base-surrogate-movielens.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": [
    "#### **Load dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 1682, 159619, 40381)"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CF', 'CFD', 'GMF', 'GMFB', 'GMFD', 'MLP', 'NeuMF']"
      ]
     },
     "execution_count": 3,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Algorithm:  base\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: 100%|██████████| 20000/20000 [6:07:09<00:00,  1.10s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Execution time: 06h 07m 10s\n",
      "-> File name: extra-CFD(16,adam)-CFD(16,adam)-base-D=20000-T(50,50)-lr(0.3,0.3)-seed(0,1)-drop(0.3,0.3)\n"
     ]
    }
   ],
   "source": [
    "# model settings\n",
    "algorithm = 'base'\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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  extra-CFD(16,adam)-CFD(16,adam)-base-D=20000-T(50,50)-lr(0.3,0.3)-seed(0,1)-drop(0.3,0.3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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>136784</td>\n",
       "      <td>-1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>87412</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.872640</td>\n",
       "      <td>0.872646</td>\n",
       "      <td>0.820138</td>\n",
       "      <td>0.295011</td>\n",
       "      <td>0.295008</td>\n",
       "      <td>0.421716</td>\n",
       "      <td>0.871350</td>\n",
       "      <td>0.871355</td>\n",
       "      <td>0.818826</td>\n",
       "      <td>0.295161</td>\n",
       "      <td>0.295158</td>\n",
       "      <td>0.419942</td>\n",
       "      <td>0.947349</td>\n",
       "      <td>0.947351</td>\n",
       "      <td>0.897312</td>\n",
       "      <td>0.947072</td>\n",
       "      <td>0.947073</td>\n",
       "      <td>0.896986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85508</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.872709</td>\n",
       "      <td>0.872720</td>\n",
       "      <td>0.819990</td>\n",
       "      <td>0.295022</td>\n",
       "      <td>0.295009</td>\n",
       "      <td>0.421733</td>\n",
       "      <td>0.871400</td>\n",
       "      <td>0.871411</td>\n",
       "      <td>0.818850</td>\n",
       "      <td>0.295155</td>\n",
       "      <td>0.295140</td>\n",
       "      <td>0.419923</td>\n",
       "      <td>0.947348</td>\n",
       "      <td>0.947354</td>\n",
       "      <td>0.897304</td>\n",
       "      <td>0.947076</td>\n",
       "      <td>0.947083</td>\n",
       "      <td>0.896996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92076</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.872672</td>\n",
       "      <td>0.872688</td>\n",
       "      <td>0.819940</td>\n",
       "      <td>0.295034</td>\n",
       "      <td>0.295013</td>\n",
       "      <td>0.421727</td>\n",
       "      <td>0.871425</td>\n",
       "      <td>0.871441</td>\n",
       "      <td>0.818727</td>\n",
       "      <td>0.295154</td>\n",
       "      <td>0.295133</td>\n",
       "      <td>0.419926</td>\n",
       "      <td>0.947344</td>\n",
       "      <td>0.947354</td>\n",
       "      <td>0.897304</td>\n",
       "      <td>0.947077</td>\n",
       "      <td>0.947087</td>\n",
       "      <td>0.896993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42530</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.872628</td>\n",
       "      <td>0.872644</td>\n",
       "      <td>0.819965</td>\n",
       "      <td>0.295052</td>\n",
       "      <td>0.295030</td>\n",
       "      <td>0.421726</td>\n",
       "      <td>0.871456</td>\n",
       "      <td>0.871472</td>\n",
       "      <td>0.818652</td>\n",
       "      <td>0.295147</td>\n",
       "      <td>0.295126</td>\n",
       "      <td>0.419938</td>\n",
       "      <td>0.947335</td>\n",
       "      <td>0.947344</td>\n",
       "      <td>0.897303</td>\n",
       "      <td>0.947080</td>\n",
       "      <td>0.947089</td>\n",
       "      <td>0.896985</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>19995</th>\n",
       "      <td>145231</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.829099</td>\n",
       "      <td>0.863460</td>\n",
       "      <td>0.768802</td>\n",
       "      <td>0.384725</td>\n",
       "      <td>0.345890</td>\n",
       "      <td>0.519089</td>\n",
       "      <td>0.827502</td>\n",
       "      <td>0.862966</td>\n",
       "      <td>0.769817</td>\n",
       "      <td>0.385148</td>\n",
       "      <td>0.345447</td>\n",
       "      <td>0.514448</td>\n",
       "      <td>0.906635</td>\n",
       "      <td>0.943540</td>\n",
       "      <td>0.863775</td>\n",
       "      <td>0.906237</td>\n",
       "      <td>0.943765</td>\n",
       "      <td>0.864213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>61591</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.829024</td>\n",
       "      <td>0.863311</td>\n",
       "      <td>0.769025</td>\n",
       "      <td>0.384706</td>\n",
       "      <td>0.345879</td>\n",
       "      <td>0.519124</td>\n",
       "      <td>0.827376</td>\n",
       "      <td>0.862867</td>\n",
       "      <td>0.769867</td>\n",
