meta-similar-users.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Library imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Library imports\n",
    "import pyforest\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from pprint import pprint\n",
    "from time import sleep\n",
    "import time\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "from turtle import forward\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import math\n",
    "import torch\n",
    "from torch.nn.parameter import Parameter\n",
    "from torch.nn.modules.module import Module\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "import models \n",
    "from models import get_accuracy\n",
    "import inspect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Create similar users from training data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train edges shape:  (159619, 3)\n",
      "similar users train edges shape:  (159619, 3)\n",
      "new train edges shape:  (319238, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1886, 1682, 319238, 40381)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = 'movielens'\n",
    "n_users = 943\n",
    "n_items = 1682\n",
    "n_similar_users = 943 # hyperparameter\n",
    "\n",
    "# load original training edges\n",
    "train_edges = np.load('data/' + dataset + '/train_edges.npy')\n",
    "test_edges = np.load('data/' + dataset + '/test_edges.npy')\n",
    "\n",
    "if n_similar_users > 0:\n",
    "    # create dicts of positive and negative edges for sampling later (only training edges, not test edges)\n",
    "    positive_edges = dict()\n",
    "    negative_edges = dict() \n",
    "\n",
    "    for u in range(n_users):\n",
    "        positive_edges[u] = []\n",
    "        negative_edges[u] = []\n",
    "\n",
    "    n_train_edges = train_edges.shape[0]\n",
    "\n",
    "    for i in range(n_train_edges):\n",
    "        u = train_edges[i, 0]\n",
    "        v = train_edges[i, 1]\n",
    "        y = train_edges[i, 2]\n",
    "        if y == 1:\n",
    "            positive_edges[u].append(v)\n",
    "        else:\n",
    "            negative_edges[u].append(v)\n",
    "\n",
    "    np.random.seed(0)\n",
    "    similar_users = np.random.choice(n_users, n_similar_users, replace=False)\n",
    "    # print('chosen {} similar users: {}'.format(n_similar_users, similar_users))\n",
    "\n",
    "    # 100% similarity for similar users \n",
    "    similar_users_train_edges = []\n",
    "    for j in range(len(similar_users)):\n",
    "        u = similar_users[j]\n",
    "        for item in positive_edges[u]:\n",
    "            similar_users_train_edges.append([j + n_users, item, 1])\n",
    "        for item in negative_edges[u]:\n",
    "            similar_users_train_edges.append([j + n_users, item, 0])\n",
    "\n",
    "    similar_users_train_edges = np.array(similar_users_train_edges)\n",
    "    np.random.seed(0)\n",
    "    np.random.shuffle(similar_users_train_edges)\n",
    "    np.random.shuffle(similar_users_train_edges)\n",
    "\n",
    "    # similar_users_train_edges \n",
    "    train_edges_new = np.concatenate((train_edges, similar_users_train_edges), axis=0)\n",
    "    np.random.seed(0)\n",
    "    np.random.shuffle(train_edges_new)\n",
    "    print('train edges shape: ', train_edges.shape)\n",
    "    print('similar users train edges shape: ', similar_users_train_edges.shape)\n",
    "    print('new train edges shape: ', train_edges_new.shape)\n",
    "else:\n",
    "    train_edges_new = train_edges\n",
    "\n",
    "# proceed with creating users, items and ratings as usual\n",
    "user_list_train = train_edges_new[:, 0]\n",
    "user_list_test = test_edges[:, 0]\n",
    "item_list_train = train_edges_new[:, 1]\n",
    "item_list_test = test_edges[:, 1]\n",
    "rating_list_train = train_edges_new[:, 2].astype('float32')\n",
    "rating_list_test = test_edges[:, 2].astype('float32')\n",
    "\n",
    "# compute new n_users, n_items and n_samples\n",
    "n_users = max(user_list_train.max(), user_list_test.max()) + 1 \n",
    "n_items = max(item_list_train.max(), item_list_test.max()) + 1\n",
    "n_samples_train = len(rating_list_train)\n",
    "n_samples_test = len(rating_list_test)\n",
    "\n",
    "n_users, n_items, n_samples_train, n_samples_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Code for surrogate attack using new users**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Algorithm:  meta-similar(943)\n",
      "\n",
      "-> T (surrogate):  150\n",
      "-> T (target):  150\n",
      "-> Delta: 10000 (3.13%)\n",
      "-> Embedding size (surrogate):  64\n",
      "-> Embedding size (target):  64\n",
      "-> Device:  cuda:7\n",
      "\n",
      "-> Surrogate:  CFD\n",
      "-> Target:  CFD\n",
      "-> Surrogate optimizer:  sgd\n",
      "-> Target optimizer:  sgd\n",
      "-> Surrogate learning rate:  1000\n",
