ESOL_pynb.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chirag1701/ENSO_waters_solubility/blob/main/ENSO_pynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "oim9R-nzjdzA"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlwZGnoUnLmZ",
        "outputId": "edb2f198-07a9-4163-853c-b0ed7ba9f505"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.3.0+cpu\n",
            "None\n",
            "Python 3.10.12\n",
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ],
      "source": [
        "!python -c \"import torch; print(torch.__version__)\"\n",
        "!python -c \"import torch; print(torch.version.cuda)\"\n",
        "!python --version\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "QbhBPdrqn64o"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "# This code is for installing RDkit library that handles molecule data\n",
        "import sys   # to interact with system python\n",
        "import os    # to interact with system-level commands\n",
        "import requests  # http requests\n",
        "import subprocess  # Run external commands and processes\n",
        "import shutil      # to perform high-level file operations\n",
        "from logging import getLogger, StreamHandler, INFO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "9mQ7BKjeqmng"
      },
      "outputs": [],
      "source": [
        "logger = getLogger(__name__)  # __ __ for internal command\n",
        "logger.addHandler(StreamHandler())\n",
        "logger.setLevel(INFO)     # only log messages with a severity level of INFO or higher will be recorded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqysVJzuAgPG",
        "outputId": "d76e6cd6-1f49-4e15-b914-494a6b090ba0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "libboost-all-dev is already the newest version (1.74.0.3ubuntu7).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.\n",
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            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
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            "  libeigen3-doc libmpfrc++-dev\n",
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            "  libeigen3-dev\n",
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            "Setting up libeigen3-dev (3.4.0-2ubuntu2) ...\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
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            "The following additional packages will be installed:\n",
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            "  qt5-gtk-platformtheme qttranslations5-l10n\n",
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            "The following NEW packages will be installed:\n",
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            "  libqt5designer5 libqt5gui5 libqt5help5 libqt5network5 libqt5printsupport5 libqt5sql5\n",
            "  libqt5sql5-sqlite libqt5svg5 libqt5test5 libqt5widgets5 libqt5xml5 libwacom-bin libwacom-common\n",
            "  libwacom9 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-render-util0 libxcb-util1\n",
            "  libxcb-xinerama0 libxcb-xinput0 libxcb-xkb1 libxkbcommon-x11-0 python3-pyqt5 python3-pyqt5.sip\n",
            "  qt5-gtk-platformtheme qttranslations5-l10n\n",
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            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
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            "Reading package lists... Done\n",
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            "libsqlite3-dev is already the newest version (3.37.2-2ubuntu0.3).\n",
            "libsqlite3-dev set to manually installed.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.\n",
            "Collecting rdkit-pypi\n",
            "  Downloading rdkit_pypi-2022.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.9 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit-pypi) (1.26.4)\n",
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            "\u001b[?25hInstalling collected packages: rdkit-pypi\n",
            "Successfully installed rdkit-pypi-2022.9.5\n"
          ]
        }
      ],
      "source": [
        "!apt-get install -y libboost-all-dev\n",
        "!apt-get install -y libeigen3-dev\n",
        "!apt-get install -y python3-pyqt5\n",
        "!apt-get install -y python3-venv\n",
        "!apt-get install -y python3-pyqt5.qtopengl\n",
        "!apt-get install -y python3-pyqt5.qtsvg\n",
        "!apt-get install -y libsqlite3-dev\n",
        "\n",
        "!pip install rdkit-pypi\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "u9WDhjEHy2Fj"
      },
      "outputs": [],
