34_·_RNN_based_molecular_property_prediction.ipynb

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    "colab": {
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      "gpuType": "T4",
      "authorship_tag": "ABX9TyPYg8FcEdegiaEpjZzLZcwv",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shumshersubashgautam/Computer-Aided-Drug-Discovery-Learning/blob/main/34_%C2%B7_RNN_based_molecular_property_prediction.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRwMpwBf2udq",
        "outputId": "3f72d28e-392a-4ee3-cef9-35d4c112e062"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'teachopencadd'...\n",
            "remote: Enumerating objects: 17115, done.\u001b[K\n",
            "remote: Counting objects: 100% (2094/2094), done.\u001b[K\n",
            "remote: Compressing objects: 100% (971/971), done.\u001b[K\n",
            "remote: Total 17115 (delta 1311), reused 1784 (delta 1121), pack-reused 15021\u001b[K\n",
            "Receiving objects: 100% (17115/17115), 849.73 MiB | 32.24 MiB/s, done.\n",
            "Resolving deltas: 100% (11503/11503), done.\n",
            "Updating files: 100% (468/468), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/volkamerlab/teachopencadd.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eVR718vv3JOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
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        "id": "_GFN3noT5euO",
        "outputId": "b133892e-5c90-4120-cb57-c6b3a183e8f4"
      },
      "execution_count": 3,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2023.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.7/29.7 MB\u001b[0m \u001b[31m50.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.22.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (8.4.0)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2023.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Molecules as text\n",
        "\n",
        "Tokenization and one-hot encoding\n",
        "\n",
        "Recurrent Neural Networks (RNNs)\n",
        "\n",
        "Vanilla RNN\n",
        "\n",
        "Training an RNN\n",
        "\n",
        "Vanishing gradients\n",
        "\n",
        "Gated Recurrent Unit"
      ],
      "metadata": {
        "id": "I88CakO1Cyo3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import re\n",
        "from collections import Counter\n",
        "from pathlib import Path\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "from torch.utils.data import DataLoader, Dataset, Subset, TensorDataset"
      ],
      "metadata": {
        "id": "9u2aLl9TAvH-"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use cuda if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# seed random generator\n",
        "_ = torch.manual_seed(42)"
      ],
      "metadata": {
        "id": "CtPWo21SDC3A"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "HERE = Path(_dh[-1])\n",
        "DATA = HERE / \"data\""
      ],
      "metadata": {
        "id": "yUf03SOvDF2S"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load the dataset\n",
        "df = pd.read_csv(os.path.join(DATA, \"/content/teachopencadd/teachopencadd/talktorials/T034_recurrent_neural_networks/data/qm9.csv.gz\"), compression=\"gzip\")\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "KHiTmt6HDHwy",
        "outputId": "0777a1a1-4744-47e1-bd8a-cd512bddb03b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  mol_id          A           B           C      mu  alpha    homo    lumo  \\\n",
              "0  gdb_1  157.71180  157.709970  157.706990  0.0000  13.21 -0.3877  0.1171   \n",
              "1  gdb_2  293.60975  293.541110  191.393970  1.6256   9.46 -0.2570  0.0829   \n",
              "2  gdb_3  799.58812  437.903860  282.945450  1.8511   6.31 -0.2928  0.0687   \n",
              "3  gdb_4    0.00000   35.610036   35.610036  0.0000  16.28 -0.2845  0.0506   \n",
              "4  gdb_5    0.00000   44.593883   44.593883  2.8937  12.99 -0.3604  0.0191   \n",
              "\n",
              "      gap       r2  ...         u0       u298       h298       g298     cv  \\\n",
              "0  0.5048  35.3641  ... -40.478930 -40.476062 -40.475117 -40.498597  6.469   \n",
              "1  0.3399  26.1563  ... -56.525887 -56.523026 -56.522082 -56.544961  6.316   \n",