      "-> Target learning rate:  1000\n",
      "-> Surrogate dropout:  0.3\n",
      "-> Target dropout:  0.3\n",
      "-> Surrogate seed:  0\n",
      "-> Target seed:  25\n",
      "\n",
      "-> Retain graph:  True\n",
      "-> Create graph:  False\n",
      "-> Save results:  True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-> Perturbations: 100%|██████████| 10000/10000 [3:04:17<00:00,  1.11s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Execution time: 03h 04m 18s\n",
      "-> File name: CFD(64,sgd)-CFD(64,sgd)-meta-similar(943)-D=10000-T(150,150)-lr(1000,1000)-seed(0,25)-drop(0.3,0.3)\n"
     ]
    }
   ],
   "source": [
    "# model settings\n",
    "algorithm = 'meta-similar({})'.format(n_similar_users)\n",
    "# algorithm = 'meta'\n",
    "surrogate = 'CFD'\n",
    "target = 'CFD'\n",
    "opt_surrogate = 'sgd'\n",
    "opt_target = 'sgd'\n",
    "lr_surrogate = 1000\n",
    "lr_target = 1000\n",
    "seed_surrogate = 0\n",
    "seed_target = 25\n",
    "dropout_surrogate = 0.3\n",
    "dropout_target = 0.3\n",
    "\n",
    "# start execution\n",
    "start_time = time.time()\n",
    "\n",
    "# GPU settings (set use_gpu = -1 if you want to use CPU)\n",
    "use_gpu = 7\n",
    "if use_gpu == -1:\n",
    "    device = 'cpu'\n",
    "else:\n",
    "    device = torch.device('cuda:{}'.format(str(use_gpu)) if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# some hyperparameters\n",
    "T_surrogate = 150\n",
    "T_target = 150\n",
    "Delta = 10000 # 5% ~ 10K perturbations for movielens\n",
    "n_factors_surrogate = 64\n",
    "n_factors_target = 64\n",
    "save_results = True\n",
    "retain_graph = True \n",
    "create_graph = False\n",
    "\n",
    "if Delta < 100:\n",
    "    save_results = False\n",
    "\n",
    "# initialize list of perturbations\n",
    "perturbations = dict()\n",
    "perturbations['edges'] = []\n",
    "perturbations['metagrad'] = []\n",
    "\n",
    "perturbations['accuracy-before-surrogate'] = []\n",
    "perturbations['accuracy-after-surrogate'] = []\n",
    "perturbations['accuracy-unseen-surrogate'] = []\n",
    "\n",
    "perturbations['loss-before-surrogate'] = []\n",
    "perturbations['loss-after-surrogate'] = []\n",
    "perturbations['loss-unseen-surrogate'] = []\n",
    "\n",
    "perturbations['accuracy-before-target'] = []\n",
    "perturbations['accuracy-after-target'] = []\n",
    "perturbations['accuracy-unseen-target'] = []\n",
    "\n",
    "perturbations['loss-before-target'] = []\n",
    "perturbations['loss-after-target'] = []\n",
    "perturbations['loss-unseen-target'] = []\n",
    "\n",
    "perturbations['auc-before-surrogate'] = []\n",
    "perturbations['auc-after-surrogate'] = []\n",
    "perturbations['auc-unseen-surrogate'] = []\n",
    "\n",
    "perturbations['auc-before-target'] = []\n",
    "perturbations['auc-after-target'] = []\n",
    "perturbations['auc-unseen-target'] = []\n",
    "\n",
    "# print hyperparam configuration\n",
    "print('-> Algorithm: ', algorithm)\n",
    "print()\n",
    "print('-> T (surrogate): ', T_surrogate)\n",
    "print('-> T (target): ', T_target)\n",
    "print('-> Delta: {} ({}%)'.format(Delta, round(Delta * 100 / n_samples_train, 2)))\n",
    "print('-> Embedding size (surrogate): ', n_factors_surrogate)\n",
    "print('-> Embedding size (target): ', n_factors_target)\n",
    "print('-> Device: ', device)\n",
    "print()\n",
    "print('-> Surrogate: ', surrogate)\n",
    "print('-> Target: ', target)\n",
    "print('-> Surrogate optimizer: ', opt_surrogate)\n",
    "print('-> Target optimizer: ', opt_target)\n",
    "print('-> Surrogate learning rate: ', lr_surrogate)\n",
    "print('-> Target learning rate: ', lr_target)\n",
    "print('-> Surrogate dropout: ', dropout_surrogate)\n",
    "print('-> Target dropout: ', dropout_target)\n",
    "print('-> Surrogate seed: ', seed_surrogate)\n",
    "print('-> Target seed: ', seed_target)\n",
    "print()\n",
    "print('-> Retain graph: ', retain_graph)\n",
    "print('-> Create graph: ', create_graph)\n",
    "print('-> Save results: ', save_results)\n",
    "\n",
    "# load users, items and ratings as tensors\n",
    "users = torch.tensor(user_list_train, device = device)\n",
    "items = torch.tensor(item_list_train, device = device)\n",
    "ratings = torch.tensor(rating_list_train, device = device, requires_grad = True)\n",
    "\n",
    "# only those edges are allowed to be perturbed whose mask entry is False\n",
    "mask = torch.ones_like(ratings).bool()\n",
    "\n",
    "if 'similar' in algorithm:\n",
    "    mask[users >= 943] = False # perturbations only allowed for newly injected similar users\n",
    "    mask[ratings == 1] = True # positive edges cannot be perturbed even for new users\n",
    "else:\n",
    "    mask[ratings == 0] = False\n",
    "\n",
    "# perturbs keeps track of perturbed edges by marking them as False\n",
