{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Link Prediction\n",
    "In this file we will give you a simple example to show how to use AutoGL to do link prediction.\n",
    "\n",
    "## Import libraries\n",
    "First, you should import some libraries and you can set the random seed before you train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autogl.datasets import build_dataset_from_name\n",
    "from autogl.solver import AutoLinkPredictor\n",
    "from autogl.solver.utils import set_seed\n",
    "import argparse\n",
    "from autogl.backend import DependentBackend\n",
    "from autogl.datasets.utils import split_edges\n",
    "from autogl.module.train.evaluation import Auc\n",
    "import yaml\n",
    "import random\n",
    "import torch\n",
    "import numpy as np\n",
    "from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n",
    "set_seed(202106)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config & Dataset\n",
    "Then, you can load dataset and graph nas methods from configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  NumNodes: 2708\n",
      "  NumEdges: 10556\n",
      "  NumFeats: 1433\n",
      "  NumClasses: 7\n",
      "  NumTrainingSamples: 140\n",
      "  NumValidationSamples: 500\n",
      "  NumTestSamples: 1000\n",
      "Done loading data from cached files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:02:49] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    }
   ],
   "source": [
    "def parse(args=None):\n",
    "    parser = ArgumentParser(\"auto link prediction\", formatter_class=ArgumentDefaultsHelpFormatter)\n",
    "    parser.add_argument('--config', type=str, default=\"../../configs/lp_gcn_benchmark.yml\")\n",
    "    parser.add_argument('--dataset', choices=['cora', 'citeseer', 'pubmed'], default='cora', type=str)\n",
    "    parser.add_argument(\"--hpo\", type=str, default=\"tpe\", help=\"hpo methods\")\n",
    "    parser.add_argument(\"--max_eval\", type=int, default=50, help=\"max hpo evaluation times\")\n",
    "    parser.add_argument(\"--seed\", type=int, default=0, help=\"random seed\")\n",
    "    parser.add_argument(\"--device\", default=0, type=int, help=\"GPU device\")\n",
    "    args = parser.parse_args(args)\n",
    "    return args\n",
    "\n",
    "args = parse('')\n",
    "\n",
    "dataset = build_dataset_from_name(args.dataset)\n",
    "dataset = split_edges(dataset, 0.8, 0.05) # split the edges for dataset\n",
    "if DependentBackend.is_dgl(): # add self-loop\n",
    "    import dgl\n",
    "    # add self loop to 0\n",
    "    data = list(dataset[0])\n",
    "    data[0] = dgl.add_self_loop(data[0])\n",
    "    dataset = [data]\n",
    "configs = yaml.load(open(args.config, \"r\").read(), Loader=yaml.FullLoader)\n",
    "configs[\"hpo\"][\"name\"] = args.hpo\n",
    "configs[\"hpo\"][\"max_evals\"] = args.max_eval\n",
    "solver = AutoLinkPredictor.from_config(configs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:LinkPredictor:Use the default train/val/test ratio in given dataset\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:03] INFO (LinkPredictor/MainThread) Use the default train/val/test ratio in given dataset\n",
      "HPO Search Phase:\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/50 [00:00<?, ?it/s]INFO:hyperopt.tpe:tpe_transform took 0.218073 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:03] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.218073 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 0 trials\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:03] INFO (hyperopt.tpe/MainThread) TPE using 0 trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:03] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:HPO:Parameter: {'trainer': {'max_epoch': 96, 'early_stopping_round': 38, 'lr': 0.038766175592297024, 'weight_decay': 2.3821416978310546e-08}, 'encoder': {'num_layers': 3, 'hidden': [111, 236], 'dropout': 0.020573287174414756, 'act': 'leaky_relu'}, 'decoder': {}} auc: 0.8238174623463882 higher_better\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:10] INFO (HPO/MainThread) Parameter: {'trainer': {'max_epoch': 96, 'early_stopping_round': 38, 'lr': 0.038766175592297024, 'weight_decay': 2.3821416978310546e-08}, 'encoder': {'num_layers': 3, 'hidden': [111, 236], 'dropout': 0.020573287174414756, 'act': 'leaky_relu'}, 'decoder': {}} auc: 0.8238174623463882 higher_better\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 1/50 [00:07<05:52,  7.19s/it]INFO:hyperopt.tpe:tpe_transform took 0.006646 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:10] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.006646 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 1/1 trials with best loss -0.823817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:10] INFO (hyperopt.tpe/MainThread) TPE using 1/1 trials with best loss -0.823817\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:10] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:HPO:Parameter: {'trainer': {'max_epoch': 92, 'early_stopping_round': 34, 'lr': 0.00927691677178042, 'weight_decay': 7.058659985819521e-10}, 'encoder': {'num_layers': 3, 'hidden': [121, 132], 'dropout': 0.034825764974073435, 'act': 'relu'}, 'decoder': {}} auc: 0.8113790781661261 higher_better\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:13] INFO (HPO/MainThread) Parameter: {'trainer': {'max_epoch': 92, 'early_stopping_round': 34, 'lr': 0.00927691677178042, 'weight_decay': 7.058659985819521e-10}, 'encoder': {'num_layers': 3, 'hidden': [121, 132], 'dropout': 0.034825764974073435, 'act': 'relu'}, 'decoder': {}} auc: 0.8113790781661261 higher_better\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 2/50 [00:09<03:34,  4.48s/it]INFO:hyperopt.tpe:tpe_transform took 0.003760 seconds\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:13] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.003760 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 2/2 trials with best loss -0.823817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:13] INFO (hyperopt.tpe/MainThread) TPE using 2/2 