GNN.ipynb

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          "model_module": "@jupyter-widgets/controls",
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            "_model_name": "DescriptionStyleModel",
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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/kangjunseo/Bioinformatics/blob/main/GNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113\n",
        "!pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cu113.html\n",
        "!pip install pytorch-lightning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-moARvuApGk",
        "outputId": "14dceeb4-b907-4a66-b25b-139503d4e9ea"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://download.pytorch.org/whl/cu113\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://data.pyg.org/whl/torch-1.12.0+cu113.html\n",
            "Collecting torch-scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-1.12.0%2Bcu113/torch_scatter-2.0.9-cp37-cp37m-linux_x86_64.whl (7.9 MB)\n",
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            "\u001b[?25hCollecting torch-sparse\n",
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            "\u001b[?25hCollecting torch-cluster\n",
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            "\u001b[K     |████████████████████████████████| 2.4 MB 69.8 MB/s \n",
            "\u001b[?25hCollecting torch-spline-conv\n",
            "  Downloading https://data.pyg.org/whl/torch-1.12.0%2Bcu113/torch_spline_conv-1.2.1-cp37-cp37m-linux_x86_64.whl (709 kB)\n",
            "\u001b[K     |████████████████████████████████| 709 kB 39.9 MB/s \n",
            "\u001b[?25hCollecting torch-geometric\n",
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            "Building wheels for collected packages: torch-geometric\n",
            "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-2.1.0.post1-py3-none-any.whl size=689859 sha256=fec5d1fab08a75604e99d04513f7ef9c3bf337d47a06c3fbd43e096b5bfb1691\n",
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            "Successfully built torch-geometric\n",
            "Installing collected packages: torch-spline-conv, torch-sparse, torch-scatter, torch-geometric, torch-cluster\n",
            "Successfully installed torch-cluster-1.6.0 torch-geometric-2.1.0.post1 torch-scatter-2.0.9 torch-sparse-0.6.15 torch-spline-conv-1.2.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pytorch-lightning\n",
            "  Downloading pytorch_lightning-1.8.2-py3-none-any.whl (798 kB)\n",
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            "\u001b[?25hCollecting lightning-utilities==0.3.*\n",
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            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning) (21.3)\n",
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            "Requirement already satisfied: fsspec[http]>2021.06.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning) (2022.10.0)\n",
            "Collecting torchmetrics>=0.7.0\n",
            "  Downloading torchmetrics-0.10.3-py3-none-any.whl (529 kB)\n",
            "\u001b[K     |████████████████████████████████| 529 kB 70.8 MB/s \n",
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            "Collecting fire\n",
            "  Downloading fire-0.4.0.tar.gz (87 kB)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.9.1->pytorch-lightning) (3.2.2)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from fire->lightning-utilities==0.3.*->pytorch-lightning) (2.1.0)\n",
            "Building wheels for collected packages: fire\n",
            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fire: filename=fire-0.4.0-py2.py3-none-any.whl size=115940 sha256=24534350155f7207c86fd3872df421dcd8b0d955e96af420d235eab8f1a1e404\n",
            "  Stored in directory: /root/.cache/pip/wheels/8a/67/fb/2e8a12fa16661b9d5af1f654bd199366799740a85c64981226\n",
            "Successfully built fire\n",
            "Installing collected packages: fire, torchmetrics, lightning-utilities, pytorch-lightning\n",
            "Successfully installed fire-0.4.0 lightning-utilities-0.3.0 pytorch-lightning-1.8.2 torchmetrics-0.10.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "-d9yjdEXxln4"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from pytorch_lightning import LightningModule"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
