2)word_Embedding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "import tensorflow as tf \n",
    "tf.random.set_seed(0) \n",
    "\n",
    "with open('wiki_space_tokenizer.txt', mode='r', encoding='utf-8') as f: \n",
    "    wiki_contents = f.read()\n",
    "wiki_contents = wiki_contents.split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1806748"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wiki_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_contents = wiki_contents[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = list() \n",
    "maxlen = 3\n",
    "tmp_content = list()\n",
    "for content in wiki_contents: \n",
    "    content = content.strip()\n",
    "    content = re.sub(r'\\([^)]*\\)', '', content)\n",
    "    content = content.lower()\n",
    "    if not content: continue \n",
    "    if content[-1] == '.':\n",
    "        word_list = list() \n",
    "        for word in content.split(): \n",
    "            tmp_idx = 0\n",
    "            for idx, char in enumerate(word): \n",
    "                if char in \"\\'\\\",.<>-•·\": \n",
    "                    if idx == 0: word_list.append(word[idx])\n",
    "                    elif tmp_idx < idx: \n",
    "                        word_list.append(word[tmp_idx:idx])\n",
    "                        word_list.append(word[idx])\n",
    "                    tmp_idx = idx+1 \n",
    "            if tmp_idx == 0: word_list.append(word) \n",
    "            elif tmp_idx < len(word): word_list.append(word[tmp_idx:])\n",
    "                \n",
    "        if len(tmp_content) > 0: \n",
    "            tmp_content = tmp_content + ['\\n'] + word_list\n",
    "        else: tmp_content = word_list \n",
    "    else:\n",
    "        if len(tmp_content) > maxlen: \n",
    "            text.append(tmp_content+['E']) \n",
    "        tmp_content = list()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "   '대한',\n",
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       "   '수론은',\n",
       "   '이런',\n",
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       "   '깊게',\n",
       "   '다루는',\n",
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       "   '정리',\n",
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       "   '세월',\n",
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       "   '이는',\n",
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       "   '환',\n",
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       "   '체',\n",
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       "   '.',\n",
       "   '함수해석학은',\n",
       "   '함수의',\n",
       "   '공간의',\n",
       "   '탐구에',\n",
       "   '주목한다',\n",
       "   '.',\n",
       "   '함수해석학의',\n",
       "   '많은',\n",
       "   '응용분야',\n",
       "   '중',\n",
       "   '하나가',\n",
       "   '양자역학이다',\n",
       "   '.',\n",
       "   '많은',\n",
       "   '문제들이',\n",
       "   '자연스럽게',\n",
       "   '양과',\n",
       "   '그',\n",
       "   '양의',\n",
       "   '변화율의',\n",
       "   '관계로',\n",
       "   '귀착되고',\n",
       "   ',',\n",
       "   '이러한',\n",
       "   '문제들이',\n",
       "   '미분방정식으로',\n",
       "   '다루어진다',\n",
       "   '.',\n",
       "   '자연의',\n",
       "   '많은',\n",
       "   '현상들이',\n",
       "   '동역학계로',\n",
       "   '기술될',\n",
       "   '수',\n",
       "   '있다',\n",
       "   '.',\n",
       "   '혼돈',\n",
       "   '이론은',\n",
       "   '이러한',\n",
       "   '예측',\n",
       "   '불가능한',\n",
       "   '현상을',\n",
       "   '탐구하는',\n",
       "   '데',\n",
       "   '상당한',\n",
       "   '기여를',\n",
       "   '한다',\n",
       "   '.',\n",
       "   '\\n',\n",
       "   '수학의',\n",
