CS598_Project_Final.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "wC9D9FHYk6vT"
      },
      "source": [
        "# Extracting Drug-drug Interactions from Biomedical Texts using Knowledge Graph Embeddings and Multi-focal Loss\n",
        "\n",
        "Our work aims to reproduce the paper ”Extract ing Drug-drug Interactions from biomedical texts using Knowledge graph embeddings and Multifocal Loss ” (Jin et al., 2022). Specifically, we will rebuild the proposed framework for DDI classification, attempt to reproduce the evaluation results, and do the ablation experiments.\n",
        "\n",
        "We verified the main claims of the paper:\n",
        "1. Claim 1- Novel DDI classification framework produce state of art result of 0.86 (F-score) :Our experiment results show a micro-F-score of 0.84  which is approximately 0.0224 less than what is claimed by the paper (86.24).Since a previous paper (Mondal, 2020) achieved 0.84, we are unable to conclude that the novel framework proposed in the paper provide state of the art results.\n",
        "2. Claim 2 - Novel Framework complements BERT based embedding with knowledge graph embedding :Framework successfully complemented language feature with drug Knowledge Graph . After adding drug knowledge embedding, recall increased by 0.03 and f-score increased by 0.01\n",
        "3. Claim 3 - KGE-MFL alleviated the problem of imbalance in dataset : MFL (F-score= 0.84) was able perform better than cross entropy loss function (F-score= 0.78) for the imbalanced DDI extraction dataset.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvWAMKNgsTNU"
      },
      "source": [
        "## Initial Setup\n",
        "\n",
        "### This entire step needs to be ran only once.\n",
        "*In case of errors, delete the corresponding folder in the working directory.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkAxE-0rsTNU"
      },
      "source": [
        "Install necessary libraries using pip, in case of errors, please comsult the respective documentation for installation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTBdd2gtCgS5",
        "outputId": "b75b3981-7cc0-498b-e2ec-2a3069055966"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "success!\n"
          ]
        }
      ],
      "source": [
        "!pip install -q matplotlib numpy pandas Requests scikit_learn stanza torch tqdm transformers\n",
        "print(\"success!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhEFqKfGKJkh"
      },
      "source": [
        "Download the data and embeddings from the paper's authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STBkFLd3KJYG",
        "outputId": "0ff8fc4d-b1e8-4540-d12f-1fe3e1f2aca8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\"data\" folder already exists.\n",
            "\"embedding\" folder already exists.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import requests\n",
        "import zipfile\n",
        "from io import BytesIO\n",
        "import platform\n",
        "import shutil\n",
        "\n",
        "def getGithubFolder(repo_url, branch, repo_name, folder_name):\n",
        "    if not os.path.exists(folder_name):\n",
        "        zip_url = f\"{repo_url}/archive/refs/heads/{branch}.zip\"\n",
        "\n",
        "        try:\n",
        "            response = requests.get(zip_url, timeout=(5, None))\n",
        "        except requests.exceptions.Timeout as err:\n",
        "            print(err)\n",
        "\n",
        "        with zipfile.ZipFile(BytesIO(response.content)) as zf:\n",
        "            for member in zf.infolist():\n",
        "                if member.filename.startswith(f\"{repo_name}/{folder_name}/\"):\n",
        "                    zf.extract(member)\n",
        "\n",
        "        if platform.system() == \"Windows\":\n",
        "            shutil.move(os.path.join(repo_name, folder_name), folder_name)\n",
        "            shutil.rmtree(repo_name)\n",
        "        else:\n",
        "            os.rename(f\"{repo_name}/{folder_name}\", folder_name)\n",
        "            os.system(f\"rm -r {repo_name}\")\n",
        "\n",
        "        print(f\"\\\"{folder_name}\\\" folder created.\")\n",
        "\n",
        "    else:\n",
        "        print(f\"\\\"{folder_name}\\\" folder already exists.\")\n",
        "\n",
        "\n",
        "getGithubFolder(\"https://github.com/NWU-IPMI/DDIE-KGE-MFL\", \"master\", \"DDIE-KGE-MFL-master\", \"data\")\n",
        "getGithubFolder(\"https://github.com/NWU-IPMI/DDIE-KGE-MFL\", \"master\", \"DDIE-KGE-MFL-master\", \"embedding\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOA4HhcwsTNW"
      },
      "source": [
        "This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in 'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUMAXl1FsTNX",
        "outputId": "d18be960-e80c-45f5-a1aa-bd83470b31f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\"MultiFocalLoss.py\" already exists.\n"
          ]
        }
      ],
      "source": [
        "def getFile(url):\n",
        "    file_name = url.split('/').pop()\n",
        "    if not os.path.exists(file_name):\n",
        "\n",
        "        try:\n",
        "            response = requests.get(url, timeout=(5, None))\n",
        "        except requests.exceptions.Timeout as err:\n",
        "            print(err)\n",
        "\n",
        "        with open(file_name, \"wb\") as f:\n",
        "            f.write(response.content)\n",
        "            print(f\"\\\"{file_name}\\\" created.\")\n",
        "    else:\n",
        "        print(f\"\\\"{file_name}\\\" already exists.\")\n",
        "\n",
        "getFile(\"https://raw.githubusercontent.com/NWU-IPMI/DDIE-KGE-MFL/master/MultiFocalLoss.py\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCnU5vyjsTNX"
      },
      "source": [
        "Import and download stanza."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "xG8TODu5uM4H",
        "outputId": "9100ddf6-0b97-415f-db75-3ae2cf8d6bb2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e8fcf684264466c8bbf9982294469bf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.5.0.json:   0%|   …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2023-05-08 23:21:42 INFO: Downloading these customized packages for language: en (English)...\n",
            "=======================\n",
            "| Processor | Package |\n",
            "-----------------------\n",
            "| tokenize  | craft   |\n",
            "| pos       | craft   |\n",
            "| lemma     | craft   |\n",
            "| depparse  | craft   |\n",
            "| pretrain  | craft   |\n",
            "=======================\n",
            "\n",
            "2023-05-08 23:21:42 INFO: File exists: stanza_resources\\en\\tokenize\\craft.pt\n",
            "2023-05-08 23:21:42 INFO: File exists: stanza_resources\\en\\pos\\craft.pt\n",
            "2023-05-08 23:21:42 INFO: File exists: stanza_resources\\en\\lemma\\craft.pt\n",
            "2023-05-08 23:21:42 INFO: File exists: stanza_resources\\en\\depparse\\craft.pt\n",
            "2023-05-08 23:21:43 INFO: File exists: stanza_resources\\en\\pretrain\\craft.pt\n",
            "2023-05-08 23:21:43 INFO: Finished downloading models and saved to stanza_resources.\n"
          ]
        }
      ],
      "source": [
        "import stanza\n",
        "stanza.download('en', package='craft', model_dir=\"stanza_resources\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdjEO9qmM2dQ"
      },
      "source": [
        "# Create Drug-drug interaction dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KecupE12tIYP"
      },
      "source": [
        "Import the necessary libraries, use pip if anything fails. The notebook was developed with Google Collab and Conda enviornemnts in mind."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yTzTAncGsTNY"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import csv\n",
        "import pickle\n",
        "import stanza\n",
        "import random\n",
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "from torch import nn, cuda\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from transformers import AutoTokenizer\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import BertModel\n",
        "from typing import List\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.decomposition import PCA\n",
        "from MultiFocalLoss import MultiFocalLoss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abCBtULxv5MT"
      },
      "source": [
        "Seed all random variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xumhq2d6syak",
        "outputId": "78a3c061-99c4-4036-fbc5-d7ae084b4b59"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x20c00919170>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "random.seed(12345)\n",
        "np.random.seed(12345)\n",
        "torch.manual_seed(12345)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy3HMc-OCkTN"
      },