        "test_set = torchvision.datasets.CIFAR10(root=',/data', train=False, download=True, transform=transform)\n",
        "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "print('*==========Train Set==========*')\n",
        "print(train_set)\n",
        "print('*========== Test Set ==========*')\n",
        "print(test_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510,
          "referenced_widgets": [
            "2dee8595cbb94a199dcd5dff221cdf29",
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        },
        "id": "rWGnUQ7Z5FsB",
        "outputId": "85bbc269-39cf-423b-f3a8-173e78e4d5bf"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dee8595cbb94a199dcd5dff221cdf29"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ,/data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acc4d9b085c443e4aa6b9a5ada0f4ed8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ,/data/cifar-10-python.tar.gz to ,/data\n",
            "*==========Train Set==========*\n",
            "Dataset CIFAR10\n",
            "    Number of datapoints: 50000\n",
            "    Root location: ./data\n",
            "    Split: Train\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
            "           )\n",
            "*========== Test Set ==========*\n",
            "Dataset CIFAR10\n",
            "    Number of datapoints: 10000\n",
            "    Root location: ,/data\n",
            "    Split: Test\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
            "           )\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "img = train_set[1][0]\n",
        "label = train_set[1][1]\n",
        "img = img.numpy()\n",
        "\n",
        "print(img.shape)\n",
        "print(f'{classes[label]}-{label}')\n",
        "\n",
        "origin_img = img / 2 + 0.5 # unnormalized\n",
        "plt.imshow(np.transpose(origin_img, (1, 2, 0)))\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.imshow(np.transpose(img, (1, 2, 0)))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "DurEZewG5-rq",
        "outputId": "5039eaf9-979f-40c3-8c1d-de494ebe6ec1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3, 32, 32)\n",
            "truck-9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from pytorch_lightning import LightningDataModule\n",
        "from torch_geometric.data import Data, Batch\n",
        "from torch.nn.functional import pad\n",
        "\n",
        "class CustomData(LightningDataModule):\n",
        "  def __init__(self, training_set, validation_set, test_set, batch_size=128, num_workers=1):\n",
        "    super().__init__()\n",
        "    self.training_set = training_set\n",
        "    self.validation_set = validation_set\n",
        "    self.test_set = test_set\n",
        "    self.batch_size = batch_size\n",
        "    self.num_workers = num_workers\n",
        "\n",
        "  #how to make a batch\n",
        "  def collate_function(self, batch):    \n",
        "    return Batch.from_data_list([Data(edge_index=data.edge_index,\n",
        "                                      x=pad(data.x, (0,3), 'constant', 0.)[:,:53],\n",
        "                                      edge_attr=data.edge_attr,\n",
        "                                      y=data.y.unsqueeze(0).float()) for data in batch])\n",
        "\n",
        "  def train_dataloader(self):\n",
        "    return DataLoader(self.training_set, batch_size=self.batch_size, \n",
        "                      num_workers=self.num_workers, shuffle=True, \n",
        "                      collate_fn=self.collate_function)\n",
        "    \n",
        "  def val_dataloader(self):\n",
        "    return DataLoader(self.validation_set, batch_size=self.batch_size, \n",
        "                      num_workers=self.num_workers, shuffle=True, \n",
        "                      collate_fn=self.collate_function)\n",
        "\n",
        "  def test_dataloader(self):\n",
        "    return DataLoader(self.test_set, batch_size=self.batch_size, \n",
        "                      num_workers=self.num_workers, \n",
        "                      collate_fn=self.collate_function)\n"
      ],
      "metadata": {
        "id": "UxburyTt6p_Z"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn import Module, Linear\n",
        "from torch_geometric.nn import GATConv, global_mean_pool\n",
        "\n",
