GIN_12Layer_MLP_4Layer_P2P.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "collapsed_sections": [
        "4KD-KUsKDiLE",
        "r0VJxZnIDpJT",
        "igYJ4y99D-bU",
        "z_dcpDhREijU",
        "fTEnWpOOEZW9",
        "8rh6Ca1XE6ar",
        "RRCYp7yuLxMe"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TruongDuyLongPTIT/Experiment2/blob/main/12GIN_MLP_4Layer_P2P.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# I. Pip & Import."
      ],
      "metadata": {
        "id": "4KD-KUsKDiLE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H7D6x31EU0fi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ef5b044-bfc1-426f-a788-0c8423314ff5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch-geometric==1.4.3 in /usr/local/lib/python3.7/dist-packages (1.4.3)\n",
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            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba->torch-geometric==1.4.3) (4.13.0)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba->torch-geometric==1.4.3) (4.1.1)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->torch-geometric==1.4.3) (2022.5)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->torch-geometric==1.4.3) (0.11.0)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: tables in /usr/local/lib/python3.7/dist-packages (from deepdish==0.3.5) (3.7.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tables->deepdish==0.3.5) (21.3)\n",
            "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.7/dist-packages (from tables->deepdish==0.3.5) (2.8.4)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tables->deepdish==0.3.5) (3.0.9)\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import os.path as osp\n",
        "import torch\n",
        "import sys\n",
        "\n",
        "os.environ['TORCH'] = torch.__version__\n",
        "\n",
        "!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install torch-geometric==1.4.3\n",
        "!pip install torch-cluster -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install deepdish==0.3.5\n",
        "\n",
        "import torch.nn as nn\n",
        "from torch.nn import Parameter\n",
        "from torch_geometric.utils import scatter_\n",
        "from torch_geometric.nn.inits import glorot, zeros\n",
        "from torch_scatter import scatter_add\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "\n",
        "from itertools import chain\n",
        "import pickle\n",
        "import h5py\n",
        "import deepdish as dd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "import inspect\n",
        "import time\n",
        "import math\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def time_since(start):\n",
        "    now = time.time()\n",
        "    s = now - start\n",
        "    m = math.floor(s / 60)\n",
        "    s -= m * 60\n",
        "    h = math.floor(m / 60)\n",
        "    m -= h * 60\n",
        "    if h == 0:\n",
        "        if m == 0:\n",
        "            return '%ds' % s\n",
        "        else:\n",
        "            return '%dm %ds' % (m, s)\n",
        "    else:\n",
        "        return '%dh %dm %ds' % (h, m, s)\n"
      ],
      "metadata": {
        "id": "X5j9SrCo4uM5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "EIm8WTU3DhDP",
        "outputId": "515ab95a-2ef6-442f-9b2c-c75391c81cc2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# II. Data"
      ],
      "metadata": {
        "id": "r0VJxZnIDpJT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data utils\n",
        "def h5group_to_dict(h5group):\n",
        "    group_dict = {k: v[()] for k, v in chain(h5group.items(), h5group.attrs.items())}\n",
        "    return group_dict\n",
        "\n",
        "def sub_dict(full_dict, *keys, to_tensor):\n",
        "    return {k: torch.tensor(full_dict[k]) if to_tensor else full_dict[k] for k in keys if k in full_dict}\n",
        "\n",
        "def build_graph_from_dict_pyg(graph_dict, to_tensor=True):\n",
        "    from torch_geometric.data import Data\n",
        "\n",
        "    g = Data(**sub_dict(graph_dict, 'edge_index', 'x', 'y', 'edge_attr', 'edge_y', to_tensor=to_tensor))\n",
        "    return g\n",
        "\n",
        "# Data loader\n",
        "class GraphDataLoader(DataLoader):\n",
        "    def __init__(self, dataset, batch_size=128, shuffle=False, num_workers=0):\n",
        "\n",
        "        def collate_graph(graph_obj_list):\n",
        "            from torch_geometric.data import Batch\n",
        "            batch = Batch.from_data_list(graph_obj_list)\n",
        "            return batch\n",
        "\n",
        "        super().__init__(\n",
        "            dataset,\n",
        "            batch_size=batch_size,\n",
        "            shuffle=shuffle,\n",
        "            collate_fn=collate_graph,\n",
        "            num_workers=num_workers)\n",
        "\n",
        "# BotnetDataset\n",
        "class BotnetDataset(Dataset):\n",
        "\n",
        "    def __init__(self, name='chord', root='data/botnet', split='train', graph_format='pyg', split_idx=None, add_nfeat_ones=True,\n",
        "                 in_memory=True):\n",
        "        super().__init__()\n",
        "        assert name in ['chord', 'debru', 'kadem', 'leet', 'c2', 'p2p']\n",
        "        assert split in ['train', 'val', 'test']\n",
        "\n",
        "        if isinstance(root, str):\n",
        "            root = osp.expanduser(osp.normpath(root))\n",
        "\n",
        "        self.name = name\n",
        "        self.root = root\n",
        "        self.split = split\n",
        "        self.split_idx = split_idx\n",
        "        self.add_nfeat_ones = add_nfeat_ones\n",
        "\n",
        "        self._graph_format = graph_format\n",
        "        if split == 'train':\n",
        "            self.path = self.processed_paths[0]\n",
        "        elif split == 'val':\n",
        "            self.path = self.processed_paths[1]\n",
        "        elif split == 'test':\n",
        "            self.path = self.processed_paths[2]\n",
        "\n",
        "        if in_memory:\n",
        "            self.data = dd.io.load(self.path)  # dictionary\n",
        "            self.data_type = 'dict'\n",
        "            self.num_graphs = self.data['num_graphs']\n",
        "        else:\n",
        "            # self.data = h5py.File(self.path, 'r')\n",
        "            self.data = None    # defer opening file in each process to make multiprocessing work\n",
        "            self.data_type = 'file'\n",
        "            with h5py.File(self.path, 'r') as f:\n",
        "                self.num_graphs = f.attrs['num_graphs']\n",
        "            \n",
        "    @property\n",
        "    def processed_dir(self):\n",
        "        return osp.join(self.root, 'processed')\n",
        "\n",
        "    @property\n",
        "    def processed_file_names(self):\n",
        "        return [self.name + '_' + s + '.hdf5' for s in ('train', 'val', 'test')]\n",
        "\n",
        "    @property\n",
        "    def processed_paths(self):\n",
        "        return [osp.join(self.processed_dir, f) for f in self.processed_file_names]\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.num_graphs\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        if self.data_type == 'dict':\n",
        "            graph_dict = self.data[str(index)]\n",
        "        elif self.data_type == 'file':\n",
        "            if self.data is None:\n",
        "                # only open once in each process\n",
        "                self.data = h5py.File(self.path, 'r')\n",
        "            graph_dict = h5group_to_dict(self.data[str(index)])\n",
        "        else:\n",
        "            raise ValueError\n",
        "\n",
        "        # graph_format == 'pyg':\n",
        "        return build_graph_from_dict_pyg(graph_dict)\n",
        "\n",
        "\n",
        "    def __iter__(self):\n",
        "        for i in range(self.num_graphs):\n",
        "            yield self[i]\n",
        "\n",
        "    def __repr__(self):\n",
        "        return f'{self.__class__.__name__}(topology: {self.name} | split: {self.split} | ' \\\n",
        "               f'#graphs: {len(self)} | graph format: {self.graph_format})'\n"
      ],
      "metadata": {
        "id": "JHHcJ7B-4BmU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# III. Measure Performancce"
      ],
      "metadata": {
        "id": "igYJ4y99D-bU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1. Metrics"
      ],
      "metadata": {
        "id": "z_dcpDhREijU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def f1(target, pred, label):\n",
        "    # F1 = 2 * (precision * recall) / (precision + recall)\n",
        "    tp = np.sum((target==label) & (pred==label))\n",
        "    fp = np.sum((target!=label) & (pred==label))\n",
        "    fn = np.sum((pred!=label) & (target==label))\n",
        "    \n",
        "    if tp+fp==0 or tp+fn==0:\n",
        "      return np.nan\n",
        "\n",
        "    precision = tp/(tp+fp)\n",
        "    recall = tp/(tp+fn)\n",
        "    \n",
        "    if precision+recall==0:\n",
        "      return np.nan\n",
        "      \n",
        "    f1 = 2 * (precision * recall) / (precision + recall)\n",
        "    return f1\n",
        "\n",
        "def f1_macro(pred, target):\n",
        "    return np.mean([f1(target, pred, label) for label in range(0, 2)])\n",
        "\n",
        "\n",
        "def accuracy(pred, target):\n",
        "    return (pred == target).sum().item() / len(target)\n",
        "\n",
        "\n",
        "def true_positive(pred, target):\n",
        "    return (target[pred == 1] == 1).sum().item()\n",
        "\n",
        "\n",
        "def false_positive(pred, target):\n",
        "    return (target[pred == 1] == 0).sum().item()\n",
        "\n",
        "\n",
        "def true_negative(pred, target):\n",
        "    return (target[pred == 0] == 0).sum().item()\n",
        "\n",
        "\n",
        "def false_negative(pred, target):\n",
        "    return (target[pred == 0] == 1).sum().item()\n",
        "\n",
        "\n",
        "def recall(pred, target):\n",
        "    try:\n",
        "        return true_positive(pred, target) / (target == 1).sum().item()\n",
        "    except:  # divide by zero\n",
        "        return -1\n",
        "\n",
        "\n",
        "def precision(pred, target):\n",
        "    try:\n",
        "        prec = true_positive(pred, target) / (pred == 1).sum().item()\n",
        "        return prec\n",
        "    except:  # divide by zero\n",
        "        return -1\n",
        "\n",
        "\n",
        "def f1_score(pred, target):\n",
        "    prec = precision(pred, target)\n",
        "    rec = recall(pred, target)\n",
        "    try:\n",
        "        return 2 * (prec * rec) / (prec + rec)\n",
        "    except:\n",
        "        return 0\n",
        "\n",
        "\n",
        "def false_positive_rate(pred, target):\n",
        "    try:\n",
        "        return false_positive(pred, target) / (target == 0).sum().item()\n",
        "    except:  # divide by zero\n",
        "        return -1\n",
        "\n",
        "\n",
        "def false_negative_rate(pred, target):\n",
        "    try:\n",
        "        return false_negative(pred, target) / (target == 1).sum().item()\n",
        "    except:  # divide by zero\n",
        "        return -1\n"
      ],
      "metadata": {
        "id": "BN-QIjMREw-7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2. Evaluation"
      ],
      "metadata": {
        "id": "fTEnWpOOEZW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_metrics(target, pred_prob, threshold=0.5):\n",
        "    if isinstance(target, torch.Tensor):\n",
        "        target = target.cpu().numpy()\n",
        "    if isinstance(pred_prob, torch.Tensor):\n",
        "        pred_prob = pred_prob.cpu().numpy()\n",
        "\n",
        "    pred = (pred_prob >= threshold).astype(int)\n",
        "\n",
        "    acc = accuracy(pred, target)\n",
        "    fpr = false_positive_rate(pred, target)\n",
        "    fnr = false_negative_rate(pred, target)\n",
        "    rec = recall(pred, target)\n",
        "    prc = precision(pred, target)\n",
        "    f1 = f1_score(pred, target)\n",
        "    f1macro = f1_macro(pred, target)\n",
        "    result_dict = {'acc': acc, 'fpr': fpr, 'fnr': fnr, 'rec': rec, 'prc': prc, 'f1': f1, 'f1_macro': f1macro}\n",
        "\n",
        "    return result_dict\n",
        "\n",
        "\n",
        "def dict_value_add(dict1, dict2):\n",
        "    result = {key: dict1.get(key, 0) + dict2.get(key, 0)\n",
        "              for key in set(dict1) | set(dict2)}\n",
        "    return result\n",
        "\n",
        "\n",
        "def dict_value_div(dict, n):\n",
        "    result = {key: value / n for key, value in dict.items()}\n",
        "    return result\n",
        "\n",
        "\n",
        "def eval_predictor(dataset, predictor):\n",
        "    result_dict_avg = {}\n",
        "    loss_avg = 0\n",
        "\n",
        "    for data in dataset:\n",
        "        # prediction\n",
        "        try:\n",
        "            pred_prob, loss = predictor(data)\n",
        "            loss_avg += loss\n",
        "        except ValueError:  # if \"too many values to unpack\"\n",
        "            pred_prob = predictor(data)\n",
        "\n",
        "        # get the ground truth target\n",
        "        # graph_format == 'pyg':\n",
        "        target = data.y\n",
        "\n",
        "        # compute the evaluation metrics\n",
        "        result_dict = eval_metrics(target, pred_prob)\n",
        "\n",
        "        result_dict_avg = dict_value_add(result_dict_avg, result_dict)\n",
        "\n",
        "    # average the metrics across all graphs in the dataset as final results\n",
        "    result_dict_avg = dict_value_div(result_dict_avg, len(dataset))\n",
        "    loss_avg = loss_avg / len(dataset)\n",
        "\n",
        "    return result_dict_avg, loss_avg\n",
        "\n",
        "\n",
        "# =================================================================================================================\n",
        "# some examples of the 'predictor' model wrapper to be fed into the above evaluation function (for PyG Data format)\n",
        "# =================================================================================================================\n",
        "class PygRandomPredictor:\n",
        "    def __init__(self):\n",
        "        # torch.manual_seed(0)\n",
        "        pass\n",
        "\n",
        "    def __call__(self, data):\n",
        "        pred_prob = torch.rand(len(data.y))\n",
        "        return pred_prob\n",
        "\n",
        "\n",
        "class PygModelPredictor:\n",
        "    def __init__(self, model, loss_fcn=torch.nn.CrossEntropyLoss()):\n",
        "        self.model = model\n",
        "        self.loss_fcn = loss_fcn\n",
        "        self.device = next(model.parameters()).device\n",
        "\n",
        "    def __call__(self, data):\n",
        "        self.model.eval()\n",
        "        data = data.to(self.device)\n",
        "        with torch.no_grad():\n",
        "            # custom the below line to adjust to your model's input format for forward pass\n",
        "            out = self.model(data.x, data.edge_index)\n",
        "            loss = self.loss_fcn(out, data.y.long())\n",
        "            pred_prob = torch.softmax(out, dim=1)[:, 1]\n",
        "        return pred_prob, loss.float()\n"
      ],
      "metadata": {
        "id": "pqjalkx1EmHc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# IV. Model"
      ],
      "metadata": {
        "id": "5fvRugR_E1QL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.1. Activation "
      ],
      "metadata": {
        "id": "8rh6Ca1XE6ar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def activation(act, negative_slope=0.2):\n",
        "    activations = nn.ModuleDict([\n",
        "        ['lrelu', nn.LeakyReLU(negative_slope)],\n",
        "        ['relu', nn.ReLU()],\n",
        "        ['elu', nn.ELU()],\n",
        "        ['none', nn.Identity()],\n",
        "    ])\n",
        "    return activations[act]"
      ],
      "metadata": {
        "id": "bzEVz1VwFjKK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.2. GIN model"
      ],
      "metadata": {
        "id": "HWrzKUrFFcNV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class GINModel(nn.Module):\n",
        "    def __init__(self, dim_input_feature, dim_hidden_feature, num_layers, num_classes, non_linear='relu', dropout=0.2):\n",
