base-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": 4,
   "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": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Algorithm:  base-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:6\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 [3:22:28<00:00,  1.21s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Execution time: 03h 22m 31s\n",
      "-> File name: CFD(64,sgd)-CFD(64,sgd)-base-similar(943)-D=10000-T(150,150)-lr(1000,1000)-seed(0,25)-drop(0.3,0.3)\n"
     ]
    }
   ],
   "source": [
    "# model settings\n",
    "algorithm = 'base-similar({})'.format(n_similar_users)\n",
    "# algorithm = 'base'\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 = 6\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  CFD(64,sgd)-CFD(64,sgd)-base-similar(943)-D=10000-T(150,150)-lr(1000,1000)-seed(0,25)-drop(0.3,0.3)\n"
     ]
    },
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       "      <th></th>\n",
       "      <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>196871</td>\n",
       "      <td>-1</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>293826</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.948643</td>\n",
       "      <td>0.948643</td>\n",
       "      <td>0.796934</td>\n",
       "      <td>0.157315</td>\n",
       "      <td>0.157315</td>\n",
       "      <td>0.507070</td>\n",
       "      <td>0.949430</td>\n",
       "      <td>0.949429</td>\n",
       "      <td>0.795597</td>\n",
       "      <td>0.155708</td>\n",
       "      <td>0.155708</td>\n",
       "      <td>0.500401</td>\n",
       "      <td>0.989739</td>\n",
       "      <td>0.989739</td>\n",
       "      <td>0.870332</td>\n",
       "      <td>0.990027</td>\n",
       "      <td>0.990027</td>\n",
       "      <td>0.871661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>253450</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.948659</td>\n",
       "      <td>0.948659</td>\n",
       "      <td>0.796736</td>\n",
       "      <td>0.157318</td>\n",
       "      <td>0.157317</td>\n",
       "      <td>0.507085</td>\n",
       "      <td>0.949442</td>\n",
       "      <td>0.949442</td>\n",
       "      <td>0.795671</td>\n",
       "      <td>0.155711</td>\n",
       "      <td>0.155711</td>\n",
       "      <td>0.500407</td>\n",
       "      <td>0.989738</td>\n",
       "      <td>0.989738</td>\n",
       "      <td>0.870327</td>\n",
       "      <td>0.990027</td>\n",
       "      <td>0.990027</td>\n",
       "      <td>0.871659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>206065</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.948675</td>\n",
       "      <td>0.948674</td>\n",
       "      <td>0.796736</td>\n",
       "      <td>0.157306</td>\n",
       "      <td>0.157305</td>\n",
       "      <td>0.507058</td>\n",
       "      <td>0.949467</td>\n",
       "      <td>0.949467</td>\n",
       "      <td>0.795523</td>\n",
       "      <td>0.155720</td>\n",
       "      <td>0.155718</td>\n",
       "      <td>0.500422</td>\n",
       "      <td>0.989740</td>\n",
       "      <td>0.989741</td>\n",
       "      <td>0.870336</td>\n",
       "      <td>0.990026</td>\n",
       "      <td>0.990026</td>\n",
       "      <td>0.871650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3796</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.948656</td>\n",
       "      <td>0.948658</td>\n",
       "      <td>0.796761</td>\n",
       "      <td>0.157315</td>\n",
       "      <td>0.157310</td>\n",
       "      <td>0.507065</td>\n",
       "      <td>0.949473</td>\n",
       "      <td>0.949476</td>\n",
       "      <td>0.795547</td>\n",
       "      <td>0.155725</td>\n",
       "      <td>0.155721</td>\n",
       "      <td>0.500410</td>\n",
       "      <td>0.989738</td>\n",
       "      <td>0.989740</td>\n",
       "      <td>0.870334</td>\n",
       "      <td>0.990024</td>\n",
       "      <td>0.990025</td>\n",
       "      <td>0.871653</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>54399</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.929949</td>\n",
       "      <td>0.941160</td>\n",
       "      <td>0.792477</td>\n",
       "      <td>0.199136</td>\n",
       "      <td>0.181931</td>\n",
       "      <td>0.510709</td>\n",
       "      <td>0.930926</td>\n",
       "      <td>0.942317</td>\n",
       "      <td>0.790174</td>\n",
       "      <td>0.197344</td>\n",
       "      <td>0.179885</td>\n",
       "      <td>0.505159</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.986756</td>\n",
       "      <td>0.865354</td>\n",
       "      <td>0.981665</td>\n",
       "      <td>0.987107</td>\n",
       "      <td>0.867002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>232159</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.929971</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.792650</td>\n",
