main_TUs_graph_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main Driver Notebook for Training Graph NNs on TU Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODELS\n",
    "- GatedGCN \n",
    "- GCN \n",
    "- GAT \n",
    "- GraphSage \n",
    "- GIN  \n",
    "- MoNet  \n",
    "- DiffPool\n",
    "- MLP\n",
    "\n",
    "### DATASET\n",
    "- DD \n",
    "- ENZYMES\n",
    "- PROTEINS_full   \n",
    "\n",
    "### TASK\n",
    "- Graph Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    IMPORTING LIBS\n",
    "\"\"\"\n",
    "import dgl\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import socket\n",
    "import time\n",
    "import random\n",
    "import glob\n",
    "import argparse, json\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from tensorboardX import SummaryWriter\n",
    "from tqdm import tqdm\n",
    "\n",
    "class DotDict(dict):\n",
    "    def __init__(self, **kwds):\n",
    "        self.update(kwds)\n",
    "        self.__dict__ = self\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# \"\"\"\n",
    "#     AUTORELOAD IPYTHON EXTENSION FOR RELOADING IMPORTED MODULES\n",
    "# \"\"\"\n",
    "\n",
    "def in_ipynb():\n",
    "    try:\n",
    "        cfg = get_ipython().config \n",
    "        return True\n",
    "    except NameError:\n",
    "        return False\n",
    "    \n",
    "notebook_mode = in_ipynb()\n",
    "print(notebook_mode)\n",
    "\n",
    "if notebook_mode == True:\n",
    "    %load_ext autoreload\n",
    "    %autoreload 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    IMPORTING CUSTOM MODULES/METHODS\n",
    "\"\"\"\n",
    "\n",
    "from nets.TUs_graph_classification.load_net import gnn_model # import GNNs\n",
    "from data.data import LoadData # import dataset\n",
    "from train.train_TUs_graph_classification import train_epoch, evaluate_network # import train functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    GPU Setup\n",
    "\"\"\"\n",
    "def gpu_setup(use_gpu, gpu_id):\n",
    "    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(gpu_id)  \n",
    "\n",
    "    if torch.cuda.is_available() and use_gpu:\n",
    "        print('cuda available with GPU:',torch.cuda.get_device_name(0))\n",
    "        device = torch.device(\"cuda\")\n",
    "    else:\n",
    "        print('cuda not available')\n",
    "        device = torch.device(\"cpu\")\n",
    "    return device\n",
    "\n",
    "\n",
    "use_gpu = True\n",
    "gpu_id = -1\n",
    "device = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I] Loading data (notebook) ...\n",
      "No Node Attribute Data\n",
      "[!] Dataset:  DD\n",
      "Time taken: 10.7704s\n",
      "[I] Finished loading.\n"
     ]
    }
   ],
   "source": [
    "# \"\"\"\n",
    "#     USER CONTROLS\n",
    "# \"\"\"\n",
    "if notebook_mode == True:\n",
    "    \n",
    "    MODEL_NAME = 'GatedGCN'\n",
    "    #MODEL_NAME = 'MoNet'\n",
    "    #MODEL_NAME = 'GCN'\n",
    "    #MODEL_NAME = 'GAT'\n",
    "    #MODEL_NAME = 'GraphSage'\n",
    "    #MODEL_NAME = 'DiffPool'\n",
    "    #MODEL_NAME = 'MLP'\n",
    "\n",
    "    DATASET_NAME = 'PROTEINS_full'\n",
    "    DATASET_NAME = 'DD'\n",
    "\n",
    "    out_dir = 'out/TUs_graph_classification/'\n",
    "    root_log_dir = out_dir + 'logs/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    root_ckpt_dir = out_dir + 'checkpoints/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "\n",
    "    print(\"[I] Loading data (notebook) ...\")\n",
    "    dataset = LoadData(DATASET_NAME)\n",
    "    trainset, valset, testset = dataset.train, dataset.val, dataset.test\n",
    "    print(\"[I] Finished loading.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_NAME = 'GatedGCN'\n",
    "#MODEL_NAME = 'GCN'\n",
    "#MODEL_NAME = 'GAT'\n",
    "#MODEL_NAME = 'GraphSage'\n",
    "#MODEL_NAME = 'MLP'\n",
    "#MODEL_NAME = 'GIN'\n",
    "#MODEL_NAME = 'MoNet'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"\"\"\n",
    "#     PARAMETERS\n",
    "# \"\"\"\n",
    "if notebook_mode == True:\n",
    "\n",
    "    n_heads = -1\n",
    "    edge_feat = False\n",
    "    pseudo_dim_MoNet = -1\n",
    "    kernel = -1\n",
    "    gnn_per_block = -1\n",
    "    embedding_dim = -1\n",
    "    pool_ratio = -1\n",
    "    n_mlp_GIN = -1\n",
    "    gated = False\n",
    "    self_loop = False\n",
    "    #self_loop = True\n",
    "    max_time = 48\n",
    "    \n",
    "\n",
    "    if MODEL_NAME == 'GatedGCN':\n",
