demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset (Cora)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: pytorch\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  NumNodes: 2485\n",
      "  NumEdges: 10138\n",
      "  NumFeats: 1433\n",
      "  NumbClasses: 7\n",
      "Done loading data from cached files.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from graphattack.data import GraphAttackDataset\n",
    "from graphattack.training import Trainer\n",
    "from graphattack.training.callbacks import ModelCheckpoint\n",
    "from graphattack.models import GCN\n",
    "from graphattack.utils import split_nodes\n",
    "from graphattack import set_seed\n",
    "from graphattack.utils import flip_graph\n",
    "\n",
    "from guard import GUARD, RandomGUARD, DegreeGUARD\n",
    "\n",
    "dataset = 'cora'\n",
    "\n",
    "data = GraphAttackDataset(dataset, verbose=True, standardize=True if dataset !='reddit' else False)\n",
    "\n",
    "g = data[0]\n",
    "y = g.ndata['label']\n",
    "splits = split_nodes(y, random_state=15)\n",
    "    \n",
    "num_feats = g.ndata['feat'].size(1)\n",
    "num_classes = data.num_classes\n",
    "y_train = y[splits.train_nodes]\n",
    "y_val = y[splits.val_nodes]\n",
    "y_test = y[splits.test_nodes]\n",
    "\n",
    "seed = 2022\n",
    "set_seed(seed)\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "g = g.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup GUARD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "defense = \"GUARD\" # RandomGUARD, DegreeGUARD\n",
    "\n",
    "if defense == \"GUARD\":\n",
    "    surrogate = GCN(num_feats, num_classes, bias=False, acts=None)\n",
    "    surrogate_trainer = Trainer(surrogate, device=device)\n",
    "    cb = ModelCheckpoint('guard.pth', monitor='val_accuracy')\n",
    "    surrogate_trainer.fit(g, y_train, splits.train_nodes, val_y=y_val, val_index=splits.val_nodes, callbacks=[cb], verbose=0)        \n",
    "    guard = GUARD(g.ndata['feat'], g.in_degrees(),  device=device)\n",
    "    guard.setup_surrogate(surrogate, y_train)\n",
    "elif defense == \"RandomGUARD\":\n",
    "    guard = RandomGUARD(g.num_nodes(), device=device)\n",
    "elif defense == \"DegreeGUARD\":\n",
    "    guard = DegreeGUARD(g.in_degrees(), device=device)\n",
    "else:\n",
    "    raise ValueError(f\"Unknown defense {defense}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup Victim Model (GCN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "100/100 [==============================] - Total: 1.26s - 12ms/step- loss: 0.053 - accuracy: 0.996 - val_loss: 0.631 - val_accuracy: 0.827\n",
      "Accuracy on test set: 84.26%\n",
      "Training...\n",
      "100/100 [==============================] - Total: 1.25s - 12ms/step- loss: 0.0599 - accuracy: 0.996 - val_loss: 0.6 - val_accuracy: 0.811\n",
      "Accuracy on test set: 84.10%\n",
      "Training...\n",
      "100/100 [==============================] - Total: 1.25s - 12ms/step- loss: 0.055 - accuracy: 1 - val_loss: 0.62 - val_accuracy: 0.823\n",
      "Accuracy on test set: 84.00%\n",
      "Training...\n",
      "100/100 [==============================] - Total: 1.23s - 12ms/step- loss: 0.057 - accuracy: 0.996 - val_loss: 0.617 - val_accuracy: 0.811\n",
      "Accuracy on test set: 83.95%\n",
      "Training...\n",
      "100/100 [==============================] - Total: 1.18s - 11ms/step- loss: 0.0488 - accuracy: 0.996 - val_loss: 0.603 - val_accuracy: 0.827\n",
      "Accuracy on test set: 84.21%\n"
     ]
    }
   ],
   "source": [
    "def get_trainer():\n",
    "    victim = GCN(num_feats, num_classes)\n",
    "    trainer = Trainer(victim, device=device)\n",
    "\n",
    "    cb = ModelCheckpoint('victim.pth', monitor='val_accuracy')\n",
    "    trainer.fit(g, y_train, splits.train_nodes, val_y=y_val, val_index=splits.val_nodes, callbacks=[cb], verbose=1)\n",
    "    acc = trainer.evaluate(g, y_test, splits.test_nodes, verbose=0).accuracy\n",
    "    print(f\"Accuracy on test set: {acc:.2%}\")\n",
    "    \n",
    "    return trainer\n",
    "\n",
    "runs = 5\n",
    "trainers = []\n",
    "\n",
    "for _ in range(runs):\n",
    "    trainers.append(get_trainer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Adversarial Edges Generated by Different Attacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "attack = 'SGAttack' # 'FGAttack', 'IGAttack'\n",
    "\n",
    "dir_name = f\"perturbations/{dataset}\"\n",
    "file_name = f\"{dir_name}/{attack}.pkl\"\n",
