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   "execution_count": null,
   "id": "e30bf074",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:16:17.863326Z",
     "start_time": "2023-06-14T17:16:17.806871Z"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import jellyfish\n",
    "import nltk\n",
    "import statistics\n",
    "from collections import Counter\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "711902a2",
   "metadata": {},
   "source": [
    "# Load KWX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bba73c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:12:46.170356Z",
     "start_time": "2023-06-14T17:12:44.665181Z"
    }
   },
   "outputs": [],
   "source": [
    "kwx = json.load(open('./data.json','r'))\n",
    "train_size = 16000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed39b665",
   "metadata": {},
   "source": [
    "# Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7df88f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:19:12.956834Z",
     "start_time": "2023-06-14T17:19:12.949734Z"
    }
   },
   "outputs": [],
   "source": [
    "def compare_two_words(w1,w2):\n",
    "    return jellyfish.jaro_distance(w1, w2)\n",
    "\n",
    "def is_related_in_list(word,ys):\n",
    "    for y in ys:\n",
    "        if compare_two_words(word.lower(),y.lower()) >= 0.9:\n",
    "            return True\n",
    "    return False \n",
    "\n",
    "def kws_precision(ys,ys_true):\n",
    "    if len(ys) >= 1:\n",
    "        count = 0 \n",
    "        for y in ys:\n",
    "            if is_related_in_list(y,ys_true):\n",
    "                count += 1\n",
    "        return count/len(ys)\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def kws_recall(ys,ys_true):\n",
    "    if not ys_true:\n",
    "        return 0\n",
    "    if len(ys) >= 1:\n",
    "        count = 0 \n",
    "        for y in ys:\n",
    "            if is_related_in_list(y,ys_true):\n",
    "                count += 1\n",
    "        if count > len(ys_true):\n",
    "            return 1\n",
    "        return count/len(ys_true)\n",
    "    else:\n",
    "        return 0 \n",
    "    \n",
    "def evaluate_kws(ys,ys_true):\n",
    "    res = {}\n",
    "    res['precision'] = kws_precision(ys,ys_true)\n",
    "    res['recall'] = kws_recall(ys,ys_true)\n",
    "    if res['precision'] or res['recall']:\n",
    "        res['f1'] = (2*res['precision']*res['recall'])/(res['precision']+res['recall'])\n",
    "    else:\n",
    "        res['f1'] = 0\n",
    "    return res \n",
    "\n",
    "\n",
    "def macro_pr(yt_pairs):\n",
    "    f1 = []\n",
    "    p = []\n",
    "    r = []\n",
    "    for pair in yt_pairs:\n",
    "        evaluate = evaluate_kws(pair[0],pair[1])\n",
    "        f1.append(evaluate['f1'])\n",
    "        p.append(evaluate['precision'])\n",
    "        r.append(evaluate['recall'])\n",
    "    return {'f1':statistics.mean(f1),\n",
    "           'precision':statistics.mean(p),\n",
    "           'recall':statistics.mean(r)}\n",
    "\n",
    "def evaluate_extractor(docs_with_kws, extractor, top=None):\n",
    "    yt_pairs = []\n",
    "    for i in tqdm(list(docs_with_kws.keys())[train_size:]):\n",
    "        pair = []\n",
    "        pair.append(extractor(docs_with_kws[i]['abstract'],top))\n",
    "        ys_true = docs_with_kws[i]['keywords']\n",
    "        if 'keywords_extra' in docs_with_kws[i]:\n",
    "            ys_true += docs_with_kws[i]['keywords_extra']\n",
    "        pair.append(ys_true)\n",
    "        yt_pairs.append(pair)\n",
    "    print(len(yt_pairs))\n",
    "    print(macro_pr(yt_pairs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949cd440",
   "metadata": {},
   "source": [
    "# Keyword Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30cd3512",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:12:51.826420Z",
     "start_time": "2023-06-14T17:12:51.821688Z"
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   "outputs": [],