      "source": [
        "from rdkit import Chem\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDztgZiPzGPi",
        "outputId": "431f7525-f5b2-4254-f0fb-56e190bea9ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.0+cpu)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.18.0+cpu)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.26.4)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (10.4.0)\n",
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          ]
        }
      ],
      "source": [
        "!pip install torch torchvision torchaudio\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsJLaxyIzWcM",
        "outputId": "74277d2b-708a-4c8f-b5aa-d12b9d3bfaf9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torch_geometric\n",
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            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (4.66.5)\n",
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            "Collecting aiohttp (from torch_geometric)\n",
            "  Downloading aiohttp-3.10.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.5 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (2.32.3)\n",
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            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (5.9.5)\n",
            "Collecting aiohappyeyeballs>=2.3.0 (from aiohttp->torch_geometric)\n",
            "  Downloading aiohappyeyeballs-2.3.5-py3-none-any.whl.metadata (5.8 kB)\n",
            "Collecting aiosignal>=1.1.2 (from aiohttp->torch_geometric)\n",
            "  Downloading aiosignal-1.3.1-py3-none-any.whl.metadata (4.0 kB)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (24.2.0)\n",
            "Collecting frozenlist>=1.1.1 (from aiohttp->torch_geometric)\n",
            "  Downloading frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multidict<7.0,>=4.5 (from aiohttp->torch_geometric)\n",
            "  Downloading multidict-6.0.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n",
            "Collecting yarl<2.0,>=1.0 (from aiohttp->torch_geometric)\n",
            "  Downloading yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (31 kB)\n",
            "Collecting async-timeout<5.0,>=4.0 (from aiohttp->torch_geometric)\n",
            "  Downloading async_timeout-4.0.3-py3-none-any.whl.metadata (4.2 kB)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch_geometric) (2.1.5)\n",
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            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric) (1.4.2)\n",
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            "\u001b[?25hDownloading aiohttp-3.10.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
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            "\u001b[?25hDownloading aiohappyeyeballs-2.3.5-py3-none-any.whl (12 kB)\n",
            "Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
            "Downloading async_timeout-4.0.3-py3-none-any.whl (5.7 kB)\n",
            "Downloading frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB)\n",
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            "\u001b[?25hDownloading multidict-6.0.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (124 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.3/124.3 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (301 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.6/301.6 kB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: multidict, frozenlist, async-timeout, aiohappyeyeballs, yarl, aiosignal, aiohttp, torch_geometric\n",
            "Successfully installed aiohappyeyeballs-2.3.5 aiohttp-3.10.2 aiosignal-1.3.1 async-timeout-4.0.3 frozenlist-1.4.1 multidict-6.0.5 torch_geometric-2.5.3 yarl-1.9.4\n"
          ]
        }
      ],
      "source": [
        "!pip install torch_geometric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "yt0ZzlLE0Aza"
      },
      "outputs": [],
      "source": [
        "import torch_geometric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "kVccMpM00SD1"
      },
      "outputs": [],
      "source": [
        "from torch_geometric.datasets import MoleculeNet   #importing MoleculeNet class from torch_geometric.datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBVRxuY_0RFn",
        "outputId": "c8c81927-d659-4ff8-e382-bcee13bbe247"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv\n",
            "Processing...\n",
            "Done!\n"
          ]
        }
      ],
      "source": [
        "# Now we need to load the ESOL datset from MoleculeNet\n",
        "dataset = MoleculeNet(root='.',name='ESOl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmlNlS0W7Uap",