        "        super().__init__()\n",
        "\n",
        "        self.dim_input_feature = dim_input_feature\n",
        "        self.dim_hidden_feature = dim_hidden_feature\n",
        "\n",
        "        self.num_layers = num_layers\n",
        "        self.num_classes = num_classes\n",
        "        \n",
        "        from torch.nn import Linear, Sequential, BatchNorm1d, ReLU, Dropout\n",
        "        from torch_geometric.nn import GINConv\n",
        "        \n",
        "        self.gin_net = nn.ModuleList()\n",
        "        for i in range(self.num_layers):\n",
        "          if i == 0:\n",
        "            self.gin_net.append(\n",
        "                GINConv(\n",
        "                  Sequential(Linear(self.dim_input_feature, self.dim_hidden_feature),\n",
        "                             BatchNorm1d(self.dim_hidden_feature), ReLU(),\n",
        "                             Linear(self.dim_hidden_feature, self.dim_hidden_feature),\n",
        "                             BatchNorm1d(self.dim_hidden_feature), ReLU(),\n",
        "                             Linear(self.dim_hidden_feature, self.dim_hidden_feature),\n",
        "                             BatchNorm1d(self.dim_hidden_feature), ReLU(),\n",
        "                             Linear(self.dim_hidden_feature, self.dim_hidden_feature), ReLU())))\n",
        "          else:\n",
        "            self.gin_net.append(\n",
        "                GINConv(\n",
        "                  Sequential(Linear(self.dim_hidden_feature, self.dim_hidden_feature),\n",
        "                             BatchNorm1d(self.dim_hidden_feature), ReLU(),\n",
        "                             Linear(self.dim_hidden_feature, self.dim_hidden_feature),\n",
        "                             BatchNorm1d(self.dim_hidden_feature), ReLU(),\n",
        "                             Linear(self.dim_hidden_feature, self.dim_hidden_feature),\n",
        "                             BatchNorm1d(self.dim_hidden_feature), ReLU(),\n",
        "                             Linear(self.dim_hidden_feature, self.dim_hidden_feature), ReLU()))) \n",
        "\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "        self.residuals = nn.ModuleList()\n",
        "        for i in range(self.num_layers):\n",
        "          if i == 0:\n",
        "            self.residuals.append(nn.Linear(self.dim_input_feature, self.dim_hidden_feature, bias=True))\n",
        "          else:\n",
        "            self.residuals.append(nn.Identity())\n",
        "\n",
        "        self.num_residuals = len(self.residuals)\n",
        "\n",
        "        self.non_linear = activation(non_linear)\n",
        "\n",
        "        # self.final_type == 'proj':\n",
        "        self.final = nn.Linear(self.dim_hidden_feature, num_classes)\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        for net in self.gin_net:\n",
        "            net.reset_parameters()\n",
        "        # self.residual_hop = 1\n",
        "        for net in self.residuals:\n",
        "            net.reset_parameters()\n",
        "        # self.final_type != 'none':\n",
        "        self.final.reset_parameters()\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        xr = None\n",
        "        add_xr_at = -1\n",
        "\n",
        "        for n, net in enumerate(self.gin_net):\n",
        "            # pass to a GIN layer with non-linear activation\n",
        "            xo = net(x, edge_index)\n",
        "            xo = self.dropout(xo)\n",
        "            # deal with residual connections\n",
        "            # self.residual_hop = 1\n",
        "            if n < self.num_residuals:\n",
        "                xr = self.residuals[n](x)\n",
        "                add_xr_at = n\n",
        "            if n == add_xr_at:\n",
        "                xo = self.non_linear(xo + xr)\n",
        "\n",
        "            x = xo\n",
        "        # size of x: (B * N, dim_hidden_feature) -> (B * N, num_classes)\n",
        "        x = self.final(x)\n",
        "\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "vLv8xNFcFqcT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# V. Load data"
      ],
      "metadata": {
        "id": "RRCYp7yuLxMe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = '/content/drive/Shareddrives/botnetdata/P2P'\n",
        "data_name = 'p2p' # 'chord', 'debru', 'kadem', 'leet', 'c2', 'p2p'\n",
        "shuffle = False\n",
        "\n",
        "# ========== load the dataset\n",
        "print('loading dataset...')\n",
        "\n",
        "train_ds = BotnetDataset(name=data_name, root=data_dir, split='train',\n",
        "                         in_memory=True, graph_format='pyg')\n",
        "val_ds = BotnetDataset(name=data_name, root=data_dir, split='val',\n",
        "                       in_memory=False, graph_format='pyg')\n",
        "test_ds = BotnetDataset(name=data_name, root=data_dir, split='test',\n",
        "                        in_memory=False, graph_format='pyg')\n"
      ],
      "metadata": {
        "id": "msBAy6CKLwl8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1571d31d-6322-4e7b-9901-02f0641c065e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading dataset...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 2\n",
        "train_loader = GraphDataLoader(train_ds, batch_size=batch_size, shuffle=bool(shuffle), num_workers=0)\n"
      ],
      "metadata": {
        "id": "q23YTuSlmot5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# VI. Train"
      ],
      "metadata": {
        "id": "PSY11P9WEX5a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============== some default parameters =============\n",
        "devid = 0\n",
        "seed = 0\n",
        "logmode = 'w'\n",
        "log_interval = 96\n",
        "\n",
        "dim_input_feature = 1\n",
        "dim_hidden_feature = 32\n",
        "act = 'relu' # 'none', 'lrelu', 'relu', 'elu'\n",
        "\n",
        "num_layers = 12\n",
        "num_classes = 2 \n",
        "\n",
        "dropout = 0.2\n",
        "bias = True\n",
        "\n",
        "lr = 0.005 # learning rate\n",
        "weight_decay = 5e-4\n",
        "epochs = 50\n",
        "save_dir = './saved_models'\n",
        "save_name = \"GIN_model.pt\"\n",
        "# ====================================================\n",
        "\n",
        "def train(model, train_loader, val_dataset, test_dataset, optimizer, scheduler=None):\n",
        "    device = next(model.parameters()).device\n",
        "    predictor = PygModelPredictor(model)\n",
        "\n",
        "    best_epoch = 0\n",
        "    max_f1_score = 0\n",
        "    start = time.time()\n",
        "    for ep in range(epochs):\n",
        "        loss_avg_train = 0\n",
        "        num_train_graph = 0\n",
        "        model.train()\n",
        "        for n, batch in enumerate(train_loader):\n",
        "            batch.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            x = model(batch.x, batch.edge_index)\n",
        "            loss = criterion(x, batch.y.long())\n",
        "\n",
        "            loss_avg_train += float(loss)\n",
        "            num_train_graph += batch.num_graphs\n",
        "\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            if num_train_graph % log_interval == 0 or n == len(train_loader) - 1:\n",
        "                with torch.no_grad():\n",
        "                    # pred = x.argmax(dim=1)\n",
        "                    pred_prob = torch.softmax(x, dim=1)[:, 1]\n",
        "                    y = batch.y.long()\n",
        "                    result_dict = eval_metrics(y, pred_prob)\n",
        "                print(f'epoch: {ep + 1}, passed number of graphs: {num_train_graph}, '\n",
        "                        f'train running loss: {loss_avg_train / num_train_graph:.5f} (passed time: {time_since(start)})')\n",
        "                print(' ' * 10 + ', '.join(['{}: {:.5f}'.format(k, v) for k, v in result_dict.items()]))\n",
        "\n",
        "        result_dict_avg, loss_avg = eval_predictor(val_dataset, predictor)\n",
        "        print(f'Validation --- epoch: {ep + 1}, loss: {loss_avg:.5f}')\n",
        "        print(' ' * 10 + ', '.join(['{}: {:.5f}'.format(k, v) for k, v in result_dict_avg.items()]))\n",
        "\n",
        "        if scheduler is not None:\n",
        "            scheduler.step(loss_avg)\n",
        "\n",
        "        if result_dict_avg['f1'] > max_f1_score:\n",
        "            save_name = f\"GIN_{ep}: {result_dict_avg['f1']}.pt\"\n",
        "            torch.save(model, os.path.join(save_dir, save_name))\n",
        "            print(f'Better model saved at {os.path.join(save_dir, save_name)}.')\n",
        "            best_epoch = ep\n",
        "            max_f1_score = result_dict_avg['f1']\n",
        "\n",
        "    best_model = torch.load(os.path.join(save_dir, save_name))\n",
        "    print('*' * 12 + f' best model obtained after epoch {best_epoch + 1}, '\n",
        "                       f'saved at {os.path.join(save_dir, save_name)} ' + '*' * 12)\n",
        "    \n",
        "    predictor = PygModelPredictor(best_model)\n",
        "\n",
        "    result_dict_avg, loss_avg = eval_predictor(test_dataset, predictor)\n",
        "    print(f'Testing --- loss: {loss_avg:.5f}')\n",
        "    print(' ' * 10 + ', '.join(['{}: {:.5f}'.format(k, v) for k, v in result_dict_avg.items()]))\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    os.makedirs(save_dir, exist_ok=True)\n",
        "\n",
        "    # ========== random seeds and device\n",
        "    random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "\n",
        "    device = torch.device(f'cuda:{devid}') if devid > -1 else torch.device('cpu')\n",
        "\n",
        "    # ========== logging setup\n",
        "    log_name = os.path.splitext(save_name)[0]\n",
        "    # logger = logging_config(__name__, folder=save_dir, name=log_name, filemode=logmode)\n",
        "    # logger = logging_config(os.path.basename(__file__), folder=save_dir, name=log_name, filemode=logmode)\n",
        "\n",
        "    print('python ' + ' '.join(sys.argv))\n",
        "    print('-' * 30)\n",
        "    #logger.info(args)\n",
        "    print('-' * 30)\n",
        "    print(time.ctime())\n",
        "    print('-' * 30)\n",
        "\n",
        "    # ========== define the model, optimizer, and loss\n",
        "\n",
        "    model = GINModel(dim_input_feature,\n",
        "                     dim_hidden_feature,\n",
        "                     num_layers,\n",
        "                     num_classes,\n",
        "                     non_linear=act,\n",
        "                     dropout=dropout)\n",
        "\n",
        "    print('model ' + '-' * 10)\n",
        "    print(repr(model))\n",
        "    model.to(device)\n",
        "\n",
        "    criterion = torch.nn.CrossEntropyLoss()\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
        "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.25, patience=1)\n",
        "\n",
        "    # ========== train the model\n",
        "    print(f\"Batch size: {batch_size}\")\n",
        "    train(model, train_loader, val_ds, test_ds, optimizer, scheduler)\n"
      ],
      "metadata": {
        "id": "3iFDupzCdBl0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7f6651d-6a65-4819-d8fe-3494620f5754"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "python /usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py -f /root/.local/share/jupyter/runtime/kernel-d22d3afc-ad51-4672-918d-0c2793297cb5.json\n",
            "------------------------------\n",
            "------------------------------\n",
            "Sun Nov  6 06:11:48 2022\n",
            "------------------------------\n",
            "model ----------\n",
            "GINModel(\n",
            "  (gin_net): ModuleList(\n",
            "    (0): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=1, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (1): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (2): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (3): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (4): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (5): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (6): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (7): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (8): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (9): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (10): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "    (11): GINConv(nn=Sequential(\n",
            "      (0): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU()\n",
            "      (3): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU()\n",
            "      (6): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU()\n",
            "      (9): Linear(in_features=32, out_features=32, bias=True)\n",
            "      (10): ReLU()\n",
            "    ))\n",
            "  )\n",
            "  (dropout): Dropout(p=0.2, inplace=False)\n",
            "  (residuals): ModuleList(\n",
            "    (0): Linear(in_features=1, out_features=32, bias=True)\n",
            "    (1): Identity()\n",
            "    (2): Identity()\n",
            "    (3): Identity()\n",
            "    (4): Identity()\n",
            "    (5): Identity()\n",
            "    (6): Identity()\n",
            "    (7): Identity()\n",
            "    (8): Identity()\n",
            "    (9): Identity()\n",
            "    (10): Identity()\n",
            "    (11): Identity()\n",
            "  )\n",
            "  (non_linear): ReLU()\n",
            "  (final): Linear(in_features=32, out_features=2, bias=True)\n",
            ")\n",
            "Batch size: 2\n",
            "epoch: 1, passed number of graphs: 96, train running loss: 0.06928 (passed time: 20s)\n",
            "          acc: 0.97829, fpr: 0.00001, fnr: 1.00000, rec: 0.00000, prc: 0.00000, f1: 0.00000, f1_macro: nan\n",
            "epoch: 1, passed number of graphs: 192, train running loss: 0.04343 (passed time: 40s)\n",
            "          acc: 0.99866, fpr: 0.00112, fnr: 0.01143, rec: 0.98857, prc: 0.95068, f1: 0.96926, f1_macro: 0.98429\n",
            "epoch: 1, passed number of graphs: 288, train running loss: 0.03138 (passed time: 1m 0s)\n",
            "          acc: 0.99932, fpr: 0.00009, fnr: 0.02778, rec: 0.97222, prc: 0.99565, f1: 0.98380, f1_macro: 0.99172\n",
            "epoch: 1, passed number of graphs: 384, train running loss: 0.02545 (passed time: 1m 20s)\n",
            "          acc: 0.99937, fpr: 0.00004, fnr: 0.02658, rec: 0.97342, prc: 0.99802, f1: 0.98556, f1_macro: 0.99262\n",
            "epoch: 1, passed number of graphs: 480, train running loss: 0.02065 (passed time: 1m 40s)\n",
            "          acc: 0.99848, fpr: 0.00148, fnr: 0.00343, rec: 0.99657, prc: 0.93717, f1: 0.96596, f1_macro: 0.98259\n",
            "epoch: 1, passed number of graphs: 576, train running loss: 0.01832 (passed time: 2m 0s)\n",
            "          acc: 0.99976, fpr: 0.00019, fnr: 0.00210, rec: 0.99790, prc: 0.99118, f1: 0.99453, f1_macro: 0.99721\n",
            "epoch: 1, passed number of graphs: 672, train running loss: 0.01585 (passed time: 2m 20s)\n",
            "          acc: 0.99960, fpr: 0.00037, fnr: 0.00176, rec: 0.99824, prc: 0.98331, f1: 0.99072, f1_macro: 0.99526\n",
            "epoch: 1, passed number of graphs: 768, train running loss: 0.01400 (passed time: 2m 40s)\n",
            "          acc: 0.99993, fpr: 0.00004, fnr: 0.00147, rec: 0.99853, prc: 0.99837, f1: 0.99845, f1_macro: 0.99921\n",
            "Validation --- epoch: 1, loss: 0.46687\n",
            "          fnr: 0.00667, prc: 0.31499, acc: 0.95288, rec: 0.99333, f1: 0.47749, f1_macro: 0.72640, fpr: 0.04801\n",
            "Better model saved at ./saved_models/GIN_0: 0.47748830624775046.pt.\n",
            "epoch: 2, passed number of graphs: 96, train running loss: 0.00179 (passed time: 3m 22s)\n",
            "          acc: 0.99686, fpr: 0.00002, fnr: 0.14388, rec: 0.85612, prc: 0.99907, f1: 0.92209, f1_macro: 0.96024\n",
            "epoch: 2, passed number of graphs: 192, train running loss: 0.00176 (passed time: 3m 43s)\n",
            "          acc: 0.99973, fpr: 0.00015, fnr: 0.00572, rec: 0.99428, prc: 0.99315, f1: 0.99372, f1_macro: 0.99679\n",
            "epoch: 2, passed number of graphs: 288, train running loss: 0.00143 (passed time: 4m 3s)\n",
            "          acc: 0.99977, fpr: 0.00008, fnr: 0.00670, rec: 0.99330, prc: 0.99607, f1: 0.99468, f1_macro: 0.99728\n",
