{
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   "id": "719e3403",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Converting Raw Text into Sequence Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "117b083a",
   "metadata": {
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     "n": "3"
    },
    "execution": {
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    },
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import random\n",
    "import re\n",
    "import torch\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87cfae33",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Reads the raw text into a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd560200",
   "metadata": {
    "attributes": {
     "classes": [],
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     "n": "5"
    },
    "execution": {
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    "origin_pos": 7,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Time Machine, by H. G. Wells [1898]\\n\\n\\n\\n\\nI\\n\\n\\nThe Time Tra'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class TimeMachine(d2l.DataModule): \n",
    "    \"\"\"The Time Machine dataset.\"\"\"\n",
    "    def _download(self):\n",
    "        fname = d2l.download(d2l.DATA_URL + 'timemachine.txt', self.root,\n",
    "                             '090b5e7e70c295757f55df93cb0a180b9691891a')\n",
    "        with open(fname) as f:\n",
    "            return f.read()\n",
    "\n",
    "data = TimeMachine()\n",
    "raw_text = data._download()\n",
    "raw_text[:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4d26c1b",
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    "execution": {
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    "origin_pos": 9,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'the time machine by h g wells i the time traveller for so it'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@d2l.add_to_class(TimeMachine)  \n",
    "def _preprocess(self, text):\n",
    "    return re.sub('[^A-Za-z]+', ' ', text).lower()\n",
    "\n",
    "text = data._preprocess(raw_text)\n",
    "text[:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc35eee3",
   "metadata": {
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     "n": "7"
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    "execution": {
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    "origin_pos": 11,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'t,h,e, ,t,i,m,e, ,m,a,c,h,i,n,e, ,b,y, ,h, ,g, ,w,e,l,l,s, '"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@d2l.add_to_class(TimeMachine)  \n",
    "def _tokenize(self, text):\n",
    "    return list(text)\n",
    "\n",
    "tokens = data._tokenize(text)\n",
    "','.join(tokens[:30])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24ef727",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Next, we introduce a class\n",
    "for constructing *vocabularies*,\n",
    "i.e., objects that associate\n",
    "each distinct token value\n",
    "with a unique index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "820d7cff",
   "metadata": {
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     "n": "8"
    },
    "execution": {
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    },
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class Vocab:  \n",
    "    \"\"\"Vocabulary for text.\"\"\"\n",
    "    def __init__(self, tokens=[], min_freq=0, reserved_tokens=[]):\n",
    "        if tokens and isinstance(tokens[0], list):\n",
    "            tokens = [token for line in tokens for token in line]\n",
    "        counter = collections.Counter(tokens)\n",
    "        self.token_freqs = sorted(counter.items(), key=lambda x: x[1],\n",
    "                                  reverse=True)\n",
    "        self.idx_to_token = list(sorted(set(['<unk>'] + reserved_tokens + [\n",
    "            token for token, freq in self.token_freqs if freq >= min_freq])))\n",
    "        self.token_to_idx = {token: idx\n",
    "                             for idx, token in enumerate(self.idx_to_token)}\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.idx_to_token)\n",
    "\n",
    "    def __getitem__(self, tokens):\n",
    "        if not isinstance(tokens, (list, tuple)):\n",
    "            return self.token_to_idx.get(tokens, self.unk)\n",
    "        return [self.__getitem__(token) for token in tokens]\n",
    "\n",
    "    def to_tokens(self, indices):\n",
    "        if hasattr(indices, '__len__') and len(indices) > 1:\n",
    "            return [self.idx_to_token[int(index)] for index in indices]\n",
    "        return self.idx_to_token[indices]\n",
    "\n",
    "    @property\n",
    "    def unk(self):\n",
    "        return self.token_to_idx['<unk>']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "483f34b2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Construct a vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da288532",
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    },
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:25.999437Z",
     "iopub.status.busy": "2023-08-18T19:29:25.998566Z",
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     "shell.execute_reply": "2023-08-18T19:29:26.019020Z"
    },
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "indices: [21, 9, 6, 0, 21, 10, 14, 6, 0, 14]\n",
      "words: ['t', 'h', 'e', ' ', 't', 'i', 'm', 'e', ' ', 'm']\n"
     ]
    }
