RD_KG_solution.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Teasotea/BioNER-and-RD/blob/main/RD_KG_solution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c31Qw3Q_n6k7"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "daVLY667npQW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import io\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3EQR-ljrEEWn"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "from nltk.tokenize import TreebankWordTokenizer as twt\n",
        "import spacy\n",
        "from spacy import displacy\n",
        "\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "import string "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9qTl2vdWjc1H"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_5PwyaMuoTJ",
        "outputId": "7106f324-d554-4dc3-efb5-a78e619dec9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import bs4\n",
        "import requests\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "\n",
        "from spacy.matcher import Matcher \n",
        "from spacy.tokens import Span \n",
        "\n",
        "import networkx as nx\n",
        "from tqdm import tqdm\n",
        "\n",
        "pd.set_option('display.max_colwidth', 200)\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWDIWjC6cL3k",
        "outputId": "9074bd5e-99f7-4e43-c46e-33a32ac38623"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'bert-sklearn'...\n",
            "remote: Enumerating objects: 259, done.\u001b[K\n",
            "remote: Total 259 (delta 0), reused 0 (delta 0), pack-reused 259\u001b[K\n",
            "Receiving objects: 100% (259/259), 516.15 KiB | 3.94 MiB/s, done.\n",
            "Resolving deltas: 100% (131/131), done.\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Processing /content/bert-sklearn/bert-sklearn/bert-sklearn\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "Requirement already satisfied: torch>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from bert-sklearn==0.3.1) (1.11.0+cu113)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from bert-sklearn==0.3.1) (1.0.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from bert-sklearn==0.3.1) (1.21.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from bert-sklearn==0.3.1) (1.3.5)\n",
            "Requirement already satisfied: boto3 in /usr/local/lib/python3.7/dist-packages (from bert-sklearn==0.3.1) (1.24.19)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from bert-sklearn==0.3.1) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from bert-sklearn==0.3.1) (4.64.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=0.4.1->bert-sklearn==0.3.1) (4.1.1)\n",
            "Requirement already satisfied: botocore<1.28.0,>=1.27.19 in /usr/local/lib/python3.7/dist-packages (from boto3->bert-sklearn==0.3.1) (1.27.19)\n",
            "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from boto3->bert-sklearn==0.3.1) (0.6.0)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from boto3->bert-sklearn==0.3.1) (1.0.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.7/dist-packages (from botocore<1.28.0,>=1.27.19->boto3->bert-sklearn==0.3.1) (1.25.11)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from botocore<1.28.0,>=1.27.19->boto3->bert-sklearn==0.3.1) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.28.0,>=1.27.19->boto3->bert-sklearn==0.3.1) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->bert-sklearn==0.3.1) (2022.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->bert-sklearn==0.3.1) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->bert-sklearn==0.3.1) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->bert-sklearn==0.3.1) (3.0.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->bert-sklearn==0.3.1) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->bert-sklearn==0.3.1) (1.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->bert-sklearn==0.3.1) (1.4.1)\n",
            "Building wheels for collected packages: bert-sklearn\n",
            "  Building wheel for bert-sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for bert-sklearn: filename=bert_sklearn-0.3.1-py3-none-any.whl size=54247 sha256=51ed7ad7c52bf62f8b6e27a3ef752ac7ee7f1c9a0b967d3af60f27829d3d519e\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/97/44/cf1e3f3e53c683f2c32e4312e7e186a6692af1ada6103d1156\n",
            "Successfully built bert-sklearn\n",
            "Installing collected packages: bert-sklearn\n",
            "  Attempting uninstall: bert-sklearn\n",
            "    Found existing installation: bert-sklearn 0.3.1\n",
            "    Uninstalling bert-sklearn-0.3.1:\n",
            "      Successfully uninstalled bert-sklearn-0.3.1\n",
            "Successfully installed bert-sklearn-0.3.1\n",
            "['glue_examples', 'demo.ipynb', 'README.md', 'bert_sklearn', '.git', 'demo_tuning_hyperparams.ipynb', 'setup.py', 'other_examples', 'Options.md', 'tests', 'LICENSE']\n"
          ]
        }
      ],
      "source": [
        "!git clone -b master https://github.com/charles9n/bert-sklearn\n",
        "!cd bert-sklearn; pip install .\n",
        "import os\n",
        "os.chdir(\"bert-sklearn\")\n",
        "print(os.listdir())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NozIv0bciUmX"
      },
      "outputs": [],
      "source": [
        "# !pip install --upgrade sagemaker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dm2VbWhUcK1Q"
      },