            "epoch: 2, passed number of graphs: 384, train running loss: 0.00127 (passed time: 4m 23s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00354, rec: 0.99646, prc: 0.99935, f1: 0.99790, f1_macro: 0.99893\n",
            "epoch: 2, passed number of graphs: 480, train running loss: 0.00140 (passed time: 4m 43s)\n",
            "          acc: 0.99213, fpr: 0.00778, fnr: 0.01176, rec: 0.98824, prc: 0.73736, f1: 0.84456, f1_macro: 0.92026\n",
            "epoch: 2, passed number of graphs: 576, train running loss: 0.00233 (passed time: 5m 3s)\n",
            "          acc: 0.99654, fpr: 0.00003, fnr: 0.16029, rec: 0.83971, prc: 0.99827, f1: 0.91215, f1_macro: 0.95519\n",
            "epoch: 2, passed number of graphs: 672, train running loss: 0.00278 (passed time: 5m 23s)\n",
            "          acc: 0.99704, fpr: 0.00000, fnr: 0.13678, rec: 0.86322, prc: 1.00000, f1: 0.92659, f1_macro: 0.96254\n",
            "epoch: 2, passed number of graphs: 768, train running loss: 0.00262 (passed time: 5m 43s)\n",
            "          acc: 0.99987, fpr: 0.00001, fnr: 0.00571, rec: 0.99429, prc: 0.99951, f1: 0.99689, f1_macro: 0.99841\n",
            "Validation --- epoch: 2, loss: 0.00346\n",
            "          fnr: 0.01975, prc: 0.99115, acc: 0.99938, rec: 0.98025, f1: 0.98410, f1_macro: 0.99189, fpr: 0.00020\n",
            "Better model saved at ./saved_models/GIN_1: 0.9840957774766091.pt.\n",
            "epoch: 3, passed number of graphs: 96, train running loss: 0.00144 (passed time: 6m 25s)\n",
            "          acc: 0.99996, fpr: 0.00003, fnr: 0.00048, rec: 0.99952, prc: 0.99872, f1: 0.99912, f1_macro: 0.99955\n",
            "epoch: 3, passed number of graphs: 192, train running loss: 0.01052 (passed time: 6m 45s)\n",
            "          acc: 0.99879, fpr: 0.00000, fnr: 0.05651, rec: 0.94349, prc: 1.00000, f1: 0.97092, f1_macro: 0.98515\n",
            "epoch: 3, passed number of graphs: 288, train running loss: 0.00907 (passed time: 7m 5s)\n",
            "          acc: 0.99958, fpr: 0.00002, fnr: 0.01863, rec: 0.98137, prc: 0.99900, f1: 0.99011, f1_macro: 0.99495\n",
            "epoch: 3, passed number of graphs: 384, train running loss: 0.00714 (passed time: 7m 25s)\n",
            "          acc: 0.99990, fpr: 0.00003, fnr: 0.00306, rec: 0.99694, prc: 0.99855, f1: 0.99774, f1_macro: 0.99885\n",
            "epoch: 3, passed number of graphs: 480, train running loss: 0.00590 (passed time: 7m 45s)\n",
            "          acc: 0.99986, fpr: 0.00006, fnr: 0.00376, rec: 0.99624, prc: 0.99738, f1: 0.99681, f1_macro: 0.99837\n",
            "epoch: 3, passed number of graphs: 576, train running loss: 0.00506 (passed time: 8m 5s)\n",
            "          acc: 0.99987, fpr: 0.00009, fnr: 0.00210, rec: 0.99790, prc: 0.99581, f1: 0.99686, f1_macro: 0.99839\n",
            "epoch: 3, passed number of graphs: 672, train running loss: 0.00454 (passed time: 8m 25s)\n",
            "          acc: 0.99920, fpr: 0.00034, fnr: 0.02141, rec: 0.97859, prc: 0.98441, f1: 0.98149, f1_macro: 0.99054\n",
            "epoch: 3, passed number of graphs: 768, train running loss: 0.00413 (passed time: 8m 45s)\n",
            "          acc: 0.99997, fpr: 0.00002, fnr: 0.00065, rec: 0.99935, prc: 0.99918, f1: 0.99927, f1_macro: 0.99963\n",
            "Validation --- epoch: 3, loss: 0.00227\n",
            "          fnr: 0.00702, prc: 0.99277, acc: 0.99969, rec: 0.99298, f1: 0.99210, f1_macro: 0.99597, fpr: 0.00016\n",
            "Better model saved at ./saved_models/GIN_2: 0.9921003329611596.pt.\n",
            "epoch: 4, passed number of graphs: 96, train running loss: 0.00068 (passed time: 9m 28s)\n",
            "          acc: 0.99994, fpr: 0.00000, fnr: 0.00271, rec: 0.99729, prc: 0.99984, f1: 0.99856, f1_macro: 0.99927\n",
            "epoch: 4, passed number of graphs: 192, train running loss: 0.00327 (passed time: 9m 48s)\n",
            "          acc: 0.99591, fpr: 0.00000, fnr: 0.19125, rec: 0.80875, prc: 1.00000, f1: 0.89427, f1_macro: 0.94609\n",
            "epoch: 4, passed number of graphs: 288, train running loss: 0.00289 (passed time: 10m 8s)\n",
            "          acc: 0.99972, fpr: 0.00000, fnr: 0.01307, rec: 0.98693, prc: 0.99983, f1: 0.99334, f1_macro: 0.99660\n",
            "epoch: 4, passed number of graphs: 384, train running loss: 0.00346 (passed time: 10m 28s)\n",
            "          acc: 0.99975, fpr: 0.00001, fnr: 0.01096, rec: 0.98904, prc: 0.99967, f1: 0.99433, f1_macro: 0.99710\n",
            "epoch: 4, passed number of graphs: 480, train running loss: 0.00294 (passed time: 10m 48s)\n",
            "          acc: 0.99986, fpr: 0.00003, fnr: 0.00490, rec: 0.99510, prc: 0.99852, f1: 0.99681, f1_macro: 0.99837\n",
            "epoch: 4, passed number of graphs: 576, train running loss: 0.00275 (passed time: 11m 8s)\n",
            "          acc: 0.99656, fpr: 0.00003, fnr: 0.15932, rec: 0.84068, prc: 0.99827, f1: 0.91272, f1_macro: 0.95548\n",
            "epoch: 4, passed number of graphs: 672, train running loss: 0.00258 (passed time: 11m 28s)\n",
            "          acc: 0.99958, fpr: 0.00031, fnr: 0.00527, rec: 0.99473, prc: 0.98606, f1: 0.99037, f1_macro: 0.99508\n",
            "epoch: 4, passed number of graphs: 768, train running loss: 0.00238 (passed time: 11m 48s)\n",
            "          acc: 0.99965, fpr: 0.00027, fnr: 0.00392, rec: 0.99608, prc: 0.98755, f1: 0.99180, f1_macro: 0.99581\n",
            "Validation --- epoch: 4, loss: 0.01025\n",
            "          fnr: 0.00686, prc: 0.88790, acc: 0.99690, rec: 0.99314, f1: 0.93450, f1_macro: 0.96646, fpr: 0.00301\n",
            "epoch: 5, passed number of graphs: 96, train running loss: 0.00075 (passed time: 12m 30s)\n",
            "          acc: 0.99992, fpr: 0.00004, fnr: 0.00223, rec: 0.99777, prc: 0.99840, f1: 0.99809, f1_macro: 0.99902\n",
            "epoch: 5, passed number of graphs: 192, train running loss: 0.00068 (passed time: 12m 51s)\n",
            "          acc: 0.99987, fpr: 0.00000, fnr: 0.00604, rec: 0.99396, prc: 0.99984, f1: 0.99689, f1_macro: 0.99841\n",
            "epoch: 5, passed number of graphs: 288, train running loss: 0.00065 (passed time: 13m 11s)\n",
            "          acc: 0.99985, fpr: 0.00003, fnr: 0.00572, rec: 0.99428, prc: 0.99852, f1: 0.99640, f1_macro: 0.99816\n",
            "epoch: 5, passed number of graphs: 384, train running loss: 0.00068 (passed time: 13m 30s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00371, rec: 0.99629, prc: 0.99935, f1: 0.99782, f1_macro: 0.99889\n",
            "epoch: 5, passed number of graphs: 480, train running loss: 0.00065 (passed time: 13m 50s)\n",
            "          acc: 0.99982, fpr: 0.00001, fnr: 0.00817, rec: 0.99183, prc: 0.99967, f1: 0.99574, f1_macro: 0.99782\n",
            "epoch: 5, passed number of graphs: 576, train running loss: 0.00063 (passed time: 14m 10s)\n",
            "          acc: 0.99982, fpr: 0.00015, fnr: 0.00161, rec: 0.99839, prc: 0.99310, f1: 0.99573, f1_macro: 0.99782\n",
            "epoch: 5, passed number of graphs: 672, train running loss: 0.00257 (passed time: 14m 30s)\n",
            "          acc: 0.97835, fpr: 0.00000, fnr: 1.00000, rec: 0.00000, prc: -1.00000, f1: 0.00000, f1_macro: nan\n",
            "epoch: 5, passed number of graphs: 768, train running loss: 0.00779 (passed time: 14m 50s)\n",
            "          acc: 0.97885, fpr: 0.00018, fnr: 0.98744, rec: 0.01256, prc: 0.60156, f1: 0.02461, f1_macro: 0.50696\n",
            "Validation --- epoch: 5, loss: 0.09239\n",
            "          fnr: 0.56112, prc: 0.24098, acc: 0.95423, rec: 0.43888, f1: 0.30226, f1_macro: 0.63929, fpr: 0.03444\n",
            "epoch: 6, passed number of graphs: 96, train running loss: 0.03104 (passed time: 15m 33s)\n",
            "          acc: 0.97703, fpr: 0.00626, fnr: 0.77620, rec: 0.22380, prc: 0.44203, f1: 0.29715, f1_macro: 0.64274\n",
            "epoch: 6, passed number of graphs: 192, train running loss: 0.03035 (passed time: 15m 53s)\n",
            "          acc: 0.97643, fpr: 0.00761, fnr: 0.75437, rec: 0.24563, prc: 0.41341, f1: 0.30817, f1_macro: 0.64809\n",
            "epoch: 6, passed number of graphs: 288, train running loss: 0.02968 (passed time: 16m 13s)\n",
            "          acc: 0.98629, fpr: 0.00347, fnr: 0.48660, rec: 0.51340, prc: 0.76207, f1: 0.61349, f1_macro: 0.80326\n",
            "epoch: 6, passed number of graphs: 384, train running loss: 0.02900 (passed time: 16m 33s)\n",
            "          acc: 0.97805, fpr: 0.00349, fnr: 0.84179, rec: 0.15821, prc: 0.50514, f1: 0.24095, f1_macro: 0.61491\n",
            "epoch: 6, passed number of graphs: 480, train running loss: 0.02848 (passed time: 16m 53s)\n",
            "          acc: 0.98334, fpr: 0.00502, fnr: 0.54312, rec: 0.45688, prc: 0.66786, f1: 0.54258, f1_macro: 0.76705\n",
            "epoch: 6, passed number of graphs: 576, train running loss: 0.02809 (passed time: 17m 13s)\n",
            "          acc: 0.98370, fpr: 0.00328, fnr: 0.61098, rec: 0.38902, prc: 0.72156, f1: 0.50551, f1_macro: 0.74861\n",
            "epoch: 6, passed number of graphs: 672, train running loss: 0.02757 (passed time: 17m 33s)\n",
            "          acc: 0.98975, fpr: 0.00013, fnr: 0.46724, rec: 0.53276, prc: 0.98873, f1: 0.69242, f1_macro: 0.84360\n",
            "epoch: 6, passed number of graphs: 768, train running loss: 0.02700 (passed time: 17m 53s)\n",
            "          acc: 0.99176, fpr: 0.00014, fnr: 0.38157, rec: 0.61843, prc: 0.98982, f1: 0.76124, f1_macro: 0.87853\n",
            "Validation --- epoch: 6, loss: 0.06234\n",
            "          fnr: 0.93342, prc: 0.92535, acc: 0.97990, rec: 0.06658, f1: 0.11482, f1_macro: nan, fpr: 0.00001\n",
            "epoch: 7, passed number of graphs: 96, train running loss: 0.01843 (passed time: 18m 36s)\n",
            "          acc: 0.99091, fpr: 0.00281, fnr: 0.29223, rec: 0.70777, prc: 0.84821, f1: 0.77165, f1_macro: 0.88351\n",
            "epoch: 7, passed number of graphs: 192, train running loss: 0.01666 (passed time: 18m 56s)\n",
            "          acc: 0.99632, fpr: 0.00001, fnr: 0.17148, rec: 0.82852, prc: 0.99921, f1: 0.90589, f1_macro: 0.95201\n",
            "epoch: 7, passed number of graphs: 288, train running loss: 0.01537 (passed time: 19m 15s)\n",
            "          acc: 0.99262, fpr: 0.00005, fnr: 0.34575, rec: 0.65425, prc: 0.99652, f1: 0.78990, f1_macro: 0.89307\n",
            "epoch: 7, passed number of graphs: 384, train running loss: 0.01445 (passed time: 19m 35s)\n",
            "          acc: 0.99461, fpr: 0.00003, fnr: 0.24327, rec: 0.75673, prc: 0.99809, f1: 0.86081, f1_macro: 0.92903\n",
            "epoch: 7, passed number of graphs: 480, train running loss: 0.01302 (passed time: 19m 55s)\n",
            "          acc: 0.99568, fpr: 0.00005, fnr: 0.19732, rec: 0.80268, prc: 0.99716, f1: 0.88941, f1_macro: 0.94360\n",
            "epoch: 7, passed number of graphs: 576, train running loss: 0.01167 (passed time: 20m 15s)\n",
            "          acc: 0.99936, fpr: 0.00024, fnr: 0.01889, rec: 0.98111, prc: 0.98894, f1: 0.98501, f1_macro: 0.99234\n",
            "epoch: 7, passed number of graphs: 672, train running loss: 0.01037 (passed time: 20m 35s)\n",
            "          acc: 0.99698, fpr: 0.00004, fnr: 0.13758, rec: 0.86242, prc: 0.99778, f1: 0.92517, f1_macro: 0.96182\n",
            "epoch: 7, passed number of graphs: 768, train running loss: 0.00930 (passed time: 20m 55s)\n",
            "          acc: 0.99963, fpr: 0.00007, fnr: 0.01419, rec: 0.98581, prc: 0.99670, f1: 0.99122, f1_macro: 0.99552\n",
            "Validation --- epoch: 7, loss: 0.00353\n",
            "          fnr: 0.02366, prc: 0.99523, acc: 0.99939, rec: 0.97634, f1: 0.98441, f1_macro: 0.99205, fpr: 0.00011\n",
            "epoch: 8, passed number of graphs: 96, train running loss: 0.00185 (passed time: 21m 38s)\n",
            "          acc: 0.99976, fpr: 0.00002, fnr: 0.00973, rec: 0.99027, prc: 0.99887, f1: 0.99455, f1_macro: 0.99722\n",
            "epoch: 8, passed number of graphs: 192, train running loss: 0.00198 (passed time: 21m 58s)\n",
            "          acc: 0.99972, fpr: 0.00004, fnr: 0.01160, rec: 0.98840, prc: 0.99835, f1: 0.99335, f1_macro: 0.99660\n",
            "epoch: 8, passed number of graphs: 288, train running loss: 0.00185 (passed time: 22m 18s)\n",
            "          acc: 0.99975, fpr: 0.00004, fnr: 0.01029, rec: 0.98971, prc: 0.99835, f1: 0.99401, f1_macro: 0.99694\n",
            "epoch: 8, passed number of graphs: 384, train running loss: 0.00164 (passed time: 22m 38s)\n",
            "          acc: 0.99976, fpr: 0.00001, fnr: 0.01047, rec: 0.98953, prc: 0.99951, f1: 0.99449, f1_macro: 0.99719\n",
            "epoch: 8, passed number of graphs: 480, train running loss: 0.00153 (passed time: 22m 58s)\n",
            "          acc: 0.99979, fpr: 0.00000, fnr: 0.00964, rec: 0.99036, prc: 1.00000, f1: 0.99516, f1_macro: 0.99753\n",
            "epoch: 8, passed number of graphs: 576, train running loss: 0.00145 (passed time: 23m 18s)\n",
            "          acc: 0.99985, fpr: 0.00000, fnr: 0.00710, rec: 0.99290, prc: 1.00000, f1: 0.99644, f1_macro: 0.99818\n",
            "epoch: 8, passed number of graphs: 672, train running loss: 0.00152 (passed time: 23m 38s)\n",
            "          acc: 0.99708, fpr: 0.00001, fnr: 0.13407, rec: 0.86593, prc: 0.99926, f1: 0.92783, f1_macro: 0.96317\n",
            "epoch: 8, passed number of graphs: 768, train running loss: 0.00149 (passed time: 23m 58s)\n",
            "          acc: 0.99972, fpr: 0.00005, fnr: 0.01093, rec: 0.98907, prc: 0.99786, f1: 0.99345, f1_macro: 0.99665\n",
            "Validation --- epoch: 8, loss: 0.00269\n",
            "          fnr: 0.01891, prc: 0.99836, acc: 0.99956, rec: 0.98109, f1: 0.98863, f1_macro: 0.99420, fpr: 0.00004\n",
            "epoch: 9, passed number of graphs: 96, train running loss: 0.00124 (passed time: 24m 40s)\n",
            "          acc: 0.99984, fpr: 0.00000, fnr: 0.00750, rec: 0.99250, prc: 1.00000, f1: 0.99624, f1_macro: 0.99808\n",
            "epoch: 9, passed number of graphs: 192, train running loss: 0.00133 (passed time: 25m 0s)\n",
            "          acc: 0.99974, fpr: 0.00000, fnr: 0.01209, rec: 0.98791, prc: 1.00000, f1: 0.99392, f1_macro: 0.99689\n",
            "epoch: 9, passed number of graphs: 288, train running loss: 0.00113 (passed time: 25m 20s)\n",
            "          acc: 0.99982, fpr: 0.00001, fnr: 0.00817, rec: 0.99183, prc: 0.99967, f1: 0.99573, f1_macro: 0.99782\n",
            "epoch: 9, passed number of graphs: 384, train running loss: 0.00105 (passed time: 25m 40s)\n",
            "          acc: 0.99980, fpr: 0.00000, fnr: 0.00902, rec: 0.99098, prc: 0.99984, f1: 0.99539, f1_macro: 0.99764\n",
            "epoch: 9, passed number of graphs: 480, train running loss: 0.00095 (passed time: 26m 0s)\n",
            "          acc: 0.99986, fpr: 0.00000, fnr: 0.00670, rec: 0.99330, prc: 1.00000, f1: 0.99664, f1_macro: 0.99828\n",
            "epoch: 9, passed number of graphs: 576, train running loss: 0.00089 (passed time: 26m 20s)\n",
            "          acc: 0.99988, fpr: 0.00002, fnr: 0.00452, rec: 0.99548, prc: 0.99903, f1: 0.99725, f1_macro: 0.99860\n",
            "epoch: 9, passed number of graphs: 672, train running loss: 0.00088 (passed time: 26m 40s)\n",
            "          acc: 0.99954, fpr: 0.00036, fnr: 0.00511, rec: 0.99489, prc: 0.98404, f1: 0.98943, f1_macro: 0.99460\n",
            "epoch: 9, passed number of graphs: 768, train running loss: 0.00089 (passed time: 27m 0s)\n",
            "          acc: 0.99994, fpr: 0.00002, fnr: 0.00179, rec: 0.99821, prc: 0.99918, f1: 0.99869, f1_macro: 0.99933\n",
            "Validation --- epoch: 9, loss: 0.00257\n",
            "          fnr: 0.00721, prc: 0.98605, acc: 0.99954, rec: 0.99279, f1: 0.98859, f1_macro: 0.99418, fpr: 0.00032\n",
            "epoch: 10, passed number of graphs: 96, train running loss: 0.00047 (passed time: 27m 42s)\n",
            "          acc: 0.99996, fpr: 0.00001, fnr: 0.00128, rec: 0.99872, prc: 0.99952, f1: 0.99912, f1_macro: 0.99955\n",
            "epoch: 10, passed number of graphs: 192, train running loss: 0.00061 (passed time: 28m 2s)\n",
            "          acc: 0.99995, fpr: 0.00001, fnr: 0.00180, rec: 0.99820, prc: 0.99951, f1: 0.99886, f1_macro: 0.99942\n",
            "epoch: 10, passed number of graphs: 288, train running loss: 0.00056 (passed time: 28m 22s)\n",
            "          acc: 0.99982, fpr: 0.00013, fnr: 0.00245, rec: 0.99755, prc: 0.99398, f1: 0.99576, f1_macro: 0.99783\n",
            "epoch: 10, passed number of graphs: 384, train running loss: 0.00057 (passed time: 28m 42s)\n",
            "          acc: 0.99991, fpr: 0.00002, fnr: 0.00338, rec: 0.99662, prc: 0.99919, f1: 0.99790, f1_macro: 0.99893\n",
            "epoch: 10, passed number of graphs: 480, train running loss: 0.00054 (passed time: 29m 2s)\n",