              "2  0.3615  19.0002  ... -76.404702 -76.401867 -76.400922 -76.422349  6.002   \n",
              "3  0.3351  59.5248  ... -77.308427 -77.305527 -77.304583 -77.327429  8.574   \n",
              "4  0.3796  48.7476  ... -93.411888 -93.409370 -93.408425 -93.431246  6.278   \n",
              "\n",
              "      u0_atom   u298_atom   h298_atom   g298_atom  smiles  \n",
              "0 -395.999595 -398.643290 -401.014647 -372.471772       C  \n",
              "1 -276.861363 -278.620271 -280.399259 -259.338802       N  \n",
              "2 -213.087624 -213.974294 -215.159658 -201.407171       O  \n",
              "3 -385.501997 -387.237686 -389.016047 -365.800724     C#C  \n",
              "4 -301.820534 -302.906752 -304.091489 -288.720028     C#N  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th></th>\n",
              "      <th>mol_id</th>\n",
              "      <th>A</th>\n",
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              "      <th>0</th>\n",
              "      <td>gdb_1</td>\n",
              "      <td>157.71180</td>\n",
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              "      <td>0.1171</td>\n",
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              "      <td>-398.643290</td>\n",
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              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>gdb_2</td>\n",
              "      <td>293.60975</td>\n",
              "      <td>293.541110</td>\n",
              "      <td>191.393970</td>\n",
              "      <td>1.6256</td>\n",
              "      <td>9.46</td>\n",
              "      <td>-0.2570</td>\n",
              "      <td>0.0829</td>\n",
              "      <td>0.3399</td>\n",
              "      <td>26.1563</td>\n",
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              "      <td>-56.523026</td>\n",
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              "      <td>6.316</td>\n",
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              "      <th>2</th>\n",
              "      <td>gdb_3</td>\n",
              "      <td>799.58812</td>\n",
              "      <td>437.903860</td>\n",
              "      <td>282.945450</td>\n",
              "      <td>1.8511</td>\n",
              "      <td>6.31</td>\n",
              "      <td>-0.2928</td>\n",
              "      <td>0.0687</td>\n",
              "      <td>0.3615</td>\n",
              "      <td>19.0002</td>\n",
              "      <td>...</td>\n",
              "      <td>-76.404702</td>\n",
              "      <td>-76.401867</td>\n",
              "      <td>-76.400922</td>\n",
              "      <td>-76.422349</td>\n",
              "      <td>6.002</td>\n",
              "      <td>-213.087624</td>\n",
              "      <td>-213.974294</td>\n",
              "      <td>-215.159658</td>\n",
              "      <td>-201.407171</td>\n",
              "      <td>O</td>\n",
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              "      <th>3</th>\n",
              "      <td>gdb_4</td>\n",
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              "      <td>35.610036</td>\n",
              "      <td>0.0000</td>\n",
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              "      <td>-387.237686</td>\n",
              "      <td>-389.016047</td>\n",
              "      <td>-365.800724</td>\n",
              "      <td>C#C</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.00000</td>\n",
              "      <td>44.593883</td>\n",
              "      <td>44.593883</td>\n",
              "      <td>2.8937</td>\n",
              "      <td>12.99</td>\n",
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              "      <td>0.0191</td>\n",
              "      <td>0.3796</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "class SmilesTokenizer(object):\n",
        "    \"\"\"\n",
        "    A simple regex-based tokenizer adapted from the deepchem smiles_tokenizer package.\n",
        "    SMILES regex pattern for the tokenization is designed by Schwaller et. al., ACS Cent. Sci 5 (2019)\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.regex_pattern = (\n",