      "source": [
        "Set cuda or cpu as the device depending on the machine configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZPfFJxNsTNY",
        "outputId": "5f0ae87d-1905-41a0-8087-19c5f78cc112"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Your device is: cuda\n"
          ]
        }
      ],
      "source": [
        "device = 'cuda' if cuda.is_available() else 'cpu'\n",
        "print(\"Your device is: \" + device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIhhCcHpwSiJ"
      },
      "source": [
        "Load Drug-drug interaction(DDI) data (Preprocessed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZvXvZ7rDk7j",
        "outputId": "ce697b02-d6a3-4a47-abdc-c1f3aa58319e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded entities dict with 7135 tokens.\n",
            "Loaded drug embeddings with 7135 entries\n",
            "Loaded training data with 22710 entries.\n",
            "Loaded test data with 4615 entries.\n"
          ]
        }
      ],
      "source": [
        "entities_dict = {}\n",
        "with open(\"data/entities.dict\", 'r', encoding='utf-8') as fr:\n",
        "    lines = csv.reader(fr, delimiter='\\t')\n",
        "    for line in lines:\n",
        "        idx = line[0]\n",
        "        entity = line[1]\n",
        "        entities_dict[entity] = int(idx)\n",
        "drug_embedding = np.load(\"./embedding/entity_embedding.npy\", allow_pickle=True)\n",
        "train_df = pd.read_csv(\"data/train.tsv\", delimiter=\"\\t\", header=None)\n",
        "test_df = pd.read_csv(\"data/test.tsv\", delimiter=\"\\t\", header=None)\n",
        "\n",
        "with pd.option_context('expand_frame_repr', False):\n",
        "    print(f\"Loaded entities dict with {len(entities_dict)} tokens.\")\n",
        "    print(f\"Loaded drug embeddings with {drug_embedding.shape[0]} entries\")\n",
        "    print(f\"Loaded training data with {train_df.shape[0]} entries.\")\n",
        "    print(f\"Loaded test data with {test_df.shape[0]} entries.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfq4TsNbwqgM"
      },
      "source": [
        "Visualization of the dataset statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "id": "d6ISE1AHwxXp",
        "outputId": "dae5bfd0-6a75-41b0-e1de-64e1509a3825"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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BAFDvL+fU1FRs2LChSrtt27at1O5jjz2G5cuXIz4+Hg8++CC8vb3RrVs3/PXXX/Xa740bN2AwGODr61vlulsvM8Xr+PDhwxgwYAAAYNmyZdi3bx+OHDmCN954o1IbzZo1w7Zt2+Dt7Y1nn30WzZo1Q7NmzSr12UlNTUV2djZUKlWVxy0lJaXBQenf1q5di9GjRyMgIADff/89Dhw4gCNHjhg/X251u8ez4vVb2+e8Op988glmzpyJdevWoU+fPnB3d8eIESNw5coVU9xdm2G3fQbCwsKMowlqw9PTE4IgYM+ePdWeI7vdeTN3d3fI5fJKHbYaYseOHUhKSsKuXbuMRwMA1HgOvr7jm2/Vpk0bdO3aFStWrMCUKVOwYsUK+Pv7Gz+QaqJQKDB9+nRMnz4d2dnZ2LZtG15//XUMHDgQ165dM37RVdcZ7tY38bp161BQUIC1a9ciKCjIePnJkyfrfb9Onz6NESNGoHfv3li2bFmV67///ntERUXh888/r3R5Xl5evfdZ8WWZnJxc5bqkpCR4enrWu+2a9nNrGDXlfurKzc0Ncrkcjz32GJ599tlqtwkJCalX256enujQoQPeeeedaq/39/c3/n/ixImYOHEiCgoK8Pfff+Ott97C0KFDcfny5Uqvr9pwc3ODIAhISUmpct2tl5nidbxq1SoolUps3LixUlCsbrhlr1690KtXL+j1ehw9ehT/+c9/8OKLL8LHxwcPP/ywsbPyli1bqt3Xv4/yNNT333+PkJAQ/Pzzz5U+m2rqDHu7x7Pi9V2X5/xWWq0W8+bNw7x585Cammo8SjBs2DBcvHix1vfL1tltGKiroUOH4r333kNiYiJGjx5dp9tqNBr07t0bv/76K955550GfwBXvIFuDSBLly6tUzv//kuttiZOnIipU6di79692LBhA6ZPnw65XF7r27u6umLUqFFITEzEiy++iLi4OLRp0wbBwcFIS0tDamqq8ZBgaWkptm7dWun21d13URSr/RKvjYSEBNx3330IDQ3FmjVroFQqq2wjCEKVx/r06dM4cOAAmjZtarzs1iM4txMZGQmNRoPvv//e2GMdAK5fv44dO3YYO0Y1VN++fQHc/AC+6667jJcfOXIEFy5cMP4VWVf/vq8ajabOt3d0dESfPn1w4sQJdOjQASqVql41VPdYDx06FJs2bUKzZs3g5uZWq7a0Wi3uu+8+lJaWYsSIETh37hyCgoLq9JxqtVp07doVa9euxf/93/8Zv6Dz8vKwYcOGStvW5XVc0/2smDTt3++/oqIifPfddzXWKJfL0a1bN7Ru3Ro//PADjh8/jocffhhDhw41dnjs1q3bbe9nfT43bq1bpVJVCgIpKSk1jibYvn17pc8FvV6Pn3/+Gc2aNTMG3Po859Xx8fHBhAkTcOrUKXz00UdmH4ZtTRgGaqlHjx6YPHkyJk6ciKNHj+Kee+6BVqtFcnIy9u7di/bt22Pq1Kk13v6DDz5Az5490a1bN8yaNQvNmzdHamoq1q9fj6VLl9YpeXfv3h1ubm54+umn8dZbb0GpVOKHH37AqVOn6nSf2rdvj7Vr1+Lzzz9H586dIZPJ7ni0ZOzYsZg+fTrGjh2LkpKSWs0sNmzYMOO8Dl5eXoiPj8dHH32EoKAgY4/5MWPG4M0338TDDz+MV199FcXFxfjkk0+g1+srtXXvvfdCpVJh7NixmDFjBoqLi/H555/jxo0bdbrvFe677z5kZ2fjv//9L86dO1fpumbNmsHLywtDhw7FggUL8NZbb6F37964dOkS5s+fj5CQkErDlpycnBAUFITff/8d/fr1g7u7Ozw9PasdeuXq6oo5c+bg9ddfx/jx4zF27FhkZmZi3rx5cHBwuG1P+rpo1aoVJk+ejP/85z+QyWS47777jKMJmjZtipdeeqle7bZv3x4AsGjRItx3332Qy+V1/lL/+OOP0bNnT/Tq1QtTp05FcHAw8vLyEB0djQ0bNlQZFVNdDbt27cKGDRvg5+cHJycntGrVCvPnz8dff/2F7t27Y9q0aWjVqhWKi4sRFxeHTZs24YsvvkCTJk3w1FNPQaPRoEePHvDz80NKSgoWLlwIFxcXY3Bq164dAODLL7+Ek5MTHBwcEBISUmMfmQULFmDQoEG499578fLLL0Ov12PRokXQarXGUSlA3V7HNb1PhwwZgg8++ADjxo3D5MmTkZmZiSVLllQJrl988QV27NiBIUOGIDAwEMXFxcbRChUjgx5++GH88MMPGDx4MF544QV07doVSqUS169fx86dOzF8+HCMHDnSWM+qVavw888/IzQ0FA4ODsbXQ20MHToUa9euxTPPPINRo0bh2rVrWLBgAfz8/Ko9NO/p6Ym+fftizpw5xtEEFy9erDS8sLbPeXW6deuGoUOHokOHDnBzc8OFCxfw3XffITIy0m6CAAD7HU1w5MiR225XU+/25cuXi926dRO1Wq2o0WjEZs2aiePHjxePHj16x32fP39efOihh0QPDw9RpVKJgYGB4oQJE8Ti4uLb1rZz584qPZr3798vRkZGio6OjqKXl5f45JNPisePHxcBiCtWrKh0P7RabbX1ZGVliaNGjRJdXV1FQRBu29v538aNGycCEHv06FHt9bf2Hn7//ffF7t27i56ensb7/cQTT4hxcXGVbrdp0yYxPDxc1Gg0YmhoqPjf//632tEEGzZsEDt27Cg6ODiIAQEB4quvvipu3ry5ymNUm9EEAGr8qXgcS0pKxFdeeUUMCAgQHRwcxIiICHHdunXVtr9t2zaxU6dOolqtFgEY93XraIIKX331ldihQwdRpVKJLi4u4vDhwyv1fK64H9U9h3caaVFBr9eLixYtElu2bCkqlUrR09NTfPTRR8Vr165V215tRhOUlJSITz75pOjl5WV87VTcNwDis88+W+U21fUqj42NFSdNmiQGBASISqVS9PLyErt37y6+/fbbd6zh5MmTYo8ePURHR0cRQKUe7unp6eK0adPEkJAQUalUiu7u7mLnzp3FN954Q8zPzxdFURRXrlwp9unTR/Tx8RFVKpXo7+8vjh49Wjx9+nSl/Xz00UdiSEiIKJfLq7y/qrN+/XrjcxoYGCi+9957DXod3+59unz5crFVq1aiWq0WQ0NDxYULF4pff/11pefjwIED4siRI8WgoCBRrVaLHh4eYu/evcX169dXqqesrExcsmSJsSadTie2bt1anDJlinjlyhXjdnFxceKAAQNEJycnEcAdRzpU97y/9957YnBwsKhWq8WwsDBx2bJl1T5GFa+lzz77TGzWrJmoVCrF1q1biz/88EOV/dTmOa9o89+jCWbNmiV26dJFdHNzMz6OL730UqURUvZAEMVbZqcgIiIiu2K3owmIiIjoJoYBIiIiO8cwQEREZOcYBoiIiOwcwwAREZGdYxggIiKycwwDREREdo5hgIiIyM4xDBAREdk5hgEiIiI7xzBARERk5xgGiIiI7BzDABERkZ1jGCAiIrJzDANERER2jmGAiIjIzjEMEBER2TmGASIiIjvHMEBERGTnGAaIiGxUXFwcBEHAyZMnzb4vQRCwbt06s++HpKGQugAiIrJ+ycnJcHNzk7oMMhOGASIiuiNfX1+pSyAz4mkCIiITiYqKwvPPP48XX3wRbm5u8PHxwZdffomCggJMnDgRTk5OaNasGTZv3my8zfnz5zF48GDodDr4+PjgscceQ0ZGhvF6g8GARYsWoXnz5lCr1QgMDMQ777xTab9Xr15Fnz594OjoiI4dO+LAgQPG6zIzMzF27Fg0adIEjo6OaN++PX766acqdU+bNg0zZsyAu7s7fH19MXfu3Erb/Ps0QWlpKZ577jn4+fnBwcEBwcHBWLhwYaVtly5diqFDh8LR0RFhYWE4cOAAoqOjERUVBa1Wi8jISMTExDT0IScT4ZEBIhunLzOguKCs8k9+GUoKy1FSVI6yEj3Kiv/595+f8lIDBJkAmUyATP6vH5kAmVx287pbLpcrZdA4qeDorILWRQ1HZxUcXVTQOKkgkwlSPwxWY+XKlZgxYwYOHz6Mn3/+GVOnTsW6deswcuRIvP766/jwww/x2GOPISEhATk5OejduzeeeuopfPDBBygqKsLMmTMxevRo7NixAwDw2muvYdmyZfjwww/Rs2dPJCcn4+LFi5X2+cYbb2DJkiVo0aIF3njjDYwdOxbR0dFQKBQoLi5G586dMXPmTDg7O+OPP/7AY489htDQUHTr1q1S3dOnT8ehQ4dw4MABTJgwAT169MC9995b5T5+8sknWL9+PX755RcEBgbi2rVruHbtWqVtFixYgA8++AAffPABZs6ciXHjxiE0NBSvvfYaAgMDMWnSJDz33HOVghFJRxBFUZS6CCK6vZLCMuSkFyEnrejmv+mF//xbhMKcUklrE2QCHHRKaF1uBoWbIeFmWNC5qeHup4WLt6NdBIaoqCjo9Xrs2bMHAKDX6+Hi4oIHHngA3377LQAgJSUFfn5+OHDgADZt2oRDhw5h69atxjauX7+Opk2b4tKlS/Dz84OXlxf++9//4sknn6yyv7i4OISEhOCrr77CE088AeDmkYa2bdviwoULaN26dbV1DhkyBGFhYViyZEm1dQNA165d0bdvX7z33nsAbv61/9tvv2HEiBGYNm0azp07h23btkEQqj6vgiBg9uzZWLBgAQDg4MGDiIyMxNdff41JkyYBAFatWoWJEyeiqKiobg8ymQWPDBBZifIyPTKu5SM79X9f9DlphcjJKEJJQbnU5dVINIgoyi1FUW7NoUSulMHN1xEeATp4+OvgHqCFh78OOje1BSu1jA4dOhj/L5fL4eHhgfbt2xsv8/HxAQCkpaXh2LFj2LlzJ3Q6XZV2YmJikJ2djZKSEvTr16/W+/Tz8zO237p1a+j1erz33nv4+eefkZiYiJKSEpSUlECr1dbYRkU7aWlp1e5vwoQJuPfee9GqVSsMGjQIQ4cOxYABA2psr+I+3/o4FBcXIzc3F87Ozre9f2R+DANEEslOK0RqbC5Sr+YgJTYXmYn5MOgb54E6fZkBGdfykXEtv9Llaq0C7n7amyEhQAd3fy08m+igcrDdjyalUlnpd0EQKl1W8Ze0wWCAwWDAsGHDsGjRoirt+Pn54erVq3Xe57/bB4D3338fH374IT766CO0b98eWq0WL774IkpLS2tso6KdijZuFRERgdjYWGzevBnbtm3D6NGj0b9/f6xevfq2Nd2uTpKW7b7jiGxIaVE5UuNykRp784s/NTYXxfllUpcluZKCciRH5yA5Osd4mSAT4NlEB//mrvBv4Qq/Fi7Q6FQSVmk+ERERWLNmDYKDg6FQVP04btGiBTQaDbZv317taYLa2LNnD4YPH45HH30UwM0v3ytXriAsLKxBtTs7O2PMmDEYM2YMRo0ahUGDBiErKwvu7u4NapekwTBAZAbF+WWIP5eJxMs3kBqbixvJBWDvnNoRDSLSE/KQnpCHUzuuAQLg5uOIDq30CHTLg2O3blA0kvHuzz77LJYtW4axY8fi1VdfhaenJ6Kjo7Fq1SosW7YMDg4OmDlzJmbMmAGVSoUePXogPT0d586dM/YRuJPmzZtjzZo12L9/P9zc3PDBBx8gJSWlQWHgww8/hJ+fH8LDwyGTyfDrr7/C19cXrq6u9W6TpMUwQGQiWckFiDudgbgzGUi5mgvRwG9/kxCBGymFKEw7gMQd3wMyGRxat4a2eyR0UVHQRERAkNnmKGl/f3/s27cPM2fOxMCBA1FSUoKgoCAMGjQIsn/u05w5c6BQKPDmm28iKSkJfn5+ePrpp2u9jzlz5iA2NhYDBw6Eo6MjJk+ejBEjRiAnJ+fON66BTqfDokWLcOXKFcjlctx1113YtGmTsWayPRxNQFRPBr0BSVeyEXc6E7FnMpCbzl7R5iIIwD2n5kOelVrlOrm7O3RRUXDq3w/a7t0hc3CQoEIi28YwQFQHxQVliD+bibgzGUg4l4XSIuvt5d+YeHgp0PHXKXfcTtBooO3RHU59+0HXJ6rRnE4gMjeGAaI7KC0qR/SxNFw+nIKk6Bwe/pdAK98cBKx6vW43ksuhjYyEy/DhcLq3P48YEN0GwwBRNUSDiGsXs3DxQApiT6ajvIzDn6TUVfwbut0/1/v2Mp0OTgMGwGX4cDh2vavaiXKI7BnDANG/5KQX4vy+ZFw+lIL8GyVSl0MAZDIB9xyZDVlelknaU/r7w/n+YXAZPhzqkBCTtElk6xgGyO4Z9AbEnsrAuT2JuHbxBsB3hFXx8paj/S+17z1fF4533w33Rx+Brk8fCHK5WfZBZAs4tJDsVm5GEc7vTcKF/ckovM1UuiQtTyHdbG0XHjyIwoMHofT3h+vYh+E6ahQ7HZJd4pEBsjsZ1/NwbHM8Yo6ncSIgG3B32TY47vvNIvsS1Go4DxkC90cfgUObNhbZJ5E1YBggu5FyNQfHNsch7kym1KVQLcnkAu7ZPwOyovw7b2xims6d4fHkE3Dq08fi+yayNIYBavSuX8zC0c3xSLx0Q+pSqI58fOVou8o8/QVqS926NTyeehLOgwaxXwE1WgwD1GjFnc7A0c1xSI3NlboUqqc2Xunw/XWu1GUAAFRBQfB4+mm43D+MoYAaHYYBalREg4jo42k4tiUemdctf2iZTCuyaDM0hzZKXUYlysBAeE6ZApfh90OoZqVBIlvEMECNgiiKuHwoBUc3xyM7tVDqcsgEFEoZev09HUKJda75oAoOhtf0l+A8YIDUpRA1GMMA2bzkmBzs/eUy0uLzpC6FTMjPT4awn6ZKXcYdaSIi4DPjVWjCw6UuhajeGAbIZuXfKMH+tdG4cqTqSnZk+9p5psB79QKpy6g1p4ED4T39JaiCgqQuhajOGAbI5pSX6XHizwQc/zMB5SV6qcshM4nM+x2aY39KXUbdKJVwe/hheD4zlZMXkU1hGCCbEn0sDfvXRiMvs1jqUsiMlGoZemx/AbJy25wZUubsDK8XX4Dbww9DkMmkLqeSqKgohIeH46OPPpK6FLIiDANkEzKu52PvL5eReDlb6lLIAgL8BLT66Rmpy2gwh44d4Dd3LhzCwqQuxSgrKwtKpRJOTk533DYuLg4hISE4ceIEwtknolHjuBiyasX5ZTi4/irO702CaGButRfuJdekLsEkik+dRuyoh+D+6CPwmjYNMq1W6pLg7u4udQlkhazr+BXRv1w6lILv3zyAc38nMgjYGaerB6UuwXT0emSt/BYxQ4Yid6v0fSCioqLw4osvAgCCg4Px7rvvYtKkSXByckJgYCC+/PJL47Yh/yzx3KlTJwiCgKioKAkqJktgGCCrU5xfhi1fnsG2FedRUlgudTlkYSqNHOoze6Quw+TKU1KQ+MILuDblaZSlWs8ImPfffx9dunTBiRMn8Mwzz2Dq1Km4ePEiAODw4cMAgG3btiE5ORlr166VslQyI4YBsipxZzLw0/xDiDluvmVrybp5u+kh6BtvCMzfvRtXh92PnA3WMbPi4MGD8cwzz6B58+aYOXMmPD09sWvXLgCAl5cXAMDDwwO+vr48xdCIMQyQVSgtLsfO7y7gj09PozDXNnuQk2m4F8ZJXYLZGXJzkfTqq7j+0kvQZ2dLWkuHDh2M/xcEAb6+vkhLS5OwIpICwwBJLulKNn5++zDO70uWuhSyAk5X9kldgsXkbd6Cq8PuR/4e6U6LKJXKSr8LggCDwSBRNSQVhgGSjL7MgH1rorHug+PIzeC8AQQ4aBVQnT8gdRkWVZ6ejmtPTUby3LkwFFrXuhoqlQoAoNdzcq/GjmGAJJF+LQ+/LDyCk38lgDNdUAVvl1IIdvqCyF71M66OHImis+ekLsXI29sbGo0GW7ZsQWpqKnJycqQuicyEYYAsShRFHN8aj9WLjiIrqUDqcsjKuBdclboESZXFJyB+3Dhk/fCD1KUAABQKBT755BMsXboU/v7+GD58uNQlkZlwBkKymNKicmz75jxiT2VIXQpZqZ5JK6C6fFTqMqyC06BB8Ht7AeQ6ndSlkB3gkQGyiKykAvz63lEGAaqRo5OCQeBfcmMv4+E/H0X0jWipSyE7wDBAZhd9LA2rFx1Fdqp1dY4i6+LtxE6kFQRnZywYVoKLeTEYt2kctsRtkbokauQYBshsRIOI/WujsXXZWZRxqWG6A7dc/gUMABAEbBgXgrPKm7MUFpUX4dXdr+L/jvwfyg2NdzImkhbDAJlFaVE5/vj8NE78mSB1KWQjdOd3S12CVbg+/C5861Z1RMG357/FlL+mIKeEPfrJ9BgGyORy0ouwevExxJ/JlLoUshE6FwWUV09LXYbkysNbY0brkzVefzjlMB7d9Ciu5TWOVR3JejAMkEklXr6B1e8dxY1kDhuk2vPWsj+JzNMDr9+bgXLh9rP/xeXG4ZE/HsHJtJOWKYzsAsMAmcz5fUlY//FJFBeUSV0K2RjXG5elLkFaCgW+fdgbcYrsWm1+o+QGntj6BDbHbjZvXWQ3GAbIJI5ticPO7y7CoOe0FVR3urM7pC5BUhdGRWC905U63abUUIqZf8/EN2e/MU9RZFcYBqjBDvwWg4Pr7HvmOKo/ZzclFNcuSV2GZIq6d8BbIcfrdVsRIt4/9j4WH1kMzh9HDcEwQPUmiiL+XnUZx7fGS10K2TAvhzypS5CM0MQfr/Zs+Iib785/h5l/z0SZnqfoqH4YBqheDAYRO769gDO7rktdCtk4t6wLUpcgCUGtxqejNEiT55ukvc1xmzF1+1QUlrEzJtUdwwDVmb7cgD+/OouLB1KkLoUaAcfT26QuQRIHx7bHLo1pj6odSj6Ep7c9jYIyjuahumEYoDopL9Vj0+dnEHM8XepSqBFw9VBCkRwndRkWl92/M973O2mWtk+kncDkPycjr9R+T79Q3TEMUK2VFpdjw39OIeEcJxMi0/BSZUtdguW1CMbLnS+adRenM07jyT+f5GyFVGsMA1QrxQVl+P3DE0i6ki11KdSIuKZXnXa3MRN0Wiy8X488WYnZ93U+8zy+2/MWUJhl9n2R7WMYoDsqzC3Fug+OIy2ehx3JhATA8cSfUldhUVseaYETqmSL7GuwWztM3fct8N1IoCjbIvsk28UwQLd189TASWQmskMSmZa7hwLyTMt8MVqD5GF34Wv3sxbZ12C3dnj3xFbIRT2QfBL4/gGgONci+ybbxDBANdKXG7D5izPIuGaaoU9E/+alvCF1CRajb98Kr7a1zEJMlYJAhcRjwPcPAiU8ukfVYxigaokGEdtWnMf1i/bzgU2W5ZJsH6sUCu5ueHNgNkoF/Z03bqBqg0CF64eBVeMATkxE1WAYoGrt+eUKoo+lSV0GNVKCzE76C8jl+HmsP64ozT8C57ZBoELs38C6ZwBOXUy3YBigKo5uiuPMgmRWHp4KyHIypC7D7KIf7IzVzuZfd6FWQaDCmV+AbXPNXhPZFoYBquT8viQcWs9Fh8i8PIXGHwRKurbDG6H1W4CoLuoUBCrs+wg4vMxsNZHtYRggo9jTGdj1g/2uHkeW45J0UuoSzErw88GMqESIgnn3U68gUGHzDODCRtMXRTaJYYAAAMnR2fhz2VmIBp5LJPOSyQVoTvwldRnmo1Ri6WhnJMvN23O/QUEAAEQDsOZJ4Nph0xZGNolhgJCZlI8/PjuN8jKD1KWQHfD0lEGWny11GWZzYmw4tjnGmnUfDQ4CFcqLgB/HABnRpimMbBbDgJ0rzC3Fxv+cQklhudSlkJ3wROMdpZLXpxMWBpww6z5MFgQqFGXdnJQon4uP2TOGATtmMIj486uzyL9h/nnSiSo4XzN/pzopCCGBmN71iln3cZ+pg0CF7Hhg9UTAYP65EMg6MQzYsUO/xyDxcrbUZZAdkSsEOBxvfP0FBEdH/N9IATmyYrPt4z63dlhojiBQIW4PsO0t87RNVo9hwE5dPZmO438mSF0G2RlPTwGy4sa3zsWOca1xWJ1otvbNHgQq7P8PcG6defdBVolhwA7lpBdi+8oLAAcOkIV56lOkLsHk0gZ3wede5pta2WJBoMLvzwLpHGJsbxgG7Ex5qR6bl55FaRE7DJLlOccfkboEkzK0aY5XOphvJUKLBwEAKM0Hfn6UixrZGYYBO7P7x0vIvM5VCMnyFCoZHE7ukLoMkxFcXTB3cD6KBfMEa0mCQIWMyzePEJDdYBiwI+f2JOLiwcZ3mJZsg5eHCKHUfB3sLEomw9pxgbioNM+0ypIGgQrnfwf2fSLd/v9l3759aN++PZRKJUaMGFHjZVR/DAN2Ii0+F3t+Nu+wJ6Lb8Sg1Xwc7S4sb2QU/uVwwS9tWEQQqbJsLxO6RugpMnz4d4eHhiI2NxTfffFPjZQ0RFxcHQRBw8uTJBrdlixgG7EBxQRm2fHkW+nLOMEjScY49JHUJJlHWuQ1ea2meiYWsKggAgKgHfpsCFGVLWkZMTAz69u2LJk2awNXVtcbLqP4YBuzAtm/OIy+zkRyeJZukcpBDfWqX1GU0mODjhZl9U6E3w1AcqwsCFXITby5qZEaiKGLx4sUIDQ2FRqNBx44dsXr1auNf65mZmZg0aRIEQcA333xT7WUAcP78eQwePBg6nQ4+Pj547LHHkJHxv1M5BoMBixYtQvPmzaFWqxEYGIh33nkHABASEgIA6NSpEwRBQFRUlFnvs7VhGGjkzv6diPgzmVKXQXbOy10PQW/jI1gUCqwY447rihyTN221QaDC6Z9v9iEwk9mzZ2PFihX4/PPPce7cObz00kt49NFHER8fj+TkZDg7O+Ojjz5CcnIyHnrooSqXjRkzBsnJyejduzfCw8Nx9OhRbNmyBampqRg9erRxP6+99hoWLVqEOXPm4Pz58/jxxx/h4+MDADh8+OaCTdu2bUNycjLWrl1rtvtrjRRSF0Dmk5tRhP1ruAAJSc+j+JrUJTTYmTER2KQ1/VTKVh8EKmx8CQiMBHTeJm22oKAAH3zwAXbs2IHIyEgAQGhoKPbu3YulS5fixx9/hCAIcHFxga+vLwBAq9VWuWzhwoWIiIjAu+++a2x7+fLlaNq0KS5fvgw/Pz98/PHH+O9//4vHH38cANCsWTP07NkTAODl5QUA8PDwMLZpTxgGGilRFLHj2wsoK7HyDxiyC7ro/VKX0CAFvcKxINCOgwAAFGYC658Hxv1s0mbPnz+P4uJi3HvvvZUuLy0tRadOnWrdzrFjx7Bz507odLoq18XExCA7OxslJSXo169fg2tujBgGGqkzu65z3QGyCmqNHOqz+6Quo96EoCZ4pftVk7drU0GgwuUtwLGVQOfHTdakwXCzY/Mff/yBgICAStep1eo6tTNs2DAsWrSoynV+fn64etX0z2FjwjDQCGWnFuLAbzFSl0EEAPB2K4dgo6vhCRoHfPSAEpmyQpO2a5NBoMLWN4DQ3oBbsEmaa9OmDdRqNRISEtC7d+96txMREYE1a9YgODgYCkXVr7YWLVpAo9Fg+/btePLJJ6tcr1KpAAB6vQ0+JybADoSNjCiK2Pn9RZSXchghWQf3wjipS6i3PWPbYp+Dafs72HQQAIDSPGDdM4DBNJ8xTk5OeOWVV/DSSy9h5cqViImJwYkTJ/Dpp59i5cqVtW7n2WefRVZWFsaOHYvDhw/j6tWr+PPPPzFp0iTo9Xo4ODhg5syZmDFjBr799lvExMTg4MGD+PrrrwEA3t7e0Gg0xo6HOTmm7yhqzRgGGpnze5OQdCVb6jKIjJwu7ZW6hHrJGtgFn/icMmmbNh8EKsTvAw5/abLmFixYgDfffBMLFy5EWFgYBg4ciA0bNhiH+9WGv78/9u3bB71ej4EDB6Jdu3Z44YUX4OLiApns5lfdnDlz8PLLL+PNN99EWFgYxowZg7S0NACAQqHAJ598gqVLl8Lf3x/Dhw832f2zBYIoily7rpEoyCnBj3MPcREishoarQJ3b3oago19zIitQjFxRDIKZWUma7PRBIEKamfg2cOAs5/UlZAJ8MhAI7Jn1WUGAbIq3i4lNhcEBGdnvD20mEHgTkpygS2zpK6CTIRhoJG4ejIdMSfSpS6DqBK3PBvrwS0I2DguBGdUaSZrslEGgQrn1wFXtkldBZkAw0AjUFpcjr9XXZa6DKIqnC7slrqEOrk+/C6sdDtnsvYadRCosOlloIzTnds6hoFG4MRfCSjILpG6DKJKHJ0UUEabZ0EfcygPD8OM1idN1p5dBAEAuBEH7LeOpY6p/hgGbFxBTglObrP9qV6p8fF2KpK6hFqTeXrg9XvTUS6YZric3QSBCns/BLL5OWTLGAZs3OENsSjnlMNkhdxyrkhdQu0oFPj2YW/EKbJN0pzdBQEAKCsE/pwtdRXUAAwDNiwrqQAX9idLXQZRtXRnd0pdQq1cHBWB9U6mCS52GQQqnF8HxP4tdRVUTwwDNmz/b9EQDbY1bIvsg85FCWX8eanLuKOi7h3wZohpFiAa5NbWfoNAhT9nAzY2lJRuYhiwUdcv3UD8mUypyyCqlrc2X+oS7kgI8MOrPRNM0tYgt7Z478Sf9h0EACD5FHBhvdRVUD0wDNggURSxf0201GUQ1cgt66LUJdyWoFbj04cckSZveGhhELjFjndMtm4BWQ7DgA26ciQV6Ql5UpdBVCPtmR1Sl3Bbh8a2xy5NfIPbYRCoRsYl4PTPUldBdcQwYGP0ZQYcXGdjs7qRXXF2U0CRaL1HrnL6d8YSv5MNbodB4DZ2LQT0ppvOmcyPYcDGnN55HXlZnO2LrJe3gxUftWoejOmdG34Kg0HgDrLjgWPfSF0F1QHDgA0pLijDsS1xUpdBdFuuGdY5ikDQabFouB55sobN1skgUEt/LwHKbGfiKXvHMGBDTu24hpJCrkpI1k17yjoXrtk6rgWOqRo2LweDQB3kpwCHv5S6CqolhgEbUV6qx9ndiVKXQXRbbh4KyNNMM1zPlJKH3YWvPM42qA0GgXrY+xFQnCt1FVQLDAM24uKBZBTns0MOWTdPVbbUJVRhaN8Sr7Y93aA2GATqqSgLOPq11FVQLTAM2ADRIOLkdi4CQtbPNa1hf32bmuDuhjkDc1Aq1P9LnEGggQ59yZEFNoBhwAbEnspATho74pB1EwRAe+Ivqcv4H5kMv4z1xxVl/WfqHMgg0HB5ScDZtVJXQXfAMGADTvxlfedgiW7l7qmALCtF6jKMoh/sgl+dL9X79gPd2mIRg4BpHPiv1BXQHTAMWLmUqzlIuZojdRlEd+Qpz5K6BKOSru3wRrP6L0DEIGBiKae5oqGVYxiwcjwqQLbCJfmU1CUAAAQ/H8yISoQo1O/2DAJmcuBTqSug22AYsGI56YWIPZkudRlEdyTIAEdr6C+gVGLZQy5IltdvFkQGATO6vBXIuCJ1FVQDhgErdmrbNS4NTjbBw1MBWa70S2qfeDgcf2rrt3YHg4C5iTw6YMUYBqxUcUEZLhxo2GxpRJbiJUh/BCsvqhMWNjlRr9syCFjIqVVAofX0LaH/YRiwUmd3J6K8lGuCk21wvl7/znqmIIQEYnq3+h2CZhCwoPIi4NgKqaugajAMWCFRFHF+b5LUZRDVikwuQHNSuvUIBEdH/N9IATmyuq/mySAggZM/Sl0BVYNhwAolR2dzmWKyGV6eMsgKpJt/fse41jisrvu6HQwCEsmMBhIOSV0F3YJhwApdOpQqdQlEteYhSjfRUPp9XfC5V93XHWAQkNjJ76WugG7BMGBl9GUGxBxPk7oMolpzTjgmyX4NbZrj5Y51XwuBQcAKnFsHlHGKdWvCMGBlYk9noKSwXOoyiGpFrpRBc8Ly/QUEVxfMH1yAYqFu75WBbm3x3sm/GASkVpILXNggdRX0LwwDVubSIeuZ253oTrw9AaHEwn/hyWT4bVwgzivrNpyxIggoDAzbVuHkD1JXQP/CMGBFivPLkHBO+olbiGrLvdzyo17iRnbBjy4X6nQbBgErFPs3kHNd6iroHwwDVuTK0VQY9JxykGyHc+wRi+6vrHMbvNaybhMLMQhYKdEAnPpJ6iroHwwDVoSnCMiWKFQyqE/vtNj+BB8vzOybCj1qH5gZBKzcSYYBa8EwYCWyUwuRGivdWG2iuvL2ECErLbHMzhQKrBjjjuuK2i/nzSBgA7JigKT6TSFNpsUwYCUuHeZRAbItHqWWO997dnQnbNLG1Hp7BgEbcmGj1BUQGAasxmWeIiAb4xRz0CL7KewZjvlBtf/rkUHAxnCIoVVgGLACafG5yM3g9MNkO1QOcqjP/G32/QiBAXi5R+2XJGYQsEEZl4CM+i0yRabDMGAF4s9yOCHZFm93PQS9eb9wBY0DPnpQhUxZYa22ZxCwXaWX/pK6BLunkLoAYhgg2+NeFG/2fewd2xb7HE7ValsGAdsiQkCRZ3sc10Tih+y2SD3VHGt7SF2VfWMYkFhRXinS4jiKgGyL05X9Zm0/a0BnfOxTuyAwgEHAJohyFbK878ZeWVd8ldYKZ65rjdfJhGxkFZTCXauSsEL7xjAgsYRzmRA5zxDZEAdHOVTn9pmtfbFVKKZ3Ol+rbQe4tcUiBgGrZXBwxXXPXthaHoFlyaFIi1VWv50I7LqUhgcimli4QqrAMCCxOJ4iIBvj7VoGwUwJVnBywoKhxSiUld1xWwYB61TuHIiLLr2wtrADfkgOQEl27bqm7bjIMCAlhgEJiQYR185nSV0GUZ24F8Sap2FBwMZHQnFGde6OmzIIWA8RAgo9O+C4JhLf3WiHP9PcgXqswv735XSU6w1QyNmvXQoMAxJKS8jjcsVkc3QX95il3ev3d8FKtzvPJ8AgID1RrkaW993YI78Ly1Jb4dy/zv/XV25xOY7F30C3UA8TVEh1xTAgoesXeVSAbIujTgHVJdMvTqTv2Bozwu7cYZBBQDoGBzdcqzj/nxSC9BrO/zfEgauZDAMSYRiQ0PWLN6QugahOvJxNPzmWzNMdswZkolww3HY7BgHLK3MJxkXnHlhT0BE/pASgLFsw6/6OxfMzUSoMAxIpL9MjOab2i64QWQP33NqvD1Arcjm+G+uDq4rbz0DHIGAZIgQUenXEMYdIfHejLf5KdQdSLbf/kwnZMBhEyGTmDR1UFcOARFJicqAvu/1fQkTWRnthl0nbu/hQZ/yuO37bbRgEzEtUOCDT+278Ldw8/3/hmqNkteSVlONSah7C/Jwlq8FeMQxI5PolHg4j26J1VkAVc9pk7RVHdsBbwQwCUjBo3JHg0RNbyiKwLDkEmVdNf/6/vo7F32AYkADDgERSYznrINkWb13t1gioDSHAD6/cEw/xNkeDGQRMq8wlBBf+Of//Y4o/ym5Y56H44/E38OjdQVKXYXcYBiSScS1f6hKI6sTtxmWTtCOo1fjsIS3SZOk1bsMg0HAiBBR4heOYw934Nqsttlv4/H99HUvgUVMpMAxIIDezCMUFd55hjciaaM/tMEk7h8e2x07NyRqvv5dBoN5EhQMyvCPxt9AFy1Jb4aKE5//rKz6zEOl5JfByUktdil1hGJBARgKPCpBtcXJVQplwqcHt5PTvjP/zO1nj9fe6tcViBoE6MWg8EO/RC5vLOuHr5GCrOv9fX8fib2BQO1+py7ArDAMSSL+WJ3UJRHXi7WiC12zzYEzvfLHGqxkEaq/MJRTnXXpgdX4HrLLi8//1dTyBYcDSGAYkwDBAtsY1s+Yv8doQdFq8N1yPPFlJtdczCNyeKMhQ4BmOIw6R+DYzDDtt5Px/fVnD5ENxcXEICQnBiRMnEB4eXqvbCIKA3377DSNGjDBrbebAMCCB9ASGAbItutPbGnT7P8e1xDHVmWqvYxConqjQIMM7ErtxF75MbYnL1zRSl2QxZxJzUFKuh1ohl7qUOklOToabm5vUZdQLw4CFFeSUoDCnVOoyiGrNxV0JeXL9VypMGXoXlnlUvwARg0BlBo0n4j174o/SCHydFIwbV+3zI7q03IArqfloF+AidSl14utru6c2uFakhfGoANkaL3X9p802tG+JV9pVP1ERg8BNpa6hONl0PN5wW4IW2R+hz5XRWBLfHDfK7DMIVLiSZtrPyi1btqBnz55wdXWFh4cHhg4dipiY/02vffjwYXTq1AkODg7o0qULTpz4X4A1GAxo0qQJvvjii0ptHj9+HIIg4OrVqwBuniZYt24dAKC0tBTPPfcc/Pz84ODggODgYCxcuNB425ycHEyePBne3t5wdnZG3759cerUnRfrMhf7frVJIIP9BcjGuKWfq9ftBDdXzBmUg1JBX+U6ew4CoiBDgVcnHFbfjZVZbbA7xQ1Ikboq63Ml1bSjrgoKCjB9+nS0b98eBQUFePPNNzFy5EicPHkSRUVFGDp0KPr27Yvvv/8esbGxeOGFF4y3lclkePjhh/HDDz/g6aefNl7+448/IjIyEqGhoVX298knn2D9+vX45ZdfEBgYiGvXruHatWsAAFEUMWTIELi7u2PTpk1wcXHB0qVL0a9fP1y+fBnu7u4mve+1wTBgYekcVki2RAAcT/5V99vJZPhlXACuKKoOR7THICAqNEj3jsQu4S4sS2mJKwn2c/6/vq6kmfaz8sEHH6z0+9dffw1vb2+cP38e+/fvh16vx/Lly+Ho6Ii2bdvi+vXrmDp1qnH7Rx55BB988AHi4+MRFBQEg8GAVatW4fXXX692fwkJCWjRogV69uwJQRAQFPS/WRV37tyJM2fOIC0tDWr1zfkUlixZgnXr1mH16tWYPHmySe97bTAMWBhPE5AtcfNQQJ6RWOfbxTzYBb86V113wJ6CgMHRE3HuvfBHaQS+Sg5Cjp2e/6+vGBOHgZiYGMyZMwcHDx5ERkYGDIabC8UlJCTgwoUL6NixIxwd/zdJU2RkZKXbd+rUCa1bt8ZPP/2EWbNmYffu3UhLS8Po0aOr3d+ECRNw7733olWrVhg0aBCGDh2KAQMGAACOHTuG/Px8eHh4VLpNUVFRpVMXlsRXpwWVFJUjL8v068ETmYuXsu5DvEq6tsPrzewzCJS6NsM55574Na8Dfk7xgT6L3bLqKz6rEKXlBqgUpnkMhw0bhqZNm2LZsmXw9/eHwWBAu3btUFpaClEUa9XGI488gh9//BGzZs3Cjz/+iIEDB8LT07PabSMiIhAbG4vNmzdj27ZtGD16NPr374/Vq1fDYDDAz88Pu3btqnI7V1fXBtzL+mMYsKC8TAYBsi0uKdUPB6yJ4OeDGVGJVRYgaqxBQBRkyPeKwBH13fgmsw3+TnHl+X8T0RtExGUWoKWPU4PbyszMxIULF7B06VL06tULALB3717j9W3atMF3332HoqIiaDQ3T+EcPHiwSjvjxo3D7NmzcezYMaxevRqff/75bffr7OyMMWPGYMyYMRg1ahQGDRqErKwsREREICUlBQqFAsHBwQ2+f6bAMGBB+TwqQDZEqGt/AaUSyx5yQbL8aqWLG1sQEJWOSPOKxC7chS9TWiCG5//NJj6z0CRhwM3NDR4eHvjyyy/h5+eHhIQEzJo1y3j9uHHj8MYbb+CJJ57A7NmzERcXhyVLllRpJyQkBN27d8cTTzyB8vJyDB8+vMZ9fvjhh/Dz80N4eDhkMhl+/fVX+Pr6wtXVFf3790dkZCRGjBiBRYsWoVWrVkhKSsKmTZswYsQIdOnSpcH3ua4YBiwo/wbDANkOd08F5DfSar39iTEd8af2ZKXLGksQMDh6Ida9FzaVdcJXSTz/bynxmQUmaUcmk2HVqlWYNm0a2rVrh1atWuGTTz5BVFQUAECn02HDhg14+umn0alTJ7Rp0waLFi2q0ukQuHmq4Nlnn8X48eONRxGqo9PpsGjRIly5cgVyuRx33XUXNm3aBJns5mmPTZs24Y033sCkSZOQnp4OX19f3HPPPfDx8THJfa4rQaztyRJqsAO/xeD41nipyyCqlda+2fBf9Uatts3v3QmTulc+pWDrQaDUrQXO6rrjl/wO+DXFB3qR5/8tbXxkEOYPbyd1GXaB8daCeGSAbIlzUi0nQAluipfuvlLpIlsMAqIgR55XBA6ruuGbrLbYm2xbs981RvGZhVKXYDcYBiyIIwnIVshkAhyP/nnH7QRHRyx5QIYc2f9e27YUBESlFqle3bELXfBlSgtcTXCQuiT6F1OdJqA7YxiwoPwb1a/YRmRtPLxkkOVn33G7neNa45D6f9MN20IQ0Gu9EeveC3+UdMJXSYHI4/l/q5WYXQRRFCEIjWuJZmvEd4GFiAYRBdkMA2QbPJF+x23SB9+Fz7z+N3+7NQeBEreWOKvrgZ/z2uPXVB+ImfxysQVlehG5xeVw0SilLqXRYxiwkIKcUhj07KtJtsH52rHbXi+GNcfLHf7XYdDagoAoyJHn3RmHlHfjm8ww7OP5f5uVXVjKMGABDAMWws6DZCtkcgGaE9tqvF5wdcHcIQUoFm5+8VtLEBBVWqR6dscOdMGXyS0QF8/z/43BjcIyBHnceTtqGIYBC2HnQbIVXl4CZEU1zAsvCPhtXCDOKy8AkD4I6LU+uOreCxtLwvFVUhAKrsolqYPM50ZBqdQl2AWGAQth50GyFZ76mufTjX/gLvzocnPdAamCQIlbK5xx6oFVue2xJtWb5/8buRuFDAOWwDBgIYU5DANkG5zij1R7eVnnNpjV8maHQUsGAVGQI9e7Cw4pu+HrjDY4lOwMJJt9t2QlbhSWSV2CXWAYsJCyEr3UJRDdkUIpg+bkjiqXC96eeK1fGvQQLRIERJUWKV49sEPsgqXJzZHA8/92K5tHBiyCYcBCyksNUpdAdEdenoBQekv/FoUC34zxRII8Gv3NGAT0Wl/EuPfChpJwLE8KREEMz/8TTxNYCsOAhZSX8sgAWT+P0sQql50d3Ql/6E6gv1tb/J+Jg0Cxe2uc0XbHqtz2WJvG8/9UFU8TWAbDgIWUl/HIAFk/59hDlX4v7BmO+UGmCwKiTIFcry44oOyG5RlhOJzk3KD2qPHjaQLLYBiwEB4ZIGunVMugOr3b+LsQGICXe1xtcBAQVToke/XEdkMElia3wPV4talKJjuQVcAjA5bAMGAhZewzQFbO28MAWfnNv8IEjQM+flCFTh4h9QoCep0fot16YX1xOL5JCkRBLpf/pfrhkQHLYBiwEH0ZjwyQdXMvvm78/96xbeHgV16nIFDsHoZT2h5YldsO69K8IGbw/D81XBE/Oy2CYcBCeGSArJ1z9H4AQNaALjjXuuyOQUCUKZDjfRcOKLrh6/QwHE1yslSpZEf0Bq7pYgkMAxbCPgNkzdQaOVRn90JsFYqNffQ1BoGK8//bDF2wNLkZEuN4/p/My8AwYBEMAxai52gCsmLebuWQaR2xc7Q3FpxZXykI6HX+iHbrhXXFHfEtz/+ThelFhgFLYBiwkDIeGSAr5l4Yj8THwvFUzBooDOUo9miDU4498GNuW/ye6g1kSF0h2SueJrAMhgELMBhEGMr5gibrpfW4jlYOSfjT/3l8ld4axxN5/p+sA8OAZTAMWIChnKcIyLqdLeiP0wUDAADDAQznUgBkJQQOSrEIhgELkCl4jpWsW25K8Z03IpKATMY0YAn8lrIAmUxguiUiqgdBzg9PS2AYsBA5jw4QEdWZwCMDFsFvKAuRK/lQExHVFbOAZfAbykLYb4CIqO54msAy+A1lIXIFX9BERHXFDoSWwTBgIUqVXOoSiIhsjkrDQW+WwDBgIUoHvqCJiOpKo1NKXYJdYBiwEJUDjwwQEdWVg04ldQl2gWHAQnioi4io7hx4ZMAiGAYsRKXmkQEiorpy0DIMWALDgIUoeWSAiKjO2GfAMhgGLETtyDBARFRXPE1gGQwDFqJzVUtdAhGRzeFpAstgGLAQJ3euCUtEVFc8TWAZDAMWomMYICKqM54msAyGAQtx8mAYICKqKw3nGbAIhgELUarkPPdFRFQHgsDO15bCMGBBOnd2IiQiqi21VgmBCxVZBMOABbETIRFR7bl4aaQuwW4wDFgQwwARUe25+ThKXYLdYBiwII4oICKqPVdfhgFLYRiwIB4ZICKqPTdfrdQl2A2GAQtiB0Iiotpz45EBi2EYsCAeGSAiqh2ZXGAHQgtiGLAgR2cVFFzKmIjojly8NJDJ+RVlKXykLUgQBHgG6KQug4jI6rlyJIFFMQxYmFeQk9QlEBFZPXYetCyGAQvzasowQER0J+w8aFkMAxbmzSMDRER3xDkGLIthwMLc/LRQKPmwExHdDk8TWBa/lSxMJhPg0YSdCImIaqJ1UUGt4WqFlsQwIAGvQJ4qICKqiW+oi9Ql2B2GAQkwDBAR1cyvuavUJdgdhgEJMAwQEdXMv4Wr1CXYHYYBCbj7ayFX8KEnIrqV0kHOflUS4DeSBORyGTwC2FOWiOhWvqEukMkEqcuwOwwDEuGpAiKiqvyasfOgFBgGJMIXPBFRVf7sPCgJhgGJNG3jAfBIGBGRkUwhwCfEWeoy7BLDgEQcnVXwZCcZIiIjr6ZOUKi4zLsUGAYkFNjGQ+oSiIisBk8RSIdhQEKBbdylLoGIyGr4NWdfKqkwDEjIt7kLlA48JEZEBIEzD0qJYUBCcrkMAS3dpC6DiEhynk10cNAqpS7DbjEMSCyoLU8VEBEFd/CUugS7xjAgscC27ERIRBTCMCAphgGJOXtq4OKtkboMIiLJaF3V8A7i/AJSYhiwAhxiSET2jEcFpMcwYAUC2W+AiOxYcEeGAakxDFiBgJZukCv5VBCR/VE6yNGkFUdVSY3fQFZAqZYjqB1PFRCR/Qnp4Am5gl9FUuMzYCVadPGRugQiIotr3tlb6hIIDANWI7iDB1ScjZCI7IjKQc4O1FaCYcBKKJRyhIZ7SV0GEZHFBHf0ZH8pK8FnwYq06MpTBURkP5p35meetWAYsCJNWrtD46ySugwiIrNTOyq4cqsVYRiwIjKZgJY8OkBEdqBVN1+OIrAifCasTFikn9QlEBGZXdt7AqQugf6FYcDKeATo4B3kJHUZRERm49/CFe5+WqnLoH9hGLBCYd15dICIGq92PCpgdRgGrFCLu3w43IaIGiWNkxKhnTiM2trwG8cKqR2VCOXCHUTUCIV192fHQSvEZ8RKtY9qInUJREQmJQhA217+UpdB1WAYsFJ+zV3hG+osdRlERCYT2NYDzp4aqcugajAMWLFO9wZJXQIRkclwOKH1YhiwYiEdPeHizRRNRLZP565GMJdqt1oMA1ZMkAkI7x8odRlERA3Wtqc/BJkgdRlUA4YBK9c60hcaJ6XUZRAR1ZtMISCsBzsOWjOGASunUMo5soCIbFpYpB+0Lmqpy6DbYBiwAe17N4GCkxARkQ2SKQR0vi9Y6jLoDvgNYwMcdEq05hTFRGSDwrr7w8ndQeoy6A4YBmxEeP+mENj3hohsiEwhoPMgDpG2BQwDNsLFyxGh4ZzPm4hsRxseFbAZDAM2pNMAJmwisg1yhQyd7+Nnlq1gGLAhPiHOCO7ABYyIyPq16eEHnRuPCtgKhgEb0/2BZpBx4g4ismJyhQwRg4KlLoPqgGHAxrj5atGGq34RkRVr09MfOjfOK2BLGAZsUNehIVA5yKUug4ioCrlCxhEENohhwAZpnFToNJBvNiKyPm16+UPryqMCtoZhwEaF92vKw3BEZFWUajmPCtgohgEbpVDJ0e3+UKnLICIyumtoCNcgsFEMAzasVTdfeDbVSV0GERHc/bXo2JeLqtkqhgEbJsgEdH+wudRlEBHhnodbQibnV4qt4jNn45q2dkdgWw+pyyAiO9ayqw8CWrpJXQY1AMNAI9D9wWYQOBEREUlA5SDnEcpGgGGgEfDw16F97wCpyyAiO9R1WCg7DTYCDAONxN0jmsHZk/OAE5HleATo0L4POw02BgwDjYRSLUefR1sDPFtARJYgAPeMbcm1UhoJhoFGpElrd7TtyXULiMj8WnfzhX9zV6nLIBNhGGhkuj/YHDp3nr8jIvNROyoQ+QA7DTYmDAONjMpBcfN0ARGRmdw9PBSOziqpyyATYhhohALbeKB1dz+pyyCiRiiwjTva3sPRS40Nw0Aj1XNUc2hdmNyJyHTUWgX6jg+DILDTYGPDMNBIqR2ViHqEpwuIyHSixrXm8sSNFMNAIxbcwRMtu/lIXQYRNQItu/qgeWdvqcsgM2EYaOR6jW4JDTv6EFED6NzUuGdsK6nLIDNiGGjkHLRK9Hs8jJMREVG9CDIB905qC7VGIXUpZEYMA3YgqK0HOg8MkroMIrJBdw0Jhn8LV6nLIDNjGLATXe8P5RuaiOokoKUrutwXLHUZZAEMA3ZCJhMw4Mm20DgppS6FiGyAg06Jeye15fLodoJhwI5oXdS494m24BBhIrotAej3eBiHEdoRhgE707S1O7oOC5W6DCKyYp0HBSG4vafUZZAFMQzYoc73BSE03EvqMojICjWL8EK3+/kHg71hGLBDgiCg34QwuPk6Sl0KEVkRr0An9JvQhtMN2yGGATulclBg8NQOUHHsMBEB0LqqMeSZDlCq5FKXQhJgGLCQqKgovPjii1KXUYmrjyPundiGExIR2TmFSobBU9uzw6AdYxgwsV27dkEQBGRnZ0tdSq0Ed/DE3cN5fpDIbglA/wlt4B3kLHUlJCGGARtWVlZmknY6DwpGhz5NTNIWEdmWbsNC0SyCCxDZO4aBeigpKcG0adPg7e0NBwcH9OzZE0eOHEFcXBz69OkDAHBzc4MgCJgwYYLxdgaDATNmzIC7uzt8fX0xd+7cSu3m5ORg8uTJ8Pb2hrOzM/r27YtTp04Zr587dy7Cw8OxfPlyhIaGQq1WQxRFk9ynnqNboMVdXOGQyJ607OaDLoODpS6DrADDQD3MmDEDa9aswcqVK3H8+HE0b94cAwcOhJOTE9asWQMAuHTpEpKTk/Hxxx8bb7dy5UpotVocOnQIixcvxvz58/HXX38BAERRxJAhQ5CSkoJNmzbh2LFjiIiIQL9+/ZCVlWVsIzo6Gr/88gvWrFmDkydPmuw+VYwwaNrG3WRtEpH18g11Qd9Hw6Qug6yEIJrqT0s7UVBQADc3N3zzzTcYN24cgJuH64ODg/Hiiy/irrvuQp8+fXDjxg24uroabxcVFQW9Xo89e/YYL+vatSv69u2L9957Dzt27MDIkSORlpYGtfp/nXiaN2+OGTNmYPLkyZg7dy7effddJCYmwsvLPPMElBaX4/cPTyAtPs8s7ROR9JzcHTBqVhc4cnlz+gePDNRRTEwMysrK0KNHD+NlSqUSXbt2xYULF2572w4dOlT63c/PD2lpaQCAY8eOIT8/Hx4eHtDpdMaf2NhYxMTEGG8TFBRktiAA3BxyOPT5jnD14RwERI2RxlmFYdM6MghQJRxkXkcVB1JunZRDFMU7TtShVFZeJEgQBBgMBgA3+xP4+flh165dVW737yMMWq22HlXXjUZ388Ni7eJjKMgpNfv+iMgyNE5KDH8xHG6+5v8cIdvCIwN11Lx5c6hUKuzdu9d4WVlZGY4ePYqwsDCoVDfTtl6vr1O7ERERSElJgUKhQPPmzSv9eHpafo5wZw8Nhk0Lh9qReZGoMXDQKnH/C53g4a+TuhSyQgwDdaTVajF16lS8+uqr2LJlC86fP4+nnnoKhYWFeOKJJxAUFARBELBx40akp6cjPz+/Vu32798fkZGRGDFiBLZu3Yq4uDjs378fs2fPxtGjR818r6rnEaDD4Gc6QK7ky4TIlqkdFbj/hXB4NmEQoOrxU74e3nvvPTz44IN47LHHEBERgejoaGzduhVubm4ICAjAvHnzMGvWLPj4+OC5556rVZuCIGDTpk245557MGnSJLRs2RIPP/ww4uLi4OMj3ZA//+auGPgk1zQnslUqzc0g4BXoJHUpZMU4moBqJfpYGv5acQ6Gcr5ciGyF0kGO+6eFwzfURepSyMoxDFCtxZ/LxJYvzqC8zCB1KUR0B0q1HMOe7wi/5q5Sl0I2gGGA6iTpSjb++PQUSovr1kGSiCxHoZJh2PMd4d/CTepSyEYwDFCdpcXnYsN/TqE43zRrIxCR6SiUMgx5tgOatOZsolR7DANUL1nJBVj/0QnOQ0BkRZQOctw3pT2ahjEIUN0wDFC95WYU4fePTiA3o1jqUojsntZFhaHPd4RnE44aoLpjGKAGKcguwe8fn8SN5AKpSyGyW+7+Wgx9riOc3B2kLoVsFMMANVhxfhk2/OckFzcikkCT1m4YNKU91BrOFkr1xzBAJlFaVI4/PjuNpCvZUpdCZDda3+2LqMdaQy7n/HHUMAwDZDL6MgN2/3QJF/YnS10KUaPXZUgwug0LlboMaiQYBsjkzuy6jr2/XoFBz5cWkanJ5AKiHmmFsO7+UpdCjQjDAJlF0pUb2PLlWRTlcS4CIlNROcgxaHJ7NG3DoYNkWgwDZDZ5WcXY/MUZpCewYyFRQ2ld1Rj6XEeuPEhmwTBAZlVeqsfOHy7i8qFUqUshslkBLV0x4Ml2cHRWSV0KNVIMA2QRJ7clYP/aGIgGvtyIak0AIgYGodv9oZBxGXEyI4YBsphrF7Pw57JzKC5gPwKiO1E7KtB/YhsEt/eUuhSyAwwDZFG5GUXY9PkZZCbmS10KkdXyDnLCwKfawdlTI3UpZCcYBsjiykr12L86Gmf/TpS6FCLrIgAd+zVF5IhmkCs4kRBZDsMASSbuTAZ2fHuBww+JAGicVej/eBgC23pIXQrZIYYBklRhbil2fncBcWcypS6FSDKBbdzRb0IbjhYgyTAMkFU4+3ci9q2+gvJSg9SlEFmMXCFDt+GhCO/fFILA0QIkHYYBsho56YXY8e1FLnZEdiGgpSuiHmkNVx9HqUshYhgg6yKKIs7suo4D666ivEQvdTlEJqfWKtDjweZcW4CsCsMAWaWc9CLs+PYCjxJQo9Kyqw96PtQCGif2DSDrwjBAVksURZz7OxEH119FSUG51OUQ1ZuzpwN6j2uFwDYcKUDWiWGArF5xQRkOb4jFub8TYeB0xmRDZDIBHfs3RdehIVCo5FKXQ1QjhgGyGZlJ+dj7yxVcv3hD6lKI7sg72Bl9Hm0FzyZOUpdCdEcMA2RzYk+lY9/qaOSkF0ldClEVakcFug4LQfveTSBwcSGyEQwDZJP05Qac2nENxzbFobSYow5IegqVDB36NkXEgECoHZVSl0NUJwwDZNMKc0tx8PcYXNyfDL6SSQoyuYC2Pf3ReXAwtC5qqcshqheGAWoU0hPysOeXy0iOzpG6FLITggC07OqLrsNCuLog2TyGAWpUrp3PwrEtcUi8nC11KdSIhXT0RLf7Q+ERoJO6FCKTYBigRik5JgfHtsQhngsgkQkFtHLD3SNC4RviInUpRCbFMECNWsb1PBzbEo+YY2nsU0D15hPijG73h6JpmLvUpRCZBcMA2YXs1EIc/zMelw6lwFDOlzzdmUwmoFmEFzr0bQrfUB4JoMaNYYDsSv6NYpz4MwHn9yVxuWSqloNOibY9/dGudxPo3Dg6gOwDwwDZpaK8UpzeeR0X9iWhIKdU6nLICng00aFDnyZo2dUHCiWnDib7wjBAds1gEJFwLhMX9iUj7kwGDHq+HeyJIBMQ0sETHfo2QUBLN6nLIZIMwwDRPwpzS3HpUAou7EvCjZRCqcshM1I7KhDWwx/tewdwjgAiMAwQVSvlag4u7EvClWNpKON0x42CTCEgqK0HWnXzRVB7D54KIPoXhgGi2ygr0SP6WCou7E/m7Ia2SAD8m7uiZVcfNIvwhoOWawYQVYdhgKiWslMLEX08DbEn05GWkAfwnWOdBMAv1AXNIrwR2skLTu4OUldEZPUYBojqIf9GCWJPpSP2VDoSL2ez46HEBAHwa+6KZhHeaNbJC1pXDgkkqguGAaIGKikqx7XzWYg/l4mEc5ko5FBFi3DycECTVm4IaOWGpmHucHRWSV0Skc1iGCAyIVEUkXE9HwnnMhF/NhOpV3NhMPAtZgqOLioEtHRDk9ZuaNLKjaMAiEyIYYDIjMpK9UiPz0NqbC5S43KQGpuL/BslUpdlE9Raxc0v/3/++nf300pdElGjxTBAZGEF2SVIjc1FSuzNcJCWkIfyEvsevqhQyuDur4V7gA6eATr4t3CFZxMdBJkgdWlEdoFhgEhiBoOIrKQCpP4TDjKTCpCbUYTi/DKpSzM5QQCcPTXwCNDBI0D7z786uHhp+MVPJCGGASIrVVpUjpz0IuSkFyE3owg5GUXI/ef3/BslEK21L4IAaHRKOLqooXVRwdXH8eaXvr8O7gFaKFWc7IfI2jAMENkgvd6AvMxiYzgoyi9DaVH5zZ/im/+WFOkr/d6gVRoFQKGQwUGnhKOzyvhFX+VfZzUcnZWQyWWmu7NEZHYMA0R2wqA3oLRIj9LicpQUlRuPLAiCAEF281/g5uI9CqUMcqUMCqUMCqUcciW/3IkaM4YBIiIiO8e4T0REZOcYBoiIiOwcwwAREZGdYxggIiKycwwDREREdo5hgIiIyM4xDBAREdk5hgEiIiI7xzBARERk5xgGiIiI7BzDABERkZ1jGCAiIrJzDANERER2jmGAiIjIzjEMEBER2TmGASIiIjvHMEBERGTnGAaIiIjsHMMAERGRnWMYICIisnMMA0RERHaOYYCIiMjOMQwQERHZOYYBIiIiO8cwQEREZOf+H4ruBWZjlq3fAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Generate Pie chart for train dataset\n",
        "df = train_df.groupby([0])[0].count()\n",
        "plt.pie(df,labels=df.index)\n",
        "plt.axis('equal')\n",
        "plt.title('Pie chart visualization of the training dataset labels')\n",
        "plt.show()\n",
        "\n",
        "# Generate Pie chart for test dataset\n",
        "df = test_df.groupby([0])[0].count()\n",
        "plt.pie(df,labels=df.index)\n",
        "plt.axis('equal')\n",
        "plt.title('Pie chart visualization of the test dataset labels')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9iPdHw7MRtRf"
      },
      "source": [
        "Functions for key-path creation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NbMQHCItsTNa",
        "outputId": "887a7269-1927-4c14-f533-d6e5f1c1f62d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2023-05-08 23:21:44 INFO: Loading these models for language: en (English):\n",
            "========================\n",
            "| Processor | Package  |\n",
            "------------------------\n",
            "| tokenize  | combined |\n",
            "| pos       | combined |\n",
            "| lemma     | combined |\n",
            "| depparse  | combined |\n",
            "========================\n",
            "\n",
            "2023-05-08 23:21:44 INFO: Using device: cuda\n",
            "2023-05-08 23:21:44 INFO: Loading: tokenize\n",
            "2023-05-08 23:21:46 INFO: Loading: pos\n",
            "2023-05-08 23:21:46 INFO: Loading: lemma\n",
            "2023-05-08 23:21:47 INFO: Loading: depparse\n",
            "2023-05-08 23:21:47 INFO: Done loading processors!\n"
          ]
        }
      ],
      "source": [
        "nlp = stanza.Pipeline('en', processors='tokenize,pos,lemma,depparse', download_method=None)\n",
        "\n",
        "def get_path_to_root(sentence, idx):\n",
        "    path = [idx]\n",
        "    while sentence.words[idx - 1].head != 0:\n",
        "        idx = sentence.words[idx - 1].head\n",
        "        path.append(idx)\n",
        "    return path\n",
        "\n",
        "\n",
        "def find_lca(sentence, drug1_idx, drug2_idx):\n",
        "    path1 = get_path_to_root(sentence, drug1_idx)\n",
        "    path2 = get_path_to_root(sentence, drug2_idx)\n",
        "    for node1 in path1:\n",
        "        if node1 in path2:\n",
        "            return node1\n",
        "\n",
        "\n",
        "def extract_key_path(sentence, drug1_idx, drug2_idx):\n",
        "    lca = find_lca(sentence, drug1_idx, drug2_idx)\n",
        "    key_path = []\n",
        "\n",
        "    def add_path_to_lca(idx, lca, key_path):\n",
        "        while idx != lca:\n",
        "            key_path.append(idx)\n",
        "            idx = sentence.words[idx - 1].head\n",
        "        return key_path\n",
        "\n",
        "    key_path = add_path_to_lca(drug1_idx, lca, key_path)\n",
        "    key_path = add_path_to_lca(drug2_idx, lca, key_path)\n",
        "\n",
        "    key_path.append(lca)\n",
        "    key_path = list(set(key_path))\n",
        "\n",
        "    return key_path\n",
        "\n",
        "\n",
        "def key_path_feature_vector(key_path: List[int], sentence):\n",
        "    # For simplicity, we use the count of each POS tag in the key path as features\n",
        "    pos_counts = {}\n",
        "    for idx in key_path:\n",
        "        pos = sentence.words[idx - 1].upos\n",
        "        if pos not in pos_counts:\n",
        "            pos_counts[pos] = 0\n",
        "        pos_counts[pos] += 1\n",
        "\n",
        "    all_pos_tags = [\"ADJ\", \"ADP\", \"ADV\", \"AUX\", \"CCONJ\", \"DET\", \"INTJ\", \"NOUN\", \"NUM\", \"PART\", \"PRON\", \"PROPN\", \"PUNCT\", \"SCONJ\", \"SYM\", \"VERB\", \"X\"]\n",
        "    feature_vector = [pos_counts.get(tag, 0) for tag in all_pos_tags]\n",
        "\n",
        "    return torch.tensor(feature_vector, dtype=torch.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1M-0EBKtwdQF"
      },
      "source": [
        "Create a CustomDataset for DDI data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Je7ayVRKsTNb"
      },
      "outputs": [],
      "source": [
        "ddi_labels = ['other', 'mechanism', 'effect', 'advise', 'int']\n",
        "label_mapping = {label: idx for idx, label in enumerate(ddi_labels)}\n",
        "\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, data, tokenizer, max_len, drug2Id, drug_embedding, cache_file=None):\n",
        "        self.data = data\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_len = max_len\n",
        "        self.drug2Id = drug2Id\n",
        "        self.drug_embedding = drug_embedding\n",
        "\n",
        "        # load data from cache if possible\n",
        "        if cache_file is not None and os.path.exists(cache_file):\n",
        "            print(f\"Reading from cache file at {cache_file}\")\n",
        "            with open(cache_file, \"rb\") as f:\n",
        "                self.preloaded_data = pickle.load(f)\n",
        "        else:\n",
        "            self.preloaded_data = []\n",
        "            for index in tqdm(range(len(data)), desc=\"Loading data\", leave=True):\n",
        "\n",
        "                text = self.data.iloc[index][1].replace('<e1>', '').replace('</e1>', '').replace('<e2>', '').replace('</e2>', '')\n",
        "\n",
        "                label_str = self.data.iloc[index][0]\n",
        "                label = label_mapping[label_str]\n",
        "                drug1 = self.data.iloc[index][2]\n",
        "                drug1Id = self.drug2Id[drug1]\n",
        "                drug1Embedding = self.drug_embedding[drug1Id]\n",
        "                drug2 = self.data.iloc[index][3]\n",
        "                drug2Id = self.drug2Id[drug2]\n",
        "                drug2Embedding = self.drug_embedding[drug2Id]\n",
        "\n",
        "                encoding = self.tokenizer.encode_plus(\n",
        "                    text,\n",
        "                    add_special_tokens=True,\n",
        "                    max_length=self.max_len,\n",
        "                    padding='max_length',\n",
        "                    truncation=True,\n",
        "                    return_token_type_ids=True,\n",
        "                    return_tensors='pt'\n",
        "                )\n",
        "\n",
        "                ### DRUG POSITIONS ###\n",
        "                # Tokenize drug names\n",
        "                drug1_tokens = self.tokenizer.tokenize(\"DRUG1\")\n",
        "                drug2_tokens = self.tokenizer.tokenize(\"DRUG2\")\n",
        "                # Find positions of drug entities\n",
        "                input_tokens = self.tokenizer.convert_ids_to_tokens(encoding[\"input_ids\"].squeeze())\n",
        "                drug1_position = self.find_entity_position(input_tokens, drug1_tokens)\n",