      "outputs": [],
      "source": [
        "from bert_sklearn import BertTokenClassifier\n",
        "from bert_sklearn import load_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vsAdRmo0c2ei"
      },
      "outputs": [],
      "source": [
        "# !nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRd1VCLdCPgS",
        "outputId": "74023837-a508-4de1-bf99-e408856ce557"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pytorch version: 1.11.0+cu113\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "print('pytorch version:', torch.__version__)\n",
        "# print('GPU:',torch.cuda.get_device_name(0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "STgPdMEAnvHD"
      },
      "outputs": [],
      "source": [
        "flatten = lambda l: [item for sublist in l for item in sublist]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXFSDJb8IHbl"
      },
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E8bT9Pq9RPPX"
      },
      "outputs": [],
      "source": [
        "with open('/content/data0.txt') as f:\n",
        "      eval_article = f.read()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "CyM7iJuiHm5k",
        "outputId": "c1d0c332-044f-4023-e699-f3178722cf86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                paragraphs  \\\n",
              "0  Transductional analysis was applied to the Pseudomonas aeruginosa mutant PAO14 (hnc-1). This mutant can utilize L-histidinol as sole source of carbon and nitrogen and has a 60-fold increased histi...   \n",
              "1  Bacteriocins produced by six strains of Rhizobium trifolii were found to be of the relatively low molecular weight, non-phage type. The molecular weights ranged from approximately 1-8 X 105 to 2-0...   \n",
              "2  The gamma haemolysin of Staphylococcus aureus \\'Smith 5R\\' was produced on Dolman-Wilson agar overlain with cellophane. Maximal yields of crude lysin with titres of 2000 to 4000 haemolytic units/m...   \n",
              "3  Arginine and methionine transport by Aspergillus nidulans mycelium was investigated. A single uptake system is responsible for the transport of arginine, lysine and ornithine. Transport is energy-...   \n",
              "4  Aphids transmitted poly-L-ornithine (PLO)-treated tobacco mosaic virus (TMV) when given acquistion and inoculation access periods as brief as 30 s and 2 min, respectively; the ability to transmit ...   \n",
              "\n",
              "                                                                                                                                                                                                 tokenized  \n",
              "0  [Transductional, analysis, was, applied, to, the, Pseudomonas, aeruginosa, mutant, PAO14, (, hnc-1, ), ., This, mutant, can, utilize, L-histidinol, as, sole, source, of, carbon, and, nitrogen, and...  \n",
              "1  [Bacteriocins, produced, by, six, strains, of, Rhizobium, trifolii, were, found, to, be, of, the, relatively, low, molecular, weight, ,, non-phage, type., The, molecular, weights, ranged, from, ap...  \n",
              "2  [The, gamma, haemolysin, of, Staphylococcus, aureus, \\'Smith, 5R\\, ', was, produced, on, Dolman-Wilson, agar, overlain, with, cellophane., Maximal, yields, of, crude, lysin, with, titres, of, 2000...  \n",
              "3  [Arginine, and, methionine, transport, by, Aspergillus, nidulans, mycelium, was, investigated., A, single, uptake, system, is, responsible, for, the, transport, of, arginine, ,, lysine, and, ornit...  \n",
              "4  [Aphids, transmitted, poly-L-ornithine, (, PLO, ), -treated, tobacco, mosaic, virus, (, TMV, ), when, given, acquistion, and, inoculation, access, periods, as, brief, as, 30, s, and, 2, min, ,, re...  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>paragraphs</th>\n",
              "      <th>tokenized</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Transductional analysis was applied to the Pseudomonas aeruginosa mutant PAO14 (hnc-1). This mutant can utilize L-histidinol as sole source of carbon and nitrogen and has a 60-fold increased histi...</td>\n",
              "      <td>[Transductional, analysis, was, applied, to, the, Pseudomonas, aeruginosa, mutant, PAO14, (, hnc-1, ), ., This, mutant, can, utilize, L-histidinol, as, sole, source, of, carbon, and, nitrogen, and...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bacteriocins produced by six strains of Rhizobium trifolii were found to be of the relatively low molecular weight, non-phage type. The molecular weights ranged from approximately 1-8 X 105 to 2-0...</td>\n",
              "      <td>[Bacteriocins, produced, by, six, strains, of, Rhizobium, trifolii, were, found, to, be, of, the, relatively, low, molecular, weight, ,, non-phage, type., The, molecular, weights, ranged, from, ap...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The gamma haemolysin of Staphylococcus aureus \\'Smith 5R\\' was produced on Dolman-Wilson agar overlain with cellophane. Maximal yields of crude lysin with titres of 2000 to 4000 haemolytic units/m...</td>\n",
              "      <td>[The, gamma, haemolysin, of, Staphylococcus, aureus, \\'Smith, 5R\\, ', was, produced, on, Dolman-Wilson, agar, overlain, with, cellophane., Maximal, yields, of, crude, lysin, with, titres, of, 2000...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arginine and methionine transport by Aspergillus nidulans mycelium was investigated. A single uptake system is responsible for the transport of arginine, lysine and ornithine. Transport is energy-...</td>\n",