        "            r\"(\\[[^\\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\\(|\\)|\\.\"\n",
        "            r\"|=|#|-|\\+|\\\\|\\/|:|~|@|\\?|>>?|\\*|\\$|\\%[0-9]{2}|[0-9])\"\n",
        "        )\n",
        "        self.regex = re.compile(self.regex_pattern)\n",
        "\n",
        "    def tokenize(self, smiles):\n",
        "        \"\"\"\n",
        "        Tokenizes SMILES string.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        smiles : str\n",
        "            Input SMILES string.\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "        List[str]\n",
        "            A list of tokens.\n",
        "        \"\"\"\n",
        "        tokens = [token for token in self.regex.findall(smiles)]\n",
        "        return tokens"
      ],
      "metadata": {
        "id": "KuTx_iU5DJbl"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_vocab(smiles_list, tokenizer, max_vocab_size):\n",
        "    \"\"\"\n",
        "    Builds a vocabulary of N=max_vocab_size most common tokens from list of SMILES strings.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    smiles_list : List[str]\n",
        "        List of SMILES strings.\n",
        "    tokenizer : SmilesTokenizer\n",
        "    max_vocab_size : int\n",
        "        Maximum size of vocabulary.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    Dict[str, int]\n",
        "        A dictionary that defines mapping of a token to its index in the vocabulary.\n",
        "    \"\"\"\n",
        "    tokenized_smiles = [tokenizer.tokenize(s) for s in smiles_list]\n",
        "    token_counter = Counter(c for s in tokenized_smiles for c in s)\n",
        "    tokens = [token for token, _ in token_counter.most_common(max_vocab_size)]\n",
        "    vocab = {token: idx for idx, token in enumerate(tokens)}\n",
        "    return vocab\n",
        "\n",
        "\n",
        "def smiles_to_ohe(smiles, tokenizer, vocab):\n",
        "    \"\"\"\n",
        "    Transforms SMILES string to one-hot encoding representation.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    smiles : str\n",
        "        Input SMILES string.\n",
        "    tokenizer : SmilesTokenizer\n",
        "    vocab : Dict[str, int]\n",
        "        A dictionary that defines mapping of a token to its index in the vocabulary.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    Tensor\n",
        "        A pytorch Tensor with shape (n_tokens, vocab_size), where n_tokens is the\n",
        "        length of tokenized input string, vocab_size is the number of tokens in\n",
        "        the vocabulary\n",
        "    \"\"\"\n",
        "    unknown_token_id = len(vocab) - 1\n",
        "    token_ids = [vocab.get(token, unknown_token_id) for token in tokenizer.tokenize(smiles)]\n",
        "    ohe = torch.eye(len(vocab))[token_ids]\n",
        "    return ohe"
      ],
      "metadata": {
        "id": "w_ZxrjFIDU-d"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = SmilesTokenizer()\n",
        "\n",
        "smiles = \"C=CCl\"\n",
        "print(\"SMILES string:\\n\\t\", smiles)\n",
        "print(\"Tokens:\\n\\t\", \", \".join(tokenizer.tokenize(smiles)))\n",
        "vocab = build_vocab([smiles], tokenizer, 3)\n",
        "print(\"Vocab:\\n\\t\", vocab)\n",
        "print(\"OHE:\\n\", np.array(smiles_to_ohe(smiles, tokenizer, vocab)).T)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2XagfxRHDXKN",
        "outputId": "44532796-6c62-413c-ddbb-f3e311d1252c"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SMILES string:\n",
            "\t C=CCl\n",
            "Tokens:\n",
            "\t C, =, C, Cl\n",
            "Vocab:\n",
            "\t {'C': 0, '=': 1, 'Cl': 2}\n",
            "OHE:\n",
            " [[1. 0. 1. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [0. 0. 0. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_size = 50000\n",
        "n_train = 40000\n",
        "n_test = n_val = 5000\n",
        "\n",
        "# get a sample\n",
        "df = df.sample(n=sample_size, axis=0, random_state=42)\n",
        "\n",
        "# select columns from the data frame\n",
        "smiles = df[\"smiles\"].tolist()\n",