   ],
   "source": [
    "vocab = Vocab(tokens)\n",
    "indices = vocab[tokens[:10]]\n",
    "print('indices:', indices)\n",
    "print('words:', vocab.to_tokens(indices))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6e6f00",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Package everything into the following\n",
    "`build` method of the `TimeMachine` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b19fc5b8",
   "metadata": {
    "attributes": {
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     "n": "10"
    },
    "execution": {
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     "shell.execute_reply": "2023-08-18T19:29:26.126623Z"
    },
    "origin_pos": 17,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(173428, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@d2l.add_to_class(TimeMachine)  \n",
    "def build(self, raw_text, vocab=None):\n",
    "    tokens = self._tokenize(self._preprocess(raw_text))\n",
    "    if vocab is None: vocab = Vocab(tokens)\n",
    "    corpus = [vocab[token] for token in tokens]\n",
    "    return corpus, vocab\n",
    "\n",
    "corpus, vocab = data.build(raw_text)\n",
    "len(corpus), len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5985d60",
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "11"
    },
    "execution": {
     "iopub.execute_input": "2023-08-18T19:29:26.131046Z",
     "iopub.status.busy": "2023-08-18T19:29:26.130467Z",
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     "shell.execute_reply": "2023-08-18T19:29:26.146169Z"
    },
    "origin_pos": 19,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 2261),\n",
       " ('i', 1267),\n",
       " ('and', 1245),\n",
       " ('of', 1155),\n",
       " ('a', 816),\n",
       " ('to', 695),\n",
       " ('was', 552),\n",
       " ('in', 541),\n",
       " ('that', 443),\n",
       " ('my', 440)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = text.split()\n",
    "vocab = Vocab(words)\n",
    "vocab.token_freqs[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdc3328",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The ten most frequent words\n",
    "*stop words*\n",
    "plot the figure of the word frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1017fe5b",
   "metadata": {
    "attributes": {
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     "n": "12"
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     "shell.execute_reply": "2023-08-18T19:29:27.116986Z"
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    "origin_pos": 21,
    "tab": [
     "pytorch"
    ]
   },
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   "source": [
    "freqs = [freq for token, freq in vocab.token_freqs]\n",
    "d2l.plot(freqs, xlabel='token: x', ylabel='frequency: n(x)',\n",
    "         xscale='log', yscale='log')"
   ]
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   },
   "source": [
    "What about the other word combinations, such as two consecutive words (bigrams), three consecutive words (trigrams)"
   ]
  },
  {
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   "id": "eb992e88",
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    },
    "origin_pos": 23,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('of--the', 309),\n",
       " ('in--the', 169),\n",
       " ('i--had', 130),\n",
       " ('i--was', 112),\n",
       " ('and--the', 109),\n",
       " ('the--time', 102),\n",
       " ('it--was', 99),\n",
       " ('to--the', 85),\n",
       " ('as--i', 78),\n",
       " ('of--a', 73)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "bigram_tokens = ['--'.join(pair) for pair in zip(words[:-1], words[1:])]\n",
    "bigram_vocab = Vocab(bigram_tokens)\n",
    "bigram_vocab.token_freqs[:10]"
   ]
  },
  {
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   "execution_count": 11,
   "id": "8a284366",
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    {
     "data": {
      "text/plain": [
       "[('the--time--traveller', 59),\n",
       " ('the--time--machine', 30),\n",
       " ('the--medical--man', 24),\n",
       " ('it--seemed--to', 16),\n",
       " ('it--was--a', 15),\n",
       " ('here--and--there', 15),\n",
       " ('seemed--to--me', 14),\n",
       " ('i--did--not', 14),\n",
       " ('i--saw--the', 13),\n",
       " ('i--began--to', 13)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "trigram_tokens = ['--'.join(triple) for triple in zip(\n",
    "    words[:-2], words[1:-1], words[2:])]\n",
    "trigram_vocab = Vocab(trigram_tokens)\n",
    "trigram_vocab.token_freqs[:10]"
   ]
  },
  {
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   "id": "7587b155",
   "metadata": {
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   "source": [
    "Visualize the token frequency"
   ]
  },
  {
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   "execution_count": 12,
   "id": "ad96dfc1",
   "metadata": {
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     "n": "15"
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   ],
   "source": [
    "bigram_freqs = [freq for token, freq in bigram_vocab.token_freqs]\n",
    "trigram_freqs = [freq for token, freq in trigram_vocab.token_freqs]\n",
    "d2l.plot([freqs, bigram_freqs, trigram_freqs], xlabel='token: x',\n",
    "         ylabel='frequency: n(x)', xscale='log', yscale='log',\n",
    "         legend=['unigram', 'bigram', 'trigram'])"
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