Scripts/Training_KGNN_models_Pykeen.ipynb

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        "# DengueDrugRep: Drug repurposing for dengue using a biomedicine knowledge graph and graph neural networks\n",
        "\n",
        "**Author:** [Sebastián Ayala Ruano][myweb]\n",
        "\n",
        "This notebook is part of my capstone project for the Scientific Programming course from the [MSc in Systems Biology][sysbio] at [Maastricht University][maasuni].\n",
        "\n",
        "<a href=\"https://colab.research.google.com/github/sayalaruano/DengueDrugRep/blob/main/Training_KGNN_models_Pykeen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "\n",
        "### About the project\n",
        "\n",
        "[Dengue][dengue] is a viral infection transmitted to humans through the bite of Aedes mosquitoes. This disease is a  neglected tropical disease that mainly affects poor populations with no access to safe water, sanitation, and high-quality healthcare. Currently, there is no specific treatment for dengue and the focus is on treating pain symptoms. Therefore, there is an urgent need to find new drugs to treat this disease.\n",
        "\n",
        "The goal of this project is to predict new repurposed drugs for dengue using a biomedical knowledge graph and graph neural networks. A knowledge graph (KG) is a heterogenous network with different types of nodes and edges, where nodes represent entities (e.g., drugs, diseases, genes, etc.) and edges are semmantic relationships between entities (e.g., drug-disease, drug-gene, etc.). Graph neural networks (GNNs) are a class of neural networks that can learn from graph data. The following figure illustrates the general structure of a knowledge graph neural network (KGNN): \n",
        "\n",
        "<figure>\n",
        "  <img src=\"../img/KGNN_pipeline.png\" alt=\"my alt text\"/>\n",
        "  <figcaption><strong>Figure 1.</strong> Anatomy of a Knowledge Graph Neural Network. Obtained from <a href=\"https://kge-tutorial-ecai2020.github.io/\">[1]</a>. </figcaption>\n",
        "</figure>\n",
        "\n",
        "\n",
        "The drug repurposing problem can be formulated as a link prediction task in a KG. The goal is to predict new drug-disease associations. For this project, four GNN algorithms, namely [PairRE][pairRE], [DistMult][distmult], [ERMLP][ermlp], and [TransR][transr], were trained to predict new drug-disease associations using the Drug Repurposing Knowledge Graph ([DRKG][drkg]). These algorithms are implemented in the [PyKEEN][pykeen] library.\n",
        "\n",
        "### Dataset\n",
        "\n",
        "The [DRKG][drkg] is a large-scale biomedical KG that integrates information from six existing databases: DrugBank, Hetionet, Global network of biomedical relationships (GNBR), String, IntAct, and DGIdb. This KG contains 97.238 nodes belonging to 13 entity-types (e.g., drugs, diseases, genes, etc.) and 5.874.257 triplets belonging to 107 edge-types. The following figure shows a schematic representation of the DRKG:\n",
        "\n",
        "<figure>\n",
        "  <img src=\"../img/DRKG.png\" alt=\"my alt text\"/>\n",
        "  <figcaption><strong>Figure 2.</strong> Interactions in the DRKG. The number next to an edge indicates the number of relation-types for that entity-pair in the KG. Obtained from <a href=\"https://github.com/gnn4dr/DRKG\">[2]</a>. </figcaption>\n",
        "</figure>\n",
        "\n",
        "### Project structure\n",
        "\n",
        "This notebook is divided into the following sections:\n",
        "\n",
        "0. [Work environment setup](#0-work-environment-setup-and-definition-of-helper-functionsclasses)\n",
        "1. [Training the KGNN models](#4-bulding-and-evaluating-machine-learning-models)\n",
        "2. [Further analysis](#5-further-analysis)\n",
        "\n",
        "### Acknowledgments and further details of the project\n",
        "\n",
        "**Credits:** Part of the code for this project was inspired by the PyKEEN [tutorials][pykeen_tutorials], a [conference paper][confpaper], and this [GitHub repository][githubrepo].\n",
        "\n",
        "You can find more details about the project in this [GitHub repository][githubrepo].\n",
        "\n",
        "[dengue]: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue\n",
        "[confpaper]: https://link.springer.com/chapter/10.1007/978-3-031-40942-4_8\n",
        "[pykeen_tutorials]: https://github.com/pykeen/pykeen/tree/master/notebooks\n",
        "[sysbio]: https://www.maastrichtuniversity.nl/education/master/systems-biology\n",
        "[maasuni]: https://www.maastrichtuniversity.nl/\n",
        "[myweb]: https://sayalaruano.github.io/\n",
        "[githubrepo]: https://github.com/sayalaruano/DengueDrugRep\n",
        "[pairRE]: http://arxiv.org/abs/2011.03798\n",
        "[distmult]: https://arxiv.org/abs/1412.6575\n",
        "[transr]: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9571/9523/\n",
        "[ermlp]: https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45634.pdf\n",
        "[pykeen]: https://github.com/pykeen/pykeen\n",
