
       "0         1  0.971562  mooc\n",
       "3         1  0.781535    一种\n",
       "3         2  0.138940    一种\n",
       "3         3  0.069470    一种\n",
       "15        2  0.162037    不好\n",
       "15        3  0.810183    不好\n",
       "17        1  0.455221    不是\n",
       "17        2  0.326825    不是\n",
       "17        3  0.221774    不是\n",
       "18        1  0.444779    不能\n",
       "18        2  0.478347    不能\n",
       "18        3  0.075528    不能\n",
       "23        2  0.873326    东西\n",
       "28        1  0.310649    中国\n",
       "28        3  0.665677    中国\n",
       "29        1  0.210067   中国化\n",
       "29        3  0.735235   中国化\n",
       "33        2  0.817999    临时\n",
       "37        1  0.974387   主动性\n",
       "43        2  0.815874    书本\n",
       "48        1  0.018125    互动\n",
       "48        2  0.978775    互动\n",
       "50        1  0.038093    交流\n",
       "50        2  0.838045    交流\n",
       "50        3  0.114279    交流\n",
       "51        2  0.253990   交流会\n",
       "51        3  0.761969   交流会\n",
       "56        2  0.944853    人文\n",
       "61        2  0.968909    以后\n",
       "64        1  0.265119    优势\n",
       "...     ...       ...   ...\n",
       "537       1  0.986916    课程\n",
       "537       2  0.012234    课程\n",
       "541       1  0.965926    质量\n",
       "543       1  0.972569    资源\n",
       "543       2  0.046313    资源\n",
       "561       1  0.561722    这种\n",
       "561       2  0.436895    这种\n",
       "564       3  0.980187    进步\n",
       "565       1  0.591392    进行\n",
       "565       2  0.336281    进行\n",
       "565       3  0.069575    进行\n",
       "570       1  0.877469    选择\n",
       "570       2  0.107008    选择\n",
       "581       1  0.751354    问题\n",
       "581       2  0.187838    问题\n",
       "581       3  0.062613    问题\n",
       "584       1  0.972660    难以\n",
       "590       1  0.828287    需要\n",
       "590       2  0.160712    需要\n",
       "595       1  0.054694   面对面\n",
       "595       2  0.929798   面对面\n",
       "596       2  0.570963   韩高英\n",
       "596       3  0.433932   韩高英\n",
       "602       1  0.597904    高校\n",
       "602       2  0.018118    高校\n",
       "602       3  0.380484    高校\n",
       "603       1  0.585134  高等教育\n",
       "603       2  0.195045  高等教育\n",
       "603       3  0.216716  高等教育\n",
       "604       3  0.907165    鼓励\n",
       "\n",
       "[267 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda, tf, vect = lda_on_chinese_articles(df = negative, n_topics = 3)\n",
    "pyLDAvis.sklearn.prepare(lda, tf, vect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\install\\miniconda\\lib\\site-packages\\pyLDAvis\\_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
     "data": {
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       "\n",
       "\n",
       "<div id=\"ldavis_el161963691292967999556888\"></div>\n",
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\"\\u8d44\\u6e90\", \"\\u8d44\\u6e90\", \"\\u8fd9\\u79cd\", \"\\u8fd9\\u79cd\", \"\\u8fdb\\u6b65\", \"\\u8fdb\\u884c\", \"\\u8fdb\\u884c\", \"\\u8fdb\\u884c\", \"\\u9009\\u62e9\", \"\\u9009\\u62e9\", \"\\u95ee\\u9898\", \"\\u95ee\\u9898\", \"\\u95ee\\u9898\", \"\\u96be\\u4ee5\", \"\\u9700\\u8981\", \"\\u9700\\u8981\", \"\\u9762\\u5bf9\\u9762\", \"\\u9762\\u5bf9\\u9762\", \"\\u97e9\\u9ad8\\u82f1\", \"\\u97e9\\u9ad8\\u82f1\", \"\\u9ad8\\u6821\", \"\\u9ad8\\u6821\", \"\\u9ad8\\u6821\", \"\\u9ad8\\u7b49\\u6559\\u80b2\", \"\\u9ad8\\u7b49\\u6559\\u80b2\", \"\\u9ad8\\u7b49\\u6559\\u80b2\", \"\\u9f13\\u52b1\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 1, 3]};\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_el161963691292967999556888\", ldavis_el161963691292967999556888_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_el161963691292967999556888\", ldavis_el161963691292967999556888_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_el161963691292967999556888\", ldavis_el161963691292967999556888_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "1      0.137078  0.085646       1        1  55.111775\n",
       "0      0.017437 -0.145236       2        1  32.684201\n",