       "   '기초를',\n",
       "   '확실히',\n",
       "   '세우기',\n",
       "   '위해',\n",
       "   ',',\n",
       "   '수리논리학과',\n",
       "   '집합론이',\n",
       "   '발전하였고',\n",
       "   ',',\n",
       "   '이와',\n",
       "   '더불어',\n",
       "   '범주론이',\n",
       "   '최근에도',\n",
       "   '발전되고',\n",
       "   '있다',\n",
       "   '.',\n",
       "   '“근본',\n",
       "   '위기”라는',\n",
       "   '말은',\n",
       "   '대략',\n",
       "   '1900년에서',\n",
       "   '1930년',\n",
       "   '사이에',\n",
       "   '일어난',\n",
       "   ',',\n",
       "   '수학의',\n",
       "   '엄밀한',\n",
       "   '기초에',\n",
       "   '대한',\n",
       "   '탐구를',\n",
       "   '상징적으로',\n",
       "   '보여주는',\n",
       "   '말이다',\n",
       "   '.',\n",
       "   '수학의',\n",
       "   '엄밀한',\n",
       "   '기초에',\n",
       "   '대한',\n",
       "   '몇',\n",
       "   '가지',\n",
       "   '의견',\n",
       "   '불일치는',\n",
       "   '오늘날에도',\n",
       "   '계속되고',\n",
       "   '있다',\n",
       "   '.',\n",
       "   '수학의',\n",
       "   '기초에',\n",
       "   '대한',\n",
       "   '위기는',\n",
       "   '그',\n",
       "   '당시',\n",
       "   '수많은',\n",
       "   '논쟁에',\n",
       "   '의해',\n",
       "   '촉발되었으며',\n",
       "   ',',\n",
       "   '그',\n",
       "   '논쟁에는',\n",
       "   '칸토어의',\n",
       "   '집합론과',\n",
       "   '브라우어',\n",
       "   '-',\n",
       "   '힐베르트',\n",
       "   '논쟁이',\n",
       "   '포함되었다',\n",
       "   '.',\n",
       "   'E']])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text), text[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38709"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_text = list() \n",
    "for elem in text: total_text += elem\n",
    "len(total_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total words:  16324\n"
     ]
    }
   ],
   "source": [
    "words = sorted(list(set(total_text)))\n",
    "word_size = len(words)\n",
    "word_indices = dict((c, i) for i, c in enumerate(words))\n",
    "indices_word = dict((i, c) for i, c in enumerate(words))\n",
    "print('total words: ', word_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequences length:  38376\n"
     ]
    }
   ],
   "source": [
    "sentences = []\n",
    "next_words = []\n",
    "for content in text: \n",
    "    for i in range(0, len(content) - maxlen, 1):\n",
    "        sentences.append([word_indices[elem] for elem in content[i: i + maxlen]])\n",
    "        next_words.append(word_indices[content[i + maxlen]])\n",
    "print(\"sequences length: \", len(sentences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical \n",
    "import numpy as np\n",
    "data_X = np.array(sentences) \n",
    "data_Y = np.array(next_words) \n",
    "data_Y = to_categorical(data_Y)\n",
    "y_size = data_Y.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Embedding, Dense, LSTM\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=word_size, \n",
    "                   output_dim=200, \n",
    "                   input_length=maxlen))\n",
    "model.add(LSTM(64))\n",
    "model.add(Dense(y_size, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 38376 samples\n",
      "Epoch 1/70\n",
      "38376/38376 - 11s - loss: 9.2170 - accuracy: 0.0643\n",
      "Epoch 2/70\n",
      "38376/38376 - 8s - loss: 8.2752 - accuracy: 0.0616\n",
      "Epoch 3/70\n",
      "38376/38376 - 8s - loss: 7.9728 - accuracy: 0.0822\n",
      "Epoch 4/70\n",
      "38376/38376 - 8s - loss: 7.7711 - accuracy: 0.0939\n",
      "Epoch 5/70\n",
      "38376/38376 - 8s - loss: 7.6218 - accuracy: 0.1054\n",
      "Epoch 6/70\n",
      "38376/38376 - 8s - loss: 7.4914 - accuracy: 0.1135\n",