        "class CustomGAT(Module):\n",
        "  def __init__(self, input_size, label_size, layer_size=64, dropout=0.1, heads=2):\n",
        "    super().__init__()\n",
        "    self.input_size = input_size\n",
        "    self.label_size = label_size\n",
        "    self.layer_size = layer_size\n",
        "    self.dropout = dropout\n",
        "    self.heads = heads\n",
        "    self.setup()\n",
        "\n",
        "  def setup(self):\n",
        "    self.first_layer = GATConv(self.input_size, self.layer_size, dropout=self.dropout, heads=self.heads, concat=False)\n",
        "    self.last_layer = GATConv(self.layer_size, self.layer_size, dropout=self.dropout, heads=self.heads, concat=False)\n",
        "    self.ffnn = Linear(self.layer_size, self.label_size)\n",
        "\n",
        "  def convert_graph_into_single_vector(self, graph_hidden, batch_index):\n",
        "    return global_mean_pool(graph_hidden, batch_index)\n",
        "\n",
        "  def forward(self, batch):\n",
        "    z = self.first_layer(batch.x, batch.edge_index)\n",
        "    z = self.last_layer(z, batch.edge_index)\n",
        "    z = self.convert_graph_into_single_vector(z, batch.batch)\n",
        "    z = self.ffnn(z)\n",
        "    return z"
      ],
      "metadata": {
        "id": "vuxUy2A56qUd"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch_geometric.datasets import TUDataset\n",
        "\n",
        "training_dataset = TUDataset('./dataset', 'Tox21_AhR_training')\n",
        "validation_dataset = TUDataset('./dataset', 'Tox21_AhR_testing')\n",
        "test_dataset = TUDataset('./dataset', 'Tox21_AhR_evaluation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKgrAvjL9iLs",
        "outputId": "613af61e-d367-4ea6-f331-eb673ec24e78"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://www.chrsmrrs.com/graphkerneldatasets/Tox21_AhR_training.zip\n",
            "Extracting dataset/Tox21_AhR_training/Tox21_AhR_training.zip\n",
            "Processing...\n",
            "Done!\n",
            "Downloading https://www.chrsmrrs.com/graphkerneldatasets/Tox21_AhR_testing.zip\n",
            "Extracting dataset/Tox21_AhR_testing/Tox21_AhR_testing.zip\n",
            "Processing...\n",
            "Done!\n",
            "Downloading https://www.chrsmrrs.com/graphkerneldatasets/Tox21_AhR_evaluation.zip\n",
            "Extracting dataset/Tox21_AhR_evaluation/Tox21_AhR_evaluation.zip\n",
            "Processing...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pytorch_lightning import LightningModule\n",
        "from torch.nn import BCEWithLogitsLoss\n",
        "from torch.optim import Adam\n",
        "\n",
        "class CustomModel(LightningModule):\n",
        "  def __init__(self, model, learning_rate=1e-3):\n",
        "    super().__init__()\n",
        "    self.model = model\n",
        "    self.lr = learning_rate\n",
        "\n",
        "  def forward(self, batch, mode):\n",
        "    z = self.model(batch)\n",
        "    loss = self.loss_function(z, batch.y)\n",
        "    self.log(f\"{mode}_loss\", loss, batch_size=batch.y.size(0), prog_bar=True, on_step=False, on_epoch=True)\n",
        "    return loss, z, batch.y\n",
        "\n",
        "  def training_step(self, batch, batch_idx):\n",
        "    loss, predict, answer = self(batch, 'train')\n",
        "    return {'loss':loss, 'predict':predict, 'answer':answer}\n",
        "\n",
        "  def validation_step(self, batch, batch_idx):\n",
        "    loss, predict, answer = self(batch, 'val')\n",
        "    return {'loss':loss, 'predict':predict, 'answer':answer}\n",
        "\n",
        "  def test_step(self, batch, batch_idx):\n",
        "    loss, predict, answer = self(batch, 'test')\n",
        "    return {'loss':loss, 'predict':predict, 'answer':answer}\n",
        "\n",
        "  def predict_step(self, batch, batch_idx):\n",
        "    predict = self.model(batch)\n",
        "    return predict\n",
        "\n",
        "  def loss_function(self, output, target):\n",
        "    return BCEWithLogitsLoss(reduction='mean')(output, target)\n",
        "\n",
        "  def configure_optimizers(self):\n",
        "    optimizer = Adam(self.parameters(), lr=self.lr)\n",
        "    return optimizer"
      ],
      "metadata": {