              "      <td>[Arginine, and, methionine, transport, by, Aspergillus, nidulans, mycelium, was, investigated., A, single, uptake, system, is, responsible, for, the, transport, of, arginine, ,, lysine, and, ornit...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aphids transmitted poly-L-ornithine (PLO)-treated tobacco mosaic virus (TMV) when given acquistion and inoculation access periods as brief as 30 s and 2 min, respectively; the ability to transmit ...</td>\n",
              "      <td>[Aphids, transmitted, poly-L-ornithine, (, PLO, ), -treated, tobacco, mosaic, virus, (, TMV, ), when, given, acquistion, and, inoculation, access, periods, as, brief, as, 30, s, and, 2, min, ,, re...</td>\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8cddfb37-af12-4c67-8eb0-b59697859c8f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "data = pd.DataFrame()\n",
        "data['paragraphs'] = eval_article.split('\\n')\n",
        "data['tokenized'] = data['paragraphs'].apply(lambda x:list(twt().tokenize(x)))\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9UUsvPpIp5E"
      },
      "source": [
        "# Load NER Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q8KplwRNrpQC"
      },
      "outputs": [],
      "source": [
        "class CPU_Unpickler(pickle.Unpickler):\n",
        "    def find_class(self, module, name):\n",
        "        if module == 'torch.storage' and name == '_load_from_bytes':\n",
        "            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')\n",
        "        else: return super().find_class(module, name)\n",
        "\n",
        "scibert_model = CPU_Unpickler(open('/content/scibert_model.pkl', 'rb')).load() #for cpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fk_NWhAY_i0h"
      },
      "outputs": [],
      "source": [
        "\n",
        "#pickled_model =  pickle.load(open('/content/scibert_model.pkl', 'rb')) #for gpu"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxeFjtk0ul3c"
      },
      "source": [
        "# Extract entity candidates and relations\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "137de940cf0f45c5a2f1acc5f50d04a4",
            "77f76b806c8445a9803c78a8f06bbe26",
            "953bbf5ad409407795dafd27ee228428",
            "80ef5ba8dd8740b8942b70bc34cc63ac",
            "5858f1e745b34502ac7f737c6c220a5c",
            "4afa3b33c86e490295e1eda78987b631",
            "85308df3631f4d50a61bd5f4420a77af",
            "0bec4c13bd17449ba03aeaddc9b9414c",
            "d0e07651fac04e2dbb65b1227505353f",
            "ece1f96f959e4560a92f5f4a73447d3a",
            "bbee3292d19044c28c36976e0c765d7b"
          ]
        },
        "id": "094JLqXvEk0y",
        "outputId": "4b1ba246-bb5b-4e04-ad35-21051c3694be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "137de940cf0f45c5a2f1acc5f50d04a4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "data_named_entities = scibert_model.predict(data['tokenized'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCSD86S-QKJp",
        "outputId": "0b92da04-cc21-4ffa-c5f0-ec99c628e975"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['O', 'O', 'B-Chemical', 'O', 'B-Chemical', 'O', 'O', 'B-Disease', 'I-Disease', 'I-Disease', 'O', 'B-Disease', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'I-Chemical', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'O', 'O', 'O', 'B-Chemical', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical', 'B-Disease', 'O', 'O', 'O', 'B-Disease', 'I-Disease', 'O', 'B-Chemical', 'B-Disease', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n"
          ]
        }
      ],
      "source": [
        "print(data_named_entities[4])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Entity extraction"
      ],
      "metadata": {
        "id": "WiHaOABHDZ2p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9FAyK9SH9ENr"
      },
      "outputs": [],
      "source": [
        "def extract_entities_from_string(string):\n",
        "  tokenized = list(twt().tokenize(string))\n",
        "  preds = scibert_model.predict(pd.Series([tokenized]))[0]\n",
        "  entities = []\n",
        "  for i, j in zip(tokenized, preds):\n",
        "    if j!= 'O':\n",
        "      entities.append(i)\n",
        "  return list(set(entities))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341,
          "referenced_widgets": [
            "8725e52149344475ab08849995590f99",
            "3f1ba01993f54d86946c56658bde120c",
            "81b189bfb2ed40d5be8c25fa49329429",
            "936629b95a484133a87b0aa4a1b9f0c8",
            "0fa5b94d74094d9a94ed7e65368d3048",
            "7c9313f129174a1faff05d0f7a199194",
            "3b9530b07efa450b8d0b312851590c80",
            "accd26379ff34bbab7fc93cdb8b49574",
            "b1f4a8c9fc9f4259a0b3000b5ab60e4d",
            "739ee888419949b0832427426b6b5de3",
            "5ed7c396c8234022a1283a341d8b0be7"
          ]
        },
        "id": "Js7w5jtP_t8A",
        "outputId": "17383312-82ab-416a-cbd1-866af51107d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8725e52149344475ab08849995590f99"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['tobacco',\n",
              " 'TMV',\n",
              " 'polyamino',\n",
              " 'KCl',\n",
