[장윤태]210916_정규세션과제.ipynb

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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BsUGyttltL5I",
    "outputId": "a2a524a9-eec2-4214-cad7-54039b4b97ac"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\admin1\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
    "from nltk.tokenize import word_tokenize\n",
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "HJRArzLSusZb"
   },
   "outputs": [],
   "source": [
    "idx_df = [(str(a['name']), str(a['history_text'])) for i,a in mydf.loc[:,[\"name\",\"history_text\"]].iterrows()]\n",
    "tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=[str(i)]) for i, _d in idx_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "axy5nl_VtPFu",
    "outputId": "a7477a98-a0ea-403f-a641-d5c1e3aa9213"
   },
   "outputs": [],
   "source": [
    "max_epochs = 100\n",
    "vec_size = 20\n",
    "alpha = 0.025\n",
    "\n",
    "model = Doc2Vec(vector_size=vec_size,\n",
    "                alpha=alpha, \n",
    "                min_alpha=0.00025,\n",
    "                min_count=1,\n",
    "                dm =1)\n",
    "  \n",
    "model.build_vocab(tagged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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    "id": "F65bVXhAXI2G",
    "outputId": "8a92d66b-8dd5-4b79-a42c-addec9cfd28d"
   },
   "outputs": [
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       "                name       real_name       full_name overall_score  \\\n",
       "5       Aayla Secura    Aayla Secura             NaN             8   \n",
       "6         Abe Sapien  Abraham Sapien  Abraham Sapien            10   \n",
       "7           Abin Sur             NaN             NaN             9   \n",
       "8        Abomination    Emil Blonsky    Emil Blonsky            22   \n",
       "9  Abra Kadabra (CW)         Unknown         Unknown            13   \n",
       "\n",
       "                                        history_text  \\\n",
       "5  ayla Secura was a Rutian Twi'lek Jedi Knight (...   \n",
       "6   Sapien began life as Langdon Everett Caul, a ...   \n",
       "7   Originally a history professor on the planet ...   \n",
       "8  Formerly known as Emil Blonsky, a spy of Sovie...   \n",
       "9  \"Abra Kadabra\" was a criminal time traveler fr...   \n",
       "\n",
       "                                         powers_text  intelligence_score  \\\n",
       "5                                                NaN                  90   \n",
       "6   Abe is a humanoid amphibious creature. He has...                  95   \n",
       "7    Abin Sur possessed an exceptionally strong s...                  75   \n",
       "8  'Blonsky''s transformation into the Abominatio...                  85   \n",
       "9   Abra Kadabra was augmented with various nanot...                 100   \n",
       "\n",
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   "source": [
    "mydf[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "WmdmnOS_tm2n",
    "outputId": "9deeb6ed-f463-4938-d253-29c9c93cdfcd"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-7123d2260fc4>:1: DeprecationWarning: Call to deprecated `docvecs` (The `docvecs` property has been renamed `dv`.).\n",
      "  model.docvecs.most_similar(\"Superman (2006)\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('Rocket Raccoon (MCU)', 0.6858669519424438),\n",
       " ('Rocket Raccoon', 0.5951510071754456),\n",
       " ('Red Tornado (CW)', 0.5688164830207825),\n",
       " ('Penguin', 0.564375638961792),\n",
       " ('Stick (MCU)', 0.5559000968933105),\n",
       " ('Steel Serpent', 0.5380349159240723),\n",
       " ('Arsenal', 0.5371323227882385),\n",
       " ('Invincible', 0.5335350632667542),\n",
       " ('Martian Manhunter', 0.5319717526435852),\n",
       " ('Jesse Quick (CW)', 0.518513023853302)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.docvecs.most_similar(\"Superman (2006)\")"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "id": "KhHQqD8UXSXN"
   },
   "outputs": [],
   "source": []
  }
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