       "2     -0.154515  0.059590       3        1  12.204024, topic_info=    Category        Freq  Term       Total  loglift  logprob\n",
       "535  Default  393.000000    课堂  393.000000  30.0000  30.0000\n",
       "69   Default  365.000000    传统  365.000000  29.0000  29.0000\n",
       "537  Default  245.000000    课程  245.000000  28.0000  28.0000\n",
       "196  Default  530.000000    学生  530.000000  27.0000  27.0000\n",
       "409  Default   37.000000    现在   37.000000  26.0000  26.0000\n",
       "391  Default  166.000000    没有  166.000000  25.0000  25.0000\n",
       "132  Default  102.000000    发展  102.000000  24.0000  24.0000\n",
       "137  Default  107.000000    取代  107.000000  23.0000  23.0000\n",
       "602  Default   55.000000    高校   55.000000  22.0000  22.0000\n",
       "326  Default  154.000000    教育  154.000000  21.0000  21.0000\n",
       "48   Default   55.000000    互动   55.000000  20.0000  20.0000\n",
       "50   Default   78.000000    交流   78.000000  19.0000  19.0000\n",
       "336  Default   76.000000    无法   76.000000  18.0000  18.0000\n",
       "596  Default   43.000000   韩高英   43.000000  17.0000  17.0000\n",
       "189  Default  382.000000    学习  382.000000  16.0000  16.0000\n",
       "191  Default   95.000000   学习者   95.000000  15.0000  15.0000\n",
       "564  Default   22.000000    进步   22.000000  14.0000  14.0000\n",
       "313  Default   33.000000    效果   33.000000  13.0000  13.0000\n",
       "223  Default   43.000000    师生   43.000000  12.0000  12.0000\n",
       "480  Default   91.000000    老师   91.000000  11.0000  11.0000\n",
       "475  Default   28.000000    网上   28.000000  10.0000  10.0000\n",
       "17   Default   85.000000    不是   85.000000   9.0000   9.0000\n",
       "208  Default   29.000000    实际   29.000000   8.0000   8.0000\n",
       "28   Default   22.000000    中国   22.000000   7.0000   7.0000\n",
       "264  Default   31.000000    情况   31.000000   6.0000   6.0000\n",
       "518  Default   31.000000    讨论   31.000000   5.0000   5.0000\n",
       "183  Default   23.000000   大学生   23.000000   4.0000   4.0000\n",
       "461  Default   15.000000    线下   15.000000   3.0000   3.0000\n",
       "595  Default   36.000000   面对面   36.000000   2.0000   2.0000\n",
       "338  Default   60.000000    时间   60.000000   1.0000   1.0000\n",
       "..       ...         ...   ...         ...      ...      ...\n",
       "311   Topic3    4.275468    放在    5.303528   1.8879  -5.8414\n",
       "387   Topic3   12.447864    氛围   16.134799   1.8440  -4.7727\n",
       "15    Topic3    4.739040    不好    6.171447   1.8393  -5.7384\n",
       "103   Topic3    4.614922    冲击    6.139037   1.8180  -5.7650\n",
       "146   Topic3    5.118667    同意    6.906186   1.8039  -5.6614\n",
       "29    Topic3    6.913173   中国化    9.520767   1.7834  -5.3608\n",
       "51    Topic3    5.604218   交流会    7.874333   1.7633  -5.5707\n",
       "28    Topic3   15.347509    中国   22.533439   1.7194  -4.5633\n",
       "183   Topic3   14.662490   大学生   23.118634   1.6481  -4.6090\n",
       "313   Topic3   20.397175    效果   33.663786   1.6024  -4.2788\n",
       "475   Topic3   14.562421    网上   28.211891   1.4421  -4.6158\n",
       "208   Topic3   14.845258    实际   29.311865   1.4231  -4.5966\n",
       "596   Topic3   18.636309   韩高英   43.785640   1.2492  -4.3691\n",
       "518   Topic3   14.700385    讨论   31.639051   1.3369  -4.6064\n",
       "602   Topic3   20.661917    高校   55.192832   1.1209  -4.2660\n",
       "69    Topic3   67.865793    传统  365.842464   0.4187  -3.0767\n",
       "535   Topic3   64.894125    课堂  393.521021   0.3010  -3.1215\n",
       "420   Topic3    7.578386    电脑   12.426259   1.6089  -5.2689\n",
       "264   Topic3   12.455764    情况   31.037450   1.1904  -4.7721\n",
       "132   Topic3   21.210795    发展  102.581642   0.5273  -4.2397\n",
       "196   Topic3   45.561604    学生  530.736552  -0.3518  -3.4752\n",
       "17    Topic3   18.518694    不是   85.672735   0.5717  -4.3755\n",
       "326   Topic3   21.985357    教育  154.573813   0.1531  -4.2039\n",
       "336   Topic3   15.874527    无法   76.759756   0.5274  -4.5295\n",
       "364   Topic3   10.331037   杜秋艳   24.835933   1.2263  -4.9591\n",
       "338   Topic3   14.021038    时间   60.600830   0.6397  -4.6537\n",
