meta-surrogate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Library imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "#### **Load dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 1682, 159619, 40381)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = 'movielens'\n",
    "\n",
    "train_edges = np.load('data/' + dataset + '/train_edges.npy')\n",
    "test_edges = np.load('data/' + dataset + '/test_edges.npy')\n",
    "\n",
    "user_list_train = train_edges[:, 0]\n",
    "user_list_test = test_edges[:, 0]\n",
    "item_list_train = train_edges[:, 1]\n",
    "item_list_test = test_edges[:, 1]\n",
    "rating_list_train = train_edges[:, 2].astype('float32')\n",
    "rating_list_test = test_edges[:, 2].astype('float32')\n",
    "\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": [
    "#### **Check available models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CF', 'CFD', 'GMF', 'GMFB', 'GMFD', 'MLP', 'NeuMF']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[m[0] for m in inspect.getmembers(models, inspect.isclass) if m[1].__module__ == 'models']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Code for surrogate meta-attack (surrogate-CF-SGD, evaluation-CF-Adam, same-init)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Algorithm:  meta\n",
      "\n",
      "-> T (surrogate):  50\n",
      "-> T (target):  50\n",
      "-> Delta: 20000 (12.53%)\n",
      "-> Embedding size (surrogate):  16\n",
      "-> Embedding size (target):  16\n",
      "-> Device:  cuda:2\n",
      "\n",
      "-> Surrogate:  CFD\n",
      "-> Target:  CFD\n",
      "-> Surrogate optimizer:  adam\n",
      "-> Target optimizer:  adam\n",
      "-> Surrogate learning rate:  0.3\n",
      "-> Target learning rate:  0.3\n",
      "-> Surrogate seed:  0\n",
      "-> Target seed:  1\n",
      "\n",
      "-> Retain graph:  True\n",
      "-> Create graph:  False\n",
      "-> Save results:  True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-> Perturbations:   0%|          | 61/20000 [01:17<7:02:27,  1.27s/it]"
     ]
    }
   ],
   "source": [
    "# model settings\n",
    "algorithm = 'meta'\n",
    "surrogate = 'CFD'\n",
    "target = 'CFD'\n",
    "opt_surrogate = 'adam'\n",
    "opt_target = 'adam'\n",
    "lr_surrogate = 0.3\n",
    "lr_target = 0.3\n",
    "seed_surrogate = 0\n",
    "seed_target = 1\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 = 2\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 = 50\n",
    "T_target = 50\n",
    "Delta = 20000 # 5% ~ 10K perturbations for movielens\n",
    "n_factors_surrogate = 16\n",
    "n_factors_target = 16\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 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",
    "perturbs = torch.ones_like(ratings).bool()\n",
    "\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\n",
    "if 'base' in algorithm:\n",
    "    edges = ratings.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 = True) # 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",
    "            mask = ratings.detach().int()\n",
    "            meta_grad[mask == 1] = 0\n",
    "            best_edge = meta_grad.argmax().item()\n",
    "            ratings[best_edge] = 1\n",
    "            perturbs[best_edge] = False\n",
    "\n",
    "            perturbations['edges'].append(best_edge)\n",
    "            perturbations['metagrad'].append(meta_grad[best_edge].item())\n",
    "\n",
    "        else:\n",
    "            best_edge = edges_to_perturb[delta]\n",
    "            ratings[best_edge] = 1 \n",
    "            perturbs[best_edge] = False\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 = 'extra-{}({},{})-{}({},{})-{}-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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  extra-CFD(16,adam)-CFD(16,adam)-meta-D=10-T(50,50)-lr(0.3,0.3)-seed(0,1)-drop(0.3,0.3)\n",
      "Max meta-gradient:  0.0005208664224483073\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",
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       "      <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",
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       "      <th>0</th>\n",
       "      <td>87518</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.872691</td>\n",
       "      <td>0.872691</td>\n",
       "      <td>0.820039</td>\n",
       "      <td>0.295008</td>\n",
       "      <td>0.295008</td>\n",
       "      <td>0.421712</td>\n",