    "        seed=41; epochs=1000; batch_size=5; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=70; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "\n",
    "    if MODEL_NAME == 'GCN':\n",
    "        seed=41; epochs=1000; batch_size=5; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=146; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "\n",
    "    if MODEL_NAME == 'GAT':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; n_heads=8; hidden_dim=19; out_dim=n_heads*hidden_dim; dropout=0.0; readout='mean'\n",
    "        print('True hidden dim:',out_dim)\n",
    "\n",
    "    if MODEL_NAME == 'GraphSage':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=108; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "\n",
    "    if MODEL_NAME == 'MLP':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=165; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        \n",
    "    if MODEL_NAME == 'DiffPool':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=32; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        n_heads=8; gnn_per_block=3; embedding_dim=32; batch_size=128; pool_ratio=0.15\n",
    "\n",
    "    if MODEL_NAME == 'GIN':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=110; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        n_mlp_GIN = 2; learn_eps_GIN=True; neighbor_aggr_GIN='sum'\n",
    "\n",
    "    if MODEL_NAME == 'MoNet':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=90; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        pseudo_dim_MoNet=2; kernel=3;\n",
    "      \n",
    "    \n",
    "\n",
    "    # generic new_params\n",
    "    net_params = {}\n",
    "    net_params['device'] = device\n",
    "    net_params['gated'] = False  # for mlpnet baseline\n",
    "    net_params['in_dim'] = dataset.all.graph_lists[0].ndata['feat'][0].shape[0]\n",
    "    net_params['residual'] = True\n",
    "    net_params['hidden_dim'] = hidden_dim\n",
    "    net_params['out_dim'] = out_dim\n",
    "    num_classes = len(np.unique(dataset.all.graph_labels))\n",
    "    net_params['n_classes'] = num_classes\n",
    "    net_params['n_heads'] = n_heads\n",
    "    net_params['L'] = L  # min L should be 2\n",
    "    net_params['readout'] = \"mean\"\n",
    "    net_params['graph_norm'] = True\n",
    "    net_params['batch_norm'] = True\n",
    "    net_params['in_feat_dropout'] = 0.0\n",
    "    net_params['dropout'] = 0.0\n",
    "    net_params['edge_feat'] = edge_feat\n",
    "    net_params['self_loop'] = self_loop\n",
    "\n",
    "    # specific for MoNet\n",
    "    net_params['pseudo_dim_MoNet'] = pseudo_dim_MoNet\n",
    "    net_params['kernel'] = kernel\n",
    "    \n",
    "    # specific for GIN\n",
    "    net_params['n_mlp_GIN'] = n_mlp_GIN\n",
    "    net_params['learn_eps_GIN'] = True\n",
    "    net_params['neighbor_aggr_GIN'] = 'sum'\n",
    "    \n",
    "    # specific for graphsage\n",
    "    net_params['sage_aggregator'] = 'meanpool'    \n",
    "\n",
    "    # specific for diffpoolnet\n",
    "    net_params['data_mode'] = 'default'\n",
    "    net_params['gnn_per_block'] = gnn_per_block\n",
    "    net_params['embedding_dim'] = embedding_dim     \n",
    "    net_params['pool_ratio'] = pool_ratio\n",
    "    net_params['linkpred'] = True\n",
    "    net_params['num_pool'] = 1\n",
    "    net_params['cat'] = False\n",
    "    net_params['batch_size'] = batch_size \n",
    "    \n",
    "    # calculate assignment dimension: pool_ratio * largest graph's maximum\n",
    "    # number of nodes  in the dataset\n",
    "    num_nodes = [dataset.all[i][0].number_of_nodes() for i in range(len(dataset.all))]\n",
    "    max_num_node = max(num_nodes)\n",
    "    net_params['assign_dim'] = int(max_num_node * net_params['pool_ratio']) * net_params['batch_size']\n",
    "\n",
    "    # setting seeds\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    if device == 'cuda':\n",
    "        torch.cuda.manual_seed(seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL DETAILS:\n",
      "\n",
      "MODEL/Total parameters: GatedGCN 116253\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "    VIEWING MODEL CONFIG AND PARAMS\n",
    "\"\"\"\n",
    "def view_model_param(MODEL_NAME, net_params):\n",
    "    model = gnn_model(MODEL_NAME, net_params)\n",
    "    total_param = 0\n",
    "    print(\"MODEL DETAILS:\\n\")\n",
    "    #print(model)\n",
    "    for param in model.parameters():\n",
    "        # print(param.data.size())\n",
    "        total_param += np.prod(list(param.data.size()))\n",
    "    print('MODEL/Total parameters:', MODEL_NAME, total_param)\n",