      "Epoch 7/70\n",
      "38376/38376 - 8s - loss: 7.3614 - accuracy: 0.1177\n",
      "Epoch 8/70\n",
      "38376/38376 - 8s - loss: 7.2237 - accuracy: 0.1251\n",
      "Epoch 9/70\n",
      "38376/38376 - 8s - loss: 7.0819 - accuracy: 0.1291\n",
      "Epoch 10/70\n",
      "38376/38376 - 8s - loss: 6.9356 - accuracy: 0.1350\n",
      "Epoch 11/70\n",
      "38376/38376 - 8s - loss: 6.7815 - accuracy: 0.1387\n",
      "Epoch 12/70\n",
      "38376/38376 - 8s - loss: 6.6218 - accuracy: 0.1434\n",
      "Epoch 13/70\n",
      "38376/38376 - 8s - loss: 6.4580 - accuracy: 0.1475\n",
      "Epoch 14/70\n",
      "38376/38376 - 8s - loss: 6.2930 - accuracy: 0.1513\n",
      "Epoch 15/70\n",
      "38376/38376 - 8s - loss: 6.1250 - accuracy: 0.1568\n",
      "Epoch 16/70\n",
      "38376/38376 - 8s - loss: 5.9591 - accuracy: 0.1639\n",
      "Epoch 17/70\n",
      "38376/38376 - 9s - loss: 5.7941 - accuracy: 0.1690\n",
      "Epoch 18/70\n",
      "38376/38376 - 9s - loss: 5.6300 - accuracy: 0.1766\n",
      "Epoch 19/70\n",
      "38376/38376 - 9s - loss: 5.4677 - accuracy: 0.1851\n",
      "Epoch 20/70\n",
      "38376/38376 - 8s - loss: 5.3069 - accuracy: 0.1955\n",
      "Epoch 21/70\n",
      "38376/38376 - 8s - loss: 5.1478 - accuracy: 0.2072\n",
      "Epoch 22/70\n",
      "38376/38376 - 8s - loss: 4.9896 - accuracy: 0.2212\n",
      "Epoch 23/70\n",
      "38376/38376 - 8s - loss: 4.8321 - accuracy: 0.2384\n",
      "Epoch 24/70\n",
      "38376/38376 - 8s - loss: 4.6755 - accuracy: 0.2550\n",
      "Epoch 25/70\n",
      "38376/38376 - 8s - loss: 4.5201 - accuracy: 0.2760\n",
      "Epoch 26/70\n",
      "38376/38376 - 8s - loss: 4.3655 - accuracy: 0.2973\n",
      "Epoch 27/70\n",
      "38376/38376 - 8s - loss: 4.2129 - accuracy: 0.3257\n",
      "Epoch 28/70\n",
      "38376/38376 - 9s - loss: 4.0640 - accuracy: 0.3516\n",
      "Epoch 29/70\n",
      "38376/38376 - 8s - loss: 3.9137 - accuracy: 0.3804\n",
      "Epoch 30/70\n",
      "38376/38376 - 9s - loss: 3.7660 - accuracy: 0.4093\n",
      "Epoch 31/70\n",
      "38376/38376 - 9s - loss: 3.6220 - accuracy: 0.4386\n",
      "Epoch 32/70\n",
      "38376/38376 - 8s - loss: 3.4789 - accuracy: 0.4693\n",
      "Epoch 33/70\n",
      "38376/38376 - 8s - loss: 3.3392 - accuracy: 0.4994\n",
      "Epoch 34/70\n",
      "38376/38376 - 8s - loss: 3.2008 - accuracy: 0.5324\n",
      "Epoch 35/70\n",
      "38376/38376 - 8s - loss: 3.0632 - accuracy: 0.5600\n",
      "Epoch 36/70\n",
      "38376/38376 - 8s - loss: 2.9310 - accuracy: 0.5907\n",
      "Epoch 37/70\n",
      "38376/38376 - 8s - loss: 2.8026 - accuracy: 0.6188\n",
      "Epoch 38/70\n",
      "38376/38376 - 8s - loss: 2.6764 - accuracy: 0.6437\n",
      "Epoch 39/70\n",
      "38376/38376 - 8s - loss: 2.5549 - accuracy: 0.6683\n",
      "Epoch 40/70\n",
      "38376/38376 - 8s - loss: 2.4357 - accuracy: 0.6926\n",
      "Epoch 41/70\n",
      "38376/38376 - 8s - loss: 2.3191 - accuracy: 0.7120\n",
      "Epoch 42/70\n",
      "38376/38376 - 9s - loss: 2.2083 - accuracy: 0.7323\n",
      "Epoch 43/70\n",
      "38376/38376 - 9s - loss: 2.0998 - accuracy: 0.7498\n",
      "Epoch 44/70\n",
      "38376/38376 - 8s - loss: 1.9965 - accuracy: 0.7677\n",
      "Epoch 45/70\n",
      "38376/38376 - 8s - loss: 1.8975 - accuracy: 0.7843\n",
      "Epoch 46/70\n",
      "38376/38376 - 8s - loss: 1.8024 - accuracy: 0.7959\n",
      "Epoch 47/70\n",
      "38376/38376 - 8s - loss: 1.7125 - accuracy: 0.8112\n",
      "Epoch 48/70\n",
      "38376/38376 - 8s - loss: 1.6267 - accuracy: 0.8226\n",
      "Epoch 49/70\n",
      "38376/38376 - 8s - loss: 1.5439 - accuracy: 0.8313\n",
      "Epoch 50/70\n",
