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   "source": [
    "# Gensim进阶教程：训练Doc2vec\n",
    "Doc2vec是Mikolov在word2vec基础上提出的另一个用于计算长文本向量的工具。它的工作原理与word2vec极为相似——只是将长文本作为一个特殊的token id引入训练语料中。\n",
    "\n",
    "在Gensim中，doc2vec也是继承于word2vec的一个子类。因此，无论是API的参数接口还是调用文本向量的方式，doc2vec与word2vec都极为相似。\n",
    "\n",
    "主要的区别是在对输入数据的预处理上。Doc2vec接受一个由LabeledSentence对象组成的迭代器作为其构造函数的输入参数。其中，LabeledSentence是Gensim内建的一个类，它接受两个List作为其初始化的参数：word list和label list。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
    "from nltk.tokenize import word_tokenize\n",
    "data = [\"I love machine learning. Its awesome.\",\n",
    "        \"I love coding in python\",\n",
    "        \"I love building chatbots\",\n",
    "        \"they chat amagingly well\"]\n",
    "tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=['SENT_%s' %str(i)]) for i, _d in enumerate(data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = Doc2Vec(size=100,\n",
    "                alpha=0.01, \n",
    "                min_count=2,\n",
    "                dm =1)\n",
    "model.build_vocab(tagged_data)\n",
    "for epoch in range(10):\n",
    "    print('iteration {0}'.format(epoch))\n",
    "    model.train(tagged_data,\n",
    "                total_examples=model.corpus_count,\n",
    "                epochs=model.iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model[\"SENT_0\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.docvecs.most_similar(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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