trials with best loss -0.823817\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:13] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:HPO:Parameter: {'trainer': {'max_epoch': 142, 'early_stopping_round': 35, 'lr': 0.02091397431735606, 'weight_decay': 8.728358614633772e-09}, 'encoder': {'num_layers': 2, 'hidden': [128], 'dropout': 0.14104896295645744, 'act': 'tanh'}, 'decoder': {}} auc: 0.9538308927047588 higher_better\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:24] INFO (HPO/MainThread) Parameter: {'trainer': {'max_epoch': 142, 'early_stopping_round': 35, 'lr': 0.02091397431735606, 'weight_decay': 8.728358614633772e-09}, 'encoder': {'num_layers': 2, 'hidden': [128], 'dropout': 0.14104896295645744, 'act': 'tanh'}, 'decoder': {}} auc: 0.9538308927047588 higher_better\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 3/50 [00:21<05:55,  7.57s/it]INFO:hyperopt.tpe:tpe_transform took 0.004439 seconds\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:24] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.004439 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 3/3 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:24] INFO (hyperopt.tpe/MainThread) TPE using 3/3 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:24] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:HPO:Parameter: {'trainer': {'max_epoch': 132, 'early_stopping_round': 29, 'lr': 0.017963261319463134, 'weight_decay': 1.039920878028204e-09}, 'encoder': {'num_layers': 2, 'hidden': [71], 'dropout': 0.09177502794266801, 'act': 'relu'}, 'decoder': {}} auc: 0.9515678953224188 higher_better\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:29] INFO (HPO/MainThread) Parameter: {'trainer': {'max_epoch': 132, 'early_stopping_round': 29, 'lr': 0.017963261319463134, 'weight_decay': 1.039920878028204e-09}, 'encoder': {'num_layers': 2, 'hidden': [71], 'dropout': 0.09177502794266801, 'act': 'relu'}, 'decoder': {}} auc: 0.9515678953224188 higher_better\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "[2022-11-09 23:03:29] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.006353 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 4/4 trials with best loss -0.953831\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:29] INFO (hyperopt.tpe/MainThread) TPE using 4/4 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:29] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:HPO:Parameter: {'trainer': {'max_epoch': 78, 'early_stopping_round': 37, 'lr': 0.024625236940264132, 'weight_decay': 5.0233612711599956e-08}, 'encoder': {'num_layers': 3, 'hidden': [71, 226], 'dropout': 0.151834136171988, 'act': 'leaky_relu'}, 'decoder': {}} auc: 0.7246650511829877 higher_better\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:35] INFO (HPO/MainThread) Parameter: {'trainer': {'max_epoch': 78, 'early_stopping_round': 37, 'lr': 0.024625236940264132, 'weight_decay': 5.0233612711599956e-08}, 'encoder': {'num_layers': 3, 'hidden': [71, 226], 'dropout': 0.151834136171988, 'act': 'leaky_relu'}, 'decoder': {}} auc: 0.7246650511829877 higher_better\n"
     ]
    },
    {
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      " 10%|█         | 5/50 [00:31<04:43,  6.31s/it]INFO:hyperopt.tpe:tpe_transform took 0.003402 seconds\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:35] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.003402 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 5/5 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:35] INFO (hyperopt.tpe/MainThread) TPE using 5/5 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:35] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:HPO:Parameter: {'trainer': {'max_epoch': 135, 'early_stopping_round': 30, 'lr': 0.005181891063109031, 'weight_decay': 5.1614599568940116e-08}, 'encoder': {'num_layers': 2, 'hidden': [139], 'dropout': 0.05673383055413166, 'act': 'elu'}, 'decoder': {}} auc: 0.8402327448699992 higher_better\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:38] INFO (HPO/MainThread) Parameter: {'trainer': {'max_epoch': 135, 'early_stopping_round': 30, 'lr': 0.005181891063109031, 'weight_decay': 5.1614599568940116e-08}, 'encoder': {'num_layers': 2, 'hidden': [139], 'dropout': 0.05673383055413166, 'act': 'elu'}, 'decoder': {}} auc: 0.8402327448699992 higher_better\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 6/50 [00:35<03:55,  5.35s/it]INFO:hyperopt.tpe:tpe_transform took 0.004568 seconds\n"
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    },
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     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:38] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.004568 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 6/6 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:38] INFO (hyperopt.tpe/MainThread) TPE using 6/6 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:38] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:HPO:Parameter: {'trainer': {'max_epoch': 59, 'early_stopping_round': 28, 'lr': 0.017915948926693917, 'weight_decay': 6.174302220575103e-08}, 'encoder': {'num_layers': 2, 'hidden': [195], 'dropout': 0.031863207309905994, 'act': 'tanh'}, 'decoder': {}} auc: 0.9523528331575026 higher_better\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:42] INFO (HPO/MainThread) Parameter: {'trainer': {'max_epoch': 59, 'early_stopping_round': 28, 'lr': 0.017915948926693917, 'weight_decay': 6.174302220575103e-08}, 'encoder': {'num_layers': 2, 'hidden': [195], 'dropout': 0.031863207309905994, 'act': 'tanh'}, 'decoder': {}} auc: 0.9523528331575026 higher_better\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 7/50 [00:39<03:26,  4.80s/it]INFO:hyperopt.tpe:tpe_transform took 0.004849 seconds\n"
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    },