              " 'acid',\n",
              " 'PLO',\n",
              " 'potato',\n",
              " 'mosaic',\n",
              " 'poly-L-ornithine',\n",
              " 'virus',\n",
              " 'poly-L-lysine',\n",
              " 'PLO-treated',\n",
              " 'rattle']"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ],
      "source": [
        "extract_entities_from_string(data['paragraphs'].iloc[4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BXbxwMTj27zy"
      },
      "outputs": [],
      "source": [
        "def isNotStopWord(word):\n",
        "    return word not in stopwords.words('english')\n",
        "\n",
        "def preprocess(sent):\n",
        "  sent = re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", sent)\n",
        "  tokens = []\n",
        "  temp = \"\"\n",
        "  words = word_tokenize(sent)\n",
        "  punctuations = '\"#$%&\\'()*+,-/:;<=>@\\\\^_`{|}~'\n",
        "  words = map(lambda x: x.translate(str.maketrans('','',punctuations)), words)\n",
        "  \n",
        "  words = map(str.lower,words)\n",
        "  words = filter(lambda x: isNotStopWord(x),words)\n",
        "  tokens = tokens + list(words)\n",
        "  temp = ' '.join(word for word in tokens)\n",
        "  return temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sI2XJRAF3GX_"
      },
      "outputs": [],
      "source": [
        "data['preprocessed'] = data['paragraphs'].apply(lambda x: preprocess(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z-VxM1E5wjYi"
      },
      "outputs": [],
      "source": [
        "def get_entity(sent):\n",
        "  ent1 = \"\" \n",
        "  ent2 = \"\" \n",
        "\n",
        "  prev_token_text = \"\" \n",
        "  prev_token_dep = \"\" \n",
        "\n",
        "  prefix = \"\"\n",
        "  modifier = \"\"\n",
        "\n",
        "  for tok in nlp(sent):\n",
        "    if tok.dep_ != \"punct\":\n",
        "      if tok.dep_ == \"compound\":\n",
        "        prefix = tok.text\n",
        "        if prev_token_dep == \"compound\":\n",
        "          prefix = prev_token_text + \" \"+ prefix\n",
        "      if tok.dep_.endswith(\"mod\")==True:\n",
        "        modifier = tok.text \n",
        "        if prev_token_dep == \"compound\":\n",
        "          modifier = prev_token_text + \" \" + modifier\n",
        "\n",
        "      if tok.dep_.find(\"subj\") == True:\n",
        "        ent1 =modifier+ \" \" + prefix + \" \" + tok.text\n",
        "        prefix = \"\"\n",
        "        modifer = \"\"\n",
        "      if tok.dep_.find(\"obj\") == True:\n",
        "        ent2 =modifier+ \" \" + prefix + \" \" + tok.text\n",
        "        prefix = \"\"\n",
        "        modifer = \"\"\n",
        "\n",
        "      prev_token_text = tok.text\n",
        "      prev_token_dep = tok.dep_\n",
        "\n",
        "  return [ent1.strip(), ent2.strip()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3uw_bS0wkyc",
        "outputId": "7854185a-604f-4ce0-d111-c9bce5fcc142"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Subj : Transductional  analysis, obj : Transductional aeruginosa mutant PAO14\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Transductional  analysis', 'Transductional aeruginosa mutant PAO14']"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ],
      "source": [
        "[ent1,ent2] = get_entity(\"Transductional analysis was applied to the Pseudomonas aeruginosa mutant PAO14 (hnc-1).\")\n",
        "print(\"Subj : {a}, obj : {b}\".format(a = ent1, b = ent2))\n",
        "get_entity(\"Transductional analysis was applied to the Pseudomonas aeruginosa mutant PAO14 (hnc-1).\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U717CvRywtTN"
      },
      "outputs": [],
      "source": [
        "data['entity_pairs']= data['preprocessed'].apply(lambda x: [get_entity(i) for i in x.split('. ')])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t1tFEY9Hwt9l"
      },
      "outputs": [],
      "source": [
        "def get_relation(sent):\n",
        "  matcher = Matcher(nlp.vocab)\n",
        "  patterns = [[{'DEP':'ROOT'}, \n",
        "            {'DEP':'prep','OP':\"?\"},\n",
        "            {'DEP':'agent','OP':\"?\"},  \n",
        "            {'POS':'ADJ','OP':\"?\"}]]\n",
        "\n",
        "  matcher.add(\"matching_1\", patterns) \n",
        "  doc = nlp(sent)\n",
        "  matches = matcher(doc)\n",
        "  if len(matches)>0:\n",
        "    k = len(matches) - 1\n",
        "    span = doc[matches[k][1]:matches[k][2]] \n",
        "  else: return None\n",
        "  return(span.text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZLFa80GAyk_O",
        "outputId": "eb5973af-01b7-44d5-df41-0d88c2ef15b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['Transductional  analysis', 'Transductional aeruginosa mutant PAO14'],\n",
              " 'applied to')"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "get_entity(\"Transductional analysis was applied to the Pseudomonas aeruginosa mutant PAO14 (hnc-1).\"), get_relation(\"Transductional analysis was applied to the Pseudomonas aeruginosa mutant PAO14 (hnc-1).\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OwAP9T_dxLV-"
      },
      "outputs": [],
      "source": [
        "data['relations'] = data['preprocessed'].apply(lambda x: [get_relation(i) for i in x.split('. ')])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OhhPK80a18ib"