    "    return total_param\n",
    "\n",
    "if notebook_mode == True:\n",
    "    view_model_param(MODEL_NAME, net_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    TRAINING CODE\n",
    "\"\"\"\n",
    "\n",
    "def train_val_pipeline(MODEL_NAME, DATASET_NAME, params, net_params, dirs):\n",
    "    avg_test_acc = []\n",
    "    avg_train_acc = []\n",
    "\n",
    "    t0 = time.time()\n",
    "    per_epoch_time = []\n",
    "\n",
    "    dataset = LoadData(DATASET_NAME)\n",
    "    \n",
    "    if MODEL_NAME in ['GCN', 'GAT']:\n",
    "        if net_params['self_loop']:\n",
    "            print(\"[!] Adding graph self-loops for GCN/GAT models (central node trick).\")\n",
    "            dataset._add_self_loops()\n",
    "    \n",
    "    trainset, valset, testset = dataset.train, dataset.val, dataset.test\n",
    "    \n",
    "    root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs\n",
    "    device = net_params['device']\n",
    "    \n",
    "    # Write the network and optimization hyper-parameters in folder config/\n",
    "    with open(write_config_file + '.txt', 'w') as f:\n",
    "        f.write(\"\"\"Dataset: {},\\nModel: {}\\n\\nparams={}\\n\\nnet_params={}\\n\\n\\nTotal Parameters: {}\\n\\n\"\"\"\\\n",
    "                .format(DATASET_NAME, MODEL_NAME, params, net_params, net_params['total_param']))\n",
    "    \n",
    "    # At any point you can hit Ctrl + C to break out of training early.\n",
    "    try:\n",
    "        for split_number in range(10):\n",
    "            t0_split = time.time()\n",
    "            log_dir = os.path.join(root_log_dir, \"RUN_\" + str(split_number))\n",
    "            writer = SummaryWriter(log_dir=log_dir)\n",
    "\n",
    "            # setting seeds\n",
    "            random.seed(params['seed'])\n",
    "            np.random.seed(params['seed'])\n",
    "            torch.manual_seed(params['seed'])\n",
    "            if device == 'cuda':\n",
    "                torch.cuda.manual_seed(params['seed'])\n",
    "\n",
    "            print(\"RUN NUMBER: \", split_number)\n",
    "            trainset, valset, testset = dataset.train[split_number], dataset.val[split_number], dataset.test[split_number]\n",
    "            print(\"Training Graphs: \", len(trainset))\n",
    "            print(\"Validation Graphs: \", len(valset))\n",
    "            print(\"Test Graphs: \", len(testset))\n",
    "            print(\"Number of Classes: \", net_params['n_classes'])\n",
    "\n",
    "            model = gnn_model(MODEL_NAME, net_params)\n",
    "            model = model.to(device)\n",
    "            optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay'])\n",
    "            scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',\n",
    "                                                             factor=params['lr_reduce_factor'],\n",
    "                                                             patience=params['lr_schedule_patience'],\n",
    "                                                             verbose=True)\n",
    "\n",
    "            epoch_train_losses, epoch_val_losses = [], []\n",
    "            epoch_train_accs, epoch_val_accs = [], [] \n",
    "\n",
    "            # batching exception for Diffpool\n",
    "            drop_last = True if MODEL_NAME == 'DiffPool' else False\n",
    "\n",
    "            train_loader = DataLoader(trainset, batch_size=params['batch_size'], shuffle=True, drop_last=drop_last, collate_fn=dataset.collate)\n",
    "            val_loader = DataLoader(valset, batch_size=params['batch_size'], shuffle=False, drop_last=drop_last, collate_fn=dataset.collate)\n",
    "            test_loader = DataLoader(testset, batch_size=params['batch_size'], shuffle=False, drop_last=drop_last, collate_fn=dataset.collate)\n",
    "\n",
    "\n",
    "            with tqdm(range(params['epochs'])) as t:\n",
    "                for epoch in t:\n",
    "\n",
    "                    t.set_description('Epoch %d' % epoch)    \n",
    "\n",
    "                    start = time.time()\n",
    "                    epoch_train_loss, epoch_train_acc, optimizer = train_epoch(model, optimizer, device, train_loader, epoch)\n",
    "                    epoch_val_loss, epoch_val_acc = evaluate_network(model, device, val_loader, epoch)\n",
    "\n",
    "                    epoch_train_losses.append(epoch_train_loss)\n",
    "                    epoch_val_losses.append(epoch_val_loss)\n",
    "                    epoch_train_accs.append(epoch_train_acc)\n",
    "                    epoch_val_accs.append(epoch_val_acc)\n",