    "perturbs = torch.ones_like(ratings).bool()\n",
    "\n",
    "# generate tensors out of validation data\n",
    "users_test = torch.tensor(user_list_test, device = device)\n",
    "items_test = torch.tensor(item_list_test, device = device)\n",
    "ratings_test = torch.tensor(rating_list_test, device = device)\n",
    "\n",
    "# sample random negative edges to perturb for baseline\n",
    "if 'base' in algorithm:\n",
    "    edges = mask.int().detach().to('cpu').numpy()\n",
    "    neg_edges = np.where(edges == 0)[0]\n",
    "    np.random.seed(0)\n",
    "    edges_to_perturb = np.random.choice(neg_edges, size=Delta, replace = False) # sample Delta edges randomly and perturb one by one inside loop \n",
    "\n",
    "# for each perturbation do the following\n",
    "for delta in tqdm(range(Delta), desc='-> Perturbations'):\n",
    "\n",
    "    # define surrogate model and it's parameters\n",
    "    torch.manual_seed(seed_surrogate)\n",
    "    model = getattr(models, surrogate)(n_users, n_items, n_factors_surrogate, dropout_surrogate).to(device)\n",
    "\n",
    "    # define optimizer and loss function\n",
    "    if 'adam' in opt_surrogate:\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr = lr_surrogate)\n",
    "    else:\n",
    "        optimizer = torch.optim.SGD(model.parameters(), lr = lr_surrogate)\n",
    "    loss_fn = nn.BCELoss(reduction = 'mean')\n",
    "\n",
    "    # inner loop training process\n",
    "    model.train()\n",
    "    for i in range(T_surrogate):\n",
    "        y_hat = model(users, items).reshape(ratings.shape)\n",
    "        loss = loss_fn(y_hat, ratings)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward(retain_graph=retain_graph, create_graph=create_graph)\n",
    "        optimizer.step()\n",
    "    model.eval()\n",
    "\n",
    "    # compute and store accuracy of model after T training steps\n",
    "    with torch.no_grad():\n",
    "        # training accuracy and loss including perturbed edges\n",
    "        y_hat = model(users, items).reshape(ratings.shape)\n",
    "        perturbations['accuracy-before-surrogate'].append(get_accuracy(y_hat, ratings))\n",
    "        perturbations['loss-before-surrogate'].append(loss_fn(y_hat, ratings).item())\n",
    "\n",
    "        # compute training AUROC including perturbed edges\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings.detach().clone().to('cpu').numpy() \n",
    "        perturbations['auc-before-surrogate'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "        # training accuracy and loss excluding perturbed edges\n",
    "        y_hat_masked = torch.masked_select(y_hat, perturbs)\n",
    "        ratings_masked = torch.masked_select(ratings, perturbs)\n",
    "        perturbations['accuracy-after-surrogate'].append(get_accuracy(y_hat_masked, ratings_masked))\n",
    "        perturbations['loss-after-surrogate'].append(loss_fn(y_hat_masked, ratings_masked).item())\n",
    "\n",
    "        # compute training AUROC excluding perturbed edges\n",
    "        y_pred = y_hat_masked.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_masked.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-after-surrogate'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # compute and store accuracy of surrogate model on unseen data\n",
    "    with torch.no_grad():\n",
    "        y_hat = model(users_test, items_test).reshape(ratings_test.shape)\n",
    "        perturbations['accuracy-unseen-surrogate'].append(get_accuracy(y_hat, ratings_test))\n",
    "        perturbations['loss-unseen-surrogate'].append(loss_fn(y_hat, ratings_test).item())\n",
    "\n",
    "        # compute unseen AUROC\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_test.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-unseen-surrogate'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # compute meta gradient\n",
    "    if 'meta' in algorithm:\n",
    "        meta_grad = torch.autograd.grad(loss, ratings)[0]\n",
    "\n",
    "    # define evaluation model\n",
    "    torch.manual_seed(seed_target)\n",
    "    eval_model = getattr(models, target)(n_users, n_items, n_factors_target, dropout_target).to(device)\n",
    "\n",
    "    # define optimizer and loss function for evaluation\n",
    "    if 'adam' in opt_target:\n",
    "        optimizer_eval = torch.optim.Adam(eval_model.parameters(), lr = lr_target)\n",
    "    else:\n",
    "        optimizer_eval = torch.optim.SGD(eval_model.parameters(), lr = lr_target)\n",
    "    loss_fn_eval = nn.BCELoss(reduction = 'mean')\n",
    "\n",
    "    # detach ratings and perturbs for eval model\n",
    "    ratings_eval = ratings.detach().clone()\n",
    "    perturbs_eval = perturbs.detach().clone()\n",
    "\n",
    "    # inner train  evaluation model\n",
    "    eval_model.train()\n",
    "    for i in range(T_target):\n",