        "outputId": "0a6d9c1b-9968-49d6-b920-7560c30fc5be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ESOL(1128)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "uBPSsEdz7WDt",
        "outputId": "2ff3ebfc-9022-49ac-ab16-c680682b1414"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch_geometric.datasets.molecule_net.MoleculeNet"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
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              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>torch_geometric.datasets.molecule_net.MoleculeNet</b><br/>def __init__(root: str, name: str, transform: Optional[Callable]=None, pre_transform: Optional[Callable]=None, pre_filter: Optional[Callable]=None, force_reload: bool=False) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/torch_geometric/datasets/molecule_net.py</a>The `MoleculeNet &lt;http://moleculenet.org/datasets-1&gt;`_ benchmark\n",
              "collection  from the `&quot;MoleculeNet: A Benchmark for Molecular Machine\n",
              "Learning&quot; &lt;https://arxiv.org/abs/1703.00564&gt;`_ paper, containing datasets\n",
              "from physical chemistry, biophysics and physiology.\n",
              "All datasets come with the additional node and edge features introduced by\n",
              "the :ogb:`null`\n",
              "`Open Graph Benchmark &lt;https://ogb.stanford.edu/docs/graphprop/&gt;`_.\n",
              "\n",
              "Args:\n",
              "    root (str): Root directory where the dataset should be saved.\n",
              "    name (str): The name of the dataset (:obj:`&quot;ESOL&quot;`, :obj:`&quot;FreeSolv&quot;`,\n",
              "        :obj:`&quot;Lipo&quot;`, :obj:`&quot;PCBA&quot;`, :obj:`&quot;MUV&quot;`, :obj:`&quot;HIV&quot;`,\n",
              "        :obj:`&quot;BACE&quot;`, :obj:`&quot;BBBP&quot;`, :obj:`&quot;Tox21&quot;`, :obj:`&quot;ToxCast&quot;`,\n",
              "        :obj:`&quot;SIDER&quot;`, :obj:`&quot;ClinTox&quot;`).\n",
              "    transform (callable, optional): A function/transform that takes in an\n",
              "        :obj:`torch_geometric.data.Data` object and returns a transformed\n",
              "        version. The data object will be transformed before every access.\n",
              "        (default: :obj:`None`)\n",
              "    pre_transform (callable, optional): A function/transform that takes in\n",
              "        an :obj:`torch_geometric.data.Data` object and returns a\n",
              "        transformed version. The data object will be transformed before\n",
              "        being saved to disk. (default: :obj:`None`)\n",
              "    pre_filter (callable, optional): A function that takes in an\n",
              "        :obj:`torch_geometric.data.Data` object and returns a boolean\n",
              "        value, indicating whether the data object should be included in the\n",
              "        final dataset. (default: :obj:`None`)\n",
              "    force_reload (bool, optional): Whether to re-process the dataset.\n",
              "        (default: :obj:`False`)\n",
              "\n",
              "**STATS:**\n",
              "\n",
              ".. list-table::\n",
              "    :widths: 20 10 10 10 10 10\n",
              "    :header-rows: 1\n",
              "\n",
              "    * - Name\n",
              "      - #graphs\n",
              "      - #nodes\n",
              "      - #edges\n",
              "      - #features\n",
              "      - #classes\n",
              "    * - ESOL\n",
              "      - 1,128\n",
              "      - ~13.3\n",
              "      - ~27.4\n",
              "      - 9\n",
              "      - 1\n",
              "    * - FreeSolv\n",
              "      - 642\n",
              "      - ~8.7\n",
              "      - ~16.8\n",
              "      - 9\n",
              "      - 1\n",
              "    * - Lipophilicity\n",
              "      - 4,200\n",
              "      - ~27.0\n",
              "      - ~59.0\n",
              "      - 9\n",
              "      - 1\n",
              "    * - PCBA\n",
              "      - 437,929\n",
              "      - ~26.0\n",
              "      - ~56.2\n",
              "      - 9\n",
              "      - 128\n",
              "    * - MUV\n",
              "      - 93,087\n",
              "      - ~24.2\n",
              "      - ~52.6\n",
              "      - 9\n",
              "      - 17\n",
              "    * - HIV\n",
              "      - 41,127\n",
              "      - ~25.5\n",
              "      - ~54.9\n",