      },
      "outputs": [],
      "source": [
        "entity_pairs = flatten(data['entity_pairs'].tolist())\n",
        "relations = flatten(data['relations'].tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-WupgABd2BKR"
      },
      "outputs": [],
      "source": [
        "subjects = [x[0] for x in entity_pairs]\n",
        "objects = [x[1] for x in entity_pairs]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxSO0HEbyXHX",
        "outputId": "90c8d165-ceb1-4a4b-baa1-95f1c407aac2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          196\n",
              "results                                     6\n",
              "effects                                     3\n",
              "transductional  analysis                    2\n",
              "specific  activity                          2\n",
              "adenyl cyclase activity                     2\n",
              "antigenic  group                            2\n",
              "intravenous kcn doxapram hydrochloride      2\n",
              "findings                                    2\n",
              "authors                                     2\n",
              "cells                                       2\n",
              "activity                                    2\n",
              "enzymatic  activity                         2\n",
              "level                                       2\n",
              "receptors                                   2\n",
              "data                                        2\n",
              "values                                      2\n",
              "pentagastrin                                2\n",
              "premature  decline                          1\n",
              "recovery function                           1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "pd.Series(subjects).value_counts()[:20]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofAPnrVW4nCS",
        "outputId": "5bb167e6-c8b6-4f82-dbb7-af6248a0874c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 824/824 [00:00<00:00, 341917.94it/s]\n"
          ]
        }
      ],
      "source": [
        "entity_pairs2 = entity_pairs\n",
        "relations2 = relations\n",
        "entity_pairs3 = []\n",
        "relations3 = []\n",
        "for i in tqdm(range(len(entity_pairs2))):\n",
        "  if entity_pairs2[i][0]!='' and entity_pairs2[i][1]!='':\n",
        "    entity_pairs3.append(entity_pairs2[i])\n",
        "    relations3.append(relations2[i])\n",
        "tqdm._instances.clear()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ssENeeZ529z",
        "outputId": "8fba4d8b-ed52-4fd5-e669-dba0294be705"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Australia', 'Arginine', 'Aspergillus']"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "sample_sent = 'Australia, Arginine and methionine transport by Aspergillus nidulans mycelium was investigated. A single uptake system is responsible for the transport of arginine, lysine and ornithine'\n",
        "nlp_ents = nlp(sample_sent).ents # tuple\n",
        "[str(word) for word in nlp_ents] #list of words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "ce62d25249ca4b299b171ea19659400c",
            "d2dcba97c1dd48f98d4a7240e6c3acc3",
            "e4bb2de49e9f4cff9b7c010df14841c5",
            "a63397b143b54c53a68990bd2efdfda0",
            "00cbe3a2c9b940cfb88492e11807e215",
            "c189e9ea9b804f239e7b20070770b6c1",
            "de2a8a1447ed42f4ae8dd4ebe41b62d1",
            "b823af2b3d7b4d1294b967afa75d854b",
            "af77c957c39744f78783d38811459f4f",
            "a76a62640d6a4c329799925dd241f6e5",
            "96586694450142658ac5be489e4df37d"
          ]
        },
        "id": "8isrdOpf7jqu",
        "outputId": "cf4bee75-907c-4826-c511-8ae13474c5cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce62d25249ca4b299b171ea19659400c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Arginine', 'ornithine', 'arginine', 'methionine', 'lysine']"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "extract_entities_from_string(sample_sent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "a60575698f09407d9e0d40310d0323f0",
            "b25ad355e28a4dedb7e1b380bc5773b7",
            "29cd86e817da48e891e9c681e4dd85ac",
            "4d9de6eb45d646758ebc411528dd509d",
            "a06c31dcaad94153846747952a5e87dd",
            "38989a1f986b4a8daf6060820c0c48c4",
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        },
        "id": "QjokaXxPIedY",
        "outputId": "9f4fcea3-2b49-4779-fcf1-632d65397795"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hypertension', 'hyperventilation']"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ],
      "source": [
        "extract_entities_from_string(entity_pairs[50][1])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Constructing e1-rel-e2 triples for building Knowledge Graps"
      ],
      "metadata": {
        "id": "4_tW1enDD_q-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_entities_from_pairs(pairs):\n",
        "  pairs_df = pd.DataFrame(pairs, columns = [\"source\", \"target\"])\n",
        "  pairs_df[\"tok_source\"] = pairs_df[\"source\"].apply(lambda x: list(twt().tokenize(x)))\n",
        "  pairs_df[\"tok_target\"] = pairs_df[\"target\"].apply(lambda x: list(twt().tokenize(x)))\n",