        "y = df[\"mu\"].to_numpy()\n",
        "\n",
        "# build a vocab using the training data\n",
        "max_vocab_size = 30\n",
        "vocab = build_vocab(smiles[:n_train], tokenizer, max_vocab_size)\n",
        "vocab_size = len(vocab)\n",
        "\n",
        "# transform smiles to one-hot encoded tensors and apply padding\n",
        "X = pad_sequence(\n",
        "    sequences=[smiles_to_ohe(smi, tokenizer, vocab) for smi in smiles],\n",
        "    batch_first=True,\n",
        "    padding_value=0,\n",
        ")\n",
        "\n",
        "# normalize the target using the training data\n",
        "train_mean = y[:n_train].mean()\n",
        "train_std = y[:n_train].std()\n",
        "y = (y - train_mean) / train_std\n",
        "\n",
        "# build dataset\n",
        "data = TensorDataset(X, torch.Tensor(y))\n",
        "\n",
        "# define loaders\n",
        "ids_train = np.arange(n_train)\n",
        "ids_val = np.arange(n_val) + n_train\n",
        "ids_test = np.arange(n_test) + n_train + n_val\n",
        "train_loader = DataLoader(\n",
        "    Subset(data, ids_train),\n",
        "    batch_size=64,\n",
        "    shuffle=True,\n",
        "    generator=torch.Generator().manual_seed(42),\n",
        ")\n",
        "val_loader = DataLoader(\n",
        "    Subset(data, ids_val), batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(42)\n",
        ")\n",
        "test_loader = DataLoader(\n",
        "    Subset(data, ids_test),\n",
        "    batch_size=1,\n",
        "    shuffle=False,\n",
        "    generator=torch.Generator().manual_seed(42),\n",
        ")"
      ],
      "metadata": {
        "id": "CYOTAOoMDZA4"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RNNRegressionModel(nn.Module):\n",
        "    \"\"\"Vanilla RNN with one recurrent layer\"\"\"\n",
        "\n",
        "    def __init__(self, input_size, hidden_size=32, num_layers=1):\n",
        "        \"\"\"\n",
        "        Vanilla RNN\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        input_size : int\n",
        "            The number of expected features in the input vector\n",
        "        hidden_size : int\n",
        "            The number of features in the hidden state\n",
        "\n",
        "        \"\"\"\n",
        "        super(RNNRegressionModel, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_size, 1)\n",
        "        self.dropout = nn.Dropout(p=0.2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n",
        "        out, hn = self.rnn(x, h0)\n",
        "        out = out[:, -1]\n",
        "        out = self.dropout(out)\n",
        "        out = self.fc(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "class GRURegressionModel(nn.Module):\n",
        "    \"\"\"GRU network with one recurrent layer\"\"\"\n",
        "\n",
        "    def __init__(self, input_size, hidden_size=32, num_layers=1):\n",
        "        \"\"\"\n",
        "        GRU network\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        input_size : int\n",
        "            The number of expected features in the input vector\n",
        "        hidden_size : int\n",
        "            The number of features in the hidden state\n",
        "\n",
        "        \"\"\"\n",
        "        super(GRURegressionModel, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "        self.gru = nn.GRU(input_size, hidden_size, num_layers=1, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_size, 1)\n",
        "        self.dropout = nn.Dropout(p=0.2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n",
        "        out, hn = self.gru(x, h0)\n",
        "        out = out[:, -1]\n",
        "        out = self.dropout(out)\n",
        "        out = self.fc(out)\n",
        "        return out"
      ],
      "metadata": {
        "id": "FURw4rbuDcFm"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ModelTrainer(object):\n",
        "    \"\"\"A class that provides training and validation infrastructure for the model and keeps track of training and validation metrics.\"\"\"\n",
        "\n",
        "    def __init__(self, model, lr, name=None, clip_gradients=False):\n",
        "        \"\"\"\n",