              "      - 9\n",
              "      - 1\n",
              "    * - BACE\n",
              "      - 1513\n",
              "      - ~34.1\n",
              "      - ~73.7\n",
              "      - 9\n",
              "      - 1\n",
              "    * - BBBP\n",
              "      - 2,050\n",
              "      - ~23.9\n",
              "      - ~51.6\n",
              "      - 9\n",
              "      - 1\n",
              "    * - Tox21\n",
              "      - 7,831\n",
              "      - ~18.6\n",
              "      - ~38.6\n",
              "      - 9\n",
              "      - 12\n",
              "    * - ToxCast\n",
              "      - 8,597\n",
              "      - ~18.7\n",
              "      - ~38.4\n",
              "      - 9\n",
              "      - 617\n",
              "    * - SIDER\n",
              "      - 1,427\n",
              "      - ~33.6\n",
              "      - ~70.7\n",
              "      - 9\n",
              "      - 27\n",
              "    * - ClinTox\n",
              "      - 1,484\n",
              "      - ~26.1\n",
              "      - ~55.5\n",
              "      - 9\n",
              "      - 2</pre>\n",
              "      <script>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Now we will investigate the data to get an intuition on its underlying structure.\n",
        "type(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xv0reso48sui",
        "outputId": "9ef062f4-ddb5-4a3c-f889-13db283140f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "dataset.num_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ECNQP8g83z3",
        "outputId": "3c3e5c15-4e63-4b57-b8ed-4831362c792c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/data/dataset.py:169: UserWarning: Found floating-point labels while calling `dataset.num_classes`. Returning the number of unique elements. Please make sure that this is expected before proceeding.\n",
            "  warnings.warn(\"Found floating-point labels while calling \"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "734"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "dataset.num_classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5gve26n8-ZN",
        "outputId": "3cb43f4b-6455-4fe3-8856-588726b33df7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1128"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "dataset.len()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9TLYShn9Oxv",
        "outputId": "36bc8d56-6b4f-4e13-9571-772a72d3f252"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(x=[32, 9], edge_index=[2, 68], edge_attr=[68, 3], smiles='OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O ', y=[1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfkMv8219YPD",
        "outputId": "c3932b9f-27c6-47f1-b9bf-b8219553e167"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32\n",
            "68\n"
          ]
        }
      ],
      "source": [
        "print(dataset[0].num_nodes)  #Sample nodes\n",
        "print(dataset[0].num_edges)  #Sample edges"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otKjbdz79hWw",
        "outputId": "7ae0a60a-ee37-4cbf-8c39-11fce76bdc77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 2, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 0, 0, 4, 0, 1],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 0, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 2, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 0, 0, 4, 0, 1],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 0, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 0],\n",
              "        [6, 0, 2, 5, 0, 0, 2, 0, 0],\n",
              "        [7, 0, 1, 5, 0, 0, 2, 0, 0],\n",
              "        [6, 0, 3, 5, 0, 0, 3, 1, 1],\n",
              "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
              "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
              "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
              "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
              "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
              "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
              "        [8, 0, 2, 5, 1, 0, 4, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Investigating node features\n",
        "dataset[0].x   # Node features of the first node"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSVNFA0SpJlx",
        "outputId": "1c9dd758-568f-4bcc-ef5e-0c8b8fd93ab6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0,  1],\n",
              "        [ 1,  0],\n",
              "        [ 1,  2],\n",
              "        [ 2,  1],\n",