    "        y_hat = eval_model(users, items).reshape(ratings_eval.shape)\n",
    "        loss_eval = loss_fn_eval(y_hat, ratings_eval)\n",
    "        optimizer_eval.zero_grad()\n",
    "        loss_eval.backward(retain_graph=retain_graph, create_graph=create_graph)\n",
    "        optimizer_eval.step()\n",
    "    eval_model.eval()\n",
    "\n",
    "    # compute and store accuracy of eval model after T training steps\n",
    "    with torch.no_grad():\n",
    "        # training accuracy and loss including perturbed edges\n",
    "        y_hat = eval_model(users, items).reshape(ratings_eval.shape)\n",
    "        perturbations['accuracy-before-target'].append(get_accuracy(y_hat, ratings_eval))\n",
    "        perturbations['loss-before-target'].append(loss_fn_eval(y_hat, ratings_eval).item())\n",
    "\n",
    "        # compute training AUROC including perturbed edges\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_eval.detach().clone().to('cpu').numpy() \n",
    "        perturbations['auc-before-target'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "        # training accuracy and loss excluding perturbed edges\n",
    "        y_hat_masked = torch.masked_select(y_hat, perturbs_eval)\n",
    "        ratings_masked = torch.masked_select(ratings_eval, perturbs_eval)\n",
    "        perturbations['accuracy-after-target'].append(get_accuracy(y_hat_masked, ratings_masked))\n",
    "        perturbations['loss-after-target'].append(loss_fn_eval(y_hat_masked, ratings_masked).item())\n",
    "\n",
    "        # compute training AUROC excluding perturbed edges\n",
    "        y_pred = y_hat_masked.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_masked.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-after-target'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # compute and store accuracy of target model on unseen data\n",
    "    with torch.no_grad():\n",
    "        y_hat = eval_model(users_test, items_test).reshape(ratings_test.shape)\n",
    "        perturbations['accuracy-unseen-target'].append(get_accuracy(y_hat, ratings_test))\n",
    "        perturbations['loss-unseen-target'].append(loss_fn(y_hat, ratings_test).item())\n",
    "\n",
    "        # compute unseen AUROC\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_test.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-unseen-target'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # select best edge and perform perturbation\n",
    "    with torch.no_grad():\n",
    "        if 'meta' in algorithm:\n",
    "            meta_grad[mask == True] = 0\n",
    "            best_edge = meta_grad.argmax().item()\n",
    "            \n",
    "            if mask[best_edge] == False:\n",
    "                # if this condition is true, it means there is not even a single eligible unperturbed edge with a positive meta-gradient\n",
    "                ratings[best_edge] = 1\n",
    "                perturbs[best_edge] = False\n",
    "                mask[best_edge] = True\n",
    "                perturbations['edges'].append(best_edge)\n",
    "                perturbations['metagrad'].append(meta_grad[best_edge].item())\n",
    "            else:\n",
    "                perturbations['edges'].append(-1)\n",
    "                perturbations['metagrad'].append(0)\n",
    "\n",
    "        else:\n",
    "            best_edge = edges_to_perturb[delta]\n",
    "            ratings[best_edge] = 1 \n",
    "            perturbs[best_edge] = False\n",
    "            mask[best_edge] = True\n",
    "\n",
    "            perturbations['edges'].append(best_edge)\n",
    "            perturbations['metagrad'].append(-1)\n",
    "\n",
    "sleep(1)\n",
    "# compute execution time\n",
    "exec_time = int(time.time() - start_time)\n",
    "exec_time = time.strftime(\"%Hh %Mm %Ss\", time.gmtime(exec_time))\n",
    "print('-> Execution time: {}'.format(exec_time))\n",
    "\n",
    "# process results\n",
    "perturbations = pd.DataFrame(perturbations)\n",
    "filename = '{}({},{})-{}({},{})-{}-D={}-T({},{})-lr({},{})-seed({},{})-drop({},{})'.format(surrogate, n_factors_surrogate, opt_surrogate, target, n_factors_target, opt_target, algorithm, Delta, T_surrogate, T_target, lr_surrogate, lr_target, seed_surrogate, seed_target, dropout_surrogate, dropout_target)\n",
    "print('-> File name: {}'.format(filename))\n",
    "if save_results:\n",
    "    perturbations.to_csv('results/' + dataset + '/' + filename + '.csv')\n",
    "    if 'similar' in algorithm:\n",
    "        np.save('results/' + dataset + '/' + 'similar-users-' + filename + '.npy', similar_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  CFD(64,sgd)-CFD(64,sgd)-meta-similar(943)-D=10000-T(150,150)-lr(1000,1000)-seed(0,25)-drop(0.3,0.3)\n",
      "Max meta-gradient:  0.00010765346087282524\n"
     ]
    },
    {
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       "      <th>edges</th>\n",