        "        Initialization.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        model : nn.Module\n",
        "            a model\n",
        "        lr : float\n",
        "            learning rate for one training step\n",
        "\n",
        "        \"\"\"\n",
        "        self.model = model\n",
        "        self.lr = lr\n",
        "        self.criterion = torch.nn.MSELoss()\n",
        "        self.optimizer = torch.optim.Adam(self.model.parameters(), self.lr)\n",
        "        self.clip_gradients = clip_gradients\n",
        "        self.model.to(device)\n",
        "\n",
        "        self.train_loss = []\n",
        "        self.batch_loss = []\n",
        "        self.val_loss = []\n",
        "\n",
        "    def _train_epoch(self, loader):\n",
        "        self.model.train()\n",
        "        epoch_loss = 0\n",
        "        batch_losses = []\n",
        "        for i, (X_batch, y_batch) in enumerate(loader):\n",
        "            X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
        "            self.optimizer.zero_grad()\n",
        "            y_pred = self.model(X_batch)\n",
        "            loss = self.criterion(y_pred, y_batch.unsqueeze(1))\n",
        "            loss.backward()\n",
        "\n",
        "            if self.clip_gradients:\n",
        "                torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1, norm_type=2)\n",
        "\n",
        "            self.optimizer.step()\n",
        "            epoch_loss += loss.item()\n",
        "            batch_losses.append(loss.item())\n",
        "\n",
        "        return epoch_loss / len(loader), batch_losses\n",
        "\n",
        "    def _eval_epoch(self, loader):\n",
        "        self.model.eval()\n",
        "        val_loss = 0\n",
        "        predictions = []\n",
        "        targets = []\n",
        "        with torch.no_grad():\n",
        "            for X_batch, y_batch in loader:\n",
        "                X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
        "                y_pred = self.model(X_batch)\n",
        "                loss = self.criterion(y_pred, y_batch.unsqueeze(1))\n",
        "                val_loss += loss.item()\n",
        "                predictions.append(y_pred.detach().cpu().numpy())\n",
        "                targets.append(y_batch.unsqueeze(1).detach().cpu().numpy())\n",
        "\n",
        "        predictions = np.concatenate(predictions).flatten()\n",
        "        targets = np.concatenate(targets).flatten()\n",
        "        return val_loss / len(loader), predictions, targets\n",
        "\n",
        "    def train(self, train_loader, val_loader, n_epochs, print_every=10):\n",
        "        \"\"\"\n",
        "        Train the model\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        train_loader :\n",
        "            a dataloader with training data\n",
        "        val_loader :\n",
        "            a dataloader with training data\n",
        "        n_epochs :\n",
        "            number of epochs to train for\n",
        "        \"\"\"\n",
        "        for e in range(n_epochs):\n",
        "            train_loss, train_loss_batches = self._train_epoch(train_loader)\n",
        "            val_loss, _, _ = self._eval_epoch(test_loader)\n",
        "            self.batch_loss += train_loss_batches\n",
        "            self.train_loss.append(train_loss)\n",
        "            self.val_loss.append(val_loss)\n",
        "            if e % print_every == 0:\n",
        "                print(f\"Epoch {e+0:03} | train_loss: {train_loss:.5f} | val_loss: {val_loss:.5f}\")\n",
        "\n",
        "    def validate(self, val_loader):\n",
        "        \"\"\"\n",
        "        Validate the model\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        val_loader :\n",
        "            a dataloader with training data\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "        Tuple[list, list, list]\n",
        "            Loss, y_predicted, y_target for each datapoint in val_loader.\n",
        "        \"\"\"\n",
        "        loss, y_pred, y_targ = self._eval_epoch(val_loader)\n",