      "38376/38376 - 8s - loss: 1.4654 - accuracy: 0.8405\n",
      "Epoch 51/70\n",
      "38376/38376 - 8s - loss: 1.3919 - accuracy: 0.8493\n",
      "Epoch 52/70\n",
      "38376/38376 - 8s - loss: 1.3200 - accuracy: 0.8576\n",
      "Epoch 53/70\n",
      "38376/38376 - 8s - loss: 1.2539 - accuracy: 0.8637\n",
      "Epoch 54/70\n",
      "38376/38376 - 8s - loss: 1.1892 - accuracy: 0.8719\n",
      "Epoch 55/70\n",
      "38376/38376 - 8s - loss: 1.1289 - accuracy: 0.8767\n",
      "Epoch 56/70\n",
      "38376/38376 - 9s - loss: 1.0718 - accuracy: 0.8838\n",
      "Epoch 57/70\n",
      "38376/38376 - 9s - loss: 1.0205 - accuracy: 0.8877\n",
      "Epoch 58/70\n",
      "38376/38376 - 8s - loss: 0.9693 - accuracy: 0.8924\n",
      "Epoch 59/70\n",
      "38376/38376 - 8s - loss: 0.9229 - accuracy: 0.8963\n",
      "Epoch 60/70\n",
      "38376/38376 - 8s - loss: 0.8779 - accuracy: 0.9006\n",
      "Epoch 61/70\n",
      "38376/38376 - 8s - loss: 0.8361 - accuracy: 0.9045\n",
      "Epoch 62/70\n",
      "38376/38376 - 8s - loss: 0.7971 - accuracy: 0.9086\n",
      "Epoch 63/70\n",
      "38376/38376 - 8s - loss: 0.7597 - accuracy: 0.9116\n",
      "Epoch 64/70\n",
      "38376/38376 - 8s - loss: 0.7254 - accuracy: 0.9141\n",
      "Epoch 65/70\n",
      "38376/38376 - 8s - loss: 0.6925 - accuracy: 0.9167\n",
      "Epoch 66/70\n",
      "38376/38376 - 8s - loss: 0.6620 - accuracy: 0.9204\n",
      "Epoch 67/70\n",
      "38376/38376 - 8s - loss: 0.6340 - accuracy: 0.9222\n",
      "Epoch 68/70\n",
      "38376/38376 - 8s - loss: 0.6057 - accuracy: 0.9246\n",
      "Epoch 69/70\n",
      "38376/38376 - 8s - loss: 0.5800 - accuracy: 0.9270\n",
      "Epoch 70/70\n",
      "38376/38376 - 8s - loss: 0.5553 - accuracy: 0.9291\n"
     ]
    }
   ],
   "source": [
    "import time \n",
    "start_time = time.time() \n",
    "\n",
    "history = model.fit(data_X, data_Y,\n",
    "          batch_size=256,\n",
    "          epochs=70, \n",
    "          verbose=2)\n",
    "\n",
    "end_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Time: 576.9808080196381'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Time: {}\".format(end_time-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['loss'])\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['accuracy', 'loss'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from nltk.translate import bleu_score\n",
    "\n",
    "def text_generation(model):\n",
    "    text_idx = 0\n",
    "    current_text = text[text_idx]\n",
    "    sentence = current_text[:maxlen]\n",
    "    generated = ' '.join(sentence)\n",
    "    print('----- Generating with seed: \"' + generated + '\"')\n",
    "    sys.stdout.write(generated)\n",
    "\n",
    "    for i in range(200): \n",
    "        x_pred = list()\n",
    "        for word in sentence: \n",
    "            x_pred.append([word_indices[word]])\n",
    "\n",
    "        x_pred = np.asarray(x_pred)\n",
    "        preds = model.predict(x_pred.T)[0] \n",
    "        next_idx = np.argmax(preds)\n",
    "        next_word = indices_word[next_idx]\n",
    "        sentence = sentence[1:] + [next_word]\n",
    "        sys.stdout.write(' ' + next_word)\n",
    "        sys.stdout.flush()\n",
    "        if next_word == 'E': break \n",
    "    print()\n",
    "\n",
    "def text_generation_res(model):\n",
    "    text_idx = 0\n",
    "    current_text = text[text_idx]\n",
    "    generated = ''\n",
    "    sentence = current_text[:maxlen]\n",
    "    generated += ' '.join(sentence)\n",
    "    \n",
    "    for i in range(min(200, len(current_text))): \n",
    "        x_pred = list()\n",
    "        for word in sentence: \n",