            "          acc: 0.99984, fpr: 0.00012, fnr: 0.00196, rec: 0.99804, prc: 0.99463, f1: 0.99633, f1_macro: 0.99812\n",
            "epoch: 10, passed number of graphs: 576, train running loss: 0.00052 (passed time: 29m 22s)\n",
            "          acc: 0.99980, fpr: 0.00018, fnr: 0.00113, rec: 0.99887, prc: 0.99198, f1: 0.99542, f1_macro: 0.99766\n",
            "epoch: 10, passed number of graphs: 672, train running loss: 0.00055 (passed time: 29m 42s)\n",
            "          acc: 0.99956, fpr: 0.00042, fnr: 0.00144, rec: 0.99856, prc: 0.98147, f1: 0.98994, f1_macro: 0.99486\n",
            "epoch: 10, passed number of graphs: 768, train running loss: 0.00059 (passed time: 30m 2s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00245, rec: 0.99755, prc: 0.99935, f1: 0.99845, f1_macro: 0.99921\n",
            "Validation --- epoch: 10, loss: 0.00176\n",
            "          fnr: 0.00767, prc: 0.99655, acc: 0.99976, rec: 0.99233, f1: 0.99368, f1_macro: 0.99678, fpr: 0.00008\n",
            "Better model saved at ./saved_models/GIN_9: 0.993677101221096.pt.\n",
            "epoch: 11, passed number of graphs: 96, train running loss: 0.00041 (passed time: 30m 45s)\n",
            "          acc: 0.99995, fpr: 0.00001, fnr: 0.00175, rec: 0.99825, prc: 0.99936, f1: 0.99880, f1_macro: 0.99939\n",
            "epoch: 11, passed number of graphs: 192, train running loss: 0.00061 (passed time: 31m 5s)\n",
            "          acc: 0.99988, fpr: 0.00000, fnr: 0.00523, rec: 0.99477, prc: 0.99984, f1: 0.99730, f1_macro: 0.99862\n",
            "epoch: 11, passed number of graphs: 288, train running loss: 0.00055 (passed time: 31m 25s)\n",
            "          acc: 0.99982, fpr: 0.00014, fnr: 0.00229, rec: 0.99771, prc: 0.99365, f1: 0.99568, f1_macro: 0.99779\n",
            "epoch: 11, passed number of graphs: 384, train running loss: 0.00056 (passed time: 31m 44s)\n",
            "          acc: 0.99993, fpr: 0.00003, fnr: 0.00209, rec: 0.99791, prc: 0.99887, f1: 0.99839, f1_macro: 0.99918\n",
            "epoch: 11, passed number of graphs: 480, train running loss: 0.00053 (passed time: 32m 4s)\n",
            "          acc: 0.99985, fpr: 0.00010, fnr: 0.00229, rec: 0.99771, prc: 0.99527, f1: 0.99649, f1_macro: 0.99821\n",
            "epoch: 11, passed number of graphs: 576, train running loss: 0.00050 (passed time: 32m 24s)\n",
            "          acc: 0.99977, fpr: 0.00022, fnr: 0.00097, rec: 0.99903, prc: 0.99024, f1: 0.99462, f1_macro: 0.99725\n",
            "epoch: 11, passed number of graphs: 672, train running loss: 0.00053 (passed time: 32m 44s)\n",
            "          acc: 0.99960, fpr: 0.00030, fnr: 0.00479, rec: 0.99521, prc: 0.98654, f1: 0.99085, f1_macro: 0.99532\n",
            "epoch: 11, passed number of graphs: 768, train running loss: 0.00058 (passed time: 33m 4s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00245, rec: 0.99755, prc: 0.99951, f1: 0.99853, f1_macro: 0.99925\n",
            "Validation --- epoch: 11, loss: 0.00170\n",
            "          fnr: 0.00829, prc: 0.99750, acc: 0.99977, rec: 0.99171, f1: 0.99384, f1_macro: 0.99686, fpr: 0.00006\n",
            "Better model saved at ./saved_models/GIN_10: 0.9938442938770403.pt.\n",
            "epoch: 12, passed number of graphs: 96, train running loss: 0.00038 (passed time: 33m 47s)\n",
            "          acc: 0.99996, fpr: 0.00001, fnr: 0.00112, rec: 0.99888, prc: 0.99936, f1: 0.99912, f1_macro: 0.99955\n",
            "epoch: 12, passed number of graphs: 192, train running loss: 0.00054 (passed time: 34m 7s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00278, rec: 0.99722, prc: 0.99967, f1: 0.99845, f1_macro: 0.99921\n",
            "epoch: 12, passed number of graphs: 288, train running loss: 0.00049 (passed time: 34m 27s)\n",
            "          acc: 0.99983, fpr: 0.00012, fnr: 0.00212, rec: 0.99788, prc: 0.99430, f1: 0.99609, f1_macro: 0.99800\n",
            "epoch: 12, passed number of graphs: 384, train running loss: 0.00051 (passed time: 34m 47s)\n",
            "          acc: 0.99989, fpr: 0.00004, fnr: 0.00322, rec: 0.99678, prc: 0.99839, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 12, passed number of graphs: 480, train running loss: 0.00048 (passed time: 35m 7s)\n",
            "          acc: 0.99983, fpr: 0.00008, fnr: 0.00425, rec: 0.99575, prc: 0.99640, f1: 0.99608, f1_macro: 0.99800\n",
            "epoch: 12, passed number of graphs: 576, train running loss: 0.00047 (passed time: 35m 27s)\n",
            "          acc: 0.99989, fpr: 0.00008, fnr: 0.00145, rec: 0.99855, prc: 0.99614, f1: 0.99734, f1_macro: 0.99864\n",
            "epoch: 12, passed number of graphs: 672, train running loss: 0.00050 (passed time: 35m 47s)\n",
            "          acc: 0.99958, fpr: 0.00031, fnr: 0.00511, rec: 0.99489, prc: 0.98591, f1: 0.99038, f1_macro: 0.99508\n",
            "epoch: 12, passed number of graphs: 768, train running loss: 0.00055 (passed time: 36m 7s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00261, rec: 0.99739, prc: 0.99967, f1: 0.99853, f1_macro: 0.99925\n",
            "Validation --- epoch: 12, loss: 0.00170\n",
            "          fnr: 0.00790, prc: 0.99717, acc: 0.99977, rec: 0.99210, f1: 0.99388, f1_macro: 0.99688, fpr: 0.00006\n",
            "Better model saved at ./saved_models/GIN_11: 0.9938754997795639.pt.\n",
            "epoch: 13, passed number of graphs: 96, train running loss: 0.00038 (passed time: 36m 49s)\n",
            "          acc: 0.99997, fpr: 0.00001, fnr: 0.00112, rec: 0.99888, prc: 0.99952, f1: 0.99920, f1_macro: 0.99959\n",
            "epoch: 13, passed number of graphs: 192, train running loss: 0.00054 (passed time: 37m 9s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00278, rec: 0.99722, prc: 0.99951, f1: 0.99836, f1_macro: 0.99916\n",
            "epoch: 13, passed number of graphs: 288, train running loss: 0.00049 (passed time: 37m 29s)\n",
            "          acc: 0.99985, fpr: 0.00013, fnr: 0.00098, rec: 0.99902, prc: 0.99415, f1: 0.99658, f1_macro: 0.99825\n",
            "epoch: 13, passed number of graphs: 384, train running loss: 0.00050 (passed time: 37m 49s)\n",
            "          acc: 0.99989, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99903, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 13, passed number of graphs: 480, train running loss: 0.00048 (passed time: 38m 9s)\n",
            "          acc: 0.99985, fpr: 0.00010, fnr: 0.00245, rec: 0.99755, prc: 0.99560, f1: 0.99657, f1_macro: 0.99825\n",
            "epoch: 13, passed number of graphs: 576, train running loss: 0.00045 (passed time: 38m 29s)\n",
            "          acc: 0.99975, fpr: 0.00022, fnr: 0.00129, rec: 0.99871, prc: 0.98992, f1: 0.99429, f1_macro: 0.99708\n",
            "epoch: 13, passed number of graphs: 672, train running loss: 0.00048 (passed time: 38m 49s)\n",
            "          acc: 0.99958, fpr: 0.00034, fnr: 0.00368, rec: 0.99632, prc: 0.98468, f1: 0.99047, f1_macro: 0.99513\n",
            "epoch: 13, passed number of graphs: 768, train running loss: 0.00053 (passed time: 39m 9s)\n",
            "          acc: 0.99997, fpr: 0.00002, fnr: 0.00065, rec: 0.99935, prc: 0.99902, f1: 0.99918, f1_macro: 0.99958\n",
            "Validation --- epoch: 13, loss: 0.01237\n",
            "          fnr: 0.00733, prc: 0.85893, acc: 0.99485, rec: 0.99267, f1: 0.90875, f1_macro: 0.95305, fpr: 0.00510\n",
            "epoch: 14, passed number of graphs: 96, train running loss: 0.00037 (passed time: 39m 51s)\n",
            "          acc: 0.99996, fpr: 0.00001, fnr: 0.00112, rec: 0.99888, prc: 0.99936, f1: 0.99912, f1_macro: 0.99955\n",
            "epoch: 14, passed number of graphs: 192, train running loss: 0.00053 (passed time: 40m 11s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00229, rec: 0.99771, prc: 0.99951, f1: 0.99861, f1_macro: 0.99929\n",
            "epoch: 14, passed number of graphs: 288, train running loss: 0.00049 (passed time: 40m 31s)\n",
            "          acc: 0.99982, fpr: 0.00017, fnr: 0.00098, rec: 0.99902, prc: 0.99237, f1: 0.99568, f1_macro: 0.99780\n",
            "epoch: 14, passed number of graphs: 384, train running loss: 0.00638 (passed time: 40m 51s)\n",
            "          acc: 0.97772, fpr: 0.00027, fnr: 1.00000, rec: 0.00000, prc: 0.00000, f1: 0.00000, f1_macro: nan\n",
            "epoch: 14, passed number of graphs: 480, train running loss: 0.01550 (passed time: 41m 11s)\n",
            "          acc: 0.98188, fpr: 0.01120, fnr: 0.33110, rec: 0.66890, prc: 0.56899, f1: 0.61491, f1_macro: 0.80282\n",
            "epoch: 14, passed number of graphs: 576, train running loss: 0.01457 (passed time: 41m 31s)\n",
            "          acc: 0.99510, fpr: 0.00010, fnr: 0.22454, rec: 0.77546, prc: 0.99421, f1: 0.87132, f1_macro: 0.93441\n",
            "epoch: 14, passed number of graphs: 672, train running loss: 0.01294 (passed time: 41m 51s)\n",
            "          acc: 0.99708, fpr: 0.00000, fnr: 0.13471, rec: 0.86529, prc: 1.00000, f1: 0.92778, f1_macro: 0.96315\n",
            "epoch: 14, passed number of graphs: 768, train running loss: 0.01155 (passed time: 42m 11s)\n",
            "          acc: 0.99977, fpr: 0.00000, fnr: 0.01044, rec: 0.98956, prc: 0.99984, f1: 0.99467, f1_macro: 0.99728\n",
            "Validation --- epoch: 14, loss: 0.00369\n",
            "          fnr: 0.02505, prc: 0.99917, acc: 0.99945, rec: 0.97495, f1: 0.98548, f1_macro: 0.99260, fpr: 0.00002\n",
            "epoch: 15, passed number of graphs: 96, train running loss: 0.00195 (passed time: 42m 54s)\n",
            "          acc: 0.99985, fpr: 0.00000, fnr: 0.00670, rec: 0.99330, prc: 0.99984, f1: 0.99656, f1_macro: 0.99824\n",
            "epoch: 15, passed number of graphs: 192, train running loss: 0.00214 (passed time: 43m 14s)\n",
            "          acc: 0.99979, fpr: 0.00000, fnr: 0.00996, rec: 0.99004, prc: 1.00000, f1: 0.99499, f1_macro: 0.99744\n",
            "epoch: 15, passed number of graphs: 288, train running loss: 0.00197 (passed time: 43m 33s)\n",
            "          acc: 0.99985, fpr: 0.00000, fnr: 0.00703, rec: 0.99297, prc: 1.00000, f1: 0.99647, f1_macro: 0.99820\n",
            "epoch: 15, passed number of graphs: 384, train running loss: 0.00181 (passed time: 43m 53s)\n",
            "          acc: 0.99983, fpr: 0.00001, fnr: 0.00693, rec: 0.99307, prc: 0.99935, f1: 0.99620, f1_macro: 0.99806\n",
            "epoch: 15, passed number of graphs: 480, train running loss: 0.00169 (passed time: 44m 13s)\n",
            "          acc: 0.99984, fpr: 0.00000, fnr: 0.00719, rec: 0.99281, prc: 1.00000, f1: 0.99639, f1_macro: 0.99816\n",
            "epoch: 15, passed number of graphs: 576, train running loss: 0.00159 (passed time: 44m 33s)\n",
            "          acc: 0.99967, fpr: 0.00001, fnr: 0.01517, rec: 0.98483, prc: 0.99967, f1: 0.99219, f1_macro: 0.99601\n",
            "epoch: 15, passed number of graphs: 672, train running loss: 0.00166 (passed time: 44m 53s)\n",
            "          acc: 0.99741, fpr: 0.00004, fnr: 0.11761, rec: 0.88239, prc: 0.99783, f1: 0.93657, f1_macro: 0.96762\n",
            "epoch: 15, passed number of graphs: 768, train running loss: 0.00162 (passed time: 45m 13s)\n",
            "          acc: 0.99982, fpr: 0.00001, fnr: 0.00799, rec: 0.99201, prc: 0.99951, f1: 0.99574, f1_macro: 0.99783\n",
            "Validation --- epoch: 15, loss: 0.00272\n",
            "          fnr: 0.01894, prc: 0.99823, acc: 0.99956, rec: 0.98106, f1: 0.98837, f1_macro: 0.99407, fpr: 0.00004\n",
            "epoch: 16, passed number of graphs: 96, train running loss: 0.00154 (passed time: 45m 56s)\n",
            "          acc: 0.99987, fpr: 0.00002, fnr: 0.00494, rec: 0.99506, prc: 0.99904, f1: 0.99704, f1_macro: 0.99849\n",
            "epoch: 16, passed number of graphs: 192, train running loss: 0.00158 (passed time: 46m 16s)\n",
            "          acc: 0.99985, fpr: 0.00000, fnr: 0.00702, rec: 0.99298, prc: 1.00000, f1: 0.99648, f1_macro: 0.99820\n",
            "epoch: 16, passed number of graphs: 288, train running loss: 0.00149 (passed time: 46m 35s)\n",
            "          acc: 0.99984, fpr: 0.00005, fnr: 0.00539, rec: 0.99461, prc: 0.99787, f1: 0.99624, f1_macro: 0.99808\n",
            "epoch: 16, passed number of graphs: 384, train running loss: 0.00140 (passed time: 46m 55s)\n",
            "          acc: 0.99986, fpr: 0.00000, fnr: 0.00628, rec: 0.99372, prc: 1.00000, f1: 0.99685, f1_macro: 0.99839\n",
            "epoch: 16, passed number of graphs: 480, train running loss: 0.00132 (passed time: 47m 15s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00441, rec: 0.99559, prc: 0.99967, f1: 0.99763, f1_macro: 0.99879\n",
            "epoch: 16, passed number of graphs: 576, train running loss: 0.00125 (passed time: 47m 35s)\n",
            "          acc: 0.99990, fpr: 0.00004, fnr: 0.00274, rec: 0.99726, prc: 0.99822, f1: 0.99774, f1_macro: 0.99884\n",
            "epoch: 16, passed number of graphs: 672, train running loss: 0.00131 (passed time: 47m 55s)\n",
            "          acc: 0.99944, fpr: 0.00045, fnr: 0.00527, rec: 0.99473, prc: 0.97985, f1: 0.98723, f1_macro: 0.99347\n",
            "epoch: 16, passed number of graphs: 768, train running loss: 0.00131 (passed time: 48m 15s)\n",
            "          acc: 0.99985, fpr: 0.00001, fnr: 0.00636, rec: 0.99364, prc: 0.99951, f1: 0.99656, f1_macro: 0.99824\n",
            "Validation --- epoch: 16, loss: 0.00222\n",
            "          fnr: 0.01089, prc: 0.99779, acc: 0.99972, rec: 0.98911, f1: 0.99266, f1_macro: 0.99626, fpr: 0.00005\n",
            "epoch: 17, passed number of graphs: 96, train running loss: 0.00090 (passed time: 48m 58s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00271, rec: 0.99729, prc: 0.99936, f1: 0.99832, f1_macro: 0.99914\n",
            "epoch: 17, passed number of graphs: 192, train running loss: 0.00107 (passed time: 49m 18s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00392, rec: 0.99608, prc: 0.99951, f1: 0.99779, f1_macro: 0.99887\n",
            "epoch: 17, passed number of graphs: 288, train running loss: 0.00104 (passed time: 49m 38s)\n",
            "          acc: 0.99981, fpr: 0.00012, fnr: 0.00376, rec: 0.99624, prc: 0.99462, f1: 0.99543, f1_macro: 0.99766\n",
            "epoch: 17, passed number of graphs: 384, train running loss: 0.00103 (passed time: 49m 58s)\n",
            "          acc: 0.99987, fpr: 0.00002, fnr: 0.00499, rec: 0.99501, prc: 0.99919, f1: 0.99709, f1_macro: 0.99851\n",
            "epoch: 17, passed number of graphs: 480, train running loss: 0.00097 (passed time: 50m 18s)\n",
            "          acc: 0.99980, fpr: 0.00009, fnr: 0.00506, rec: 0.99494, prc: 0.99575, f1: 0.99534, f1_macro: 0.99762\n",
            "epoch: 17, passed number of graphs: 576, train running loss: 0.00093 (passed time: 50m 38s)\n",
            "          acc: 0.99991, fpr: 0.00003, fnr: 0.00274, rec: 0.99726, prc: 0.99855, f1: 0.99790, f1_macro: 0.99893\n",
            "epoch: 17, passed number of graphs: 672, train running loss: 0.00101 (passed time: 50m 57s)\n",
            "          acc: 0.99956, fpr: 0.00033, fnr: 0.00527, rec: 0.99473, prc: 0.98512, f1: 0.98990, f1_macro: 0.99484\n",
            "epoch: 17, passed number of graphs: 768, train running loss: 0.00103 (passed time: 51m 17s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00473, rec: 0.99527, prc: 0.99967, f1: 0.99747, f1_macro: 0.99871\n",
            "Validation --- epoch: 17, loss: 0.00210\n",
            "          fnr: 0.00995, prc: 0.99735, acc: 0.99973, rec: 0.99005, f1: 0.99291, f1_macro: 0.99639, fpr: 0.00006\n",
            "epoch: 18, passed number of graphs: 96, train running loss: 0.00081 (passed time: 52m 0s)\n",
            "          acc: 0.99993, fpr: 0.00002, fnr: 0.00223, rec: 0.99777, prc: 0.99920, f1: 0.99848, f1_macro: 0.99922\n",
            "epoch: 18, passed number of graphs: 192, train running loss: 0.00096 (passed time: 52m 20s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00359, rec: 0.99641, prc: 0.99951, f1: 0.99796, f1_macro: 0.99896\n",
            "epoch: 18, passed number of graphs: 288, train running loss: 0.00093 (passed time: 52m 40s)\n",
            "          acc: 0.99985, fpr: 0.00009, fnr: 0.00278, rec: 0.99722, prc: 0.99592, f1: 0.99657, f1_macro: 0.99825\n",
            "epoch: 18, passed number of graphs: 384, train running loss: 0.00093 (passed time: 53m 0s)\n",
            "          acc: 0.99992, fpr: 0.00002, fnr: 0.00274, rec: 0.99726, prc: 0.99919, f1: 0.99823, f1_macro: 0.99909\n",
            "epoch: 18, passed number of graphs: 480, train running loss: 0.00088 (passed time: 53m 20s)\n",
            "          acc: 0.99984, fpr: 0.00007, fnr: 0.00392, rec: 0.99608, prc: 0.99673, f1: 0.99641, f1_macro: 0.99816\n",