        "[drkg]: https://github.com/gnn4dr/DRKG"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 0. Work environment setup\n",
        "### 0.1. Installing PyKEEN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnAgYIEmtL67",
        "outputId": "4d1cca91-d64f-47ce-aa46-1f2bb4089b8e"
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      "outputs": [],
      "source": [
        "# Install packages if they're not already found\n",
        "! pip install --upgrade pip\n",
        "! python -c \"import pykeen\" || pip install git+https://github.com/pykeen/pykeen.git\n",
        "! python -c \"import wordcloud\" || pip install wordcloud"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 0.2. Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ce5_ZDL8tL67",
        "outputId": "b7625b04-67ea-4e82-d59f-9f1056fff821"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "\n",
        "import pykeen\n",
        "from pykeen.datasets import DRKG\n",
        "from pykeen.pipeline import pipeline\n",
        "from pykeen import predict\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'svg'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "JnU24aS6tL68",
        "outputId": "4c8117ba-2a4b-4ba8-a93b-f6e11724e1ca"
      },
      "outputs": [],
      "source": [
        "pykeen.env()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "cy8-djVhtL68"
      },
      "source": [
        "## 1. Training the KGNN models"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1.1. General evaluation\n",
        "These models are evaluated using all the triplets in the DRKG, so evaluation results reflect the link prediction performance for all the entity-pairs in the KG."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 1.1.1. PairRE"
      ]
    },
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      "source": [
        "# Define the PairRE KGNN model\n",
        "result_PairRE_50ep_genev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"PairRE\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=50,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_PairRE_genev.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the PairRE model to a directory\n",
        "PairRE_dir_genenv = \"results_PairRE_50ep_genenv\"\n",
        "result_PairRE_50ep_genev.save_to_directory(PairRE_dir_genenv)\n",
        "os.listdir(PairRE_dir_genenv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_PairRE_50epochs.zip results_PairRE_50ep_genenv"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 1.1.2. DistMult"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the DisMult KGNN model\n",
        "result_DisMult_50ep_genev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"DISMULT\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=50,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_DisMult_genev.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the DisMult model to a directory\n",
        "DisMult_dir_genenv = \"results_DisMult_50ep_genenv\"\n",
        "result_DisMult_50ep_genev.save_to_directory(DisMult_dir_genenv)\n",
        "os.listdir(DisMult_dir_genenv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_DisMult_50epochs.zip results_DisMult_50ep_genenv"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 1.1.3. ERMLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the ERMLP KGNN model\n",
        "result_ERMLP_50ep_genev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"ERMLP\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=50,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_ERMLP_genev.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the ERMLP model to a directory\n",
        "ERMLP_dir_genenv = \"results_ERMLP_50ep_genenv\"\n",
        "result_ERMLP_50ep_genev.save_to_directory(ERMLP_dir_genenv)\n",
        "os.listdir(ERMLP_dir_genenv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_ERMLP_50epochs.zip results_ERMLP_50ep_genenv"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 1.1.4. TransR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the TransR KGNN model\n",
        "result_TransR_50ep_genev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"TransR\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=50,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_TransR_genev.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the TransR model to a directory\n",
        "TransR_dir_genenv = \"results_TransR_50ep_genenv\"\n",
        "result_TransR_50ep_genev.save_to_directory(TransR_dir_genenv)\n",
        "os.listdir(TransR_dir_genenv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_TransR_50epochs.zip results_TransR_50ep_genenv"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1.2. Drug repurposing evaluation\n",
        "Instead of evaluating the models using all the triplets in the KG, we can evaluate the models using only the triplets that involve drugs and diseases. This is the drug repurposing evaluation. In this way, the models are evaluated only on the task of predicting new drug-disease associations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# List of the drug-disease relations in DRKG\n",