       "137   Topic3   15.627000    取代  107.465847   0.1752  -4.5452\n",
       "480   Topic3   13.134942    老师   91.787935   0.1592  -4.7190\n",
       "489   Topic3   11.607482    能够   55.194677   0.5442  -4.8426\n",
       "603   Topic3   10.389837  高等教育   46.143248   0.6125  -4.9534\n",
       "\n",
       "[204 rows x 6 columns], token_table=      Topic      Freq  Term\n",
       "term                       \n",
       "0         1  0.971562  mooc\n",
       "3         1  0.781535    一种\n",
       "3         2  0.138940    一种\n",
       "3         3  0.069470    一种\n",
       "15        2  0.162037    不好\n",
       "15        3  0.810183    不好\n",
       "17        1  0.455221    不是\n",
       "17        2  0.326825    不是\n",
       "17        3  0.221774    不是\n",
       "18        1  0.444779    不能\n",
       "18        2  0.478347    不能\n",
       "18        3  0.075528    不能\n",
       "23        2  0.873326    东西\n",
       "28        1  0.310649    中国\n",
       "28        3  0.665677    中国\n",
       "29        1  0.210067   中国化\n",
       "29        3  0.735235   中国化\n",
       "33        2  0.817999    临时\n",
       "37        1  0.974387   主动性\n",
       "43        2  0.815874    书本\n",
       "48        1  0.018125    互动\n",
       "48        2  0.978775    互动\n",
       "50        1  0.038093    交流\n",
       "50        2  0.838045    交流\n",
       "50        3  0.114279    交流\n",
       "51        2  0.253990   交流会\n",
       "51        3  0.761969   交流会\n",
       "56        2  0.944853    人文\n",
       "61        2  0.968909    以后\n",
       "64        1  0.265119    优势\n",
       "...     ...       ...   ...\n",
       "537       1  0.986916    课程\n",
       "537       2  0.012234    课程\n",
       "541       1  0.965926    质量\n",
       "543       1  0.972569    资源\n",
       "543       2  0.046313    资源\n",
       "561       1  0.561722    这种\n",
       "561       2  0.436895    这种\n",
       "564       3  0.980187    进步\n",
       "565       1  0.591392    进行\n",
       "565       2  0.336281    进行\n",
       "565       3  0.069575    进行\n",
       "570       1  0.877469    选择\n",
       "570       2  0.107008    选择\n",
       "581       1  0.751354    问题\n",
       "581       2  0.187838    问题\n",
       "581       3  0.062613    问题\n",
       "584       1  0.972660    难以\n",
       "590       1  0.828287    需要\n",
       "590       2  0.160712    需要\n",
       "595       1  0.054694   面对面\n",
       "595       2  0.929798   面对面\n",
       "596       2  0.570963   韩高英\n",
       "596       3  0.433932   韩高英\n",
       "602       1  0.597904    高校\n",
       "602       2  0.018118    高校\n",
       "602       3  0.380484    高校\n",
       "603       1  0.585134  高等教育\n",
       "603       2  0.195045  高等教育\n",
       "603       3  0.216716  高等教育\n",
       "604       3  0.907165    鼓励\n",
       "\n",
       "[267 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 3])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.sklearn.prepare(lda, tf, vect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考 https://github.com/bmabey/pyLDAvis/issues/132\n",
    "    \n",
    "```python\n",
    "D:\\install\\miniconda\\lib\\site-packages\\pyLDAvis\\_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
    "of pandas will change to not sort by default.\n",
    "\n",
    "To accept the future behavior, pass 'sort=False'.\n",
    "\n",
    "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
    "\n",
    "  return pd.concat([default_term_info] + list(topic_dfs))\n",
    "```\n",
    "\n",
    "重新安装后依然没有解决。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.24.2'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pyldavis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "with open(\"model/sklearn-lda.pkl\", 'wb') as fp:\n",
    "    pkl.dump(lda, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.decomposition.online_lda.LatentDirichletAllocation'>\n"
     ]
    }
   ],
   "source": [
    "with open(\"model/sklearn-lda.pkl\", 'rb') as fp:\n",
    "    model0 = pkl.load(fp)\n",
    "    print(model0.__class__)"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
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