{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10.6 求近义词和类比词\n",
    "## 10.6.1 使用预训练的词向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['charngram.100d', 'fasttext.en.300d', 'fasttext.simple.300d', 'glove.42B.300d', 'glove.840B.300d', 'glove.twitter.27B.25d', 'glove.twitter.27B.50d', 'glove.twitter.27B.100d', 'glove.twitter.27B.200d', 'glove.6B.50d', 'glove.6B.100d', 'glove.6B.200d', 'glove.6B.300d'])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torchtext.vocab as vocab\n",
    "\n",
    "print(torch.__version__)\n",
    "vocab.pretrained_aliases.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['glove.42B.300d',\n",
       " 'glove.840B.300d',\n",
       " 'glove.twitter.27B.25d',\n",
       " 'glove.twitter.27B.50d',\n",
       " 'glove.twitter.27B.100d',\n",
       " 'glove.twitter.27B.200d',\n",
       " 'glove.6B.50d',\n",
       " 'glove.6B.100d',\n",
       " 'glove.6B.200d',\n",
       " 'glove.6B.300d']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[key for key in vocab.pretrained_aliases.keys()\n",
    "        if \"glove\" in key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cache_dir = \"/Users/tangshusen/Datasets/glove\"\n",
    "# glove = vocab.pretrained_aliases[\"glove.6B.50d\"](cache=cache_dir)\n",
    "glove = vocab.GloVe(name='6B', dim=50, cache=cache_dir) # 与上面等价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一共包含400000个词。\n"
     ]
    }
   ],
   "source": [
    "print(\"一共包含%d个词。\" % len(glove.stoi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3366, 'beautiful')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glove.stoi['beautiful'], glove.itos[3366]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.6.2 应用预训练词向量\n",
    "### 10.6.2.1 求近义词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def knn(W, x, k):\n",
    "    # 添加的1e-9是为了数值稳定性\n",
    "    cos = torch.matmul(W, x.view((-1,))) / (\n",
    "        (torch.sum(W * W, dim=1) + 1e-9).sqrt() * torch.sum(x * x).sqrt())\n",
    "    _, topk = torch.topk(cos, k=k)\n",
    "    topk = topk.cpu().numpy()\n",
    "    return topk, [cos[i].item() for i in topk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_similar_tokens(query_token, k, embed):\n",
    "    topk, cos = knn(embed.vectors,\n",
    "                    embed.vectors[embed.stoi[query_token]], k+1)\n",
    "    for i, c in zip(topk[1:], cos[1:]):  # 除去输入词\n",
    "        print('cosine sim=%.3f: %s' % (c, (embed.itos[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosine sim=0.856: chips\n",
      "cosine sim=0.749: intel\n",
      "cosine sim=0.749: electronics\n"
     ]
    }
   ],
   "source": [
    "get_similar_tokens('chip', 3, glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosine sim=0.839: babies\n",
      "cosine sim=0.800: boy\n",
      "cosine sim=0.792: girl\n"
     ]
    }
   ],
   "source": [
    "get_similar_tokens('baby', 3, glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosine sim=0.921: lovely\n",
      "cosine sim=0.893: gorgeous\n",
      "cosine sim=0.830: wonderful\n"
     ]
    }
   ],
   "source": [
    "get_similar_tokens('beautiful', 3, glove)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.6.2.2 求类比词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_analogy(token_a, token_b, token_c, embed):\n",
    "    vecs = [embed.vectors[embed.stoi[t]] \n",
    "                for t in [token_a, token_b, token_c]]\n",
    "    x = vecs[1] - vecs[0] + vecs[2]\n",
    "    topk, cos = knn(embed.vectors, x, 1)\n",
    "    return embed.itos[topk[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'daughter'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_analogy('man', 'woman', 'son', glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'japan'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_analogy('beijing', 'china', 'tokyo', glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'biggest'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_analogy('bad', 'worst', 'big', glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'went'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_analogy('do', 'did', 'go', glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}