            "epoch: 18, passed number of graphs: 576, train running loss: 0.00085 (passed time: 53m 40s)\n",
            "          acc: 0.99989, fpr: 0.00005, fnr: 0.00291, rec: 0.99709, prc: 0.99774, f1: 0.99742, f1_macro: 0.99868\n",
            "epoch: 18, passed number of graphs: 672, train running loss: 0.00093 (passed time: 54m 0s)\n",
            "          acc: 0.99948, fpr: 0.00037, fnr: 0.00703, rec: 0.99297, prc: 0.98323, f1: 0.98807, f1_macro: 0.99390\n",
            "epoch: 18, passed number of graphs: 768, train running loss: 0.00096 (passed time: 54m 20s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00522, rec: 0.99478, prc: 0.99934, f1: 0.99706, f1_macro: 0.99850\n",
            "Validation --- epoch: 18, loss: 0.00204\n",
            "          fnr: 0.00927, prc: 0.99714, acc: 0.99974, rec: 0.99073, f1: 0.99315, f1_macro: 0.99651, fpr: 0.00006\n",
            "epoch: 19, passed number of graphs: 96, train running loss: 0.00077 (passed time: 55m 2s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00239, rec: 0.99761, prc: 0.99952, f1: 0.99856, f1_macro: 0.99927\n",
            "epoch: 19, passed number of graphs: 192, train running loss: 0.00100 (passed time: 55m 22s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00523, rec: 0.99477, prc: 0.99967, f1: 0.99722, f1_macro: 0.99858\n",
            "epoch: 19, passed number of graphs: 288, train running loss: 0.00097 (passed time: 55m 42s)\n",
            "          acc: 0.99980, fpr: 0.00012, fnr: 0.00392, rec: 0.99608, prc: 0.99445, f1: 0.99527, f1_macro: 0.99758\n",
            "epoch: 19, passed number of graphs: 384, train running loss: 0.00096 (passed time: 56m 2s)\n",
            "          acc: 0.99992, fpr: 0.00003, fnr: 0.00258, rec: 0.99742, prc: 0.99887, f1: 0.99815, f1_macro: 0.99905\n",
            "epoch: 19, passed number of graphs: 480, train running loss: 0.00091 (passed time: 56m 22s)\n",
            "          acc: 0.99988, fpr: 0.00006, fnr: 0.00310, rec: 0.99690, prc: 0.99739, f1: 0.99714, f1_macro: 0.99854\n",
            "epoch: 19, passed number of graphs: 576, train running loss: 0.00087 (passed time: 56m 42s)\n",
            "          acc: 0.99988, fpr: 0.00006, fnr: 0.00258, rec: 0.99742, prc: 0.99710, f1: 0.99726, f1_macro: 0.99860\n",
            "epoch: 19, passed number of graphs: 672, train running loss: 0.00095 (passed time: 57m 2s)\n",
            "          acc: 0.99960, fpr: 0.00031, fnr: 0.00431, rec: 0.99569, prc: 0.98607, f1: 0.99086, f1_macro: 0.99533\n",
            "epoch: 19, passed number of graphs: 768, train running loss: 0.00097 (passed time: 57m 22s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00489, rec: 0.99511, prc: 0.99967, f1: 0.99738, f1_macro: 0.99866\n",
            "Validation --- epoch: 19, loss: 0.00202\n",
            "          fnr: 0.00930, prc: 0.99721, acc: 0.99974, rec: 0.99070, f1: 0.99317, f1_macro: 0.99652, fpr: 0.00006\n",
            "epoch: 20, passed number of graphs: 96, train running loss: 0.00081 (passed time: 58m 4s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00287, rec: 0.99713, prc: 0.99968, f1: 0.99840, f1_macro: 0.99918\n",
            "epoch: 20, passed number of graphs: 192, train running loss: 0.00094 (passed time: 58m 24s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00425, rec: 0.99575, prc: 0.99934, f1: 0.99755, f1_macro: 0.99875\n",
            "epoch: 20, passed number of graphs: 288, train running loss: 0.00091 (passed time: 58m 44s)\n",
            "          acc: 0.99983, fpr: 0.00010, fnr: 0.00359, rec: 0.99641, prc: 0.99559, f1: 0.99600, f1_macro: 0.99796\n",
            "epoch: 20, passed number of graphs: 384, train running loss: 0.00091 (passed time: 59m 4s)\n",
            "          acc: 0.99985, fpr: 0.00003, fnr: 0.00548, rec: 0.99452, prc: 0.99887, f1: 0.99669, f1_macro: 0.99831\n",
            "epoch: 20, passed number of graphs: 480, train running loss: 0.00086 (passed time: 59m 24s)\n",
            "          acc: 0.99986, fpr: 0.00007, fnr: 0.00327, rec: 0.99673, prc: 0.99690, f1: 0.99681, f1_macro: 0.99837\n",
            "epoch: 20, passed number of graphs: 576, train running loss: 0.00083 (passed time: 59m 44s)\n",
            "          acc: 0.99991, fpr: 0.00003, fnr: 0.00274, rec: 0.99726, prc: 0.99871, f1: 0.99798, f1_macro: 0.99897\n",
            "epoch: 20, passed number of graphs: 672, train running loss: 0.00092 (passed time: 1h 0m 4s)\n",
            "          acc: 0.99955, fpr: 0.00034, fnr: 0.00527, rec: 0.99473, prc: 0.98466, f1: 0.98967, f1_macro: 0.99472\n",
            "epoch: 20, passed number of graphs: 768, train running loss: 0.00095 (passed time: 1h 0m 24s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00424, rec: 0.99576, prc: 0.99967, f1: 0.99771, f1_macro: 0.99883\n",
            "Validation --- epoch: 20, loss: 0.00201\n",
            "          fnr: 0.00939, prc: 0.99767, acc: 0.99975, rec: 0.99061, f1: 0.99337, f1_macro: 0.99662, fpr: 0.00005\n",
            "epoch: 21, passed number of graphs: 96, train running loss: 0.00081 (passed time: 1h 1m 6s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00223, rec: 0.99777, prc: 0.99952, f1: 0.99864, f1_macro: 0.99931\n",
            "epoch: 21, passed number of graphs: 192, train running loss: 0.00092 (passed time: 1h 1m 26s)\n",
            "          acc: 0.99987, fpr: 0.00000, fnr: 0.00621, rec: 0.99379, prc: 1.00000, f1: 0.99689, f1_macro: 0.99841\n",
            "epoch: 21, passed number of graphs: 288, train running loss: 0.00090 (passed time: 1h 1m 46s)\n",
            "          acc: 0.99981, fpr: 0.00012, fnr: 0.00376, rec: 0.99624, prc: 0.99462, f1: 0.99543, f1_macro: 0.99766\n",
            "epoch: 21, passed number of graphs: 384, train running loss: 0.00089 (passed time: 1h 2m 6s)\n",
            "          acc: 0.99987, fpr: 0.00002, fnr: 0.00483, rec: 0.99517, prc: 0.99903, f1: 0.99709, f1_macro: 0.99851\n",
            "epoch: 21, passed number of graphs: 480, train running loss: 0.00084 (passed time: 1h 2m 26s)\n",
            "          acc: 0.99983, fpr: 0.00008, fnr: 0.00392, rec: 0.99608, prc: 0.99624, f1: 0.99616, f1_macro: 0.99804\n",
            "epoch: 21, passed number of graphs: 576, train running loss: 0.00081 (passed time: 1h 2m 46s)\n",
            "          acc: 0.99989, fpr: 0.00005, fnr: 0.00307, rec: 0.99693, prc: 0.99774, f1: 0.99734, f1_macro: 0.99864\n",
            "epoch: 21, passed number of graphs: 672, train running loss: 0.00089 (passed time: 1h 3m 6s)\n",
            "          acc: 0.99960, fpr: 0.00032, fnr: 0.00415, rec: 0.99585, prc: 0.98561, f1: 0.99070, f1_macro: 0.99525\n",
            "epoch: 21, passed number of graphs: 768, train running loss: 0.00092 (passed time: 1h 3m 26s)\n",
            "          acc: 0.99986, fpr: 0.00001, fnr: 0.00620, rec: 0.99380, prc: 0.99951, f1: 0.99665, f1_macro: 0.99829\n",
            "Validation --- epoch: 21, loss: 0.00201\n",
            "          fnr: 0.00931, prc: 0.99760, acc: 0.99975, rec: 0.99069, f1: 0.99337, f1_macro: 0.99662, fpr: 0.00005\n",
            "epoch: 22, passed number of graphs: 96, train running loss: 0.00079 (passed time: 1h 4m 8s)\n",
            "          acc: 0.99995, fpr: 0.00002, fnr: 0.00144, rec: 0.99856, prc: 0.99920, f1: 0.99888, f1_macro: 0.99943\n",
            "epoch: 22, passed number of graphs: 192, train running loss: 0.00092 (passed time: 1h 4m 28s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00392, rec: 0.99608, prc: 0.99967, f1: 0.99787, f1_macro: 0.99891\n",
            "epoch: 22, passed number of graphs: 288, train running loss: 0.00089 (passed time: 1h 4m 48s)\n",
            "          acc: 0.99982, fpr: 0.00011, fnr: 0.00327, rec: 0.99673, prc: 0.99478, f1: 0.99576, f1_macro: 0.99783\n",
            "epoch: 22, passed number of graphs: 384, train running loss: 0.00088 (passed time: 1h 5m 8s)\n",
            "          acc: 0.99989, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99903, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 22, passed number of graphs: 480, train running loss: 0.00084 (passed time: 1h 5m 28s)\n",
            "          acc: 0.99986, fpr: 0.00005, fnr: 0.00425, rec: 0.99575, prc: 0.99755, f1: 0.99665, f1_macro: 0.99829\n",
            "epoch: 22, passed number of graphs: 576, train running loss: 0.00082 (passed time: 1h 5m 48s)\n",
            "          acc: 0.99988, fpr: 0.00003, fnr: 0.00420, rec: 0.99580, prc: 0.99854, f1: 0.99717, f1_macro: 0.99855\n",
            "epoch: 22, passed number of graphs: 672, train running loss: 0.00089 (passed time: 1h 6m 8s)\n",
            "          acc: 0.99958, fpr: 0.00031, fnr: 0.00511, rec: 0.99489, prc: 0.98606, f1: 0.99045, f1_macro: 0.99512\n",
            "epoch: 22, passed number of graphs: 768, train running loss: 0.00091 (passed time: 1h 6m 28s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00326, rec: 0.99674, prc: 0.99935, f1: 0.99804, f1_macro: 0.99900\n",
            "Validation --- epoch: 22, loss: 0.00200\n",
            "          fnr: 0.00912, prc: 0.99737, acc: 0.99975, rec: 0.99088, f1: 0.99335, f1_macro: 0.99661, fpr: 0.00006\n",
            "epoch: 23, passed number of graphs: 96, train running loss: 0.00084 (passed time: 1h 7m 10s)\n",
            "          acc: 0.99995, fpr: 0.00001, fnr: 0.00191, rec: 0.99809, prc: 0.99952, f1: 0.99880, f1_macro: 0.99939\n",
            "epoch: 23, passed number of graphs: 192, train running loss: 0.00093 (passed time: 1h 7m 30s)\n",
            "          acc: 0.99992, fpr: 0.00002, fnr: 0.00294, rec: 0.99706, prc: 0.99902, f1: 0.99804, f1_macro: 0.99900\n",
            "epoch: 23, passed number of graphs: 288, train running loss: 0.00087 (passed time: 1h 7m 50s)\n",
            "          acc: 0.99987, fpr: 0.00006, fnr: 0.00343, rec: 0.99657, prc: 0.99722, f1: 0.99689, f1_macro: 0.99841\n",
            "epoch: 23, passed number of graphs: 384, train running loss: 0.00087 (passed time: 1h 8m 10s)\n",
            "          acc: 0.99989, fpr: 0.00002, fnr: 0.00419, rec: 0.99581, prc: 0.99919, f1: 0.99750, f1_macro: 0.99872\n",
            "epoch: 23, passed number of graphs: 480, train running loss: 0.00083 (passed time: 1h 8m 30s)\n",
            "          acc: 0.99989, fpr: 0.00004, fnr: 0.00343, rec: 0.99657, prc: 0.99820, f1: 0.99738, f1_macro: 0.99866\n",
            "epoch: 23, passed number of graphs: 576, train running loss: 0.00080 (passed time: 1h 8m 50s)\n",
            "          acc: 0.99983, fpr: 0.00013, fnr: 0.00178, rec: 0.99822, prc: 0.99389, f1: 0.99605, f1_macro: 0.99798\n",
            "epoch: 23, passed number of graphs: 672, train running loss: 0.00088 (passed time: 1h 9m 10s)\n",
            "          acc: 0.99955, fpr: 0.00034, fnr: 0.00527, rec: 0.99473, prc: 0.98466, f1: 0.98967, f1_macro: 0.99472\n",
            "epoch: 23, passed number of graphs: 768, train running loss: 0.00091 (passed time: 1h 9m 29s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00506, rec: 0.99494, prc: 0.99951, f1: 0.99722, f1_macro: 0.99858\n",
            "Validation --- epoch: 23, loss: 0.00200\n",
            "          fnr: 0.00937, prc: 0.99765, acc: 0.99975, rec: 0.99063, f1: 0.99337, f1_macro: 0.99662, fpr: 0.00005\n",
            "epoch: 24, passed number of graphs: 96, train running loss: 0.00093 (passed time: 1h 10m 12s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00415, rec: 0.99585, prc: 0.99952, f1: 0.99768, f1_macro: 0.99882\n",
            "epoch: 24, passed number of graphs: 192, train running loss: 0.00098 (passed time: 1h 10m 32s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00490, rec: 0.99510, prc: 0.99951, f1: 0.99730, f1_macro: 0.99862\n",
            "epoch: 24, passed number of graphs: 288, train running loss: 0.00095 (passed time: 1h 10m 52s)\n",
            "          acc: 0.99979, fpr: 0.00014, fnr: 0.00359, rec: 0.99641, prc: 0.99348, f1: 0.99494, f1_macro: 0.99742\n",
            "epoch: 24, passed number of graphs: 384, train running loss: 0.00092 (passed time: 1h 11m 12s)\n",
            "          acc: 0.99991, fpr: 0.00002, fnr: 0.00322, rec: 0.99678, prc: 0.99919, f1: 0.99798, f1_macro: 0.99897\n",
            "epoch: 24, passed number of graphs: 480, train running loss: 0.00087 (passed time: 1h 11m 32s)\n",
            "          acc: 0.99981, fpr: 0.00012, fnr: 0.00343, rec: 0.99657, prc: 0.99446, f1: 0.99551, f1_macro: 0.99771\n",
            "epoch: 24, passed number of graphs: 576, train running loss: 0.00083 (passed time: 1h 11m 52s)\n",
            "          acc: 0.99984, fpr: 0.00004, fnr: 0.00565, rec: 0.99435, prc: 0.99806, f1: 0.99620, f1_macro: 0.99806\n",
            "epoch: 24, passed number of graphs: 672, train running loss: 0.00092 (passed time: 1h 12m 11s)\n",
            "          acc: 0.99958, fpr: 0.00034, fnr: 0.00384, rec: 0.99616, prc: 0.98483, f1: 0.99047, f1_macro: 0.99513\n",
            "epoch: 24, passed number of graphs: 768, train running loss: 0.00094 (passed time: 1h 12m 31s)\n",
            "          acc: 0.99989, fpr: 0.00000, fnr: 0.00506, rec: 0.99494, prc: 0.99984, f1: 0.99738, f1_macro: 0.99866\n",
            "Validation --- epoch: 24, loss: 0.00200\n",
            "          fnr: 0.00941, prc: 0.99767, acc: 0.99975, rec: 0.99059, f1: 0.99336, f1_macro: 0.99662, fpr: 0.00005\n",
            "epoch: 25, passed number of graphs: 96, train running loss: 0.00075 (passed time: 1h 13m 14s)\n",
            "          acc: 0.99996, fpr: 0.00001, fnr: 0.00160, rec: 0.99840, prc: 0.99952, f1: 0.99896, f1_macro: 0.99947\n",
            "epoch: 25, passed number of graphs: 192, train running loss: 0.00088 (passed time: 1h 13m 34s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00457, rec: 0.99543, prc: 0.99967, f1: 0.99755, f1_macro: 0.99875\n",
            "epoch: 25, passed number of graphs: 288, train running loss: 0.00088 (passed time: 1h 13m 54s)\n",
            "          acc: 0.99983, fpr: 0.00010, fnr: 0.00376, rec: 0.99624, prc: 0.99559, f1: 0.99592, f1_macro: 0.99791\n",
            "epoch: 25, passed number of graphs: 384, train running loss: 0.00087 (passed time: 1h 14m 14s)\n",
            "          acc: 0.99989, fpr: 0.00003, fnr: 0.00354, rec: 0.99646, prc: 0.99871, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 25, passed number of graphs: 480, train running loss: 0.00083 (passed time: 1h 14m 34s)\n",
            "          acc: 0.99984, fpr: 0.00008, fnr: 0.00359, rec: 0.99641, prc: 0.99624, f1: 0.99633, f1_macro: 0.99812\n",
            "epoch: 25, passed number of graphs: 576, train running loss: 0.00081 (passed time: 1h 14m 53s)\n",
            "          acc: 0.99988, fpr: 0.00006, fnr: 0.00291, rec: 0.99709, prc: 0.99742, f1: 0.99726, f1_macro: 0.99860\n",
            "epoch: 25, passed number of graphs: 672, train running loss: 0.00087 (passed time: 1h 15m 13s)\n",
            "          acc: 0.99948, fpr: 0.00033, fnr: 0.00895, rec: 0.99105, prc: 0.98523, f1: 0.98813, f1_macro: 0.99393\n",
            "epoch: 25, passed number of graphs: 768, train running loss: 0.00090 (passed time: 1h 15m 33s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00473, rec: 0.99527, prc: 0.99967, f1: 0.99747, f1_macro: 0.99871\n",
            "Validation --- epoch: 25, loss: 0.00200\n",
            "          fnr: 0.00917, prc: 0.99756, acc: 0.99975, rec: 0.99083, f1: 0.99343, f1_macro: 0.99665, fpr: 0.00005\n",
            "epoch: 26, passed number of graphs: 96, train running loss: 0.00084 (passed time: 1h 16m 16s)\n",
            "          acc: 0.99996, fpr: 0.00001, fnr: 0.00112, rec: 0.99888, prc: 0.99936, f1: 0.99912, f1_macro: 0.99955\n",
            "epoch: 26, passed number of graphs: 192, train running loss: 0.00096 (passed time: 1h 16m 36s)\n",
            "          acc: 0.99983, fpr: 0.00001, fnr: 0.00735, rec: 0.99265, prc: 0.99951, f1: 0.99607, f1_macro: 0.99799\n",
            "epoch: 26, passed number of graphs: 288, train running loss: 0.00092 (passed time: 1h 16m 56s)\n",
            "          acc: 0.99985, fpr: 0.00011, fnr: 0.00180, rec: 0.99820, prc: 0.99495, f1: 0.99657, f1_macro: 0.99825\n",
            "epoch: 26, passed number of graphs: 384, train running loss: 0.00091 (passed time: 1h 17m 16s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00322, rec: 0.99678, prc: 0.99952, f1: 0.99814, f1_macro: 0.99905\n",
            "epoch: 26, passed number of graphs: 480, train running loss: 0.00086 (passed time: 1h 17m 35s)\n",
            "          acc: 0.99984, fpr: 0.00010, fnr: 0.00278, rec: 0.99722, prc: 0.99543, f1: 0.99633, f1_macro: 0.99812\n",
            "epoch: 26, passed number of graphs: 576, train running loss: 0.00083 (passed time: 1h 17m 55s)\n",
            "          acc: 0.99990, fpr: 0.00004, fnr: 0.00291, rec: 0.99709, prc: 0.99838, f1: 0.99774, f1_macro: 0.99884\n",
            "epoch: 26, passed number of graphs: 672, train running loss: 0.00091 (passed time: 1h 18m 15s)\n",