       "      <td>0.199139</td>\n",
       "      <td>0.181934</td>\n",
       "      <td>0.510687</td>\n",
       "      <td>0.930901</td>\n",
       "      <td>0.942304</td>\n",
       "      <td>0.790297</td>\n",
       "      <td>0.197344</td>\n",
       "      <td>0.179884</td>\n",
       "      <td>0.505154</td>\n",
       "      <td>0.981291</td>\n",
       "      <td>0.986755</td>\n",
       "      <td>0.865359</td>\n",
       "      <td>0.981666</td>\n",
       "      <td>0.987107</td>\n",
       "      <td>0.867005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>76245</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.929971</td>\n",
       "      <td>0.941179</td>\n",
       "      <td>0.792452</td>\n",
       "      <td>0.199141</td>\n",
       "      <td>0.181936</td>\n",
       "      <td>0.510645</td>\n",
       "      <td>0.930889</td>\n",
       "      <td>0.942294</td>\n",
       "      <td>0.790223</td>\n",
       "      <td>0.197351</td>\n",
       "      <td>0.179891</td>\n",
       "      <td>0.505180</td>\n",
       "      <td>0.981291</td>\n",
       "      <td>0.986756</td>\n",
       "      <td>0.865371</td>\n",
       "      <td>0.981664</td>\n",
       "      <td>0.987105</td>\n",
       "      <td>0.866997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>101906</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.929977</td>\n",
       "      <td>0.941201</td>\n",
       "      <td>0.792601</td>\n",
       "      <td>0.199143</td>\n",
       "      <td>0.181937</td>\n",
       "      <td>0.510673</td>\n",
       "      <td>0.930898</td>\n",
       "      <td>0.942313</td>\n",
       "      <td>0.790198</td>\n",
       "      <td>0.197354</td>\n",
       "      <td>0.179893</td>\n",
       "      <td>0.505188</td>\n",
       "      <td>0.981291</td>\n",
       "      <td>0.986756</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>0.981663</td>\n",
       "      <td>0.987105</td>\n",
       "      <td>0.866992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>309785</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.929955</td>\n",
       "      <td>0.941185</td>\n",
       "      <td>0.792526</td>\n",
       "      <td>0.199138</td>\n",
       "      <td>0.181934</td>\n",
       "      <td>0.510654</td>\n",
       "      <td>0.930867</td>\n",
       "      <td>0.942287</td>\n",
       "      <td>0.790124</td>\n",
       "      <td>0.197354</td>\n",
       "      <td>0.179894</td>\n",
       "      <td>0.505213</td>\n",
       "      <td>0.981293</td>\n",
       "      <td>0.986757</td>\n",
       "      <td>0.865368</td>\n",
       "      <td>0.981664</td>\n",
       "      <td>0.987106</td>\n",
       "      <td>0.866979</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     196871        -1                   0.948621                  0.948621   \n",
       "1     293826        -1                   0.948643                  0.948643   \n",
       "2     253450        -1                   0.948659                  0.948659   \n",
       "3     206065        -1                   0.948675                  0.948674   \n",
       "4       3796        -1                   0.948656                  0.948658   \n",
       "...      ...       ...                        ...                       ...   \n",
       "9995   54399        -1                   0.929949                  0.941160   \n",
       "9996  232159        -1                   0.929971                  0.941176   \n",
       "9997   76245        -1                   0.929971                  0.941179   \n",
       "9998  101906        -1                   0.929977                  0.941201   \n",
       "9999  309785        -1                   0.929955                  0.941185   \n",
       "\n",
       "      accuracy-unseen-surrogate  loss-before-surrogate  loss-after-surrogate  \\\n",
       "0                      0.796736               0.157307              0.157307   \n",
       "1                      0.796934               0.157315              0.157315   \n",
       "2                      0.796736               0.157318              0.157317   \n",
       "3                      0.796736               0.157306              0.157305   \n",
       "4                      0.796761               0.157315              0.157310   \n",
       "...                         ...                    ...                   ...   \n",
       "9995                   0.792477               0.199136              0.181931   \n",
       "9996                   0.792650               0.199139              0.181934   \n",
       "9997                   0.792452               0.199141              0.181936   \n",
       "9998                   0.792601               0.199143              0.181937   \n",