    "\n",
    "                    writer.add_scalar('train/_loss', epoch_train_loss, epoch)\n",
    "                    writer.add_scalar('val/_loss', epoch_val_loss, epoch)\n",
    "                    writer.add_scalar('train/_acc', epoch_train_acc, epoch)\n",
    "                    writer.add_scalar('val/_acc', epoch_val_acc, epoch)\n",
    "                    writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch)\n",
    "\n",
    "                    _, epoch_test_acc = evaluate_network(model, device, test_loader, epoch)\n",
    "                    t.set_postfix(time=time.time()-start, lr=optimizer.param_groups[0]['lr'],\n",
    "                                  train_loss=epoch_train_loss, val_loss=epoch_val_loss,\n",
    "                                  train_acc=epoch_train_acc, val_acc=epoch_val_acc,\n",
    "                                  test_acc=epoch_test_acc)  \n",
    "\n",
    "                    per_epoch_time.append(time.time()-start)\n",
    "\n",
    "                    # Saving checkpoint\n",
    "                    ckpt_dir = os.path.join(root_ckpt_dir, \"RUN_\" + str(split_number))\n",
    "                    if not os.path.exists(ckpt_dir):\n",
    "                        os.makedirs(ckpt_dir)\n",
    "                    torch.save(model.state_dict(), '{}.pkl'.format(ckpt_dir + \"/epoch_\" + str(epoch)))\n",
    "\n",
    "                    files = glob.glob(ckpt_dir + '/*.pkl')\n",
    "                    for file in files:\n",
    "                        epoch_nb = file.split('_')[-1]\n",
    "                        epoch_nb = int(epoch_nb.split('.')[0])\n",
    "                        if epoch_nb < epoch-1:\n",
    "                            os.remove(file)\n",
    "\n",
    "                    scheduler.step(epoch_val_loss)\n",
    "\n",
    "                    if optimizer.param_groups[0]['lr'] < params['min_lr']:\n",
    "                        print(\"\\n!! LR EQUAL TO MIN LR SET.\")\n",
    "                        break\n",
    "                        \n",
    "                    # Stop training after params['max_time'] hours\n",
    "                    if time.time()-t0_split > params['max_time']*3600/10:       # Dividing max_time by 10, since there are 10 runs in TUs\n",
    "                        print('-' * 89)\n",
    "                        print(\"Max_time for one train-val-test split experiment elapsed {:.3f} hours, so stopping\".format(params['max_time']/10))\n",
    "                        break\n",
    "\n",
    "            _, test_acc = evaluate_network(model, device, test_loader, epoch)   \n",
    "            _, train_acc = evaluate_network(model, device, train_loader, epoch)    \n",
    "            avg_test_acc.append(test_acc)   \n",
    "            avg_train_acc.append(train_acc)\n",
    "\n",
    "            print(\"Test Accuracy [LAST EPOCH]: {:.4f}\".format(test_acc))\n",
    "            print(\"Train Accuracy [LAST EPOCH]: {:.4f}\".format(train_acc))\n",
    "    \n",
    "    except KeyboardInterrupt:\n",
    "        print('-' * 89)\n",
    "        print('Exiting from training early because of KeyboardInterrupt')\n",
    "        \n",
    "    \n",
    "    print(\"TOTAL TIME TAKEN: {:.4f}hrs\".format((time.time()-t0)/3600))\n",
    "    print(\"AVG TIME PER EPOCH: {:.4f}s\".format(np.mean(per_epoch_time)))\n",
    "\n",
    "    # Final test accuracy value averaged over 10-fold\n",
    "    print(\"\"\"\\n\\n\\nFINAL RESULTS\\n\\nTEST ACCURACY averaged: {:.4f} with s.d. {:.4f}\"\"\"\\\n",
    "          .format(np.mean(np.array(avg_test_acc))*100, np.std(avg_test_acc)*100))\n",
    "    print(\"\\nAll splits Test Accuracies:\\n\", avg_test_acc)\n",
    "    print(\"\"\"\\n\\n\\nFINAL RESULTS\\n\\nTRAIN ACCURACY averaged: {:.4f} with s.d. {:.4f}\"\"\"\\\n",
    "          .format(np.mean(np.array(avg_train_acc))*100, np.std(avg_train_acc)*100))\n",
    "    print(\"\\nAll splits Train Accuracies:\\n\", avg_train_acc)\n",
    "\n",
    "    writer.close()\n",
    "\n",
    "    \"\"\"\n",
    "        Write the results in out/results folder\n",
    "    \"\"\"\n",
    "    with open(write_file_name + '.txt', 'w') as f:\n",
    "        f.write(\"\"\"Dataset: {},\\nModel: {}\\n\\nparams={}\\n\\nnet_params={}\\n\\n{}\\n\\nTotal Parameters: {}\\n\\n\n",
    "    FINAL RESULTS\\nTEST ACCURACY averaged: {:.4f} with s.d. {:.4f}\\nTRAIN ACCURACY averaged: {:.4f} with s.d. {:.4f}\\n\\n\n",
    "    Total Time Taken: {:.4f} hrs\\nAverage Time Per Epoch: {:.4f} s\\n\\n\\nAll Splits Test Accuracies: {}\"\"\"\\\n",