        "        return loss, y_pred, y_targ"
      ],
      "metadata": {
        "id": "1IzQqUPBDenl"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn = ModelTrainer(\n",
        "    model=RNNRegressionModel(vocab_size, hidden_size=32),\n",
        "    lr=1e-3,\n",
        ")"
      ],
      "metadata": {
        "id": "0z-0P-S1Dha-"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_gru = ModelTrainer(\n",
        "    model=GRURegressionModel(vocab_size, hidden_size=32),\n",
        "    lr=1e-3,\n",
        ")"
      ],
      "metadata": {
        "id": "QefatLGuEw6x"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn.train(train_loader, val_loader, 51)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W80fmOPwDjEV",
        "outputId": "d09617e6-afe8-4941-ec58-4d365934014a"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 000 | train_loss: 0.85954 | val_loss: 0.63604\n",
            "Epoch 010 | train_loss: 0.55200 | val_loss: 0.50211\n",
            "Epoch 020 | train_loss: 0.49238 | val_loss: 0.44084\n",
            "Epoch 030 | train_loss: 0.45500 | val_loss: 0.41160\n",
            "Epoch 040 | train_loss: 0.43041 | val_loss: 0.40063\n",
            "Epoch 050 | train_loss: 0.41760 | val_loss: 0.39637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_gru.train(train_loader, val_loader, 51)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqS56o_sHk_o",
        "outputId": "fe2b0f87-2b65-419e-b745-6b50f01b579f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 000 | train_loss: 0.75208 | val_loss: 0.54450\n",
            "Epoch 010 | train_loss: 0.43109 | val_loss: 0.42034\n",
            "Epoch 020 | train_loss: 0.37559 | val_loss: 0.38635\n",
            "Epoch 030 | train_loss: 0.34615 | val_loss: 0.35569\n",
            "Epoch 040 | train_loss: 0.32072 | val_loss: 0.34151\n",
            "Epoch 050 | train_loss: 0.30018 | val_loss: 0.34079\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_rnn.train_loss, label=f\"RNN train\")\n",
        "plt.plot(model_rnn.val_loss, label=f\"RNN val\")\n",
        "plt.plot(model_gru.train_loss, label=f\"GRU train\")\n",
        "plt.plot(model_gru.val_loss, label=f\"GRU val\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "RiJVj49TDllf",
        "outputId": "cebadeab-e9ab-4356-e722-3217ad338543"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fac15912c50>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, axarr = plt.subplots(1, 2, figsize=(9, 5), constrained_layout=True)\n",
        "\n",
        "f.suptitle(\"Ground truth vs predicted values\")\n",
        "\n",
        "loss, y_pred, y_targ = model_rnn.validate(test_loader)\n",
        "print(f\"RNN test loss: {loss:.3f}\")\n",
        "\n",
        "_ = axarr[0].scatter(y_pred, y_targ, alpha=0.5, s=0.5)\n",
        "_ = axarr[0].plot(y_targ, y_targ, lw=0.4, c=\"r\")\n",
        "_ = axarr[0].set_title(\"RNN\")\n",
        "\n",
        "loss, y_pred, y_targ = model_gru.validate(test_loader)\n",
        "print(f\"GRU test loss: {loss:.3f}\")\n",
        "\n",
        "_ = axarr[1].scatter(y_pred, y_targ, alpha=0.5, s=0.5)\n",
        "_ = axarr[1].plot(y_targ, y_targ, lw=0.4, c=\"r\")\n",
        "_ = axarr[1].set_title(\"GRU\")\n",
        "\n",
        "for ax in axarr:\n",
        "    _ = ax.set_xlabel(\"y_pred\")\n",
        "    _ = ax.set_ylabel(\"y_true\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "W71eOh66GKdo",
        "outputId": "eabcc891-e23a-4e49-f06c-5be63ccfc610"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RNN test loss: 0.396\n",
            "GRU test loss: 0.341\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion**\n",
        "Natural language processing-based models proved to be a powerful tool for a wide range of molecular tasks. In this talktorial we introduced the basics of Recurrent Neural Network architectures and demonstrated their application to the regression task on the QM9 dataset. We have learned how to pre-process SMILES strings, build a model using PyTorch, and train the model to predict the dipole moment of the molecule."
      ],
      "metadata": {
        "id": "9HF7ux-eGS3A"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nZy2IEPzGRqf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}