       "      <td>0.871337</td>\n",
       "      <td>0.871337</td>\n",
       "      <td>0.818776</td>\n",
       "      <td>0.295165</td>\n",
       "      <td>0.295165</td>\n",
       "      <td>0.419914</td>\n",
       "      <td>0.947351</td>\n",
       "      <td>0.947351</td>\n",
       "      <td>0.897315</td>\n",
       "      <td>0.947068</td>\n",
       "      <td>0.947068</td>\n",
       "      <td>0.896990</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134191</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.872565</td>\n",
       "      <td>0.872564</td>\n",
       "      <td>0.820262</td>\n",
       "      <td>0.295010</td>\n",
       "      <td>0.295011</td>\n",
       "      <td>0.421559</td>\n",
       "      <td>0.871406</td>\n",
       "      <td>0.871405</td>\n",
       "      <td>0.818950</td>\n",
       "      <td>0.295200</td>\n",
       "      <td>0.295201</td>\n",
       "      <td>0.419886</td>\n",
       "      <td>0.947350</td>\n",
       "      <td>0.947349</td>\n",
       "      <td>0.897367</td>\n",
       "      <td>0.947052</td>\n",
       "      <td>0.947052</td>\n",
       "      <td>0.897036</td>\n",
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       "      <th>2</th>\n",
       "      <td>117937</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.872609</td>\n",
       "      <td>0.872608</td>\n",
       "      <td>0.820559</td>\n",
       "      <td>0.295049</td>\n",
       "      <td>0.295052</td>\n",
       "      <td>0.419465</td>\n",
       "      <td>0.871450</td>\n",
       "      <td>0.871455</td>\n",
       "      <td>0.818974</td>\n",
       "      <td>0.295220</td>\n",
       "      <td>0.295213</td>\n",
       "      <td>0.419998</td>\n",
       "      <td>0.947334</td>\n",
       "      <td>0.947333</td>\n",
       "      <td>0.897376</td>\n",
       "      <td>0.947048</td>\n",
       "      <td>0.947051</td>\n",
       "      <td>0.896993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36569</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.872628</td>\n",
       "      <td>0.872626</td>\n",
       "      <td>0.820336</td>\n",
       "      <td>0.295063</td>\n",
       "      <td>0.295067</td>\n",
       "      <td>0.419256</td>\n",
       "      <td>0.871481</td>\n",
       "      <td>0.871485</td>\n",
       "      <td>0.818950</td>\n",
       "      <td>0.295217</td>\n",
       "      <td>0.295209</td>\n",
       "      <td>0.420075</td>\n",
       "      <td>0.947330</td>\n",
       "      <td>0.947328</td>\n",
       "      <td>0.897401</td>\n",
       "      <td>0.947050</td>\n",
       "      <td>0.947054</td>\n",
       "      <td>0.896955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150402</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.872659</td>\n",
       "      <td>0.872656</td>\n",
       "      <td>0.820386</td>\n",
       "      <td>0.295089</td>\n",
       "      <td>0.295092</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>0.871444</td>\n",
       "      <td>0.871453</td>\n",
       "      <td>0.818652</td>\n",
       "      <td>0.295233</td>\n",
       "      <td>0.295209</td>\n",
       "      <td>0.420064</td>\n",
       "      <td>0.947321</td>\n",
       "      <td>0.947320</td>\n",
       "      <td>0.897323</td>\n",
       "      <td>0.947044</td>\n",
       "      <td>0.947054</td>\n",
       "      <td>0.896949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34680</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.872678</td>\n",
       "      <td>0.872674</td>\n",
       "      <td>0.820188</td>\n",
       "      <td>0.295158</td>\n",
       "      <td>0.295164</td>\n",
       "      <td>0.419393</td>\n",
       "      <td>0.871362</td>\n",
       "      <td>0.871377</td>\n",
       "      <td>0.818826</td>\n",
       "      <td>0.295252</td>\n",
       "      <td>0.295220</td>\n",
       "      <td>0.420062</td>\n",
       "      <td>0.947296</td>\n",
       "      <td>0.947294</td>\n",
       "      <td>0.897308</td>\n",
       "      <td>0.947033</td>\n",
       "      <td>0.947047</td>\n",
       "      <td>0.896949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>143750</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>0.872640</td>\n",
       "      <td>0.872642</td>\n",
       "      <td>0.820039</td>\n",
       "      <td>0.295216</td>\n",
       "      <td>0.295207</td>\n",
       "      <td>0.419362</td>\n",
       "      <td>0.871237</td>\n",