    "perturbation = torch.load(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1830, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = list(perturbation.keys())[1]\n",
    "target_label = y[target].item()\n",
    "\n",
    "# clean graph\n",
    "g\n",
    "\n",
    "# perturbed graph\n",
    "edges = perturbation[target] # [2, M]\n",
    "attack_g = flip_graph(g, edges)\n",
    "\n",
    "# defensed graph\n",
    "defense_g = guard(attack_g, target, k=200)\n",
    "    \n",
    "target, target_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results on Clean, Perturbed, and Defensed Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = []\n",
    "for graph in [g, attack_g, defense_g]:\n",
    "    res = []\n",
    "    for trainer in trainers:\n",
    "        # Important for SGC\n",
    "        trainer.cache_clear()\n",
    "        res.append(trainer.predict(graph, target).cpu().numpy())\n",
    "    results.append(res)\n",
    "        \n",
    "results = np.array(results)\n",
    "results.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results on Clean Graph vs. Perturbed Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_xlabel(ix, correct):\n",
    "    if ix == correct:\n",
    "        return \"Class {}\\n(correct)\".format(ix)\n",
    "    return \"Class {}\".format(ix)\n",
    "\n",
    "\n",
    "figure = plt.figure(figsize=(12, 4), dpi=300)\n",
    "plt.subplot(1, 2, 1)\n",
    "center_ixs_clean = []\n",
    "for ix, block in enumerate(results[0].T):\n",
    "    x_ixs = np.arange(len(block)) + ix*(len(block)+2)\n",
    "    center_ixs_clean.append(np.mean(x_ixs))\n",
    "    color = '#555555'\n",
    "    if ix == y[target]:\n",
    "        color = 'darkgreen'\n",
    "    plt.bar(x_ixs, block, color=color)\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.ylim((-.05, 1.05))\n",
    "plt.ylabel(\"Predicted probability\")\n",
    "ax.set_xticks(center_ixs_clean)\n",
    "ax.set_xticklabels([make_xlabel(k, y[target]) for k in range(num_classes)])\n",
    "ax.set_title(f\"Predicted class probabilities for node {target} on clean data\\n({runs} re-trainings)\")\n",
    "\n",
    "fig = plt.subplot(1, 2, 2)\n",
    "center_ixs_retrain = []\n",
    "for ix, block in enumerate(results[1].T):\n",
    "    x_ixs = np.arange(len(block)) + ix*(len(block)+2)\n",
    "    center_ixs_retrain.append(np.mean(x_ixs))\n",
    "    color = '#555555'\n",
    "    if ix == y[target]:\n",
    "        color = 'darkgreen'\n",
    "    plt.bar(x_ixs, block, color=color)\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.ylim((-.05, 1.05))\n",
    "ax.set_xticks(center_ixs_retrain)\n",
    "ax.set_xticklabels([make_xlabel(k, y[target]) for k in range(num_classes)])\n",
    "ax.set_title(f\"Predicted class probabilities for node {target} after attack\\n({runs} re-trainings)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results on Perturbed Graph vs. Defensed Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(12, 4), dpi=300)\n",
    "plt.subplot(1, 2, 1)\n",
    "center_ixs_clean = []\n",
    "for ix, block in enumerate(results[1].T):\n",
    "    x_ixs = np.arange(len(block)) + ix*(len(block)+2)\n",
    "    center_ixs_clean.append(np.mean(x_ixs))\n",
    "    color = '#555555'\n",
    "    if ix == y[target]:\n",
    "        color = 'darkgreen'\n",
    "    plt.bar(x_ixs, block, color=color)\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.ylim((-.05, 1.05))\n",
    "plt.ylabel(\"Predicted probability\")\n",
    "ax.set_xticks(center_ixs_clean)\n",
    "ax.set_xticklabels([make_xlabel(k, y[target]) for k in range(num_classes)])\n",
    "ax.set_title(f\"Predicted class probabilities for node {target} after perturbations\\n({runs} re-trainings)\")\n",
    "\n",
    "fig = plt.subplot(1, 2, 2)\n",
    "center_ixs_retrain = []\n",
    "for ix, block in enumerate(results[2].T):\n",
    "    x_ixs = np.arange(len(block)) + ix*(len(block)+2)\n",
    "    center_ixs_retrain.append(np.mean(x_ixs))\n",
    "    color = '#555555'\n",
    "    if ix == y[target]:\n",
    "        color = 'darkgreen'\n",
    "    plt.bar(x_ixs, block, color=color)\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.ylim((-.05, 1.05))\n",
    "ax.set_xticks(center_ixs_retrain)\n",
    "ax.set_xticklabels([make_xlabel(k, y[target]) for k in range(num_classes)])\n",
    "ax.set_title(f\"Predicted class probabilities for node {target} after attack, \\nfollowed with universal defense GUARD\\n({runs} re-trainings)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}