        "\n",
        "  pairs_df[\"ner_source\"] = scibert_model.predict(pairs_df[\"tok_source\"])\n",
        "  pairs_df[\"ner_target\"] = scibert_model.predict(pairs_df[\"tok_target\"])\n",
        "\n",
        "  def extract_words(tok, ner):\n",
        "    word_list = []\n",
        "    for i,j in zip(tok,ner):\n",
        "      if j!= 'O':\n",
        "        word_list.append(i)\n",
        "    if len(word_list)==0:\n",
        "      return None\n",
        "    else: return word_list\n",
        "  pairs_df['source+ner'] = pairs_df.apply(lambda x: extract_words(x['tok_source'],x['ner_source']), axis=1)\n",
        "  pairs_df['target+ner'] = pairs_df.apply(lambda x: extract_words(x['tok_target'],x['ner_target']), axis=1)\n",
        "  return pairs_df"
      ],
      "metadata": {
        "id": "YKXOwzMMQH23"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pair_df = extract_entities_from_pairs(entity_pairs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
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        "id": "kjEuzAvbRIBr",
        "outputId": "b947e876-bc99-4f96-f656-47a6af8cd1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "display_data",
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              "Predicting:   0%|          | 0/52 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "pair_df['relations'] = relations"
      ],
      "metadata": {
        "id": "8HcBeLHR6u8i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pair_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ppI6FPgI63M4",
        "outputId": "3f2a37ec-15c4-4d4e-ec75-2a9efb1235fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       source                                  target  \\\n",
              "0    transductional  analysis  transductional aeruginosa mutant pao14   \n",
              "1                sole  60fold        histidinol dehydrogenase content   \n",
              "2    transductional  analysis             structural hnc1 locus genes   \n",
              "3            cotransduced  iv                      structural 97 gene   \n",
              "4                        data               structural data hnc1 gene   \n",
              "..                        ...                                     ...   \n",
              "819   diamide cystamine nano2         acidsoluble thiol content heart   \n",
              "820                 compounds                                           \n",
              "821                                          probably  catecholamines   \n",
              "822                                                 coronary  vessels   \n",
              "823                                                                     \n",
              "\n",
              "                      tok_source                                   tok_target  \\\n",
              "0     [transductional, analysis]  [transductional, aeruginosa, mutant, pao14]   \n",
              "1                 [sole, 60fold]         [histidinol, dehydrogenase, content]   \n",
              "2     [transductional, analysis]             [structural, hnc1, locus, genes]   \n",
              "3             [cotransduced, iv]                       [structural, 97, gene]   \n",
              "4                         [data]               [structural, data, hnc1, gene]   \n",
              "..                           ...                                          ...   \n",
              "819  [diamide, cystamine, nano2]         [acidsoluble, thiol, content, heart]   \n",
              "820                  [compounds]                                           []   \n",
              "821                           []                   [probably, catecholamines]   \n",
              "822                           []                          [coronary, vessels]   \n",
              "823                           []                                           []   \n",
              "\n",
              "             ner_source    ner_target   source+ner target+ner  \\\n",
              "0                [O, O]  [O, O, O, O]         None       None   \n",
              "1                [O, O]     [O, O, O]         None       None   \n",
              "2                [O, O]  [O, O, O, O]         None       None   \n",
              "3                [O, O]     [O, O, O]         None       None   \n",
              "4                   [O]  [O, O, O, O]         None       None   \n",
              "..                  ...           ...          ...        ...   \n",
              "819  [O, B-Chemical, O]  [O, O, O, O]  [cystamine]       None   \n",
              "820                 [O]           [O]         None       None   \n",
              "821                 [O]        [O, O]         None       None   \n",
              "822                 [O]        [O, O]         None       None   \n",
              "823                 [O]           [O]         None       None   \n",
              "\n",
              "                 relations  \n",
              "0                  applied  \n",
              "1                  utilize  \n",
              "2                      see  \n",
              "3                    genes  \n",
              "4                 obtained  \n",
              "..                     ...  \n",
              "819  decreased acidsoluble  \n",
              "820                   None  \n",