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     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:42] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.004849 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hyperopt.tpe:TPE using 7/7 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:42] INFO (hyperopt.tpe/MainThread) TPE using 7/7 trials with best loss -0.953831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Ignore passed dec since enc is a whole model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-11-09 23:03:42] WARNING (root/MainThread) Ignore passed dec since enc is a whole model\n"
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      " 14%|█▍        | 7/50 [00:40<04:08,  5.78s/it]\n"
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    },
    {
     "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-21-4938daf3de29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_limit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3600\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluation_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAuc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\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      2\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_leaderboard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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      3\u001b[0m \u001b[0mauc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"auc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"test auc: {:.4f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauc\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/home/jcai/code/AutoGL/autogl/solver/classifier/link_predictor.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, time_limit, inplace, train_split, val_split, evaluation_method, seed)\u001b[0m\n\u001b[1;32m    362\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    363\u001b[0m                 optimized, _ = self.hpo_module.optimize(\n\u001b[0;32m--> 364\u001b[0;31m                     \u001b[0mtrainer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compose_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_limit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtime_for_each_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    365\u001b[0m                 )\n\u001b[1;32m    366\u001b[0m             \u001b[0;31m# to save memory, all the trainer derived will be mapped to cpu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jcai/code/AutoGL/autogl/module/hpo/advisorbase.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, trainer, dataset, time_limit, memory_limit)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mnew_parameter_values_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameter_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m             \u001b[0mdecoded_json\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial_para\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode_para\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_parameter_values_json\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0mcurrent_trainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecoded_json\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    168\u001b[0m             \u001b[0mnew_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameter_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial_para\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m             \u001b[0mnew_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Completed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jcai/code/AutoGL/autogl/module/hpo/advisorbase.py\u001b[0m in \u001b[0;36mfn\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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    145\u001b[0m             \u001b[0mcurrent_trainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduplicate_from_hyper_parameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m             \u001b[0mcurrent_trainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\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    147\u001b[0m             \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_higher_better\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrent_trainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_valid_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_higher_better\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jcai/code/AutoGL/autogl/module/train/link_prediction_full.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, dataset, keep_valid_result)\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyg_dgl\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'dgl'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_only_dgl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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    379\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkeep_valid_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_predict_only_dgl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jcai/code/AutoGL/autogl/module/train/link_prediction_full.py\u001b[0m in \u001b[0;36m_train_only_dgl\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    314\u001b[0m                 )\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[0m\u001b[1;32m    317\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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    318\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_scheduler_type\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jcai/virtualenvs/py3_hetgnn/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    243\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\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    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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/home/jcai/virtualenvs/py3_hetgnn/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    145\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    146\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "solver.fit(dataset, time_limit=3600, evaluation_method=[Auc], seed=args.seed)\n",
    "solver.get_leaderboard().show()\n",
    "auc = solver.evaluate(metric=\"auc\")\n",
    "print(\"test auc: {:.4f}\".format(auc))"
   ]
  }
 ],
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