   "source": [
    "kw_start = [\n",
    "    'NN',\n",
    "    'NNP',\n",
    "    'JJ',\n",
    "    'NNS',\n",
    "    'VBG',\n",
    "    'RB',\n",
    "    'VBN'\n",
    "]\n",
    "\n",
    "kw_end = [\n",
    "    'NN',\n",
    "    'NNP',\n",
    "    'NNS',\n",
    "    'VBG',\n",
    "    'JJ'\n",
    "]\n",
    "\n",
    "kw_split = [\n",
    "    '.',\n",
    "    ','\n",
    "]\n",
    "def keywords_selector(text):\n",
    "    tokens = nltk.word_tokenize(text)\n",
    "    tags = nltk.pos_tag(tokens)\n",
    "    res = set()\n",
    "    for i in range(len(tags)):\n",
    "        if tags[i][1] in kw_start:\n",
    "            end = i+4 if i+4 <= len(tags) - 1 else len(tags) - 1\n",
    "            cut = tags[i:end]\n",
    "            for j in range(len(cut)):\n",
    "                if cut[j][0] in kw_split:\n",
    "                    cut = cut[:j]\n",
    "                    break \n",
    "            for k in range(len(cut)):\n",
    "                if cut[k][1] in kw_end:\n",
    "                    res.add(' '.join([m[0] for m in cut][:k+1]))\n",
    "    return res \n",
    "    "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "00bf7e61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:14:54.344161Z",
     "start_time": "2023-06-14T17:13:30.555540Z"
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   },
   "outputs": [],
   "source": [
    "# scoreboard to record all combinations.\n",
    "history_score = {}\n",
    "# global dictionary to record all links between present and absent keywords\n",
    "global_kwx_index = {}\n",
    "for i in tqdm(list(kwx.keys())[:train_size]):\n",
    "    ys = kwx[i]['keywords']\n",
    "    candidates = keywords_selector(kwx[i]['abstract'])\n",
    "    for c in candidates:\n",
    "        if not c in history_score:\n",
    "            history_score[c] = 0\n",
    "        if is_related_in_list(c,ys):\n",
    "            history_score[c] += 100\n",
    "        else:\n",
    "            history_score[c] -= 1\n",
    "    for y in ys:\n",
    "        if y in global_kwx_index:\n",
    "            global_kwx_index[y].update(ys)\n",
    "        else:\n",
    "            global_kwx_index[y] = Counter(ys)\n",
    "print(len(history_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf0bc909",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:20:03.965152Z",
     "start_time": "2023-06-14T17:20:03.960665Z"
    }
   },
   "outputs": [],
   "source": [
    "def kwx_kws(text,top=None):\n",
    "    res = []\n",
    "    score = []\n",
    "    candidates = keywords_selector(text)\n",
    "    for c in candidates:\n",
    "        if c not in history_score:\n",
    "            res.append(c)\n",
    "            score.append(1)\n",
    "        elif history_score[c]>0:\n",
    "            res.append(c)\n",
    "            score.append(history_score[c])\n",
    "        if c in global_kwx_index:\n",
    "            for r in global_kwx_index[c]:\n",
    "                if r not in candidates:\n",
    "                    res.append(r)\n",
    "                    score.append(global_kwx_index[c][r])\n",
    "    if not res:\n",
    "        res = ['None']\n",
    "    sorted_res = [x for _, x in sorted(zip(score, res))][::-1]\n",
    "    if top and len(sorted_res)>top:\n",
    "        return sorted_res[:top]\n",
    "    else:\n",
    "        return sorted_res"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "86f377c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:17:18.438467Z",
     "start_time": "2023-06-14T17:16:21.507730Z"
    }
   },
   "outputs": [],
   "source": [
    "evaluate_extractor(kwx, kwx_kws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdfec2e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-14T17:20:24.682626Z",
     "start_time": "2023-06-14T17:20:06.517802Z"
    }
   },
   "outputs": [],
   "source": [
    "evaluate_extractor(kwx, kwx_kws, 10)"
   ]
  }
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