        "                drug2_position = self.find_entity_position(input_tokens, drug2_tokens)\n",
        "                if drug1_position == -1 or drug2_position == -1:\n",
        "                    print(f\"Error at index {index} in the data set. Reported by tokenizer. Skipping.\")\n",
        "                    print(drug1_position, drug2_position)\n",
        "                    print(text)\n",
        "                    continue\n",
        "\n",
        "                ### KEY-PATH WITH STANZA ###\n",
        "                doc = nlp(text)\n",
        "                key_path_feature = None\n",
        "                for sentence in doc.sentences:\n",
        "                    drug1_position_stanza = self.find_entity_position_stanza(sentence, \"DRUG1\")\n",
        "                    drug2_position_stanza = self.find_entity_position_stanza(sentence, \"DRUG2\")\n",
        "                    # Check if both DRUG1 and DRUG2 are found in the current sentence\n",
        "                    if drug1_position_stanza != -1 and drug2_position_stanza != -1:\n",
        "                        # Get key path\n",
        "                        key_path = extract_key_path(sentence, drug1_position_stanza, drug2_position_stanza)\n",
        "                        key_path_feature = key_path_feature_vector(key_path, sentence)\n",
        "                        # Stop searching, as both DRUG1 and DRUG2 have been found\n",
        "                        break\n",
        "                if key_path_feature is None:\n",
        "                    key_path_feature = torch.zeros(17, dtype=torch.float32)\n",
        "\n",
        "                text = text.replace(\"DRUG1\", drug1)\n",
        "                text = text.replace(\"DRUG2\", drug2)\n",
        "\n",
        "                self.preloaded_data.append({\n",
        "                    \"text\": text,\n",
        "                    \"input_ids\": encoding[\"input_ids\"],\n",
        "                    \"attention_mask\": encoding[\"attention_mask\"],\n",
        "                    \"token_type_ids\": encoding[\"token_type_ids\"],\n",
        "                    \"label\": torch.tensor(label, dtype=torch.long),\n",
        "                    \"drug1Emb\": torch.from_numpy(drug1Embedding),\n",
        "                    \"drug2Emb\": torch.from_numpy(drug2Embedding),\n",
        "                    \"drug1_position\": torch.tensor(drug1_position, dtype=torch.int),\n",
        "                    \"drug2_position\": torch.tensor(drug2_position, dtype=torch.int),\n",
        "                    \"key_path_feature\": key_path_feature\n",
        "                })\n",
        "            if cache_file is not None:\n",
        "                print(f\"Writing to cache file at {cache_file}\")\n",
        "                with open(cache_file, \"wb\") as f:\n",
        "                    pickle.dump(self.preloaded_data, f)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.preloaded_data)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return self.preloaded_data[index]\n",
        "\n",
        "    def find_entity_position(self, input_tokens, entity_tokens):\n",
        "        for i in range(len(input_tokens) - len(entity_tokens) + 1):\n",
        "            if input_tokens[i:i + len(entity_tokens)] == entity_tokens:\n",
        "                return i\n",
        "        return -1\n",
        "\n",
        "    def find_entity_position_stanza(self, sentence, entity):\n",
        "        for i, word in enumerate(sentence.words):\n",
        "            if word.text == entity:\n",
        "                return i + 1\n",
        "        return -1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tv-_X0Z-xr89"
      },
      "source": [
        "Create Training and Test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fLmZLIkTxoYI",
        "outputId": "d8ef4aaa-98c0-4cd6-9af7-f3baa0502ff4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading training dataset:\n",
            "Reading from cache file at train.cache\n",
            "Loading test dataset:\n",
            "Reading from cache file at test.cache\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext\")\n",
        "tokenizer.add_special_tokens({\"additional_special_tokens\": [\"<e1>\", \"</e1>\", \"<e2>\", \"</e2>\"]})\n",
        "\n",
        "max_len = 390\n",
        "\n",
        "print(\"Loading training dataset:\")\n",
        "train_dataset = CustomDataset(train_df, tokenizer, max_len, entities_dict, drug_embedding, cache_file=\"train.cache\")\n",
        "print(\"Loading test dataset:\")\n",
        "test_dataset = CustomDataset(test_df, tokenizer, max_len, entities_dict, drug_embedding, cache_file=\"test.cache\")\n",
        "\n",
        "train_size = 32\n",
        "test_size = 16\n",
        "\n",
        "train_params = {'batch_size': train_size,\n",
        "                'shuffle': True,\n",
        "                'num_workers': 0\n",
        "                }\n",
        "\n",
        "test_params = {'batch_size': test_size,\n",
        "               'shuffle': True,\n",
        "               'num_workers': 0\n",
        "               }\n",
        "\n",
        "training_loader = DataLoader(train_dataset, **train_params)\n",
        "testing_loader = DataLoader(test_dataset, **test_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3NP_kg6yyYH"
      },
      "source": [
        "\n",
        "# Create DDI classifier model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GLMIAM8OEeGv",
        "outputId": "153d303d-2562-4a37-d615-273c0e0fd960"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "C:\\Users\\alyosharomanov\\anaconda3\\envs\\DDIE-KGE-MFL\\lib\\site-packages\\torch\\nn\\modules\\rnn.py:71: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "BertDDIClassifier(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30526, 768)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.3, inplace=False)\n",
              "  (linear): Linear(in_features=2000, out_features=200, bias=True)\n",
              "  (classifier): Linear(in_features=768, out_features=5, bias=True)\n",
              "  (key_path_lstm): LSTM(17, 768, batch_first=True, dropout=0.5, bidirectional=True)\n",
              "  (key_path_linear): Linear(in_features=1536, out_features=768, bias=True)\n",
              "  (MLP1): FCLayer(\n",
              "    (dropout): Dropout(p=0.3, inplace=False)\n",
              "    (linear): Linear(in_features=1936, out_features=768, bias=True)\n",
              "    (activation): ReLU()\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class FCLayer(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim, dropout_rate=0.0, use_activation=True):\n",
        "        super(FCLayer, self).__init__()\n",
        "        self.use_activation = use_activation\n",
        "        self.dropout = nn.Dropout(dropout_rate)\n",
        "        self.linear = nn.Linear(input_dim, output_dim)\n",
        "        self.activation = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.dropout(x)\n",
        "        if self.use_activation:\n",
        "            x = self.activation(x)\n",
        "        return self.linear(x)\n",
        "\n",
        "\n",
        "class BertDDIClassifier(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(BertDDIClassifier, self).__init__()\n",
        "        self.bert = BertModel.from_pretrained(\"microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext\")\n",
        "        for param in self.bert.parameters():\n",
        "            param.requires_grad = True\n",
        "        self.bert.resize_token_embeddings(len(tokenizer))\n",
        "        self.dropout = torch.nn.Dropout(0.3)\n",
        "        self.linear = nn.Linear(2000, 200)\n",
        "        self.classifier = torch.nn.Linear(768, 5)\n",
        "        self.bert_layer_weights = nn.Parameter(torch.rand(13, 1))\n",
        "        self.key_path_lstm = nn.LSTM(input_size=17, hidden_size=768, num_layers=1, batch_first=True, dropout=0.5, bidirectional=True)\n",
        "        self.key_path_linear = nn.Linear(768 * 2, 768)\n",
        "        self.MLP1 = FCLayer((768 + 200) * 2, 768, 0.3)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask, token_type_ids, drug1Embs, drug2Embs, drug1_position, drug2_position, key_path_feature, return_latent_space=False):\n",
        "        output = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, output_hidden_states=True)\n",
        "\n",
        "        # position feature\n",
        "        position_feature = torch.abs(drug1_position - drug2_position).unsqueeze(1).to(device)\n",
        "        position_feature = position_feature / input_ids.shape[1]\n",
        "\n",
        "        # lanaguage feature\n",
        "        hidden_states = torch.stack(output[2])  # [13,Batch, 200, 768]\n",