       "      <th>metagrad</th>\n",
       "      <th>accuracy-before-surrogate</th>\n",
       "      <th>accuracy-after-surrogate</th>\n",
       "      <th>accuracy-unseen-surrogate</th>\n",
       "      <th>loss-before-surrogate</th>\n",
       "      <th>loss-after-surrogate</th>\n",
       "      <th>loss-unseen-surrogate</th>\n",
       "      <th>accuracy-before-target</th>\n",
       "      <th>accuracy-after-target</th>\n",
       "      <th>accuracy-unseen-target</th>\n",
       "      <th>loss-before-target</th>\n",
       "      <th>loss-after-target</th>\n",
       "      <th>loss-unseen-target</th>\n",
       "      <th>auc-before-surrogate</th>\n",
       "      <th>auc-after-surrogate</th>\n",
       "      <th>auc-unseen-surrogate</th>\n",
       "      <th>auc-before-target</th>\n",
       "      <th>auc-after-target</th>\n",
       "      <th>auc-unseen-target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98090</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.948621</td>\n",
       "      <td>0.948621</td>\n",
       "      <td>0.796736</td>\n",
       "      <td>0.157307</td>\n",
       "      <td>0.157307</td>\n",
       "      <td>0.507076</td>\n",
       "      <td>0.949448</td>\n",
       "      <td>0.949448</td>\n",
       "      <td>0.795622</td>\n",
       "      <td>0.155716</td>\n",
       "      <td>0.155716</td>\n",
       "      <td>0.500380</td>\n",
       "      <td>0.989741</td>\n",
       "      <td>0.989741</td>\n",
       "      <td>0.870335</td>\n",
       "      <td>0.990026</td>\n",
       "      <td>0.990026</td>\n",
       "      <td>0.871659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166148</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.948587</td>\n",
       "      <td>0.948587</td>\n",
       "      <td>0.796736</td>\n",
       "      <td>0.157310</td>\n",
       "      <td>0.157310</td>\n",
       "      <td>0.507059</td>\n",
       "      <td>0.949452</td>\n",
       "      <td>0.949451</td>\n",
       "      <td>0.795572</td>\n",
       "      <td>0.155717</td>\n",
       "      <td>0.155718</td>\n",
       "      <td>0.500383</td>\n",
       "      <td>0.989740</td>\n",
       "      <td>0.989740</td>\n",
       "      <td>0.870338</td>\n",
       "      <td>0.990026</td>\n",
       "      <td>0.990026</td>\n",
       "      <td>0.871656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143155</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.948493</td>\n",
       "      <td>0.948493</td>\n",
       "      <td>0.796067</td>\n",
       "      <td>0.157421</td>\n",
       "      <td>0.157421</td>\n",
       "      <td>0.507882</td>\n",
       "      <td>0.949433</td>\n",
       "      <td>0.949432</td>\n",
       "      <td>0.795523</td>\n",
       "      <td>0.155724</td>\n",
       "      <td>0.155725</td>\n",
       "      <td>0.500382</td>\n",
       "      <td>0.989728</td>\n",
       "      <td>0.989728</td>\n",
       "      <td>0.869973</td>\n",
       "      <td>0.990025</td>\n",
       "      <td>0.990025</td>\n",
       "      <td>0.871656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>293825</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.948499</td>\n",
       "      <td>0.948499</td>\n",
       "      <td>0.796117</td>\n",
       "      <td>0.157410</td>\n",
       "      <td>0.157411</td>\n",
       "      <td>0.507914</td>\n",
       "      <td>0.949411</td>\n",
       "      <td>0.949410</td>\n",
       "      <td>0.795572</td>\n",
       "      <td>0.155734</td>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.500405</td>\n",
       "      <td>0.989730</td>\n",
       "      <td>0.989730</td>\n",
       "      <td>0.869957</td>\n",
       "      <td>0.990022</td>\n",
       "      <td>0.990022</td>\n",
       "      <td>0.871645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>298621</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.948371</td>\n",
       "      <td>0.948370</td>\n",
       "      <td>0.796018</td>\n",
       "      <td>0.157422</td>\n",
       "      <td>0.157423</td>\n",
       "      <td>0.508609</td>\n",
       "      <td>0.949423</td>\n",
       "      <td>0.949423</td>\n",
       "      <td>0.795523</td>\n",
       "      <td>0.155736</td>\n",
       "      <td>0.155736</td>\n",
       "      <td>0.500393</td>\n",
       "      <td>0.989675</td>\n",
       "      <td>0.989675</td>\n",
       "      <td>0.869901</td>\n",
       "      <td>0.990022</td>\n",
       "      <td>0.990023</td>\n",
       "      <td>0.871647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>81497</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.925485</td>\n",
       "      <td>0.935520</td>\n",
       "      <td>0.791115</td>\n",
       "      <td>0.218264</td>\n",
       "      <td>0.200964</td>\n",
       "      <td>0.511736</td>\n",
       "      <td>0.935496</td>\n",