    "          .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'],\n",
    "                  np.mean(np.array(avg_test_acc))*100, np.std(avg_test_acc)*100,\n",
    "                  np.mean(np.array(avg_train_acc))*100, np.std(avg_train_acc)*100,\n",
    "               (time.time()-t0)/3600, np.mean(per_epoch_time), avg_test_acc))\n",
    "        \n",
    "\n",
    "    # send results to gmail\n",
    "    try:\n",
    "        from gmail import send\n",
    "        subject = 'Result for Dataset: {}, Model: {}'.format(DATASET_NAME, MODEL_NAME)\n",
    "        body = \"\"\"Dataset: {},\\nModel: {}\\n\\nparams={}\\n\\nnet_params={}\\n\\n{}\\n\\nTotal Parameters: {}\\n\\n\n",
    "    FINAL RESULTS\\nTEST ACCURACY averaged: {:.4f} with s.d. {:.4f}\\nTRAIN ACCURACY averaged: {:.4f} with s.d. {:.4f}\\n\\n\n",
    "    Total Time Taken: {:.4f} hrs\\nAverage Time Per Epoch: {:.4f} s\\n\\n\\nAll Splits Test Accuracies: {}\"\"\"\\\n",
    "          .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'],\n",
    "                  np.mean(np.array(avg_test_acc))*100, np.std(avg_test_acc)*100,\n",
    "                  np.mean(np.array(avg_train_acc))*100, np.std(avg_train_acc)*100,\n",
    "               (time.time()-t0)/3600, np.mean(per_epoch_time), avg_test_acc)\n",
    "        send(subject, body)\n",
    "    except:\n",
    "        pass\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convert main_TUs_graph_classification.ipynb to main_TUs_graph_classification.py\n",
      "Clean main_TUs_graph_classification.py\n",
      "Done. \n",
      "No Node Attribute Data\n",
      "[!] Dataset:  DD\n",
      "Time taken: 12.4625s\n",
      "cuda not available\n",
      "MODEL DETAILS:\n",
      "\n",
      "MODEL/Total parameters: GatedGCN 116253\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-5aa8016bc128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0mcleaner_main\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'main_TUs_graph_classification'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m     \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-5aa8016bc128>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(notebook_mode, config)\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0mnet_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_param'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mview_model_param\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMODEL_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m     \u001b[0mtrain_val_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMODEL_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDATASET_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-11-3591dda36824>\u001b[0m in \u001b[0;36mtrain_val_pipeline\u001b[0;34m(MODEL_NAME, DATASET_NAME, params, net_params, dirs)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mper_epoch_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoadData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATASET_NAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mMODEL_NAME\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'GCN'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'GAT'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/data/data.py\u001b[0m in \u001b[0;36mLoadData\u001b[0;34m(DATASET_NAME)\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mTU_DATASETS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'COLLAB'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ENZYMES'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'DD'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PROTEINS_full'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mDATASET_NAME\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mTU_DATASETS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mTUsDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATASET_NAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;31m# handling for SBM datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/data/TUs.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m#dataset = TUDataset(self.name, hidden_size=1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLegacyTUDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# dgl 4.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0;31m# frankenstein has labels 0 and 2; so correcting them as 0 and 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/data/tu.