              "        [ 2,  3],\n",
              "        [ 2, 30],\n",
              "        [ 3,  2],\n",
              "        [ 3,  4],\n",
              "        [ 4,  3],\n",
              "        [ 4,  5],\n",
              "        [ 4, 26],\n",
              "        [ 5,  4],\n",
              "        [ 5,  6],\n",
              "        [ 6,  5],\n",
              "        [ 6,  7],\n",
              "        [ 7,  6],\n",
              "        [ 7,  8],\n",
              "        [ 7, 24],\n",
              "        [ 8,  7],\n",
              "        [ 8,  9],\n",
              "        [ 9,  8],\n",
              "        [ 9, 10],\n",
              "        [ 9, 20],\n",
              "        [10,  9],\n",
              "        [10, 11],\n",
              "        [11, 10],\n",
              "        [11, 12],\n",
              "        [11, 14],\n",
              "        [12, 11],\n",
              "        [12, 13],\n",
              "        [13, 12],\n",
              "        [14, 11],\n",
              "        [14, 15],\n",
              "        [14, 19],\n",
              "        [15, 14],\n",
              "        [15, 16],\n",
              "        [16, 15],\n",
              "        [16, 17],\n",
              "        [17, 16],\n",
              "        [17, 18],\n",
              "        [18, 17],\n",
              "        [18, 19],\n",
              "        [19, 14],\n",
              "        [19, 18],\n",
              "        [20,  9],\n",
              "        [20, 21],\n",
              "        [20, 22],\n",
              "        [21, 20],\n",
              "        [22, 20],\n",
              "        [22, 23],\n",
              "        [22, 24],\n",
              "        [23, 22],\n",
              "        [24,  7],\n",
              "        [24, 22],\n",
              "        [24, 25],\n",
              "        [25, 24],\n",
              "        [26,  4],\n",
              "        [26, 27],\n",
              "        [26, 28],\n",
              "        [27, 26],\n",
              "        [28, 26],\n",
              "        [28, 29],\n",
              "        [28, 30],\n",
              "        [29, 28],\n",
              "        [30,  2],\n",
              "        [30, 28],\n",
              "        [30, 31],\n",
              "        [31, 30]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "dataset[0].edge_index.t()   # returns the edge index tensor of the first graph of deataset\n",
        "# < shows the connectivity of edges  as a 2XN tensor , where N= no. of edges >"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FI-3owrvpnIF",
        "outputId": "7ca0603a-f53e-4ffc-b7a1-801e84e5577e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.7700]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# graph level prediction label for the first graph\n",
        "dataset[0].y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "qa1n6PSTqRaw",
        "outputId": "f2a58334-f6e6-433e-a7d7-ec4c3961adb1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Now we will convert SMILES to RDKIT molecules\n",
        "dataset.smiles[0]   # or dataset[0][\"smiles\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "S-8ft0vJqeDP",
        "outputId": "789fc04a-e5c7-4019-bde2-7914ef16fc81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7ad129107f40>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Rdkit is used for visualising molecules in their real shape\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "molecule = Chem.MolFromSmiles(dataset.smiles[0])  # converts the SMILES string in the dataset to Molecule\n",
        "molecule"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-KTB4RgsRqI",
        "outputId": "156fe91c-4089-4cfb-9206-521359af22e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rdkit.Chem.rdchem.Mol"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "type(molecule)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdMXiZE6smJK",
        "outputId": "9e845baa-205a-457f-fa3d-9ad6d83e6802"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GCN(\n",
            "  (initial_conv): GCNConv(9, 64)\n",
            "  (conv1): GCNConv(64, 64)\n",
            "  (conv2): GCNConv(64, 64)\n",
            "  (conv3): GCNConv(64, 64)\n",
            "  (out): Linear(in_features=128, out_features=1, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# We will build a class that inherits from torch.nn.Module  ( with its in-built structure)\n",
        "import torch\n",
        "from torch.nn import Linear # for performing a linear transformation on the input data\n",