        "        hidden_states = hidden_states[:, :, :1, :]  # [13,Batch,1,768] # Select embedding for CLS token (1st token)\n",
        "        weighted_bert_layers = hidden_states * self.bert_layer_weights.view(13, 1, 1, 1)  # [13, Batch ,1 , 768]\n",
        "        summed = torch.sum(weighted_bert_layers, (2, 3))  # [13,Batch]\n",
        "        summed = torch.permute(summed, [1, 0])  # [Batch,13]\n",
        "        bert_layer_attn = torch.nn.functional.softmax(summed, dim=1)  # [Batch, 13]\n",
        "        hidden_states = torch.permute(hidden_states, [1, 0, 2, 3])  # [Batch, 13,1,768]\n",
        "        language_feature = hidden_states * bert_layer_attn.view(bert_layer_attn.shape[0], bert_layer_attn.shape[1], 1, 1)\n",
        "        language_feature = torch.sum(language_feature, (2, 1))  # [Batch,768]\n",
        "\n",
        "        # key-path feature\n",
        "        key_path_feature = key_path_feature.unsqueeze(1)\n",
        "        lstm_output, _ = self.key_path_lstm(key_path_feature)\n",
        "        key_path_feature = lstm_output.squeeze(1)\n",
        "        key_path_feature = self.key_path_linear(key_path_feature)\n",
        "\n",
        "        # synthetical feature\n",
        "        pooled_output = (position_feature + language_feature + key_path_feature) / 3\n",
        "\n",
        "        # knowledege feature\n",
        "        drug1 = self.linear(drug1Embs)\n",
        "        drug2 = self.linear(drug2Embs)\n",
        "\n",
        "        # final\n",
        "        pooled_output = torch.cat([output.pooler_output, pooled_output, drug1, drug2], dim=1)\n",
        "\n",
        "        if return_latent_space:\n",
        "            return pooled_output\n",
        "\n",
        "        pooled_output = self.MLP1(pooled_output)\n",
        "        logits = self.classifier(self.dropout(pooled_output))\n",
        "        return logits\n",
        "\n",
        "\n",
        "model = BertDDIClassifier()\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNpzrJ9SzB3S"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfeXEIXySRrn"
      },
      "source": [
        "Initialize loss function with weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TIPeSQ8asTNd"
      },
      "outputs": [],
      "source": [
        "lossfn = MultiFocalLoss(len(ddi_labels), [0.8, 0.07, 0.08, 0.04, 0.01])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ns_qtqCWSWHg"
      },
      "source": [
        "Set number of epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fYeZ5q_wsTNe"
      },
      "outputs": [],
      "source": [
        "epochs = 6 if device == \"cuda\" else 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwE3COKnSYhv"
      },
      "source": [
        "Initialize schedule with warmup."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rtVgM1CIsTNe",
        "outputId": "be40a0a9-7a88-4467-a6c6-910fa6d62091"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total training steps (for scheduler): 4260\n"
          ]
        }
      ],
      "source": [
        "from transformers import get_linear_schedule_with_warmup\n",
        "\n",
        "optimizer = torch.optim.Adam(params=model.parameters(), lr=2e-5, eps=1e-8)\n",
        "t_total = len(training_loader) * epochs\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=t_total)\n",
        "\n",
        "print(f\"Total training steps (for scheduler): {t_total}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGL3wPt3Scuc"
      },
      "source": [
        "DDI compute metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F5-NEACAsTNe"
      },
      "outputs": [],
      "source": [
        "def ddi_compute_metrics(preds, labels):\n",
        "    preds = preds.cpu().numpy()\n",
        "    labels = labels.cpu().numpy()\n",
        "\n",
        "    p, r, f, s = precision_recall_fscore_support(y_pred=preds, y_true=labels, labels=[1, 2, 3, 4], average='micro')\n",
        "    result = {\n",
        "        \"Precision\": p,\n",
        "        \"Recall\": r,\n",
        "        \"microF\": f\n",
        "    }\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM5dvKfBSph9"
      },
      "source": [
        "Training loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fLxxKDz9v0FF"
      },
      "outputs": [],
      "source": [
        "def train(epoch):\n",
        "    model.train()\n",
        "    losses = []\n",
        "\n",
        "    for _, data in enumerate(tqdm(training_loader, desc=f\"Epoch {epoch}/{epochs}\", leave=True), 0):\n",
        "        ids = data['input_ids'].to(device, dtype=torch.long).squeeze()\n",
        "        mask = data['attention_mask'].to(device, dtype=torch.long).squeeze()\n",
        "        token_type_ids = data['token_type_ids'].to(device, dtype=torch.long).squeeze()\n",
        "        labels = data['label'].to(device, dtype=torch.long)\n",
        "        drug1Embs = data['drug1Emb'].to(device, dtype=torch.float32)\n",
        "        drug2Embs = data['drug2Emb'].to(device, dtype=torch.float32)\n",
        "        drug1_position = data['drug1_position'].to(device, dtype=torch.int)\n",
        "        drug2_position = data['drug2_position'].to(device, dtype=torch.int)\n",
        "        key_path_feature = data['key_path_feature'].to(device, dtype=torch.float32)\n",
        "        output = model(ids, mask, token_type_ids, drug1Embs, drug2Embs, drug1_position, drug2_position, key_path_feature)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = lossfn(output, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        losses.append(loss.item())\n",
        "\n",
        "    return sum(losses) / len(losses)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NOXrFoqDSrup"
      },
      "source": [
        "Evaluate loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CBl1Br-2tj04"
      },
      "outputs": [],
      "source": [
        "def evaluate(epoch):\n",
        "    model.eval()\n",
        "    outputs = []\n",
        "    labels = []\n",
        "    val_losses = []\n",
        "    latent_space = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for _, data in enumerate(tqdm(testing_loader, desc=f\"Evaluating {epoch}/{epochs}\", leave=True), 0):\n",
        "            ids = data['input_ids'].to(device, dtype=torch.long).squeeze()\n",
        "            mask = data['attention_mask'].to(device, dtype=torch.long).squeeze()\n",
        "            token_type_ids = data['token_type_ids'].to(device, dtype=torch.long).squeeze()\n",
        "            label = data['label'].to(device, dtype=torch.long)\n",
        "            drug1Embs = data['drug1Emb'].to(device, dtype=torch.float32)\n",
        "            drug2Embs = data['drug2Emb'].to(device, dtype=torch.float32)\n",
        "            drug1_position = data['drug1_position'].to(device, dtype=torch.int)\n",
        "            drug2_position = data['drug2_position'].to(device, dtype=torch.int)\n",
        "            key_path_feature = data['key_path_feature'].to(device, dtype=torch.float32)\n",
        "            output = model(ids, mask, token_type_ids, drug1Embs, drug2Embs, drug1_position, drug2_position, key_path_feature)\n",
        "\n",
        "            latent_vectors = model(ids, mask, token_type_ids, drug1Embs, drug2Embs, drug1_position, drug2_position, key_path_feature, return_latent_space=True)\n",
        "            latent_space.append(latent_vectors.cpu().numpy())\n",
        "\n",
        "            val_loss = lossfn(output, label)\n",
        "            val_losses.append(val_loss.item())\n",
        "            outputs.append(output)\n",
        "            labels.append(label)\n",
        "        latent_space = np.vstack(latent_space)\n",
        "\n",
        "    return torch.cat(outputs, 0), torch.cat(labels, 0), sum(val_losses) / len(val_losses), latent_space"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10GDXDXWS9lz"
      },
      "source": [
        "Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NuaJP-uIMKnE",
        "scrolled": false,
        "outputId": "5395341f-c6a6-4429-df8d-a7b0630719a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training...\n",
            "Number of Epochs: 6\n",
            "Train samples: 22710\n",
            "Train batch size: 32\n",
            "Eval samples: 4615\n",
            "Eval batch size: 16\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 1/6: 100%|█████████████████████████████████████████████████████████████████████| 710/710 [05:24<00:00,  2.19it/s]\n",
            "Evaluating 1/6: 100%|████████████████████████████████████████████████████████████████| 289/289 [00:47<00:00,  6.07it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Epoch 1 = {'Precision': 0.7748478701825557, 'Recall': 0.7876288659793814, 'microF': 0.7811860940695297}\n",
            "Train Loss: 0.08576441309021288\n",
            "Eval Loss: 0.07001840669555416\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 2/6: 100%|█████████████████████████████████████████████████████████████████████| 710/710 [05:15<00:00,  2.25it/s]\n",