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, use_pandas, hidden_size, max_allow_node)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             DS_edge_list = self._idx_from_zero(\n\u001b[0;32m---> 42\u001b[0;31m                 np.genfromtxt(self._file_path(\"A\"), delimiter=\",\", dtype=int))\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         DS_indicator = self._idx_from_zero(\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mgenfromtxt\u001b[0;34m(fname, dtype, comments, delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows, encoding)\u001b[0m\n\u001b[1;32m   2203\u001b[0m                         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mttype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m             \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0musemask\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def main(notebook_mode=False,config=None):\n",
    "    \n",
    "    \"\"\"\n",
    "        USER CONTROLS\n",
    "    \"\"\"\n",
    "    \n",
    "    # terminal mode\n",
    "    if notebook_mode==False:\n",
    "        \n",
    "        parser = argparse.ArgumentParser()\n",
    "        parser.add_argument('--config', help=\"Please give a config.json file with training/model/data/param details\")\n",
    "        parser.add_argument('--gpu_id', help=\"Please give a value for gpu id\")\n",
    "        parser.add_argument('--model', help=\"Please give a value for model name\")\n",
    "        parser.add_argument('--dataset', help=\"Please give a value for dataset name\")\n",
    "        parser.add_argument('--out_dir', help=\"Please give a value for out_dir\")\n",
    "        parser.add_argument('--seed', help=\"Please give a value for seed\")\n",
    "        parser.add_argument('--epochs', help=\"Please give a value for epochs\")\n",
    "        parser.add_argument('--batch_size', help=\"Please give a value for batch_size\")\n",
    "        parser.add_argument('--init_lr', help=\"Please give a value for init_lr\")\n",
    "        parser.add_argument('--lr_reduce_factor', help=\"Please give a value for lr_reduce_factor\")\n",
    "        parser.add_argument('--lr_schedule_patience', help=\"Please give a value for lr_schedule_patience\")\n",
    "        parser.add_argument('--min_lr', help=\"Please give a value for min_lr\")\n",
    "        parser.add_argument('--weight_decay', help=\"Please give a value for weight_decay\")\n",
    "        parser.add_argument('--print_epoch_interval', help=\"Please give a value for print_epoch_interval\")    \n",
    "        parser.add_argument('--L', help=\"Please give a value for L\")\n",
    "        parser.add_argument('--hidden_dim', help=\"Please give a value for hidden_dim\")\n",
    "        parser.add_argument('--out_dim', help=\"Please give a value for out_dim\")\n",
    "        parser.add_argument('--residual', help=\"Please give a value for residual\")\n",
    "        parser.add_argument('--edge_feat', help=\"Please give a value for edge_feat\")\n",
    "        parser.add_argument('--readout', help=\"Please give a value for readout\")\n",
    "        parser.add_argument('--kernel', help=\"Please give a value for kernel\")\n",
    "        parser.add_argument('--n_heads', help=\"Please give a value for n_heads\")\n",
    "        parser.add_argument('--gated', help=\"Please give a value for gated\")\n",
    "        parser.add_argument('--in_feat_dropout', help=\"Please give a value for in_feat_dropout\")\n",
    "        parser.add_argument('--dropout', help=\"Please give a value for dropout\")\n",
    "        parser.add_argument('--graph_norm', help=\"Please give a value for graph_norm\")\n",
    "        parser.add_argument('--batch_norm', help=\"Please give a value for batch_norm\")\n",
    "        parser.add_argument('--sage_aggregator', help=\"Please give a value for sage_aggregator\")\n",
    "        parser.add_argument('--data_mode', help=\"Please give a value for data_mode\")\n",
    "        parser.add_argument('--num_pool', help=\"Please give a value for num_pool\")\n",
    "        parser.add_argument('--gnn_per_block', help=\"Please give a value for gnn_per_block\")\n",
    "        parser.add_argument('--embedding_dim', help=\"Please give a value for embedding_dim\")\n",
    "        parser.add_argument('--pool_ratio', help=\"Please give a value for pool_ratio\")\n",
    "        parser.add_argument('--linkpred', help=\"Please give a value for linkpred\")\n",
    "        parser.add_argument('--cat', help=\"Please give a value for cat\")\n",
    "        parser.add_argument('--self_loop', help=\"Please give a value for self_loop\")\n",
    "        parser.add_argument('--max_time', help=\"Please give a value for max_time\")\n",