            "          acc: 0.99961, fpr: 0.00030, fnr: 0.00431, rec: 0.99569, prc: 0.98639, f1: 0.99101, f1_macro: 0.99541\n",
            "epoch: 26, passed number of graphs: 768, train running loss: 0.00093 (passed time: 1h 18m 35s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00473, rec: 0.99527, prc: 0.99967, f1: 0.99747, f1_macro: 0.99871\n",
            "Validation --- epoch: 26, loss: 0.00200\n",
            "          fnr: 0.00902, prc: 0.99730, acc: 0.99975, rec: 0.99098, f1: 0.99336, f1_macro: 0.99662, fpr: 0.00006\n",
            "epoch: 27, passed number of graphs: 96, train running loss: 0.00070 (passed time: 1h 19m 18s)\n",
            "          acc: 0.99976, fpr: 0.00001, fnr: 0.01021, rec: 0.98979, prc: 0.99936, f1: 0.99455, f1_macro: 0.99722\n",
            "epoch: 27, passed number of graphs: 192, train running loss: 0.00090 (passed time: 1h 19m 38s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00310, rec: 0.99690, prc: 0.99951, f1: 0.99820, f1_macro: 0.99908\n",
            "epoch: 27, passed number of graphs: 288, train running loss: 0.00088 (passed time: 1h 19m 58s)\n",
            "          acc: 0.99981, fpr: 0.00012, fnr: 0.00327, rec: 0.99673, prc: 0.99430, f1: 0.99551, f1_macro: 0.99771\n",
            "epoch: 27, passed number of graphs: 384, train running loss: 0.00088 (passed time: 1h 20m 18s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00387, rec: 0.99613, prc: 0.99952, f1: 0.99782, f1_macro: 0.99889\n",
            "epoch: 27, passed number of graphs: 480, train running loss: 0.00084 (passed time: 1h 20m 37s)\n",
            "          acc: 0.99983, fpr: 0.00013, fnr: 0.00163, rec: 0.99837, prc: 0.99398, f1: 0.99617, f1_macro: 0.99804\n",
            "epoch: 27, passed number of graphs: 576, train running loss: 0.00081 (passed time: 1h 20m 57s)\n",
            "          acc: 0.99984, fpr: 0.00010, fnr: 0.00274, rec: 0.99726, prc: 0.99533, f1: 0.99629, f1_macro: 0.99810\n",
            "epoch: 27, passed number of graphs: 672, train running loss: 0.00089 (passed time: 1h 21m 17s)\n",
            "          acc: 0.99956, fpr: 0.00025, fnr: 0.00911, rec: 0.99089, prc: 0.98884, f1: 0.98986, f1_macro: 0.99482\n",
            "epoch: 27, passed number of graphs: 768, train running loss: 0.00092 (passed time: 1h 21m 37s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00392, rec: 0.99608, prc: 0.99967, f1: 0.99788, f1_macro: 0.99891\n",
            "Validation --- epoch: 27, loss: 0.00200\n",
            "          fnr: 0.00917, prc: 0.99753, acc: 0.99975, rec: 0.99083, f1: 0.99341, f1_macro: 0.99664, fpr: 0.00005\n",
            "epoch: 28, passed number of graphs: 96, train running loss: 0.00085 (passed time: 1h 22m 20s)\n",
            "          acc: 0.99996, fpr: 0.00002, fnr: 0.00128, rec: 0.99872, prc: 0.99920, f1: 0.99896, f1_macro: 0.99947\n",
            "epoch: 28, passed number of graphs: 192, train running loss: 0.00099 (passed time: 1h 22m 40s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00408, rec: 0.99592, prc: 0.99951, f1: 0.99771, f1_macro: 0.99883\n",
            "epoch: 28, passed number of graphs: 288, train running loss: 0.00098 (passed time: 1h 23m 0s)\n",
            "          acc: 0.99986, fpr: 0.00005, fnr: 0.00441, rec: 0.99559, prc: 0.99771, f1: 0.99665, f1_macro: 0.99829\n",
            "epoch: 28, passed number of graphs: 384, train running loss: 0.00095 (passed time: 1h 23m 19s)\n",
            "          acc: 0.99990, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99919, f1: 0.99766, f1_macro: 0.99880\n",
            "epoch: 28, passed number of graphs: 480, train running loss: 0.00090 (passed time: 1h 23m 39s)\n",
            "          acc: 0.99979, fpr: 0.00016, fnr: 0.00278, rec: 0.99722, prc: 0.99301, f1: 0.99511, f1_macro: 0.99750\n",
            "epoch: 28, passed number of graphs: 576, train running loss: 0.00086 (passed time: 1h 23m 59s)\n",
            "          acc: 0.99991, fpr: 0.00006, fnr: 0.00178, rec: 0.99822, prc: 0.99742, f1: 0.99782, f1_macro: 0.99889\n",
            "epoch: 28, passed number of graphs: 672, train running loss: 0.00093 (passed time: 1h 24m 19s)\n",
            "          acc: 0.99960, fpr: 0.00033, fnr: 0.00368, rec: 0.99632, prc: 0.98515, f1: 0.99070, f1_macro: 0.99525\n",
            "epoch: 28, passed number of graphs: 768, train running loss: 0.00095 (passed time: 1h 24m 39s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00408, rec: 0.99592, prc: 0.99967, f1: 0.99779, f1_macro: 0.99887\n",
            "Validation --- epoch: 28, loss: 0.00200\n",
            "          fnr: 0.00903, prc: 0.99741, acc: 0.99975, rec: 0.99097, f1: 0.99342, f1_macro: 0.99664, fpr: 0.00006\n",
            "epoch: 29, passed number of graphs: 96, train running loss: 0.00073 (passed time: 1h 25m 22s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00255, rec: 0.99745, prc: 0.99968, f1: 0.99856, f1_macro: 0.99927\n",
            "epoch: 29, passed number of graphs: 192, train running loss: 0.00090 (passed time: 1h 25m 42s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00506, rec: 0.99494, prc: 0.99951, f1: 0.99722, f1_macro: 0.99858\n",
            "epoch: 29, passed number of graphs: 288, train running loss: 0.00087 (passed time: 1h 26m 1s)\n",
            "          acc: 0.99981, fpr: 0.00014, fnr: 0.00229, rec: 0.99771, prc: 0.99349, f1: 0.99560, f1_macro: 0.99775\n",
            "epoch: 29, passed number of graphs: 384, train running loss: 0.00086 (passed time: 1h 26m 21s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00403, rec: 0.99597, prc: 0.99935, f1: 0.99766, f1_macro: 0.99880\n",
            "epoch: 29, passed number of graphs: 480, train running loss: 0.00082 (passed time: 1h 26m 41s)\n",
            "          acc: 0.99983, fpr: 0.00013, fnr: 0.00212, rec: 0.99788, prc: 0.99414, f1: 0.99601, f1_macro: 0.99796\n",
            "epoch: 29, passed number of graphs: 576, train running loss: 0.00079 (passed time: 1h 27m 1s)\n",
            "          acc: 0.99980, fpr: 0.00015, fnr: 0.00258, rec: 0.99742, prc: 0.99309, f1: 0.99525, f1_macro: 0.99757\n",
            "epoch: 29, passed number of graphs: 672, train running loss: 0.00086 (passed time: 1h 27m 21s)\n",
            "          acc: 0.99957, fpr: 0.00034, fnr: 0.00447, rec: 0.99553, prc: 0.98482, f1: 0.99015, f1_macro: 0.99496\n",
            "epoch: 29, passed number of graphs: 768, train running loss: 0.00089 (passed time: 1h 27m 41s)\n",
            "          acc: 0.99984, fpr: 0.00000, fnr: 0.00718, rec: 0.99282, prc: 0.99984, f1: 0.99632, f1_macro: 0.99812\n",
            "Validation --- epoch: 29, loss: 0.00200\n",
            "          fnr: 0.00909, prc: 0.99738, acc: 0.99975, rec: 0.99091, f1: 0.99337, f1_macro: 0.99662, fpr: 0.00006\n",
            "epoch: 30, passed number of graphs: 96, train running loss: 0.00074 (passed time: 1h 28m 23s)\n",
            "          acc: 0.99996, fpr: 0.00002, fnr: 0.00096, rec: 0.99904, prc: 0.99920, f1: 0.99912, f1_macro: 0.99955\n",
            "epoch: 30, passed number of graphs: 192, train running loss: 0.00088 (passed time: 1h 28m 43s)\n",
            "          acc: 0.99989, fpr: 0.00000, fnr: 0.00506, rec: 0.99494, prc: 0.99984, f1: 0.99738, f1_macro: 0.99866\n",
            "epoch: 30, passed number of graphs: 288, train running loss: 0.00087 (passed time: 1h 29m 3s)\n",
            "          acc: 0.99981, fpr: 0.00013, fnr: 0.00310, rec: 0.99690, prc: 0.99413, f1: 0.99551, f1_macro: 0.99771\n",
            "epoch: 30, passed number of graphs: 384, train running loss: 0.00086 (passed time: 1h 29m 23s)\n",
            "          acc: 0.99989, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99903, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 30, passed number of graphs: 480, train running loss: 0.00082 (passed time: 1h 29m 43s)\n",
            "          acc: 0.99981, fpr: 0.00010, fnr: 0.00408, rec: 0.99592, prc: 0.99543, f1: 0.99567, f1_macro: 0.99779\n",
            "epoch: 30, passed number of graphs: 576, train running loss: 0.00079 (passed time: 1h 30m 3s)\n",
            "          acc: 0.99990, fpr: 0.00004, fnr: 0.00274, rec: 0.99726, prc: 0.99822, f1: 0.99774, f1_macro: 0.99884\n",
            "epoch: 30, passed number of graphs: 672, train running loss: 0.00088 (passed time: 1h 30m 23s)\n",
            "          acc: 0.99961, fpr: 0.00031, fnr: 0.00415, rec: 0.99585, prc: 0.98623, f1: 0.99102, f1_macro: 0.99541\n",
            "epoch: 30, passed number of graphs: 768, train running loss: 0.00090 (passed time: 1h 30m 43s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00489, rec: 0.99511, prc: 0.99951, f1: 0.99730, f1_macro: 0.99862\n",
            "Validation --- epoch: 30, loss: 0.00201\n",
            "          fnr: 0.00924, prc: 0.99758, acc: 0.99975, rec: 0.99076, f1: 0.99340, f1_macro: 0.99664, fpr: 0.00005\n",
            "epoch: 31, passed number of graphs: 96, train running loss: 0.00073 (passed time: 1h 31m 25s)\n",
            "          acc: 0.99992, fpr: 0.00002, fnr: 0.00287, rec: 0.99713, prc: 0.99904, f1: 0.99808, f1_macro: 0.99902\n",
            "epoch: 31, passed number of graphs: 192, train running loss: 0.00086 (passed time: 1h 31m 45s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00441, rec: 0.99559, prc: 0.99967, f1: 0.99763, f1_macro: 0.99879\n",
            "epoch: 31, passed number of graphs: 288, train running loss: 0.00085 (passed time: 1h 32m 5s)\n",
            "          acc: 0.99980, fpr: 0.00014, fnr: 0.00310, rec: 0.99690, prc: 0.99365, f1: 0.99527, f1_macro: 0.99758\n",
            "epoch: 31, passed number of graphs: 384, train running loss: 0.00085 (passed time: 1h 32m 25s)\n",
            "          acc: 0.99988, fpr: 0.00002, fnr: 0.00451, rec: 0.99549, prc: 0.99919, f1: 0.99734, f1_macro: 0.99864\n",
            "epoch: 31, passed number of graphs: 480, train running loss: 0.00082 (passed time: 1h 32m 45s)\n",
            "          acc: 0.99981, fpr: 0.00012, fnr: 0.00343, rec: 0.99657, prc: 0.99462, f1: 0.99559, f1_macro: 0.99775\n",
            "epoch: 31, passed number of graphs: 576, train running loss: 0.00079 (passed time: 1h 33m 5s)\n",
            "          acc: 0.99989, fpr: 0.00006, fnr: 0.00226, rec: 0.99774, prc: 0.99710, f1: 0.99742, f1_macro: 0.99868\n",
            "epoch: 31, passed number of graphs: 672, train running loss: 0.00086 (passed time: 1h 33m 25s)\n",
            "          acc: 0.99960, fpr: 0.00033, fnr: 0.00352, rec: 0.99648, prc: 0.98531, f1: 0.99086, f1_macro: 0.99533\n",
            "epoch: 31, passed number of graphs: 768, train running loss: 0.00089 (passed time: 1h 33m 45s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00457, rec: 0.99543, prc: 0.99951, f1: 0.99747, f1_macro: 0.99871\n",
            "Validation --- epoch: 31, loss: 0.00201\n",
            "          fnr: 0.00883, prc: 0.99687, acc: 0.99974, rec: 0.99117, f1: 0.99323, f1_macro: 0.99655, fpr: 0.00007\n",
            "epoch: 32, passed number of graphs: 96, train running loss: 0.00087 (passed time: 1h 34m 27s)\n",
            "          acc: 0.99996, fpr: 0.00001, fnr: 0.00112, rec: 0.99888, prc: 0.99936, f1: 0.99912, f1_macro: 0.99955\n",
            "epoch: 32, passed number of graphs: 192, train running loss: 0.00094 (passed time: 1h 34m 47s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00278, rec: 0.99722, prc: 0.99951, f1: 0.99836, f1_macro: 0.99916\n",
            "epoch: 32, passed number of graphs: 288, train running loss: 0.00090 (passed time: 1h 35m 7s)\n",
            "          acc: 0.99980, fpr: 0.00015, fnr: 0.00261, rec: 0.99739, prc: 0.99333, f1: 0.99535, f1_macro: 0.99763\n",
            "epoch: 32, passed number of graphs: 384, train running loss: 0.00091 (passed time: 1h 35m 27s)\n",
            "          acc: 0.99991, fpr: 0.00002, fnr: 0.00322, rec: 0.99678, prc: 0.99919, f1: 0.99798, f1_macro: 0.99897\n",
            "epoch: 32, passed number of graphs: 480, train running loss: 0.00086 (passed time: 1h 35m 47s)\n",
            "          acc: 0.99984, fpr: 0.00007, fnr: 0.00425, rec: 0.99575, prc: 0.99689, f1: 0.99632, f1_macro: 0.99812\n",
            "epoch: 32, passed number of graphs: 576, train running loss: 0.00082 (passed time: 1h 36m 7s)\n",
            "          acc: 0.99986, fpr: 0.00010, fnr: 0.00226, rec: 0.99774, prc: 0.99565, f1: 0.99669, f1_macro: 0.99831\n",
            "epoch: 32, passed number of graphs: 672, train running loss: 0.00089 (passed time: 1h 36m 27s)\n",
            "          acc: 0.99958, fpr: 0.00033, fnr: 0.00447, rec: 0.99553, prc: 0.98514, f1: 0.99030, f1_macro: 0.99504\n",
            "epoch: 32, passed number of graphs: 768, train running loss: 0.00092 (passed time: 1h 36m 47s)\n",
            "          acc: 0.99984, fpr: 0.00001, fnr: 0.00718, rec: 0.99282, prc: 0.99967, f1: 0.99624, f1_macro: 0.99808\n",
            "Validation --- epoch: 32, loss: 0.00200\n",
            "          fnr: 0.00925, prc: 0.99759, acc: 0.99975, rec: 0.99075, f1: 0.99340, f1_macro: 0.99664, fpr: 0.00005\n",
            "epoch: 33, passed number of graphs: 96, train running loss: 0.00078 (passed time: 1h 37m 30s)\n",
            "          acc: 0.99996, fpr: 0.00001, fnr: 0.00128, rec: 0.99872, prc: 0.99936, f1: 0.99904, f1_macro: 0.99951\n",
            "epoch: 33, passed number of graphs: 192, train running loss: 0.00089 (passed time: 1h 37m 50s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00343, rec: 0.99657, prc: 0.99934, f1: 0.99796, f1_macro: 0.99896\n",
            "epoch: 33, passed number of graphs: 288, train running loss: 0.00085 (passed time: 1h 38m 9s)\n",
            "          acc: 0.99982, fpr: 0.00010, fnr: 0.00408, rec: 0.99592, prc: 0.99559, f1: 0.99575, f1_macro: 0.99783\n",
            "epoch: 33, passed number of graphs: 384, train running loss: 0.00084 (passed time: 1h 38m 29s)\n",
            "          acc: 0.99990, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99919, f1: 0.99766, f1_macro: 0.99880\n",
            "epoch: 33, passed number of graphs: 480, train running loss: 0.00082 (passed time: 1h 38m 49s)\n",
            "          acc: 0.99849, fpr: 0.00007, fnr: 0.06664, rec: 0.93336, prc: 0.99686, f1: 0.96406, f1_macro: 0.98165\n",
            "epoch: 33, passed number of graphs: 576, train running loss: 0.00080 (passed time: 1h 39m 9s)\n",
            "          acc: 0.99980, fpr: 0.00012, fnr: 0.00387, rec: 0.99613, prc: 0.99468, f1: 0.99540, f1_macro: 0.99765\n",
            "epoch: 33, passed number of graphs: 672, train running loss: 0.00088 (passed time: 1h 39m 29s)\n",
            "          acc: 0.99960, fpr: 0.00030, fnr: 0.00495, rec: 0.99505, prc: 0.98653, f1: 0.99077, f1_macro: 0.99528\n",
            "epoch: 33, passed number of graphs: 768, train running loss: 0.00090 (passed time: 1h 39m 49s)\n",
            "          acc: 0.99988, fpr: 0.00000, fnr: 0.00555, rec: 0.99445, prc: 0.99984, f1: 0.99714, f1_macro: 0.99854\n",
            "Validation --- epoch: 33, loss: 0.00200\n",
            "          fnr: 0.00902, prc: 0.99727, acc: 0.99975, rec: 0.99098, f1: 0.99335, f1_macro: 0.99661, fpr: 0.00006\n",
            "epoch: 34, passed number of graphs: 96, train running loss: 0.00075 (passed time: 1h 40m 32s)\n",
            "          acc: 0.99994, fpr: 0.00002, fnr: 0.00160, rec: 0.99840, prc: 0.99904, f1: 0.99872, f1_macro: 0.99935\n",
            "epoch: 34, passed number of graphs: 192, train running loss: 0.00088 (passed time: 1h 40m 52s)\n",
            "          acc: 0.99992, fpr: 0.00002, fnr: 0.00294, rec: 0.99706, prc: 0.99918, f1: 0.99812, f1_macro: 0.99904\n",
            "epoch: 34, passed number of graphs: 288, train running loss: 0.00090 (passed time: 1h 41m 12s)\n",
            "          acc: 0.99982, fpr: 0.00010, fnr: 0.00376, rec: 0.99624, prc: 0.99527, f1: 0.99575, f1_macro: 0.99783\n",
            "epoch: 34, passed number of graphs: 384, train running loss: 0.00090 (passed time: 1h 41m 31s)\n",
            "          acc: 0.99980, fpr: 0.00001, fnr: 0.00838, rec: 0.99162, prc: 0.99935, f1: 0.99547, f1_macro: 0.99768\n",
            "epoch: 34, passed number of graphs: 480, train running loss: 0.00087 (passed time: 1h 41m 51s)\n",
            "          acc: 0.99766, fpr: 0.00006, fnr: 0.10552, rec: 0.89448, prc: 0.99691, f1: 0.94292, f1_macro: 0.97086\n",
            "epoch: 34, passed number of graphs: 576, train running loss: 0.00084 (passed time: 1h 42m 11s)\n",
            "          acc: 0.99988, fpr: 0.00008, fnr: 0.00226, rec: 0.99774, prc: 0.99645, f1: 0.99710, f1_macro: 0.99852\n",
            "epoch: 34, passed number of graphs: 672, train running loss: 0.00091 (passed time: 1h 42m 31s)\n",
            "          acc: 0.99958, fpr: 0.00032, fnr: 0.00479, rec: 0.99521, prc: 0.98560, f1: 0.99038, f1_macro: 0.99508\n",
            "epoch: 34, passed number of graphs: 768, train running loss: 0.00094 (passed time: 1h 42m 51s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00359, rec: 0.99641, prc: 0.99967, f1: 0.99804, f1_macro: 0.99900\n",