        "drud_disease_relations = ['DRUGBANK::treats::Compound:Disease',\n",
        "                        'GNBR::C::Compound:Disease',\n",
        "                        'GNBR::J::Compound:Disease',\n",
        "                        'GNBR::Mp::Compound:Disease',\n",
        "                        'GNBR::Pa::Compound:Disease',\n",
        "                        'GNBR::Pr::Compound:Disease',\n",
        "                        'GNBR::Sa::Compound:Disease',\n",
        "                        'GNBR::T::Compound:Disease',\n",
        "                        'Hetionet::CpD::Compound:Disease',\n",
        "                        'Hetionet::CtD::Compound:Disease']"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 1.2.1. PairRE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "result_PairRE_drev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"PairRE\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    evaluation_relation_whitelist=drud_disease_relations,\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=10,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_PairRE_10.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the PairRE model to a directory\n",
        "PairRE_dir_drev = \"results_PairRE_10ep_drev\"\n",
        "result_PairRE_drev.save_to_directory(PairRE_dir_drev)\n",
        "os.listdir(PairRE_dir_drev)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_PairRE_drev.zip results_PairRE_10ep_drev"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 1.2.2. DistMult"
      ]
    },
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      "outputs": [],
      "source": [
        "# Define the DisMult KGNN model\n",
        "result_DisMult_drev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"DISMULT\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    evaluation_relation_whitelist=drud_disease_relations,\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=10,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_DisMult_10.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the DisMult model to a directory\n",
        "DisMult_dir_drev = \"results_DisMult_10ep_drev\"\n",
        "result_DisMult_drev.save_to_directory(DisMult_dir_drev)\n",
        "os.listdir(DisMult_dir_drev)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_DisMult_drev.zip results_DisMult_10ep_drev"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### 1.2.3. ERMLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the ERMLP KGNN model\n",
        "result_ERMLP_drev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"ERMLP\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    evaluation_relation_whitelist=drud_disease_relations,\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=10,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_ERMLP_10.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the ERMLP model to a directory\n",
        "ERMLP_dir_drev = \"results_ERMLP_10ep_drev\"\n",
        "result_ERMLP_drev.save_to_directory(ERMLP_dir_drev)\n",
        "os.listdir(ERMLP_dir_drev)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_ERMLP_drev.zip results_ERMLP_10ep_drev"
      ]
    },
    {
      "attachments": {},
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      "metadata": {},
      "source": [
        "#### 1.2.4. TransR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the TransR KGNN model\n",
        "result_TransR_drev = pipeline(\n",
        "    dataset=\"DRKG\",\n",
        "    model=\"TransR\",\n",
        "    loss = \"MarginRankingLoss\",\n",
        "    device='cuda',\n",
        "    evaluation_relation_whitelist=drud_disease_relations,\n",
        "    # Training configuration\n",
        "    training_kwargs=dict(\n",
        "        num_epochs=10,\n",
        "        use_tqdm_batch=False,\n",
        "        checkpoint_name='results_TransR_10.pt',\n",
        "        checkpoint_on_failure=True,\n",
        "        checkpoint_frequency=2\n",
        "    ),\n",
        "    # Runtime configuration\n",
        "    random_seed=1235,\n",
        ")\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Save the TransR model to a directory\n",
        "TransR_dir_drev = \"results_TransR_10ep_drev\"\n",
        "result_TransR_drev.save_to_directory(TransR_dir_drev)\n",
        "os.listdir(TransR_dir_drev)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Zip the directory to download it\n",
        "!zip -r result_TransR_drev.zip results_TransR_10ep_drev"
      ]
    },
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        "id": "pe6ivsPftL6-"
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        "## 2. Further analysis\n",
        "After training the KGNN models on Google Colab, I downloaded them to my local machine. Then, I ran the scripts to perform the internal and external evaluation. These scripts and the results are available in this [GitHub repository](https://github.com/sayalaruano/DengueDrugRep)."
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