              "821               conclude  \n",
              "822      decrease coronary  \n",
              "823                   None  \n",
              "\n",
              "[824 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-530d4fed-affb-479d-8ac7-a520a450d27e\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>source</th>\n",
              "      <th>target</th>\n",
              "      <th>tok_source</th>\n",
              "      <th>tok_target</th>\n",
              "      <th>ner_source</th>\n",
              "      <th>ner_target</th>\n",
              "      <th>source+ner</th>\n",
              "      <th>target+ner</th>\n",
              "      <th>relations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>transductional  analysis</td>\n",
              "      <td>transductional aeruginosa mutant pao14</td>\n",
              "      <td>[transductional, analysis]</td>\n",
              "      <td>[transductional, aeruginosa, mutant, pao14]</td>\n",
              "      <td>[O, O]</td>\n",
              "      <td>[O, O, O, O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>applied</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sole  60fold</td>\n",
              "      <td>histidinol dehydrogenase content</td>\n",
              "      <td>[sole, 60fold]</td>\n",
              "      <td>[histidinol, dehydrogenase, content]</td>\n",
              "      <td>[O, O]</td>\n",
              "      <td>[O, O, O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>utilize</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>transductional  analysis</td>\n",
              "      <td>structural hnc1 locus genes</td>\n",
              "      <td>[transductional, analysis]</td>\n",
              "      <td>[structural, hnc1, locus, genes]</td>\n",
              "      <td>[O, O]</td>\n",
              "      <td>[O, O, O, O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>see</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cotransduced  iv</td>\n",
              "      <td>structural 97 gene</td>\n",
              "      <td>[cotransduced, iv]</td>\n",
              "      <td>[structural, 97, gene]</td>\n",
              "      <td>[O, O]</td>\n",
              "      <td>[O, O, O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>genes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>data</td>\n",
              "      <td>structural data hnc1 gene</td>\n",
              "      <td>[data]</td>\n",
              "      <td>[structural, data, hnc1, gene]</td>\n",
              "      <td>[O]</td>\n",
              "      <td>[O, O, O, O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>obtained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>819</th>\n",
              "      <td>diamide cystamine nano2</td>\n",
              "      <td>acidsoluble thiol content heart</td>\n",
              "      <td>[diamide, cystamine, nano2]</td>\n",
              "      <td>[acidsoluble, thiol, content, heart]</td>\n",
              "      <td>[O, B-Chemical, O]</td>\n",
              "      <td>[O, O, O, O]</td>\n",
              "      <td>[cystamine]</td>\n",
              "      <td>None</td>\n",
              "      <td>decreased acidsoluble</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820</th>\n",
              "      <td>compounds</td>\n",
              "      <td></td>\n",
              "      <td>[compounds]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[O]</td>\n",
              "      <td>[O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td></td>\n",
              "      <td>probably  catecholamines</td>\n",
              "      <td>[]</td>\n",
              "      <td>[probably, catecholamines]</td>\n",
              "      <td>[O]</td>\n",
              "      <td>[O, O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>conclude</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>822</th>\n",
              "      <td></td>\n",
              "      <td>coronary  vessels</td>\n",
              "      <td>[]</td>\n",
              "      <td>[coronary, vessels]</td>\n",
              "      <td>[O]</td>\n",
              "      <td>[O, O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>decrease coronary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>823</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[O]</td>\n",
              "      <td>[O]</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>824 rows × 9 columns</p>\n",
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "relation_df = pair_df[['tok_source','tok_target','ner_source','ner_target','source+ner','relations', 'target+ner']].dropna().reset_index(drop=True)\n",
        "relation_df.to_csv('relation_pairs.csv', index = False)"
      ],
      "metadata": {
        "id": "W6QJUHjI7lI2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_pair_df = pair_df[['source+ner','relations','target+ner']]\n",
        "ner_pair_df = ner_pair_df.dropna().reset_index(drop=True)"
      ],
      "metadata": {
        "id": "UW8YEkAU6XNI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_pair_df = ner_pair_df.rename(columns={'source+ner':'source', 'relations':'relation','target+ner':'target' }).drop_duplicates().reset_index(drop=True)\n",
        "ner_pair_df['source'] = ner_pair_df['source'].apply(lambda x: ''.join(x))\n",
        "ner_pair_df['target'] = ner_pair_df['target'].apply(lambda x: ''.join(x))\n",