       "      <td>0.871257</td>\n",
       "      <td>0.818875</td>\n",
       "      <td>0.295309</td>\n",
       "      <td>0.295273</td>\n",
       "      <td>0.419919</td>\n",
       "      <td>0.947273</td>\n",
       "      <td>0.947277</td>\n",
       "      <td>0.897288</td>\n",
       "      <td>0.947008</td>\n",
       "      <td>0.947024</td>\n",
       "      <td>0.896947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13471</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.872459</td>\n",
       "      <td>0.872466</td>\n",
       "      <td>0.820113</td>\n",
       "      <td>0.295250</td>\n",
       "      <td>0.295238</td>\n",
       "      <td>0.419157</td>\n",
       "      <td>0.871218</td>\n",
       "      <td>0.871238</td>\n",
       "      <td>0.818801</td>\n",
       "      <td>0.295301</td>\n",
       "      <td>0.295264</td>\n",
       "      <td>0.419683</td>\n",
       "      <td>0.947257</td>\n",
       "      <td>0.947263</td>\n",
       "      <td>0.897318</td>\n",
       "      <td>0.947017</td>\n",
       "      <td>0.947033</td>\n",
       "      <td>0.896986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39302</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.872572</td>\n",
       "      <td>0.872578</td>\n",
       "      <td>0.820188</td>\n",
       "      <td>0.295257</td>\n",
       "      <td>0.295245</td>\n",
       "      <td>0.419011</td>\n",
       "      <td>0.871218</td>\n",
       "      <td>0.871243</td>\n",
       "      <td>0.818875</td>\n",
       "      <td>0.295334</td>\n",
       "      <td>0.295291</td>\n",
       "      <td>0.419510</td>\n",
       "      <td>0.947255</td>\n",
       "      <td>0.947260</td>\n",
       "      <td>0.897364</td>\n",
       "      <td>0.947005</td>\n",
       "      <td>0.947024</td>\n",
       "      <td>0.897028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>114006</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.872590</td>\n",
       "      <td>0.872602</td>\n",
       "      <td>0.820386</td>\n",
       "      <td>0.295310</td>\n",
       "      <td>0.295271</td>\n",
       "      <td>0.418944</td>\n",
       "      <td>0.871225</td>\n",
       "      <td>0.871255</td>\n",
       "      <td>0.819024</td>\n",
       "      <td>0.295407</td>\n",
       "      <td>0.295336</td>\n",
       "      <td>0.419438</td>\n",
       "      <td>0.947243</td>\n",
       "      <td>0.947256</td>\n",
       "      <td>0.897380</td>\n",
       "      <td>0.946984</td>\n",
       "      <td>0.947011</td>\n",
       "      <td>0.897060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    edges  metagrad  accuracy-before-surrogate  accuracy-after-surrogate  \\\n",
       "0   87518  0.000521                   0.872691                  0.872691   \n",
       "1  134191  0.000471                   0.872565                  0.872564   \n",
       "2  117937  0.000457                   0.872609                  0.872608   \n",
       "3   36569  0.000453                   0.872628                  0.872626   \n",
       "4  150402  0.000420                   0.872659                  0.872656   \n",
       "5   34680  0.000394                   0.872678                  0.872674   \n",
       "6  143750  0.000373                   0.872640                  0.872642   \n",
       "7   13471  0.000367                   0.872459                  0.872466   \n",
       "8   39302  0.000358                   0.872572                  0.872578   \n",
       "9  114006  0.000352                   0.872590                  0.872602   \n",
       "\n",
       "   accuracy-unseen-surrogate  loss-before-surrogate  loss-after-surrogate  \\\n",
       "0                   0.820039               0.295008              0.295008   \n",
       "1                   0.820262               0.295010              0.295011   \n",
       "2                   0.820559               0.295049              0.295052   \n",
       "3                   0.820336               0.295063              0.295067   \n",
       "4                   0.820386               0.295089              0.295092   \n",
       "5                   0.820188               0.295158              0.295164   \n",
       "6                   0.820039               0.295216              0.295207   \n",
       "7                   0.820113               0.295250              0.295238   \n",
       "8                   0.820188               0.295257              0.295245   \n",