        "# it is used in NN to project the input data to a new space governed by weights and biases\n",
        "import torch.nn.functional as F # torch.nn.functional is a toolkit for nn's consisting of loss functions, activation functions ,etc\n",
        "from torch_geometric.nn import GCNConv, TopKPooling, global_mean_pool\n",
        "from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp\n",
        "embedding_size = 64\n",
        "\n",
        "class GCN(torch.nn.Module):\n",
        "  def __init__(self):   # constructor of the GCN class\n",
        "  # Now we will call the contructor of the parent class torch.nn.module\n",
        "    super(GCN, self).__init__()\n",
        "    torch.manual_seed(42)\n",
        "\n",
        "    #GCN layers\n",
        "    self.initial_conv = GCNConv(dataset.num_features, embedding_size)  # first GCN layer which processes the input features\n",
        "    self.conv1 = GCNConv(embedding_size, embedding_size) # input and output embedding size\n",
        "    self.conv2 = GCNConv(embedding_size, embedding_size)\n",
        "    self.conv3 = GCNConv(embedding_size, embedding_size)\n",
        "\n",
        "    # output layer\n",
        "    self.out = Linear(2*embedding_size, 1)  # gmp and gap are concatenated\n",
        "  def forward(self, x, edge_index, batch_index): # x is the node features of your graph\n",
        "    # first conv layer                           # edge_index = connection of edges\n",
        "    hidden = self.initial_conv(x,edge_index )              # batch_index = which nodes belong to which graph\n",
        "    hidden = F.tanh(hidden)\n",
        "    # Other conv layers\n",
        "    hidden = self.conv1(hidden, edge_index)\n",
        "    hidden = F.tanh(hidden)\n",
        "    hidden = self.conv2(hidden, edge_index)\n",
        "    hidden = F.tanh(hidden)\n",
        "    hidden = self.conv3(hidden, edge_index)\n",
        "    hidden - F.tanh(hidden)\n",
        "    # Global Pooling\n",
        "    # stack different aggregations\n",
        "    hidden = torch.cat([gmp(hidden,batch_index),gap(hidden,batch_index)], dim = 1)    # Returns batch-wise graph-level-outputs by taking the channel-wise\n",
        "                                                                                      # maximum across the node dimension.\n",
        "    # Apply a final linear classifier\n",
        "    out = self.out(hidden)\n",
        "    return out, hidden\n",
        "\n",
        "model = GCN()\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7ExtBK3TRPD",
        "outputId": "16b9cef5-bad4-4f2d-84ea-358d63792f08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of parameters : 13249\n"
          ]
        }
      ],
      "source": [
        "print( \"No. of parameters : {}\".format(sum(p.numel() for p in model.parameters())))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSqNwD3FHB3Y"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDzYdEszHTrv"
      },
      "source": [
        "# **TRAINING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "_0dqQCRhzyys"
      },
      "outputs": [],
      "source": [
        "from torch_geometric.data import DataLoader\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "4JWaAAIA1he8"
      },
      "outputs": [],
      "source": [
        "# Root mean squared error\n",
        "mse = torch.nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(),lr=0.0007)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JhgO4a4L2YRE",
        "outputId": "44312f1d-0210-4bf3-91db-fa23cf32839c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "50vA8Tz82l-R"
      },
      "outputs": [],
      "source": [
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "YpDhfmKt24-c"
      },
      "outputs": [],
      "source": [
        "# Wrap data in a loader\n",
        "data_size = len(dataset)\n",
        "num_graphs_per_batch = 64\n",
        "Loader = DataLoader(dataset[:int(data_size*0.8)], batch_size = num_graphs_per_batch, shuffle = True)\n",
        "ValLoader = DataLoader(dataset[int(data_size*0.8):int(data_size*0.9)], batch_size = num_graphs_per_batch, shuffle = True)\n",
        "TestLoader = DataLoader(dataset[int(data_size*0.9):], batch_size = num_graphs_per_batch, shuffle = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wn0UCqDNITOK",
        "outputId": "14081ddd-a835-4e69-e8b0-b2ac9b4e2178"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0 |   Train Loss 10.099656105041504   |    Validation Loss 3.3124107122421265\n",
            "Epoch 100 |   Train Loss 0.7967323660850525   |    Validation Loss 0.9220696985721588\n",