        "ner_pair_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "_zX6WPID9Ptn",
        "outputId": "81fb2ecf-35c3-450a-9d17-4a9fc532b81a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            source              relation  \\\n",
              "0                       cholicacid                   fed   \n",
              "1                    noradrenaline            determined   \n",
              "2                          glycine                heated   \n",
              "3                         leukemic               treated   \n",
              "4   alphaketobetamethylvalericacid                raised   \n",
              "5                          ammonia               effects   \n",
              "6                          choline              observed   \n",
              "7            doxapramhydrochloride  studied anesthetized   \n",
              "8                  organophosphate             increases   \n",
              "9                    coronaryheart        decreased oral   \n",
              "10                    promethazine             inhibited   \n",
              "11        nitratehydrogenpotassium     secretion nitrate   \n",
              "12                        shigella               promise   \n",
              "13                          oxygen               studied   \n",
              "\n",
              "                           target  \n",
              "0                     glutathione  \n",
              "1   noradrenalinepancuronium14cna  \n",
              "2                 imidazoleglynpa  \n",
              "3                        leukemic  \n",
              "4                           amino  \n",
              "5                         ammonia  \n",
              "6                        acidosis  \n",
              "7                    normothermia  \n",
              "8                 organophosphate  \n",
              "9                     propranolol  \n",
              "10                      arthritis  \n",
              "11                       chloride  \n",
              "12                    shigellosis  \n",
              "13       normovolemichemodilution  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5bd25037-b748-4d7a-80a0-9ebd309ee38a\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>source</th>\n",
              "      <th>relation</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cholicacid</td>\n",
              "      <td>fed</td>\n",
              "      <td>glutathione</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>noradrenaline</td>\n",
              "      <td>determined</td>\n",
              "      <td>noradrenalinepancuronium14cna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>glycine</td>\n",
              "      <td>heated</td>\n",
              "      <td>imidazoleglynpa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>leukemic</td>\n",
              "      <td>treated</td>\n",
              "      <td>leukemic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alphaketobetamethylvalericacid</td>\n",
              "      <td>raised</td>\n",
              "      <td>amino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ammonia</td>\n",
              "      <td>effects</td>\n",
              "      <td>ammonia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>choline</td>\n",
              "      <td>observed</td>\n",
              "      <td>acidosis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>doxapramhydrochloride</td>\n",
              "      <td>studied anesthetized</td>\n",
              "      <td>normothermia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>organophosphate</td>\n",
              "      <td>increases</td>\n",
              "      <td>organophosphate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>coronaryheart</td>\n",
              "      <td>decreased oral</td>\n",
              "      <td>propranolol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>promethazine</td>\n",
              "      <td>inhibited</td>\n",
              "      <td>arthritis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>nitratehydrogenpotassium</td>\n",
              "      <td>secretion nitrate</td>\n",
              "      <td>chloride</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>shigella</td>\n",
              "      <td>promise</td>\n",
              "      <td>shigellosis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>oxygen</td>\n",
              "      <td>studied</td>\n",
              "      <td>normovolemichemodilution</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ner_pair_df.to_csv('/content/relations1.tsv', sep=\"\\t\")"
      ],
      "metadata": {
        "id": "leICb2CI-agn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "G=nx.from_pandas_edgelist(ner_pair_df, \"source\", \"target\", \n",
        "                          edge_attr=True, create_using=nx.MultiDiGraph())\n",
        "pos = nx.spring_layout(G, k = 0.7)\n",
        "nx.draw(G, with_labels=True, node_color='skyblue', edge_cmap=plt.cm.Blues, pos = pos)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 895
        },
        "id": "G41pWCj6_Y6S",
        "outputId": "ce62f02f-3544-402b-b1c2-8b4fe93b5e0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": 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