            "Validation --- epoch: 34, loss: 0.00200\n",
            "          fnr: 0.00900, prc: 0.99726, acc: 0.99975, rec: 0.99100, f1: 0.99335, f1_macro: 0.99661, fpr: 0.00006\n",
            "epoch: 35, passed number of graphs: 96, train running loss: 0.00073 (passed time: 1h 43m 34s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00239, rec: 0.99761, prc: 0.99936, f1: 0.99848, f1_macro: 0.99922\n",
            "epoch: 35, passed number of graphs: 192, train running loss: 0.00089 (passed time: 1h 43m 54s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00294, rec: 0.99706, prc: 0.99935, f1: 0.99820, f1_macro: 0.99908\n",
            "epoch: 35, passed number of graphs: 288, train running loss: 0.00087 (passed time: 1h 44m 13s)\n",
            "          acc: 0.99981, fpr: 0.00012, fnr: 0.00327, rec: 0.99673, prc: 0.99446, f1: 0.99559, f1_macro: 0.99775\n",
            "epoch: 35, passed number of graphs: 384, train running loss: 0.00088 (passed time: 1h 44m 33s)\n",
            "          acc: 0.99989, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99903, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 35, passed number of graphs: 480, train running loss: 0.00083 (passed time: 1h 44m 53s)\n",
            "          acc: 0.99980, fpr: 0.00013, fnr: 0.00359, rec: 0.99641, prc: 0.99413, f1: 0.99527, f1_macro: 0.99758\n",
            "epoch: 35, passed number of graphs: 576, train running loss: 0.00080 (passed time: 1h 45m 13s)\n",
            "          acc: 0.99985, fpr: 0.00010, fnr: 0.00242, rec: 0.99758, prc: 0.99533, f1: 0.99645, f1_macro: 0.99819\n",
            "epoch: 35, passed number of graphs: 672, train running loss: 0.00087 (passed time: 1h 45m 33s)\n",
            "          acc: 0.99960, fpr: 0.00034, fnr: 0.00336, rec: 0.99664, prc: 0.98484, f1: 0.99071, f1_macro: 0.99525\n",
            "epoch: 35, passed number of graphs: 768, train running loss: 0.00090 (passed time: 1h 45m 53s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00424, rec: 0.99576, prc: 0.99935, f1: 0.99755, f1_macro: 0.99875\n",
            "Validation --- epoch: 35, loss: 0.00200\n",
            "          fnr: 0.00902, prc: 0.99732, acc: 0.99975, rec: 0.99098, f1: 0.99338, f1_macro: 0.99662, fpr: 0.00006\n",
            "epoch: 36, passed number of graphs: 96, train running loss: 0.00091 (passed time: 1h 46m 36s)\n",
            "          acc: 0.99992, fpr: 0.00002, fnr: 0.00271, rec: 0.99729, prc: 0.99920, f1: 0.99824, f1_macro: 0.99910\n",
            "epoch: 36, passed number of graphs: 192, train running loss: 0.00097 (passed time: 1h 46m 56s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00392, rec: 0.99608, prc: 0.99967, f1: 0.99787, f1_macro: 0.99891\n",
            "epoch: 36, passed number of graphs: 288, train running loss: 0.00091 (passed time: 1h 47m 16s)\n",
            "          acc: 0.99984, fpr: 0.00011, fnr: 0.00261, rec: 0.99739, prc: 0.99495, f1: 0.99616, f1_macro: 0.99804\n",
            "epoch: 36, passed number of graphs: 384, train running loss: 0.00090 (passed time: 1h 47m 36s)\n",
            "          acc: 0.99988, fpr: 0.00003, fnr: 0.00419, rec: 0.99581, prc: 0.99887, f1: 0.99734, f1_macro: 0.99864\n",
            "epoch: 36, passed number of graphs: 480, train running loss: 0.00087 (passed time: 1h 47m 56s)\n",
            "          acc: 0.99981, fpr: 0.00013, fnr: 0.00310, rec: 0.99690, prc: 0.99414, f1: 0.99551, f1_macro: 0.99771\n",
            "epoch: 36, passed number of graphs: 576, train running loss: 0.00084 (passed time: 1h 48m 16s)\n",
            "          acc: 0.99989, fpr: 0.00005, fnr: 0.00274, rec: 0.99726, prc: 0.99758, f1: 0.99742, f1_macro: 0.99868\n",
            "epoch: 36, passed number of graphs: 672, train running loss: 0.00090 (passed time: 1h 48m 36s)\n",
            "          acc: 0.99958, fpr: 0.00030, fnr: 0.00575, rec: 0.99425, prc: 0.98668, f1: 0.99045, f1_macro: 0.99512\n",
            "epoch: 36, passed number of graphs: 768, train running loss: 0.00093 (passed time: 1h 48m 56s)\n",
            "          acc: 0.99983, fpr: 0.00000, fnr: 0.00799, rec: 0.99201, prc: 0.99984, f1: 0.99591, f1_macro: 0.99791\n",
            "Validation --- epoch: 36, loss: 0.00200\n",
            "          fnr: 0.00915, prc: 0.99750, acc: 0.99975, rec: 0.99085, f1: 0.99340, f1_macro: 0.99664, fpr: 0.00006\n",
            "epoch: 37, passed number of graphs: 96, train running loss: 0.00081 (passed time: 1h 49m 38s)\n",
            "          acc: 0.99993, fpr: 0.00002, fnr: 0.00223, rec: 0.99777, prc: 0.99920, f1: 0.99848, f1_macro: 0.99922\n",
            "epoch: 37, passed number of graphs: 192, train running loss: 0.00095 (passed time: 1h 49m 58s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00474, rec: 0.99526, prc: 0.99951, f1: 0.99738, f1_macro: 0.99866\n",
            "epoch: 37, passed number of graphs: 288, train running loss: 0.00092 (passed time: 1h 50m 18s)\n",
            "          acc: 0.99983, fpr: 0.00008, fnr: 0.00425, rec: 0.99575, prc: 0.99624, f1: 0.99600, f1_macro: 0.99795\n",
            "epoch: 37, passed number of graphs: 384, train running loss: 0.00092 (passed time: 1h 50m 38s)\n",
            "          acc: 0.99990, fpr: 0.00002, fnr: 0.00354, rec: 0.99646, prc: 0.99903, f1: 0.99774, f1_macro: 0.99885\n",
            "epoch: 37, passed number of graphs: 480, train running loss: 0.00087 (passed time: 1h 50m 58s)\n",
            "          acc: 0.99982, fpr: 0.00014, fnr: 0.00229, rec: 0.99771, prc: 0.99382, f1: 0.99576, f1_macro: 0.99783\n",
            "epoch: 37, passed number of graphs: 576, train running loss: 0.00083 (passed time: 1h 51m 18s)\n",
            "          acc: 0.99987, fpr: 0.00007, fnr: 0.00258, rec: 0.99742, prc: 0.99661, f1: 0.99701, f1_macro: 0.99847\n",
            "epoch: 37, passed number of graphs: 672, train running loss: 0.00091 (passed time: 1h 51m 38s)\n",
            "          acc: 0.99959, fpr: 0.00035, fnr: 0.00336, rec: 0.99664, prc: 0.98453, f1: 0.99055, f1_macro: 0.99517\n",
            "epoch: 37, passed number of graphs: 768, train running loss: 0.00093 (passed time: 1h 51m 58s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00343, rec: 0.99657, prc: 0.99951, f1: 0.99804, f1_macro: 0.99900\n",
            "Validation --- epoch: 37, loss: 0.00200\n",
            "          fnr: 0.00921, prc: 0.99756, acc: 0.99975, rec: 0.99079, f1: 0.99340, f1_macro: 0.99664, fpr: 0.00005\n",
            "epoch: 38, passed number of graphs: 96, train running loss: 0.00070 (passed time: 1h 52m 40s)\n",
            "          acc: 0.99995, fpr: 0.00001, fnr: 0.00175, rec: 0.99825, prc: 0.99952, f1: 0.99888, f1_macro: 0.99943\n",
            "epoch: 38, passed number of graphs: 192, train running loss: 0.00089 (passed time: 1h 53m 0s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00425, rec: 0.99575, prc: 0.99951, f1: 0.99763, f1_macro: 0.99879\n",
            "epoch: 38, passed number of graphs: 288, train running loss: 0.00087 (passed time: 1h 53m 20s)\n",
            "          acc: 0.99979, fpr: 0.00013, fnr: 0.00408, rec: 0.99592, prc: 0.99397, f1: 0.99494, f1_macro: 0.99741\n",
            "epoch: 38, passed number of graphs: 384, train running loss: 0.00087 (passed time: 1h 53m 40s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00338, rec: 0.99662, prc: 0.99952, f1: 0.99806, f1_macro: 0.99901\n",
            "epoch: 38, passed number of graphs: 480, train running loss: 0.00082 (passed time: 1h 54m 0s)\n",
            "          acc: 0.99987, fpr: 0.00004, fnr: 0.00441, rec: 0.99559, prc: 0.99820, f1: 0.99689, f1_macro: 0.99841\n",
            "epoch: 38, passed number of graphs: 576, train running loss: 0.00079 (passed time: 1h 54m 20s)\n",
            "          acc: 0.99989, fpr: 0.00006, fnr: 0.00242, rec: 0.99758, prc: 0.99742, f1: 0.99750, f1_macro: 0.99872\n",
            "epoch: 38, passed number of graphs: 672, train running loss: 0.00088 (passed time: 1h 54m 40s)\n",
            "          acc: 0.99959, fpr: 0.00032, fnr: 0.00463, rec: 0.99537, prc: 0.98576, f1: 0.99054, f1_macro: 0.99516\n",
            "epoch: 38, passed number of graphs: 768, train running loss: 0.00091 (passed time: 1h 55m 0s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00424, rec: 0.99576, prc: 0.99951, f1: 0.99763, f1_macro: 0.99879\n",
            "Validation --- epoch: 38, loss: 0.00200\n",
            "          fnr: 0.00934, prc: 0.99766, acc: 0.99975, rec: 0.99066, f1: 0.99339, f1_macro: 0.99663, fpr: 0.00005\n",
            "epoch: 39, passed number of graphs: 96, train running loss: 0.00075 (passed time: 1h 55m 43s)\n",
            "          acc: 0.99996, fpr: 0.00002, fnr: 0.00096, rec: 0.99904, prc: 0.99904, f1: 0.99904, f1_macro: 0.99951\n",
            "epoch: 39, passed number of graphs: 192, train running loss: 0.00091 (passed time: 1h 56m 3s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00523, rec: 0.99477, prc: 0.99951, f1: 0.99714, f1_macro: 0.99854\n",
            "epoch: 39, passed number of graphs: 288, train running loss: 0.00088 (passed time: 1h 56m 22s)\n",
            "          acc: 0.99982, fpr: 0.00011, fnr: 0.00343, rec: 0.99657, prc: 0.99478, f1: 0.99567, f1_macro: 0.99779\n",
            "epoch: 39, passed number of graphs: 384, train running loss: 0.00088 (passed time: 1h 56m 42s)\n",
            "          acc: 0.99990, fpr: 0.00002, fnr: 0.00338, rec: 0.99662, prc: 0.99903, f1: 0.99782, f1_macro: 0.99889\n",
            "epoch: 39, passed number of graphs: 480, train running loss: 0.00083 (passed time: 1h 57m 2s)\n",
            "          acc: 0.99982, fpr: 0.00014, fnr: 0.00212, rec: 0.99788, prc: 0.99382, f1: 0.99584, f1_macro: 0.99788\n",
            "epoch: 39, passed number of graphs: 576, train running loss: 0.00080 (passed time: 1h 57m 22s)\n",
            "          acc: 0.99991, fpr: 0.00005, fnr: 0.00210, rec: 0.99790, prc: 0.99774, f1: 0.99782, f1_macro: 0.99889\n",
            "epoch: 39, passed number of graphs: 672, train running loss: 0.00090 (passed time: 1h 57m 42s)\n",
            "          acc: 0.99960, fpr: 0.00032, fnr: 0.00415, rec: 0.99585, prc: 0.98576, f1: 0.99078, f1_macro: 0.99529\n",
            "epoch: 39, passed number of graphs: 768, train running loss: 0.00092 (passed time: 1h 58m 2s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00538, rec: 0.99462, prc: 0.99951, f1: 0.99706, f1_macro: 0.99850\n",
            "Validation --- epoch: 39, loss: 0.00200\n",
            "          fnr: 0.00914, prc: 0.99751, acc: 0.99975, rec: 0.99086, f1: 0.99342, f1_macro: 0.99665, fpr: 0.00006\n",
            "epoch: 40, passed number of graphs: 96, train running loss: 0.00076 (passed time: 1h 58m 45s)\n",
            "          acc: 0.99994, fpr: 0.00002, fnr: 0.00207, rec: 0.99793, prc: 0.99920, f1: 0.99856, f1_macro: 0.99927\n",
            "epoch: 40, passed number of graphs: 192, train running loss: 0.00094 (passed time: 1h 59m 5s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00310, rec: 0.99690, prc: 0.99967, f1: 0.99828, f1_macro: 0.99912\n",
            "epoch: 40, passed number of graphs: 288, train running loss: 0.00096 (passed time: 1h 59m 25s)\n",
            "          acc: 0.99973, fpr: 0.00015, fnr: 0.00621, rec: 0.99379, prc: 0.99330, f1: 0.99355, f1_macro: 0.99670\n",
            "epoch: 40, passed number of graphs: 384, train running loss: 0.00093 (passed time: 1h 59m 44s)\n",
            "          acc: 0.99988, fpr: 0.00003, fnr: 0.00419, rec: 0.99581, prc: 0.99871, f1: 0.99726, f1_macro: 0.99860\n",
            "epoch: 40, passed number of graphs: 480, train running loss: 0.00090 (passed time: 2h 0m 4s)\n",
            "          acc: 0.99982, fpr: 0.00011, fnr: 0.00327, rec: 0.99673, prc: 0.99511, f1: 0.99592, f1_macro: 0.99791\n",
            "epoch: 40, passed number of graphs: 576, train running loss: 0.00085 (passed time: 2h 0m 24s)\n",
            "          acc: 0.99985, fpr: 0.00010, fnr: 0.00210, rec: 0.99790, prc: 0.99533, f1: 0.99661, f1_macro: 0.99827\n",
            "epoch: 40, passed number of graphs: 672, train running loss: 0.00092 (passed time: 2h 0m 44s)\n",
            "          acc: 0.99954, fpr: 0.00034, fnr: 0.00591, rec: 0.99409, prc: 0.98496, f1: 0.98950, f1_macro: 0.99463\n",
            "epoch: 40, passed number of graphs: 768, train running loss: 0.00094 (passed time: 2h 1m 4s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00424, rec: 0.99576, prc: 0.99935, f1: 0.99755, f1_macro: 0.99875\n",
            "Validation --- epoch: 40, loss: 0.00200\n",
            "          fnr: 0.00928, prc: 0.99762, acc: 0.99975, rec: 0.99072, f1: 0.99340, f1_macro: 0.99664, fpr: 0.00005\n",
            "epoch: 41, passed number of graphs: 96, train running loss: 0.00080 (passed time: 2h 1m 47s)\n",
            "          acc: 0.99987, fpr: 0.00001, fnr: 0.00542, rec: 0.99458, prc: 0.99952, f1: 0.99704, f1_macro: 0.99849\n",
            "epoch: 41, passed number of graphs: 192, train running loss: 0.00092 (passed time: 2h 2m 7s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00523, rec: 0.99477, prc: 0.99967, f1: 0.99722, f1_macro: 0.99858\n",
            "epoch: 41, passed number of graphs: 288, train running loss: 0.00090 (passed time: 2h 2m 26s)\n",
            "          acc: 0.99978, fpr: 0.00015, fnr: 0.00376, rec: 0.99624, prc: 0.99332, f1: 0.99478, f1_macro: 0.99733\n",
            "epoch: 41, passed number of graphs: 384, train running loss: 0.00089 (passed time: 2h 2m 46s)\n",
            "          acc: 0.99989, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99903, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 41, passed number of graphs: 480, train running loss: 0.00085 (passed time: 2h 3m 6s)\n",
            "          acc: 0.99978, fpr: 0.00016, fnr: 0.00310, rec: 0.99690, prc: 0.99300, f1: 0.99495, f1_macro: 0.99742\n",
            "epoch: 41, passed number of graphs: 576, train running loss: 0.00081 (passed time: 2h 3m 26s)\n",
            "          acc: 0.99985, fpr: 0.00010, fnr: 0.00226, rec: 0.99774, prc: 0.99549, f1: 0.99661, f1_macro: 0.99827\n",
            "epoch: 41, passed number of graphs: 672, train running loss: 0.00088 (passed time: 2h 3m 46s)\n",
            "          acc: 0.99962, fpr: 0.00032, fnr: 0.00320, rec: 0.99680, prc: 0.98578, f1: 0.99126, f1_macro: 0.99553\n",
            "epoch: 41, passed number of graphs: 768, train running loss: 0.00091 (passed time: 2h 4m 6s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00473, rec: 0.99527, prc: 0.99934, f1: 0.99730, f1_macro: 0.99862\n",
            "Validation --- epoch: 41, loss: 0.00200\n",
            "          fnr: 0.00908, prc: 0.99747, acc: 0.99975, rec: 0.99092, f1: 0.99343, f1_macro: 0.99665, fpr: 0.00006\n",
            "epoch: 42, passed number of graphs: 96, train running loss: 0.00077 (passed time: 2h 4m 48s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00191, rec: 0.99809, prc: 0.99936, f1: 0.99872, f1_macro: 0.99935\n",
            "epoch: 42, passed number of graphs: 192, train running loss: 0.00092 (passed time: 2h 5m 8s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00474, rec: 0.99526, prc: 0.99951, f1: 0.99738, f1_macro: 0.99866\n",
            "epoch: 42, passed number of graphs: 288, train running loss: 0.00090 (passed time: 2h 5m 28s)\n",
            "          acc: 0.99983, fpr: 0.00011, fnr: 0.00278, rec: 0.99722, prc: 0.99495, f1: 0.99608, f1_macro: 0.99800\n",
            "epoch: 42, passed number of graphs: 384, train running loss: 0.00089 (passed time: 2h 5m 48s)\n",
            "          acc: 0.99983, fpr: 0.00003, fnr: 0.00644, rec: 0.99356, prc: 0.99887, f1: 0.99620, f1_macro: 0.99806\n",
            "epoch: 42, passed number of graphs: 480, train running loss: 0.00085 (passed time: 2h 6m 8s)\n",
            "          acc: 0.99978, fpr: 0.00015, fnr: 0.00343, rec: 0.99657, prc: 0.99332, f1: 0.99494, f1_macro: 0.99742\n",
            "epoch: 42, passed number of graphs: 576, train running loss: 0.00081 (passed time: 2h 6m 28s)\n",
            "          acc: 0.99989, fpr: 0.00004, fnr: 0.00323, rec: 0.99677, prc: 0.99822, f1: 0.99750, f1_macro: 0.99872\n",
            "epoch: 42, passed number of graphs: 672, train running loss: 0.00088 (passed time: 2h 6m 48s)\n",
            "          acc: 0.99960, fpr: 0.00032, fnr: 0.00431, rec: 0.99569, prc: 0.98576, f1: 0.99070, f1_macro: 0.99525\n",
            "epoch: 42, passed number of graphs: 768, train running loss: 0.00092 (passed time: 2h 7m 8s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00375, rec: 0.99625, prc: 0.99951, f1: 0.99788, f1_macro: 0.99891\n",
            "Validation --- epoch: 42, loss: 0.00200\n",
            "          fnr: 0.00914, prc: 0.99751, acc: 0.99975, rec: 0.99086, f1: 0.99341, f1_macro: 0.99664, fpr: 0.00006\n",
            "epoch: 43, passed number of graphs: 96, train running loss: 0.00082 (passed time: 2h 7m 51s)\n",