            "Evaluating 2/6: 100%|████████████████████████████████████████████████████████████████| 289/289 [00:43<00:00,  6.58it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Epoch 2 = {'Precision': 0.775811209439528, 'Recall': 0.8134020618556701, 'microF': 0.7941620533467538}\n",
            "Train Loss: 0.028096414538933304\n",
            "Eval Loss: 0.06688281831489255\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 3/6: 100%|█████████████████████████████████████████████████████████████████████| 710/710 [05:02<00:00,  2.35it/s]\n",
            "Evaluating 3/6: 100%|████████████████████████████████████████████████████████████████| 289/289 [00:43<00:00,  6.57it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Epoch 3 = {'Precision': 0.832271762208068, 'Recall': 0.8082474226804124, 'microF': 0.8200836820083682}\n",
            "Train Loss: 0.018790982492205777\n",
            "Eval Loss: 0.06282491227486521\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 4/6: 100%|█████████████████████████████████████████████████████████████████████| 710/710 [05:07<00:00,  2.31it/s]\n",
            "Evaluating 4/6: 100%|████████████████████████████████████████████████████████████████| 289/289 [00:45<00:00,  6.28it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Epoch 4 = {'Precision': 0.7860696517412935, 'Recall': 0.8144329896907216, 'microF': 0.7999999999999999}\n",
            "Train Loss: 0.012677453342508721\n",
            "Eval Loss: 0.07327604648275672\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 5/6: 100%|█████████████████████████████████████████████████████████████████████| 710/710 [04:57<00:00,  2.39it/s]\n",
            "Evaluating 5/6: 100%|████████████████████████████████████████████████████████████████| 289/289 [00:43<00:00,  6.64it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Epoch 5 = {'Precision': 0.8579610538373424, 'Recall': 0.7721649484536083, 'microF': 0.812805208898535}\n",
            "Train Loss: 0.010166855729487868\n",
            "Eval Loss: 0.07984050094246634\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 6/6: 100%|█████████████████████████████████████████████████████████████████████| 710/710 [04:57<00:00,  2.39it/s]\n",
            "Evaluating 6/6: 100%|████████████████████████████████████████████████████████████████| 289/289 [00:43<00:00,  6.70it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics for Epoch 6 = {'Precision': 0.8511087645195353, 'Recall': 0.8309278350515464, 'microF': 0.8408972352634324}\n",
            "Train Loss: 0.007608007194306685\n",
            "Eval Loss: 0.07194845522488032\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Training...\")\n",
        "print(f\"Number of Epochs: {epochs}\")\n",
        "print(f\"Train samples: {len(train_dataset)}\")\n",
        "print(f\"Train batch size: {train_size}\")\n",
        "print(f\"Eval samples: {len(test_dataset)}\")\n",
        "print(f\"Eval batch size: {test_size}\")\n",
        "\n",
        "all_train_losses, all_eval_losses, all_results = [], [], []\n",
        "for epoch in range(1, epochs + 1):\n",
        "    train_loss = train(epoch)\n",
        "    all_train_losses.append(train_loss)\n",
        "\n",
        "    outputs, labels, eval_loss, _ = evaluate(epoch)\n",
        "    all_eval_losses.append(eval_loss)\n",
        "\n",
        "    ddimetrics = ddi_compute_metrics(preds=torch.argmax(outputs, dim=1), labels=labels)\n",
        "    all_results.append(ddimetrics)\n",
        "\n",
        "    print(f\"Metrics for Epoch {epoch} = {ddimetrics}\")\n",
        "    print(f\"Train Loss: {train_loss}\")\n",
        "    print(f\"Eval Loss: {eval_loss}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMqsqwIuzWCL"
      },
      "source": [
        "# Evaluation and Visualisation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-WWH5-DjCGEY",
        "outputId": "47fbf8e7-45e3-47b6-cd63-df4003001f0b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Evaluating 6/6: 100%|████████████████████████████████████████████████████████████████| 289/289 [00:44<00:00,  6.57it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Metrics = {'Precision': 0.8511087645195353, 'Recall': 0.8309278350515464, 'microF': 0.8408972352634324}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "outputs, labels, val_losses, latent_space = evaluate(epoch)\n",
        "ddimetrics = ddi_compute_metrics(preds=torch.argmax(outputs, dim=1), labels=labels)\n",
        "print(f\"Metrics = {ddimetrics}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KR8Zp4R-tw2s",
        "outputId": "233d55dd-ed92-4339-a6b2-e49b96c17d2a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "epochs_list = list(range(1, epochs + 1))\n",
        "\n",
        "plt.plot(epochs_list, all_train_losses, label='Training Loss')\n",
        "plt.plot(epochs_list, all_eval_losses, label='Validation Loss', linestyle='--')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Losses')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VSFHjvYUsTNh",
        "outputId": "92942217-be19-4962-d849-de7969494d6e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "precision = [d['Precision'] for d in all_results]\n",
        "recall = [d['Recall'] for d in all_results]\n",
        "microF = [d['microF'] for d in all_results]\n",
        "\n",
        "plt.plot(epochs_list, precision, label='Precision', linestyle='--')\n",
        "plt.plot(epochs_list, recall, label='Recall', linestyle='--')\n",
        "plt.plot(epochs_list, microF, label='microF', marker='o')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Score')\n",
        "plt.title('Precision, Recall, and microF Scores')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zmLWj0G1sTNh"
      },
      "outputs": [],
      "source": [
        "# t-SNE\n",
        "tsne = TSNE(n_components=2, random_state=42)\n",
        "reduced_latent_space_tsne = tsne.fit_transform(latent_space)\n",
        "\n",
        "# PCA\n",
        "pca = PCA(n_components=2)\n",
        "reduced_latent_space_pca = pca.fit_transform(latent_space)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V-BfmThpsTNh",
        "outputId": "a8fe543d-09df-491e-ce3c-c06d5c1d2ec6"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# t-SNE\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(reduced_latent_space_tsne[:, 0], reduced_latent_space_tsne[:, 1])\n",
        "plt.title(\"t-SNE Visualization of Latent Space\")\n",
        "plt.xlabel(\"Dimension 1\")\n",
        "plt.ylabel(\"Dimension 2\")\n",
        "plt.show()\n",
        "\n",
        "# PCA\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(reduced_latent_space_pca[:, 0], reduced_latent_space_pca[:, 1])\n",
        "plt.title(\"PCA Visualization of Latent Space\")\n",
        "plt.xlabel(\"Principal Component 1\")\n",
        "plt.ylabel(\"Principal Component 2\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ubdAELwuF9q"
      },
      "source": [
        "# Summary and Conclusion\n",
        "\n",
        "Our experiment results show the a micro F-score\n",
        "of 0.84 for DDI classification which is approxi-\n",
        "mately 0.0224 less than what is claimed by the pa-\n",
        "per (86.24).Since a previous paper (Mondal, 2020)\n",
        "achieved 0.84, we are unable to conclude that novel\n",
        "framework proposed in the paper provide state of\n",
        "the art results on DDIExtraction 2013 dataset. Also\n",
        "we observed that framework successfully comple-\n",
        "mented language feature with Drug Knowledge\n",
        "Graph . After adding drug knowledge\n",
        "embedding, recall increased by 0.03 and microF\n",
        "increased by 0.01. Also, As claimed by the paper,\n",
        "MFL was able perform better than cross entropy\n",
        "loss function for the imbalanced DDI extraction\n",
        "dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6EELU5HkI7X"
      },
      "source": [
        "# References\n",
        "Xin Jin, Xia Sun, Jiacheng Chen, and Richard Sutcliffe. 2022. Extracting Drug-drug Interactions from Biomedical Texts using Knowledge Graph Embeddings and Multi-focal Loss. In Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management (CIKM '22). Association for Computing Machinery, New York, NY, USA, 884–893. https://doi.org/10.1145/3511808.3557318\n",
        "\n",
        "Gu, Y., Tinn, R., Cheng, H., Lucas, M.R., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2020). Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. ACM Transactions on Computing for Healthcare (HEALTH), 3, 1 - 23.\n",
        "\n",
        "Lin, T., Goyal, P., Girshick, R.B., He, K., & Dollár, P. (2017). Focal Loss for Dense Object Detection. 2017 IEEE International Conference on Computer Vision (ICCV), 2999-3007.\n",
        "\n"
      ]
    }
  ],
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