       "9                   0.820386               0.295310              0.295271   \n",
       "\n",
       "   loss-unseen-surrogate  accuracy-before-target  accuracy-after-target  \\\n",
       "0               0.421712                0.871337               0.871337   \n",
       "1               0.421559                0.871406               0.871405   \n",
       "2               0.419465                0.871450               0.871455   \n",
       "3               0.419256                0.871481               0.871485   \n",
       "4               0.419355                0.871444               0.871453   \n",
       "5               0.419393                0.871362               0.871377   \n",
       "6               0.419362                0.871237               0.871257   \n",
       "7               0.419157                0.871218               0.871238   \n",
       "8               0.419011                0.871218               0.871243   \n",
       "9               0.418944                0.871225               0.871255   \n",
       "\n",
       "   accuracy-unseen-target  loss-before-target  loss-after-target  \\\n",
       "0                0.818776            0.295165           0.295165   \n",
       "1                0.818950            0.295200           0.295201   \n",
       "2                0.818974            0.295220           0.295213   \n",
       "3                0.818950            0.295217           0.295209   \n",
       "4                0.818652            0.295233           0.295209   \n",
       "5                0.818826            0.295252           0.295220   \n",
       "6                0.818875            0.295309           0.295273   \n",
       "7                0.818801            0.295301           0.295264   \n",
       "8                0.818875            0.295334           0.295291   \n",
       "9                0.819024            0.295407           0.295336   \n",
       "\n",
       "   loss-unseen-target  auc-before-surrogate  auc-after-surrogate  \\\n",
       "0            0.419914              0.947351             0.947351   \n",
       "1            0.419886              0.947350             0.947349   \n",
       "2            0.419998              0.947334             0.947333   \n",
       "3            0.420075              0.947330             0.947328   \n",
       "4            0.420064              0.947321             0.947320   \n",
       "5            0.420062              0.947296             0.947294   \n",
       "6            0.419919              0.947273             0.947277   \n",
       "7            0.419683              0.947257             0.947263   \n",
       "8            0.419510              0.947255             0.947260   \n",
       "9            0.419438              0.947243             0.947256   \n",
       "\n",
       "   auc-unseen-surrogate  auc-before-target  auc-after-target  \\\n",
       "0              0.897315           0.947068          0.947068   \n",
       "1              0.897367           0.947052          0.947052   \n",
       "2              0.897376           0.947048          0.947051   \n",
       "3              0.897401           0.947050          0.947054   \n",
       "4              0.897323           0.947044          0.947054   \n",
       "5              0.897308           0.947033          0.947047   \n",
       "6              0.897288           0.947008          0.947024   \n",
       "7              0.897318           0.947017          0.947033   \n",
       "8              0.897364           0.947005          0.947024   \n",
       "9              0.897380           0.946984          0.947011   \n",
       "\n",
       "   auc-unseen-target  \n",
       "0           0.896990  \n",
       "1           0.897036  \n",
       "2           0.896993  \n",
       "3           0.896955  \n",
       "4           0.896949  \n",
       "5           0.896949  \n",
       "6           0.896947  \n",
       "7           0.896986  \n",
       "8           0.897028  \n",
       "9           0.897060  "
      ]
     },
     "execution_count": 18,
     "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": "markdown",
   "metadata": {},
   "source": [
    "#### **Plots**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  extra-CFD(8,adam)-CFD(8,adam)-meta-D=20000-T(25,25)-lr(0.3,0.3)-seed(0,1)-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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