       "      <td>0.942705</td>\n",
       "      <td>0.788118</td>\n",
       "      <td>0.194774</td>\n",
       "      <td>0.183326</td>\n",
       "      <td>0.512540</td>\n",
       "      <td>0.977431</td>\n",
       "      <td>0.983915</td>\n",
       "      <td>0.864118</td>\n",
       "      <td>0.983069</td>\n",
       "      <td>0.986837</td>\n",
       "      <td>0.864328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>259523</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.925466</td>\n",
       "      <td>0.935507</td>\n",
       "      <td>0.790966</td>\n",
       "      <td>0.218273</td>\n",
       "      <td>0.200970</td>\n",
       "      <td>0.511763</td>\n",
       "      <td>0.935474</td>\n",
       "      <td>0.942682</td>\n",
       "      <td>0.788267</td>\n",
       "      <td>0.194775</td>\n",
       "      <td>0.183325</td>\n",
       "      <td>0.512541</td>\n",
       "      <td>0.977430</td>\n",
       "      <td>0.983915</td>\n",
       "      <td>0.864107</td>\n",
       "      <td>0.983070</td>\n",
       "      <td>0.986839</td>\n",
       "      <td>0.864326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>284128</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.925457</td>\n",
       "      <td>0.935510</td>\n",
       "      <td>0.791040</td>\n",
       "      <td>0.218287</td>\n",
       "      <td>0.200980</td>\n",
       "      <td>0.511754</td>\n",
       "      <td>0.935459</td>\n",
       "      <td>0.942663</td>\n",
       "      <td>0.788168</td>\n",
       "      <td>0.194773</td>\n",
       "      <td>0.183322</td>\n",
       "      <td>0.512538</td>\n",
       "      <td>0.977426</td>\n",
       "      <td>0.983914</td>\n",
       "      <td>0.864111</td>\n",
       "      <td>0.983070</td>\n",
       "      <td>0.986839</td>\n",
       "      <td>0.864328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>71079</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.925504</td>\n",
       "      <td>0.935548</td>\n",
       "      <td>0.791090</td>\n",
       "      <td>0.218301</td>\n",
       "      <td>0.200990</td>\n",
       "      <td>0.511773</td>\n",
       "      <td>0.935446</td>\n",
       "      <td>0.942643</td>\n",
       "      <td>0.788192</td>\n",
       "      <td>0.194785</td>\n",
       "      <td>0.183328</td>\n",
       "      <td>0.512546</td>\n",
       "      <td>0.977423</td>\n",
       "      <td>0.983913</td>\n",
       "      <td>0.864103</td>\n",
       "      <td>0.983067</td>\n",
       "      <td>0.986839</td>\n",
       "      <td>0.864327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>124036</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.925479</td>\n",
       "      <td>0.935529</td>\n",
       "      <td>0.791238</td>\n",
       "      <td>0.218306</td>\n",
       "      <td>0.200995</td>\n",
       "      <td>0.511796</td>\n",
       "      <td>0.935406</td>\n",
       "      <td>0.942611</td>\n",
       "      <td>0.788242</td>\n",
       "      <td>0.194788</td>\n",
       "      <td>0.183331</td>\n",
       "      <td>0.512504</td>\n",
       "      <td>0.977422</td>\n",
       "      <td>0.983912</td>\n",
       "      <td>0.864097</td>\n",
       "      <td>0.983067</td>\n",
       "      <td>0.986838</td>\n",
       "      <td>0.864345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       edges  metagrad  accuracy-before-surrogate  accuracy-after-surrogate  \\\n",
       "0      98090  0.000108                   0.948621                  0.948621   \n",
       "1     166148  0.000106                   0.948587                  0.948587   \n",
       "2     143155  0.000101                   0.948493                  0.948493   \n",
       "3     293825  0.000100                   0.948499                  0.948499   \n",
       "4     298621  0.000099                   0.948371                  0.948370   \n",
       "...      ...       ...                        ...                       ...   \n",
       "9995   81497  0.000018                   0.925485                  0.935520   \n",
       "9996  259523  0.000018                   0.925466                  0.935507   \n",
       "9997  284128  0.000018                   0.925457                  0.935510   \n",
       "9998   71079  0.000018                   0.925504                  0.935548   \n",
       "9999  124036  0.000018                   0.925479                  0.935529   \n",
       "\n",
       "      accuracy-unseen-surrogate  loss-before-surrogate  loss-after-surrogate  \\\n",
       "0                      0.796736               0.157307              0.157307   \n",
       "1                      0.796736               0.157310              0.157310   \n",
       "2                      0.796067               0.157421              0.157421   \n",
       "3                      0.796117               0.157410              0.157411   \n",