            "Epoch 200 |   Train Loss 0.4805862009525299   |    Validation Loss 0.8655951023101807\n",
            "Epoch 300 |   Train Loss 0.6116620898246765   |    Validation Loss 0.7185283303260803\n",
            "Epoch 400 |   Train Loss 0.4156852662563324   |    Validation Loss 0.7121057361364365\n",
            "Epoch 500 |   Train Loss 0.6477764844894409   |    Validation Loss 0.6074101328849792\n",
            "Epoch 600 |   Train Loss 0.49396684765815735   |    Validation Loss 0.612003356218338\n",
            "Epoch 700 |   Train Loss 0.21474678814411163   |    Validation Loss 0.4947803020477295\n",
            "Epoch 800 |   Train Loss 0.264352947473526   |    Validation Loss 0.5492172390222549\n",
            "Epoch 900 |   Train Loss 0.19119061529636383   |    Validation Loss 0.44058121740818024\n",
            "Epoch 1000 |   Train Loss 0.07432744652032852   |    Validation Loss 0.42949078977108\n",
            "Epoch 1100 |   Train Loss 0.05991722643375397   |    Validation Loss 0.4972299337387085\n",
            "Epoch 1200 |   Train Loss 0.047058284282684326   |    Validation Loss 0.48625628650188446\n",
            "Epoch 1300 |   Train Loss 0.04470298811793327   |    Validation Loss 0.521039679646492\n",
            "Epoch 1400 |   Train Loss 0.03636741638183594   |    Validation Loss 0.5434441119432449\n",
            "Epoch 1500 |   Train Loss 0.04745817184448242   |    Validation Loss 0.5035093128681183\n",
            "Epoch 1600 |   Train Loss 0.19678761065006256   |    Validation Loss 0.5917820185422897\n",
            "Epoch 1700 |   Train Loss 0.3070102035999298   |    Validation Loss 0.47927315533161163\n",
            "Epoch 1800 |   Train Loss 0.03709955886006355   |    Validation Loss 0.44581346213817596\n",
            "Epoch 1900 |   Train Loss 0.03162933513522148   |    Validation Loss 0.591758131980896\n"
          ]
        }
      ],
      "source": [
        "def train(dataset):\n",
        "  model.train()\n",
        "  for batch in Loader:\n",
        "    batch.to(device)\n",
        "    optimizer.zero_grad()\n",
        "    # passing the node features and connection info\n",
        "    # forward pass\n",
        "    pred, embedding = model(batch.x.float(), batch.edge_index, batch.batch)   # unpacking into 2 variables\n",
        "    loss = mse(pred, batch.y)\n",
        "    loss.backward()  # Calculates gradients\n",
        "    optimizer.step()  # updates the parameters using the calculated gradients\n",
        "  return loss.item(),embedding\n",
        "\n",
        "def validate(ValLoader):\n",
        "  model.eval()\n",
        "  val_loss = 0.0\n",
        "  with torch.no_grad():\n",
        "    for batch in ValLoader:\n",
        "      batch.to(device)\n",
        "      pred, embedding = model(batch.x.float(), batch.edge_index, batch.batch)\n",
        "      loss = mse(pred, batch.y)\n",
        "      val_loss += loss.item()\n",
        "      val_loss /= len(ValLoader)\n",
        "  return val_loss\n",
        "\n",
        "\n",
        "\n",
        "  print(\"Starting training..\")\n",
        "train_losses = []\n",
        "val_losses = []\n",
        "for epoch in range(2000):\n",
        "  train_loss,h = train(dataset)   # h are the embeddings\n",
        "  train_losses.append(train_loss)\n",
        "  val_loss = validate(ValLoader)\n",
        "  val_losses.append(val_loss)\n",
        "  if epoch%100==0:\n",
        "    print(f\"Epoch {epoch} |   Train Loss {train_loss}   |    Validation Loss {val_loss}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38EuGwjeO767"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLZhnmXbPGZo"
      },
      "source": [
        "#**Visualising the training loss using seaborn**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uakCD-SpO5GI"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "EDn7Oz5H1XZm",
        "outputId": "dd05a472-795b-4797-afd3-ddbff4f8e40b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "loss_indices = [i for i,l in enumerate(train_losses)]\n",
        "plt = sns.lineplot(x=loss_indices, y=train_losses)\n",
        "plt.set(xlabel = \"Epochs\", ylabel = \"Loss\")\n",
        "plt2 = sns.lineplot(x=loss_indices, y=val_losses)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZhavLee35iTg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGVdh-mMPFxX"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dY3ltdPAVEsP"
      },
      "source": [
        "# **Checking on test dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "mQ46MxXXVS80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "b796e154-3864-4652-97d4-00ada88f3f86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    y_real    y_pred\n",