    "        args = parser.parse_args()\n",
    "        with open(args.config) as f:\n",
    "            config = json.load(f)\n",
    "            \n",
    "\n",
    "        # device\n",
    "        if args.gpu_id is not None:\n",
    "            config['gpu']['id'] = int(args.gpu_id)\n",
    "            config['gpu']['use'] = True\n",
    "        device = gpu_setup(config['gpu']['use'], config['gpu']['id'])\n",
    "\n",
    "        # model, dataset, out_dir\n",
    "        if args.model is not None:\n",
    "            MODEL_NAME = args.model\n",
    "        else:\n",
    "            MODEL_NAME = config['model']\n",
    "        if args.dataset is not None:\n",
    "            DATASET_NAME = args.dataset\n",
    "        else:\n",
    "            DATASET_NAME = config['dataset']\n",
    "        dataset = LoadData(DATASET_NAME)\n",
    "        if args.out_dir is not None:\n",
    "            out_dir = args.out_dir\n",
    "        else:\n",
    "            out_dir = config['out_dir']\n",
    "\n",
    "        # parameters\n",
    "        params = config['params']\n",
    "        if args.seed is not None:\n",
    "            params['seed'] = int(args.seed)\n",
    "        if args.epochs is not None:\n",
    "            params['epochs'] = int(args.epochs)\n",
    "        if args.batch_size is not None:\n",
    "            params['batch_size'] = int(args.batch_size)\n",
    "        if args.init_lr is not None:\n",
    "            params['init_lr'] = float(args.init_lr)\n",
    "        if args.lr_reduce_factor is not None:\n",
    "            params['lr_reduce_factor'] = float(args.lr_reduce_factor)\n",
    "        if args.lr_schedule_patience is not None:\n",
    "            params['lr_schedule_patience'] = int(args.lr_schedule_patience)\n",
    "        if args.min_lr is not None:\n",
    "            params['min_lr'] = float(args.min_lr)\n",
    "        if args.weight_decay is not None:\n",
    "            params['weight_decay'] = float(args.weight_decay)\n",
    "        if args.print_epoch_interval is not None:\n",
    "            params['print_epoch_interval'] = int(args.print_epoch_interval)\n",
    "        if args.max_time is not None:\n",
    "            params['max_time'] = float(args.max_time)\n",
    "\n",
    "        # network parameters\n",
    "        net_params = config['net_params']\n",
    "        net_params['device'] = device\n",
    "        net_params['gpu_id'] = config['gpu']['id']\n",
    "        net_params['batch_size'] = params['batch_size']\n",
    "        if args.L is not None:\n",
    "            net_params['L'] = int(args.L)\n",
    "        if args.hidden_dim is not None:\n",
    "            net_params['hidden_dim'] = int(args.hidden_dim)\n",
    "        if args.out_dim is not None:\n",
    "            net_params['out_dim'] = int(args.out_dim)   \n",
    "        if args.residual is not None:\n",
    "            net_params['residual'] = True if args.residual=='True' else False\n",
    "        if args.edge_feat is not None:\n",
    "            net_params['edge_feat'] = True if args.edge_feat=='True' else False\n",
    "        if args.readout is not None:\n",
    "            net_params['readout'] = args.readout\n",
    "        if args.kernel is not None:\n",
    "            net_params['kernel'] = int(args.kernel)\n",
    "        if args.n_heads is not None:\n",
    "            net_params['n_heads'] = int(args.n_heads)\n",
    "        if args.gated is not None:\n",
    "            net_params['gated'] = True if args.gated=='True' else False\n",
    "        if args.in_feat_dropout is not None:\n",
    "            net_params['in_feat_dropout'] = float(args.in_feat_dropout)\n",
    "        if args.dropout is not None:\n",
    "            net_params['dropout'] = float(args.dropout)\n",
    "        if args.graph_norm is not None:\n",
    "            net_params['graph_norm'] = True if args.graph_norm=='True' else False\n",
    "        if args.batch_norm is not None:\n",
    "            net_params['batch_norm'] = True if args.batch_norm=='True' else False\n",
    "        if args.sage_aggregator is not None:\n",
    "            net_params['sage_aggregator'] = args.sage_aggregator\n",
    "        if args.data_mode is not None:\n",
    "            net_params['data_mode'] = args.data_mode\n",
    "        if args.num_pool is not None:\n",
    "            net_params['num_pool'] = int(args.num_pool)\n",
    "        if args.gnn_per_block is not None:\n",
    "            net_params['gnn_per_block'] = int(args.gnn_per_block)\n",