       "4                      0.796018               0.157422              0.157423   \n",
       "...                         ...                    ...                   ...   \n",
       "9995                   0.791115               0.218264              0.200964   \n",
       "9996                   0.790966               0.218273              0.200970   \n",
       "9997                   0.791040               0.218287              0.200980   \n",
       "9998                   0.791090               0.218301              0.200990   \n",
       "9999                   0.791238               0.218306              0.200995   \n",
       "\n",
       "      loss-unseen-surrogate  accuracy-before-target  accuracy-after-target  \\\n",
       "0                  0.507076                0.949448               0.949448   \n",
       "1                  0.507059                0.949452               0.949451   \n",
       "2                  0.507882                0.949433               0.949432   \n",
       "3                  0.507914                0.949411               0.949410   \n",
       "4                  0.508609                0.949423               0.949423   \n",
       "...                     ...                     ...                    ...   \n",
       "9995               0.511736                0.935496               0.942705   \n",
       "9996               0.511763                0.935474               0.942682   \n",
       "9997               0.511754                0.935459               0.942663   \n",
       "9998               0.511773                0.935446               0.942643   \n",
       "9999               0.511796                0.935406               0.942611   \n",
       "\n",
       "      accuracy-unseen-target  loss-before-target  loss-after-target  \\\n",
       "0                   0.795622            0.155716           0.155716   \n",
       "1                   0.795572            0.155717           0.155718   \n",
       "2                   0.795523            0.155724           0.155725   \n",
       "3                   0.795572            0.155734           0.155735   \n",
       "4                   0.795523            0.155736           0.155736   \n",
       "...                      ...                 ...                ...   \n",
       "9995                0.788118            0.194774           0.183326   \n",
       "9996                0.788267            0.194775           0.183325   \n",
       "9997                0.788168            0.194773           0.183322   \n",
       "9998                0.788192            0.194785           0.183328   \n",
       "9999                0.788242            0.194788           0.183331   \n",
       "\n",
       "      loss-unseen-target  auc-before-surrogate  auc-after-surrogate  \\\n",
       "0               0.500380              0.989741             0.989741   \n",
       "1               0.500383              0.989740             0.989740   \n",
       "2               0.500382              0.989728             0.989728   \n",
       "3               0.500405              0.989730             0.989730   \n",
       "4               0.500393              0.989675             0.989675   \n",
       "...                  ...                   ...                  ...   \n",
       "9995            0.512540              0.977431             0.983915   \n",
       "9996            0.512541              0.977430             0.983915   \n",
       "9997            0.512538              0.977426             0.983914   \n",
       "9998            0.512546              0.977423             0.983913   \n",
       "9999            0.512504              0.977422             0.983912   \n",
       "\n",
       "      auc-unseen-surrogate  auc-before-target  auc-after-target  \\\n",
       "0                 0.870335           0.990026          0.990026   \n",
       "1                 0.870338           0.990026          0.990026   \n",
       "2                 0.869973           0.990025          0.990025   \n",
       "3                 0.869957           0.990022          0.990022   \n",
       "4                 0.869901           0.990022          0.990023   \n",
       "...                    ...                ...               ...   \n",
       "9995              0.864118           0.983069          0.986837   \n",
       "9996              0.864107           0.983070          0.986839   \n",
       "9997              0.864111           0.983070          0.986839   \n",
       "9998              0.864103           0.983067          0.986839   \n",
       "9999              0.864097           0.983067          0.986838   \n",
       "\n",
       "      auc-unseen-target  \n",
       "0              0.871659  \n",
       "1              0.871656  \n",
       "2              0.871656  \n",