            "          acc: 0.99995, fpr: 0.00001, fnr: 0.00175, rec: 0.99825, prc: 0.99936, f1: 0.99880, f1_macro: 0.99939\n",
            "epoch: 43, passed number of graphs: 192, train running loss: 0.00092 (passed time: 2h 8m 11s)\n",
            "          acc: 0.99987, fpr: 0.00000, fnr: 0.00604, rec: 0.99396, prc: 0.99984, f1: 0.99689, f1_macro: 0.99841\n",
            "epoch: 43, passed number of graphs: 288, train running loss: 0.00090 (passed time: 2h 8m 31s)\n",
            "          acc: 0.99982, fpr: 0.00012, fnr: 0.00294, rec: 0.99706, prc: 0.99446, f1: 0.99576, f1_macro: 0.99783\n",
            "epoch: 43, passed number of graphs: 384, train running loss: 0.00090 (passed time: 2h 8m 51s)\n",
            "          acc: 0.99987, fpr: 0.00003, fnr: 0.00467, rec: 0.99533, prc: 0.99887, f1: 0.99709, f1_macro: 0.99851\n",
            "epoch: 43, passed number of graphs: 480, train running loss: 0.00085 (passed time: 2h 9m 11s)\n",
            "          acc: 0.99984, fpr: 0.00007, fnr: 0.00425, rec: 0.99575, prc: 0.99706, f1: 0.99640, f1_macro: 0.99816\n",
            "epoch: 43, passed number of graphs: 576, train running loss: 0.00082 (passed time: 2h 9m 31s)\n",
            "          acc: 0.99986, fpr: 0.00009, fnr: 0.00226, rec: 0.99774, prc: 0.99581, f1: 0.99677, f1_macro: 0.99835\n",
            "epoch: 43, passed number of graphs: 672, train running loss: 0.00090 (passed time: 2h 9m 51s)\n",
            "          acc: 0.99934, fpr: 0.00022, fnr: 0.02077, rec: 0.97923, prc: 0.99014, f1: 0.98465, f1_macro: 0.99216\n",
            "epoch: 43, passed number of graphs: 768, train running loss: 0.00093 (passed time: 2h 10m 11s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00457, rec: 0.99543, prc: 0.99967, f1: 0.99755, f1_macro: 0.99875\n",
            "Validation --- epoch: 43, loss: 0.00200\n",
            "          fnr: 0.00901, prc: 0.99728, acc: 0.99975, rec: 0.99099, f1: 0.99336, f1_macro: 0.99661, fpr: 0.00006\n",
            "epoch: 44, passed number of graphs: 96, train running loss: 0.00077 (passed time: 2h 10m 53s)\n",
            "          acc: 0.99996, fpr: 0.00002, fnr: 0.00128, rec: 0.99872, prc: 0.99920, f1: 0.99896, f1_macro: 0.99947\n",
            "epoch: 44, passed number of graphs: 192, train running loss: 0.00091 (passed time: 2h 11m 13s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00359, rec: 0.99641, prc: 0.99951, f1: 0.99796, f1_macro: 0.99896\n",
            "epoch: 44, passed number of graphs: 288, train running loss: 0.00088 (passed time: 2h 11m 33s)\n",
            "          acc: 0.99981, fpr: 0.00013, fnr: 0.00294, rec: 0.99706, prc: 0.99381, f1: 0.99543, f1_macro: 0.99767\n",
            "epoch: 44, passed number of graphs: 384, train running loss: 0.00088 (passed time: 2h 11m 53s)\n",
            "          acc: 0.99987, fpr: 0.00002, fnr: 0.00483, rec: 0.99517, prc: 0.99903, f1: 0.99709, f1_macro: 0.99851\n",
            "epoch: 44, passed number of graphs: 480, train running loss: 0.00084 (passed time: 2h 12m 13s)\n",
            "          acc: 0.99980, fpr: 0.00014, fnr: 0.00278, rec: 0.99722, prc: 0.99365, f1: 0.99543, f1_macro: 0.99767\n",
            "epoch: 44, passed number of graphs: 576, train running loss: 0.00082 (passed time: 2h 12m 33s)\n",
            "          acc: 0.99990, fpr: 0.00004, fnr: 0.00307, rec: 0.99693, prc: 0.99822, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 44, passed number of graphs: 672, train running loss: 0.00089 (passed time: 2h 12m 53s)\n",
            "          acc: 0.99961, fpr: 0.00033, fnr: 0.00336, rec: 0.99664, prc: 0.98531, f1: 0.99094, f1_macro: 0.99537\n",
            "epoch: 44, passed number of graphs: 768, train running loss: 0.00091 (passed time: 2h 13m 13s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00277, rec: 0.99723, prc: 0.99951, f1: 0.99837, f1_macro: 0.99917\n",
            "Validation --- epoch: 44, loss: 0.00200\n",
            "          fnr: 0.00894, prc: 0.99717, acc: 0.99975, rec: 0.99106, f1: 0.99334, f1_macro: 0.99661, fpr: 0.00006\n",
            "epoch: 45, passed number of graphs: 96, train running loss: 0.00071 (passed time: 2h 13m 55s)\n",
            "          acc: 0.99994, fpr: 0.00001, fnr: 0.00239, rec: 0.99761, prc: 0.99952, f1: 0.99856, f1_macro: 0.99927\n",
            "epoch: 45, passed number of graphs: 192, train running loss: 0.00088 (passed time: 2h 14m 15s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00523, rec: 0.99477, prc: 0.99951, f1: 0.99714, f1_macro: 0.99854\n",
            "epoch: 45, passed number of graphs: 288, train running loss: 0.00086 (passed time: 2h 14m 35s)\n",
            "          acc: 0.99982, fpr: 0.00010, fnr: 0.00392, rec: 0.99608, prc: 0.99543, f1: 0.99575, f1_macro: 0.99783\n",
            "epoch: 45, passed number of graphs: 384, train running loss: 0.00087 (passed time: 2h 14m 55s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00435, rec: 0.99565, prc: 0.99951, f1: 0.99758, f1_macro: 0.99876\n",
            "epoch: 45, passed number of graphs: 480, train running loss: 0.00084 (passed time: 2h 15m 15s)\n",
            "          acc: 0.99983, fpr: 0.00011, fnr: 0.00294, rec: 0.99706, prc: 0.99511, f1: 0.99608, f1_macro: 0.99800\n",
            "epoch: 45, passed number of graphs: 576, train running loss: 0.00080 (passed time: 2h 15m 35s)\n",
            "          acc: 0.99985, fpr: 0.00010, fnr: 0.00226, rec: 0.99774, prc: 0.99533, f1: 0.99653, f1_macro: 0.99823\n",
            "epoch: 45, passed number of graphs: 672, train running loss: 0.00089 (passed time: 2h 15m 55s)\n",
            "          acc: 0.99960, fpr: 0.00031, fnr: 0.00447, rec: 0.99553, prc: 0.98592, f1: 0.99070, f1_macro: 0.99525\n",
            "epoch: 45, passed number of graphs: 768, train running loss: 0.00091 (passed time: 2h 16m 15s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00294, rec: 0.99706, prc: 0.99935, f1: 0.99820, f1_macro: 0.99908\n",
            "Validation --- epoch: 45, loss: 0.00200\n",
            "          fnr: 0.00893, prc: 0.99711, acc: 0.99974, rec: 0.99107, f1: 0.99331, f1_macro: 0.99659, fpr: 0.00006\n",
            "epoch: 46, passed number of graphs: 96, train running loss: 0.00067 (passed time: 2h 16m 58s)\n",
            "          acc: 0.99993, fpr: 0.00002, fnr: 0.00207, rec: 0.99793, prc: 0.99888, f1: 0.99840, f1_macro: 0.99918\n",
            "epoch: 46, passed number of graphs: 192, train running loss: 0.00090 (passed time: 2h 17m 18s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00523, rec: 0.99477, prc: 0.99951, f1: 0.99714, f1_macro: 0.99854\n",
            "epoch: 46, passed number of graphs: 288, train running loss: 0.00090 (passed time: 2h 17m 38s)\n",
            "          acc: 0.99981, fpr: 0.00011, fnr: 0.00408, rec: 0.99592, prc: 0.99510, f1: 0.99551, f1_macro: 0.99771\n",
            "epoch: 46, passed number of graphs: 384, train running loss: 0.00089 (passed time: 2h 17m 58s)\n",
            "          acc: 0.99988, fpr: 0.00001, fnr: 0.00483, rec: 0.99517, prc: 0.99951, f1: 0.99734, f1_macro: 0.99864\n",
            "epoch: 46, passed number of graphs: 480, train running loss: 0.00085 (passed time: 2h 18m 17s)\n",
            "          acc: 0.99980, fpr: 0.00014, fnr: 0.00294, rec: 0.99706, prc: 0.99381, f1: 0.99543, f1_macro: 0.99767\n",
            "epoch: 46, passed number of graphs: 576, train running loss: 0.00082 (passed time: 2h 18m 37s)\n",
            "          acc: 0.99983, fpr: 0.00012, fnr: 0.00210, rec: 0.99790, prc: 0.99437, f1: 0.99613, f1_macro: 0.99802\n",
            "epoch: 46, passed number of graphs: 672, train running loss: 0.00089 (passed time: 2h 18m 57s)\n",
            "          acc: 0.99959, fpr: 0.00033, fnr: 0.00415, rec: 0.99585, prc: 0.98545, f1: 0.99062, f1_macro: 0.99521\n",
            "epoch: 46, passed number of graphs: 768, train running loss: 0.00091 (passed time: 2h 19m 17s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00375, rec: 0.99625, prc: 0.99967, f1: 0.99796, f1_macro: 0.99896\n",
            "Validation --- epoch: 46, loss: 0.00200\n",
            "          fnr: 0.00913, prc: 0.99749, acc: 0.99975, rec: 0.99087, f1: 0.99341, f1_macro: 0.99664, fpr: 0.00006\n",
            "epoch: 47, passed number of graphs: 96, train running loss: 0.00080 (passed time: 2h 20m 0s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00319, rec: 0.99681, prc: 0.99936, f1: 0.99808, f1_macro: 0.99902\n",
            "epoch: 47, passed number of graphs: 192, train running loss: 0.00091 (passed time: 2h 20m 20s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00294, rec: 0.99706, prc: 0.99935, f1: 0.99820, f1_macro: 0.99908\n",
            "epoch: 47, passed number of graphs: 288, train running loss: 0.00088 (passed time: 2h 20m 40s)\n",
            "          acc: 0.99982, fpr: 0.00013, fnr: 0.00229, rec: 0.99771, prc: 0.99398, f1: 0.99584, f1_macro: 0.99788\n",
            "epoch: 47, passed number of graphs: 384, train running loss: 0.00090 (passed time: 2h 21m 0s)\n",
            "          acc: 0.99987, fpr: 0.00001, fnr: 0.00548, rec: 0.99452, prc: 0.99935, f1: 0.99693, f1_macro: 0.99843\n",
            "epoch: 47, passed number of graphs: 480, train running loss: 0.00086 (passed time: 2h 21m 20s)\n",
            "          acc: 0.99982, fpr: 0.00012, fnr: 0.00310, rec: 0.99690, prc: 0.99462, f1: 0.99576, f1_macro: 0.99783\n",
            "epoch: 47, passed number of graphs: 576, train running loss: 0.00082 (passed time: 2h 21m 40s)\n",
            "          acc: 0.99990, fpr: 0.00006, fnr: 0.00194, rec: 0.99806, prc: 0.99726, f1: 0.99766, f1_macro: 0.99880\n",
            "epoch: 47, passed number of graphs: 672, train running loss: 0.00089 (passed time: 2h 22m 0s)\n",
            "          acc: 0.99963, fpr: 0.00031, fnr: 0.00320, rec: 0.99680, prc: 0.98625, f1: 0.99150, f1_macro: 0.99565\n",
            "epoch: 47, passed number of graphs: 768, train running loss: 0.00091 (passed time: 2h 22m 19s)\n",
            "          acc: 0.99992, fpr: 0.00001, fnr: 0.00326, rec: 0.99674, prc: 0.99951, f1: 0.99812, f1_macro: 0.99904\n",
            "Validation --- epoch: 47, loss: 0.00200\n",
            "          fnr: 0.00899, prc: 0.99721, acc: 0.99975, rec: 0.99101, f1: 0.99333, f1_macro: 0.99660, fpr: 0.00006\n",
            "epoch: 48, passed number of graphs: 96, train running loss: 0.00076 (passed time: 2h 23m 2s)\n",
            "          acc: 0.99993, fpr: 0.00002, fnr: 0.00191, rec: 0.99809, prc: 0.99888, f1: 0.99848, f1_macro: 0.99923\n",
            "epoch: 48, passed number of graphs: 192, train running loss: 0.00089 (passed time: 2h 23m 22s)\n",
            "          acc: 0.99988, fpr: 0.00000, fnr: 0.00523, rec: 0.99477, prc: 0.99984, f1: 0.99730, f1_macro: 0.99862\n",
            "epoch: 48, passed number of graphs: 288, train running loss: 0.00093 (passed time: 2h 23m 42s)\n",
            "          acc: 0.99981, fpr: 0.00014, fnr: 0.00229, rec: 0.99771, prc: 0.99349, f1: 0.99560, f1_macro: 0.99775\n",
            "epoch: 48, passed number of graphs: 384, train running loss: 0.00093 (passed time: 2h 24m 2s)\n",
            "          acc: 0.99988, fpr: 0.00002, fnr: 0.00451, rec: 0.99549, prc: 0.99919, f1: 0.99734, f1_macro: 0.99864\n",
            "epoch: 48, passed number of graphs: 480, train running loss: 0.00087 (passed time: 2h 24m 22s)\n",
            "          acc: 0.99982, fpr: 0.00012, fnr: 0.00327, rec: 0.99673, prc: 0.99478, f1: 0.99576, f1_macro: 0.99783\n",
            "epoch: 48, passed number of graphs: 576, train running loss: 0.00085 (passed time: 2h 24m 42s)\n",
            "          acc: 0.99991, fpr: 0.00004, fnr: 0.00242, rec: 0.99758, prc: 0.99822, f1: 0.99790, f1_macro: 0.99893\n",
            "epoch: 48, passed number of graphs: 672, train running loss: 0.00093 (passed time: 2h 25m 2s)\n",
            "          acc: 0.99959, fpr: 0.00034, fnr: 0.00384, rec: 0.99616, prc: 0.98499, f1: 0.99055, f1_macro: 0.99517\n",
            "epoch: 48, passed number of graphs: 768, train running loss: 0.00095 (passed time: 2h 25m 22s)\n",
            "          acc: 0.99984, fpr: 0.00001, fnr: 0.00669, rec: 0.99331, prc: 0.99934, f1: 0.99632, f1_macro: 0.99812\n",
            "Validation --- epoch: 48, loss: 0.00200\n",
            "          fnr: 0.00922, prc: 0.99758, acc: 0.99975, rec: 0.99078, f1: 0.99341, f1_macro: 0.99664, fpr: 0.00005\n",
            "epoch: 49, passed number of graphs: 96, train running loss: 0.00070 (passed time: 2h 26m 4s)\n",
            "          acc: 0.99993, fpr: 0.00001, fnr: 0.00287, rec: 0.99713, prc: 0.99952, f1: 0.99832, f1_macro: 0.99914\n",
            "epoch: 49, passed number of graphs: 192, train running loss: 0.00084 (passed time: 2h 26m 24s)\n",
            "          acc: 0.99991, fpr: 0.00000, fnr: 0.00425, rec: 0.99575, prc: 0.99984, f1: 0.99779, f1_macro: 0.99887\n",
            "epoch: 49, passed number of graphs: 288, train running loss: 0.00084 (passed time: 2h 26m 44s)\n",
            "          acc: 0.99979, fpr: 0.00013, fnr: 0.00359, rec: 0.99641, prc: 0.99381, f1: 0.99510, f1_macro: 0.99750\n",
            "epoch: 49, passed number of graphs: 384, train running loss: 0.00084 (passed time: 2h 27m 4s)\n",
            "          acc: 0.99990, fpr: 0.00002, fnr: 0.00387, rec: 0.99613, prc: 0.99919, f1: 0.99766, f1_macro: 0.99880\n",
            "epoch: 49, passed number of graphs: 480, train running loss: 0.00081 (passed time: 2h 27m 24s)\n",
            "          acc: 0.99983, fpr: 0.00012, fnr: 0.00261, rec: 0.99739, prc: 0.99479, f1: 0.99608, f1_macro: 0.99800\n",
            "epoch: 49, passed number of graphs: 576, train running loss: 0.00078 (passed time: 2h 27m 44s)\n",
            "          acc: 0.99988, fpr: 0.00007, fnr: 0.00258, rec: 0.99742, prc: 0.99677, f1: 0.99710, f1_macro: 0.99852\n",
            "epoch: 49, passed number of graphs: 672, train running loss: 0.00086 (passed time: 2h 28m 4s)\n",
            "          acc: 0.99954, fpr: 0.00025, fnr: 0.00959, rec: 0.99041, prc: 0.98852, f1: 0.98946, f1_macro: 0.99462\n",
            "epoch: 49, passed number of graphs: 768, train running loss: 0.00090 (passed time: 2h 28m 24s)\n",
            "          acc: 0.99991, fpr: 0.00001, fnr: 0.00343, rec: 0.99657, prc: 0.99935, f1: 0.99796, f1_macro: 0.99896\n",
            "Validation --- epoch: 49, loss: 0.00200\n",
            "          fnr: 0.00892, prc: 0.99711, acc: 0.99974, rec: 0.99108, f1: 0.99332, f1_macro: 0.99659, fpr: 0.00006\n",
            "epoch: 50, passed number of graphs: 96, train running loss: 0.00070 (passed time: 2h 29m 6s)\n",
            "          acc: 0.99991, fpr: 0.00002, fnr: 0.00319, rec: 0.99681, prc: 0.99920, f1: 0.99800, f1_macro: 0.99898\n",
            "epoch: 50, passed number of graphs: 192, train running loss: 0.00087 (passed time: 2h 29m 26s)\n",
            "          acc: 0.99990, fpr: 0.00001, fnr: 0.00441, rec: 0.99559, prc: 0.99951, f1: 0.99755, f1_macro: 0.99875\n",
            "epoch: 50, passed number of graphs: 288, train running loss: 0.00086 (passed time: 2h 29m 46s)\n",
            "          acc: 0.99981, fpr: 0.00014, fnr: 0.00278, rec: 0.99722, prc: 0.99365, f1: 0.99543, f1_macro: 0.99767\n",
            "epoch: 50, passed number of graphs: 384, train running loss: 0.00087 (passed time: 2h 30m 6s)\n",
            "          acc: 0.99987, fpr: 0.00002, fnr: 0.00516, rec: 0.99484, prc: 0.99919, f1: 0.99701, f1_macro: 0.99847\n",
            "epoch: 50, passed number of graphs: 480, train running loss: 0.00083 (passed time: 2h 30m 26s)\n",
            "          acc: 0.99981, fpr: 0.00010, fnr: 0.00441, rec: 0.99559, prc: 0.99559, f1: 0.99559, f1_macro: 0.99775\n",
            "epoch: 50, passed number of graphs: 576, train running loss: 0.00080 (passed time: 2h 30m 46s)\n",
            "          acc: 0.99988, fpr: 0.00006, fnr: 0.00274, rec: 0.99726, prc: 0.99709, f1: 0.99718, f1_macro: 0.99856\n",
            "epoch: 50, passed number of graphs: 672, train running loss: 0.00088 (passed time: 2h 31m 6s)\n",
            "          acc: 0.99961, fpr: 0.00032, fnr: 0.00384, rec: 0.99616, prc: 0.98577, f1: 0.99094, f1_macro: 0.99537\n",
            "epoch: 50, passed number of graphs: 768, train running loss: 0.00090 (passed time: 2h 31m 25s)\n",
            "          acc: 0.99989, fpr: 0.00001, fnr: 0.00473, rec: 0.99527, prc: 0.99934, f1: 0.99730, f1_macro: 0.99862\n",
            "Validation --- epoch: 50, loss: 0.00200\n",
            "          fnr: 0.00893, prc: 0.99716, acc: 0.99975, rec: 0.99107, f1: 0.99334, f1_macro: 0.99660, fpr: 0.00006\n",
            "************ best model obtained after epoch 12, saved at ./saved_models/GIN_11: 0.9938754997795639.pt ************\n",
            "Testing --- loss: 0.00109\n",
            "          fnr: 0.00391, prc: 0.99581, acc: 0.99982, rec: 0.99609, f1: 0.99572, f1_macro: 0.99782, fpr: 0.00009\n"
          ]
        }
      ]
    }
  ]
}