    "        if args.embedding_dim is not None:\n",
    "            net_params['embedding_dim'] = int(args.embedding_dim)\n",
    "        if args.pool_ratio is not None:\n",
    "            net_params['pool_ratio'] = float(args.pool_ratio)\n",
    "        if args.linkpred is not None:\n",
    "            net_params['linkpred'] = True if args.linkpred=='True' else False\n",
    "        if args.cat is not None:\n",
    "            net_params['cat'] = True if args.cat=='True' else False\n",
    "        if args.self_loop is not None:\n",
    "            net_params['self_loop'] = True if args.self_loop=='True' else False\n",
    "\n",
    "            \n",
    "    # notebook mode\n",
    "    if notebook_mode:\n",
    "        \n",
    "        # parameters\n",
    "        params = config['params']\n",
    "        \n",
    "        # dataset\n",
    "        DATASET_NAME = config['dataset']\n",
    "        dataset = LoadData(DATASET_NAME)\n",
    "        \n",
    "        # device\n",
    "        device = gpu_setup(config['gpu']['use'], config['gpu']['id'])\n",
    "        out_dir = config['out_dir']\n",
    "        \n",
    "        # GNN model\n",
    "        MODEL_NAME = config['model']\n",
    "        \n",
    "        # network parameters\n",
    "        net_params = config['net_params']\n",
    "        net_params['device'] = device\n",
    "        net_params['gpu_id'] = config['gpu']['id']\n",
    "        net_params['batch_size'] = params['batch_size']\n",
    "      \n",
    "    \n",
    "    # TUs\n",
    "    net_params['in_dim'] = dataset.all.graph_lists[0].ndata['feat'][0].shape[0]\n",
    "    num_classes = len(np.unique(dataset.all.graph_labels))\n",
    "    net_params['n_classes'] = num_classes\n",
    "    \n",
    "    if MODEL_NAME == 'DiffPool':\n",
    "        # calculate assignment dimension: pool_ratio * largest graph's maximum\n",
    "        # number of nodes  in the dataset\n",
    "        num_nodes = [dataset.all[i][0].number_of_nodes() for i in range(len(dataset.all))]\n",
    "        max_num_node = max(num_nodes)\n",
    "        net_params['assign_dim'] = int(max_num_node * net_params['pool_ratio']) * net_params['batch_size']\n",
    "    \n",
    "    root_log_dir = out_dir + 'logs/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    root_ckpt_dir = out_dir + 'checkpoints/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    write_file_name = out_dir + 'results/result_' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    write_config_file = out_dir + 'configs/config_' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    dirs = root_log_dir, root_ckpt_dir, write_file_name, write_config_file\n",
    "\n",
    "    if not os.path.exists(out_dir + 'results'):\n",
    "        os.makedirs(out_dir + 'results')\n",
    "        \n",
    "    if not os.path.exists(out_dir + 'configs'):\n",
    "        os.makedirs(out_dir + 'configs')\n",
    "\n",
    "    net_params['total_param'] = view_model_param(MODEL_NAME, net_params)\n",
    "    train_val_pipeline(MODEL_NAME, DATASET_NAME, params, net_params, dirs)\n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "if notebook_mode==True:\n",
    "    \n",
    "    config = {}\n",
    "    # gpu config\n",
    "    gpu = {}\n",
    "    gpu['use'] = use_gpu\n",
    "    gpu['id'] = gpu_id\n",
    "    config['gpu'] = gpu\n",
    "    # GNN model, dataset, out_dir\n",
    "    config['model'] = MODEL_NAME\n",
    "    config['dataset'] = DATASET_NAME\n",
    "    config['out_dir'] = out_dir\n",
    "    # parameters\n",
    "    params = {}\n",
    "    params['seed'] = seed\n",
    "    params['epochs'] = epochs\n",
    "    params['batch_size'] = batch_size\n",
    "    params['init_lr'] = init_lr\n",
    "    params['lr_reduce_factor'] = lr_reduce_factor \n",
    "    params['lr_schedule_patience'] = lr_schedule_patience\n",
    "    params['min_lr'] = min_lr\n",
    "    params['weight_decay'] = weight_decay\n",
    "    params['print_epoch_interval'] = 5\n",
    "    params['max_time'] = max_time\n",
    "    config['params'] = params\n",
    "    # network parameters\n",
    "    config['net_params'] = net_params\n",
    "    \n",
    "    # convert to .py format\n",
    "    from utils.cleaner_main import *\n",
    "    cleaner_main('main_TUs_graph_classification')\n",
    "    \n",
    "    main(True,config)\n",
    "    \n",
    "else:\n",
    "    \n",
    "    main()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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