       "3              0.871645  \n",
       "4              0.871647  \n",
       "...                 ...  \n",
       "9995           0.864328  \n",
       "9996           0.864326  \n",
       "9997           0.864328  \n",
       "9998           0.864327  \n",
       "9999           0.864345  \n",
       "\n",
       "[10000 rows x 20 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('File name: ', filename)\n",
    "if 'meta' in algorithm:\n",
    "    print('Max meta-gradient: ', perturbations['metagrad'].max())\n",
    "perturbations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  CFD(64,sgd)-CFD(64,sgd)-meta-similar(943)-D=10000-T(150,150)-lr(1000,1000)-seed(0,25)-drop(0.3,0.3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Process perturbations data\n",
    "perturbations_reset = perturbations.reset_index()\n",
    "perturbations_reset.columns = ['# Perturbations', 'Perturbed edge', 'Meta gradient', 'Accuracy including perturbed edges (surrogate)', 'Accuracy excluding perturbed edges (surrogate)', 'Accuracy unseen (surrogate)', 'Loss including perturbed edges (surrogate)', 'Loss excluding perturbed edges (surrogate)', 'Loss unseen (surrogate)', 'Accuracy including perturbed edges (target)', 'Accuracy excluding perturbed edges (target)', 'Accuracy unseen (target)', 'Loss including perturbed edges (target)', 'Loss excluding perturbed edges (target)', 'Loss unseen (target)', 'AUROC including perturbed edges (surrogate)', 'AUROC excluding perturbed edges (surrogate)', 'AUROC unseen (surrogate)', 'AUROC including perturbed edges (target)', 'AUROC excluding perturbed edges (target)', 'AUROC unseen (target)']\n",
    "perturbations_reset\n",
    "\n",
    "# Figure specifications\n",
    "sns.set_theme()\n",
    "figsize = (7, 6)\n",
    "print('File name: ', filename)\n",
    "\n",
    "# Loss on training data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss excluding perturbed edges (surrogate)', color = 'blue').set_title('Comparison of loss on training data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss excluding perturbed edges (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(labels = ['Loss (surrogate)', 'Loss (target)'])\n",
    "if save_results: \n",
    "    fig.savefig('plots/' + dataset + '/losses-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# Loss on unseen data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss unseen (surrogate)', color = 'blue').set_title('Comparison of loss on unseen data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss unseen (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(labels = ['Loss unseen (surrogate)', 'Loss unseen (target)'])\n",
    "if save_results: \n",
    "    fig.savefig('plots/' + dataset + '/losses-unseen-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# Accuracy on training data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy excluding perturbed edges (surrogate)', color = 'blue').set_title('Comparison of accuracy on training data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy excluding perturbed edges (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(labels = ['Accuracy (surrogate)', 'Accuracy (target)'])\n",
    "if save_results:\n",
    "    fig.savefig('plots/' + dataset + '/accuracy-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# Accuracy on unseen data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy unseen (surrogate)', color = 'blue').set_title('Comparison of accuracy on unseen data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy unseen (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(labels = ['Accuracy unseen (surrogate)', 'Accuracy unseen (target)'])\n",
    "if save_results: \n",
    "    fig.savefig('plots/' + dataset + '/accuracy-unseen-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# AUROC on training data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC excluding perturbed edges (surrogate)', color = 'blue').set_title('Comparison of AUROC on training data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC excluding perturbed edges (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('AUROC')\n",
    "plt.legend(labels = ['AUROC (surrogate)', 'AUROC (target)'])\n",
    "if save_results:\n",
    "    fig.savefig('plots/' + dataset + '/AUROC-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# AUROC on unseen data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC unseen (surrogate)', color = 'blue').set_title('Comparison of AUROC on unseen data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC unseen (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('AUROC')\n",
    "plt.legend(labels = ['AUROC unseen (surrogate)', 'AUROC unseen (target)'])\n",
    "if save_results:\n",
    "    fig.savefig('plots/' + dataset + '/AUROC-unseen-' + filename + '.png')\n",
    "plt.show()"
   ]
  }
 ],
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