    "            x_pred.append([word_indices[word]])\n",
    "\n",
    "        x_pred = np.asarray(x_pred)\n",
    "        preds = model.predict(x_pred.T)[0] \n",
    "        next_idx = np.argmax(preds)\n",
    "        next_word = indices_word[next_idx]\n",
    "        sentence = sentence[1:] + [next_word]\n",
    "        generated = generated + ' ' + next_word\n",
    "        if next_word == 'E': break\n",
    "        \n",
    "    return generated, text_idx\n",
    "\n",
    "def get_BLEU_score(model): \n",
    "    predict_text, text_idx = text_generation_res(model) \n",
    "    pred_len = len(predict_text) \n",
    "    original_text = text[text_idx] \n",
    "    original_text = original_text[:pred_len]\n",
    "    BLEUscore = bleu_score.sentence_bleu([' '.join(original_text)], predict_text)\n",
    "    return BLEUscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Generating with seed: \"제임스 얼 \"\"\n",
      "제임스 얼 \" 지미 \" 카터 주니어는 민주당 출신 미국 39번째 대통령이다 . \n",
      " 지미 카터는 대한민국과의 관계에서도 중요한 영향을 미쳤던 대통령 중 하나다 . 인권 문제와 주한미군 철수 문제로 한때 한미 관계가 불편하기도 했다 . 1978년 대한민국에 대한 조선민주주의인민공화국의 위협에 대비해 한미연합사를 창설하면서 , 1982년까지 3단계에 걸쳐 주한미군을 철수하기로 했다 . 그러나 주한미군사령부와 정보기관 · 의회의 반대에 부딪혀 주한미군은 완전철수 대신 6 , 000명을 감축하는 데 그쳤다 . 또한 박정희 정권의 인권 문제 등과의 논란으로 불협화음을 냈으나 , 1979년 6월 하순 , 대한민국을 방문하여 관계가 다소 회복되었다 . \n",
      " 1979년 ~ 1980년 대한민국의 정치적 격변기 당시의 대통령이었던 그는 이에 대해 애매한 태도를 보였고 , 이는 후에 대한민국 내에서 고조되는 반미 운동의 한 원인이 됐다 . 10월 26일 , 박정희 전 대통령이었다 . 또한 \" 15억 원이나 관리하면서 박연차로부터 노무현이 3억 원이나 빌릴 때에도 그 돈을 내놓지 않고 차명 계좌에 은닉하고 있었다는 것을 믿을 국민은 없다 \" 라고 판시하였다 . \n",
      " 2009년 5월 23일 , 노무현의 자살함으로써 노무현에 대한 검찰 수사를 종료하였다 . \n",
      " 2009년 5월 23일 , 노무현의 자살함으로써 노무현에 대한 검찰 수사를 종료하였다 . \n",
      " 2009년 5월 23일 , 노무현의 자살함으로써 노무현에 대한 검찰 수사를 종료하였다 . \n",
      " 2009년 5월 23일 , 노무현의 자살함으로써 노무현에 대한 검찰 수사를 종료하였다 . \n",
      "\n"
     ]
    }
   ],
   "source": [
    "text_generation(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.054145498666060284"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_BLEU_score(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rouge-1': {'f': 0.3902439024390244, 'p': 0.38095238095238093, 'r': 0.4},\n",
       " 'rouge-4': {'f': 0.0, 'p': 0.0, 'r': 0.0},\n",
       " 'rouge-3': {'f': 0.05405405405405406,\n",
       "  'p': 0.05263157894736842,\n",
       "  'r': 0.05555555555555555},\n",
       " 'rouge-2': {'f': 0.15384615384615385, 'p': 0.15, 'r': 0.15789473684210525},\n",
       " 'rouge-l': {'f': 0.4565251580949447,\n",
       "  'p': 0.44743049129966744,\n",
       "  'r': 0.4659972203002852},\n",
       " 'rouge-w': {'f': 0.21685693894947203,\n",
       "  'p': 0.2859632609331056,\n",
       "  'r': 0.17465060484356942}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rouge import Rouge\n",
    "\n",
    "evalutaor = Rouge(metrics=['rouge-n', 'rouge-l', 'rouge-w'],\n",
    "                           max_n=4,\n",
    "                           apply_avg='Avg',\n",
    "                           alpha=0.5, # Default F1_score\n",
    "                           weight_factor=1.2)\n",
    "def get_ROUGE_score(model, evaluator): \n",
    "    predict_text, text_idx = text_generation_res(model) \n",
    "    pred_len = len(predict_%text) \n",
    "    predict_text = ' '.join([elem[0] for elem in predict_text])\n",
    "    original_text = text[text_idx] \n",
    "    original_text = ' '.join([elem[0] for elem in original_text[:pred_len]]) \n",
    "    score = evalutaor.get_scores(original_text, predict_text)\n",
    "    return score\n",
    "get_ROUGE_score(model, evalutaor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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