              "0   -0.400 -0.761782\n",
              "1   -1.210 -1.539821\n",
              "2   -1.340 -1.660373\n",
              "3   -3.370 -3.319162\n",
              "4   -2.460 -2.262365\n",
              "..     ...       ...\n",
              "59  -3.900 -3.625201\n",
              "60  -1.470 -1.483460\n",
              "61  -4.900 -4.320014\n",
              "62   1.020  1.472258\n",
              "63  -3.451 -2.700935\n",
              "\n",
              "[64 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdb491ce-689d-40d0-a168-cededa50ca2f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y_real</th>\n",
              "      <th>y_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.400</td>\n",
              "      <td>-0.761782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.210</td>\n",
              "      <td>-1.539821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.340</td>\n",
              "      <td>-1.660373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-3.370</td>\n",
              "      <td>-3.319162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-2.460</td>\n",
              "      <td>-2.262365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>-3.900</td>\n",
              "      <td>-3.625201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>-1.470</td>\n",
              "      <td>-1.483460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>-4.900</td>\n",
              "      <td>-4.320014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>1.020</td>\n",
              "      <td>1.472258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>-3.451</td>\n",
              "      <td>-2.700935</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdb491ce-689d-40d0-a168-cededa50ca2f')\"\n",
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              "\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "        document.querySelector('#df-cdb491ce-689d-40d0-a168-cededa50ca2f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-cdb491ce-689d-40d0-a168-cededa50ca2f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-cb3b0cc3-5337-4362-9e7e-f104c0c22ecd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_6f844371-5fb5-4aee-b2cc-9c3936878b4c\">\n",
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              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6f844371-5fb5-4aee-b2cc-9c3936878b4c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 64,\n  \"fields\": [\n    {\n      \"column\": \"y_real\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8890266759224181,\n        \"min\": -6.860000133514404,\n        \"max\": 1.0199999809265137,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          -4.449999809265137,\n          -5.409999847412109,\n          -0.4000000059604645\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y_pred\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.702355037045201,\n        \"min\": -7.115146636962891,\n        \"max\": 1.4722579717636108,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          -2.903811454772949,\n          -1.9294817447662354,\n          -0.7617820501327515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "import pandas as pd\n",
        "test_batch = next(iter(TestLoader))\n",
        "test_batch.to(device)\n",
        "with torch.no_grad():\n",
        "  pred, embedding = model(test_batch.x.float(), test_batch.edge_index, test_batch.batch)\n",
        "  df = pd.DataFrame()\n",
        "  df['y_real'] = test_batch.y.tolist()\n",
        "  df['y_pred'] = pred.tolist()\n",
        "df['y_real']  = df['y_real'].apply(lambda row: row[0])\n",
        "df['y_pred'] = df['y_pred'].apply(lambda row: row[0])\n",
        "df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "RS8VgVbxaslS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "a48ff41a-0333-4150-daf1-ede8cefbf9c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='y_real', ylabel='y_pred'>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt = sns.scatterplot(data = df, x = 'y_real', y = 'y_pred')\n",
        "plt.set(xlim = (-7,2), ylim = (-7,2))\n",
        "plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWTuZonjbXn8"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-c258pibYt4"
      },
      "source": [
        "#**We have achieved decent performance on the prediction of water solubility values for the ESOL dataset**"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "gpuType": "V28",
      "provenance": [],
      "authorship_tag": "ABX9TyM1jHaKMHqM6av66aFuRxci",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}