       "9999                   0.792526               0.199138              0.181934   \n",
       "\n",
       "      loss-unseen-surrogate  accuracy-before-target  accuracy-after-target  \\\n",
       "0                  0.507076                0.949448               0.949448   \n",
       "1                  0.507070                0.949430               0.949429   \n",
       "2                  0.507085                0.949442               0.949442   \n",
       "3                  0.507058                0.949467               0.949467   \n",
       "4                  0.507065                0.949473               0.949476   \n",
       "...                     ...                     ...                    ...   \n",
       "9995               0.510709                0.930926               0.942317   \n",
       "9996               0.510687                0.930901               0.942304   \n",
       "9997               0.510645                0.930889               0.942294   \n",
       "9998               0.510673                0.930898               0.942313   \n",
       "9999               0.510654                0.930867               0.942287   \n",
       "\n",
       "      accuracy-unseen-target  loss-before-target  loss-after-target  \\\n",
       "0                   0.795622            0.155716           0.155716   \n",
       "1                   0.795597            0.155708           0.155708   \n",
       "2                   0.795671            0.155711           0.155711   \n",
       "3                   0.795523            0.155720           0.155718   \n",
       "4                   0.795547            0.155725           0.155721   \n",
       "...                      ...                 ...                ...   \n",
       "9995                0.790174            0.197344           0.179885   \n",
       "9996                0.790297            0.197344           0.179884   \n",
       "9997                0.790223            0.197351           0.179891   \n",
       "9998                0.790198            0.197354           0.179893   \n",
       "9999                0.790124            0.197354           0.179894   \n",
       "\n",
       "      loss-unseen-target  auc-before-surrogate  auc-after-surrogate  \\\n",
       "0               0.500380              0.989741             0.989741   \n",
       "1               0.500401              0.989739             0.989739   \n",
       "2               0.500407              0.989738             0.989738   \n",
       "3               0.500422              0.989740             0.989741   \n",
       "4               0.500410              0.989738             0.989740   \n",
       "...                  ...                   ...                  ...   \n",
       "9995            0.505159              0.981292             0.986756   \n",
       "9996            0.505154              0.981291             0.986755   \n",
       "9997            0.505180              0.981291             0.986756   \n",
       "9998            0.505188              0.981291             0.986756   \n",
       "9999            0.505213              0.981293             0.986757   \n",
       "\n",
       "      auc-unseen-surrogate  auc-before-target  auc-after-target  \\\n",
       "0                 0.870335           0.990026          0.990026   \n",
       "1                 0.870332           0.990027          0.990027   \n",
       "2                 0.870327           0.990027          0.990027   \n",
       "3                 0.870336           0.990026          0.990026   \n",
       "4                 0.870334           0.990024          0.990025   \n",
       "...                    ...                ...               ...   \n",
       "9995              0.865354           0.981665          0.987107   \n",
       "9996              0.865359           0.981666          0.987107   \n",
       "9997              0.865371           0.981664          0.987105   \n",
       "9998              0.865360           0.981663          0.987105   \n",
       "9999              0.865368           0.981664          0.987106   \n",
       "\n",
       "      auc-unseen-target  \n",
       "0              0.871659  \n",
       "1              0.871661  \n",
       "2              0.871659  \n",
       "3              0.871650  \n",
       "4              0.871653  \n",
       "...                 ...  \n",
       "9995           0.867002  \n",
       "9996           0.867005  \n",
       "9997           0.866997  \n",
       "9998           0.866992  \n",
       "9999           0.866979  \n",
       "\n",
       "[10000 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  CFD(64,sgd)-CFD(64,sgd)-base-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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