        "id": "MldYMQKu_2NE"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pytorch_lightning import Trainer\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')\n",
        "\n",
        "data_module = CustomData(training_dataset, validation_dataset, test_dataset)\n",
        "\n",
        "gat = CustomGAT(53, 1)\n",
        "model = CustomModel(gat)\n",
        "\n",
        "trainer = Trainer(max_epochs=1, accelerator='gpu', devices=[0])\n",
        "trainer.fit(model, datamodule=data_module)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335,
          "referenced_widgets": [
            "6578809032bb4deaa2642554515d60bc",
            "631921eadba14779a47752158398ab60",
            "e2fd071c192540e5be478721d80cd17c",
            "b15b46d27d6e44f69b3efb78ca96e5f4",
            "7937b94aecb2438590637d8db93a0b2e",
            "7dd5d5bdf5674fe8b4aa5bddfc22fc11",
            "33fbaae169ff485c8c99d50765588f28",
            "72d1e1fba956448e8d6f8279eaa39249",
            "20ec64633fbf48fdb38dacd5ba75518d",
            "b0b1bbc701054d30945731ddf735b2ed",
            "d5e6199ace434a8293f18d3abf30702e",
            "df7ef99d3baa49beaabcb142f1957928",
            "5107b246a69f481fa028c35581287eff",
            "c635f875bc184e44a21439a42e52a4cc",
            "f7a3ffc225634ed386a4050db349f459",
            "cd7919841d494ba49084b70cdf6b8e5a",
            "abdb509e44bd43f1b5753ea5d0bdd6a0",
            "815f2a8e73c545638f4105806381a620",
            "20c8f8a8060a4517b5d1cf2f1ea150c9",
            "7bddd5b851d04dc78aea2c8168be5c0d",
            "ef7fbe821f73400c8935f327d30b2b09",
            "ce6fa03a08264eb28dd975c22f805284",
            "784ecfabdd7f4c76b1fb05409a41f03c",
            "6d8c89f5e4e94f9898370b86a64b7af3",
            "076bbef4f61a4bf58b811bf4b21ce0af",
            "24b3dad659594ad38b726432056b101c",
            "de95f13e32cf464cbddd8cd0a8b317e7",
            "3129be17c4774248a84a5768f3b287b3",
            "5f11d3c976bb4effa208ba1ef6061cb0",
            "e16e5a0e57f549c786b15081df816b97",
            "075f395b6e414ed79eee1aa4fd913912",
            "229d28539bff400997086c1ccbad94af",
            "e173aef92ce44ecc91add1fb76ec8f91"
          ]
        },
        "id": "VvQ7VrnSCPn6",
        "outputId": "7163c7b7-0a23-44a5-c9c2-84c0c4a1b49e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name  | Type      | Params\n",
            "------------------------------------\n",
            "0 | model | CustomGAT | 15.7 K\n",
            "------------------------------------\n",
            "15.7 K    Trainable params\n",
            "0         Non-trainable params\n",
            "15.7 K    Total params\n",
            "0.063     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6578809032bb4deaa2642554515d60bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df7ef99d3baa49beaabcb142f1957928"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "784ecfabdd7f4c76b1fb05409a41f03c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=1` reached.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchmetrics.functional import auroc\n",
        "\n",
        "outputs = trainer.predict(model, dataloaders=data_module.test_dataloader())\n",
        "y = torch.concat(outputs)\n",
        "x = torch.concat([batch.y for batch in data_module.test_dataloader()]).int()\n",
        "\n",
        "evaluation = auroc(y, x)\n",
        "print(f\"auc-roc: {evaluation}\")"
      ],
      "metadata": {
        "id": "wfB__xVbCxIW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "0741e10158dc4af78d0c30e0172773e3",
            "293a3d69d20e416f86b8f7aa01eb3d21",
            "44574b14fbec41c689b1985f7c47713b",
            "51813e2fa19c481daa7c944880fcdd05",
            "c522b3ddf172415c9ac5351ab78fdaa9",
            "c2883a57e0b5450da463388c233c249a",
            "c11fd27e7f08440b8d989c06800122a8",
            "3316cd774bb94c3993c137632e538a15",
            "32e9f701798a4c59b36610405bc84769",
            "c7fb0968ef924f9fa0919c2a1395ff21",
            "3ecf48419734443dabbcaf1a9a0f202b"
          ]
        },
        "outputId": "87832715-b422-47d1-871b-3d15bbb17e18"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 64it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0741e10158dc4af78d0c30e0172773e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auc-roc: 0.4305001199245453\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WkPiSuoWCttW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}