       "      <td>0.385135</td>\n",
       "      <td>0.345436</td>\n",
       "      <td>0.514471</td>\n",
       "      <td>0.906642</td>\n",
       "      <td>0.943537</td>\n",
       "      <td>0.863752</td>\n",
       "      <td>0.906244</td>\n",
       "      <td>0.943770</td>\n",
       "      <td>0.864216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>13732</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.828999</td>\n",
       "      <td>0.863310</td>\n",
       "      <td>0.768678</td>\n",
       "      <td>0.384721</td>\n",
       "      <td>0.345895</td>\n",
       "      <td>0.519111</td>\n",
       "      <td>0.827376</td>\n",
       "      <td>0.862873</td>\n",
       "      <td>0.769718</td>\n",
       "      <td>0.385143</td>\n",
       "      <td>0.345440</td>\n",
       "      <td>0.514509</td>\n",
       "      <td>0.906635</td>\n",
       "      <td>0.943535</td>\n",
       "      <td>0.863771</td>\n",
       "      <td>0.906240</td>\n",
       "      <td>0.943770</td>\n",
       "      <td>0.864199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>76125</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.828999</td>\n",
       "      <td>0.863310</td>\n",
       "      <td>0.768678</td>\n",
       "      <td>0.384721</td>\n",
       "      <td>0.345895</td>\n",
       "      <td>0.519111</td>\n",
       "      <td>0.827376</td>\n",
       "      <td>0.862873</td>\n",
       "      <td>0.769718</td>\n",
       "      <td>0.385143</td>\n",
       "      <td>0.345440</td>\n",
       "      <td>0.514509</td>\n",
       "      <td>0.906635</td>\n",
       "      <td>0.943535</td>\n",
       "      <td>0.863771</td>\n",
       "      <td>0.906240</td>\n",
       "      <td>0.943770</td>\n",
       "      <td>0.864199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>52858</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.829118</td>\n",
       "      <td>0.863499</td>\n",
       "      <td>0.769000</td>\n",
       "      <td>0.384719</td>\n",
       "      <td>0.345892</td>\n",
       "      <td>0.519150</td>\n",
       "      <td>0.827282</td>\n",
       "      <td>0.862893</td>\n",
       "      <td>0.769817</td>\n",
       "      <td>0.385156</td>\n",
       "      <td>0.345445</td>\n",
       "      <td>0.514500</td>\n",
       "      <td>0.906635</td>\n",
       "      <td>0.943541</td>\n",
       "      <td>0.863745</td>\n",
       "      <td>0.906229</td>\n",
       "      <td>0.943769</td>\n",
       "      <td>0.864204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        edges  metagrad  accuracy-before-surrogate  accuracy-after-surrogate  \\\n",
       "0      136784        -1                   0.872691                  0.872691   \n",
       "1       87412        -1                   0.872640                  0.872646   \n",
       "2       85508        -1                   0.872709                  0.872720   \n",
       "3       92076        -1                   0.872672                  0.872688   \n",
       "4       42530        -1                   0.872628                  0.872644   \n",
       "...       ...       ...                        ...                       ...   \n",
       "19995  145231        -1                   0.829099                  0.863460   \n",
       "19996   61591        -1                   0.829024                  0.863311   \n",
       "19997   13732        -1                   0.828999                  0.863310   \n",
       "19998   76125        -1                   0.828999                  0.863310   \n",
       "19999   52858        -1                   0.829118                  0.863499   \n",
       "\n",
       "       accuracy-unseen-surrogate  loss-before-surrogate  loss-after-surrogate  \\\n",
       "0                       0.820039               0.295008              0.295008   \n",
       "1                       0.820138               0.295011              0.295008   \n",
       "2                       0.819990               0.295022              0.295009   \n",
       "3                       0.819940               0.295034              0.295013   \n",
       "4                       0.819965               0.295052              0.295030   \n",
       "...                          ...                    ...                   ...   \n",
       "19995                   0.768802               0.384725              0.345890   \n",
       "19996                   0.769025               0.384706              0.345879   \n",
       "19997                   0.768678               0.384721              0.345895   \n",
       "19998                   0.768678               0.384721              0.345895   \n",
       "19999                   0.769000               0.384719              0.345892   \n",
       "\n",
       "       loss-unseen-surrogate  accuracy-before-target  accuracy-after-target  \\\n",
       "0                   0.421712                0.871337               0.871337   \n",
       "1                   0.421716                0.871350               0.871355   \n",
       "2                   0.421733                0.871400               0.871411   \n",
       "3                   0.421727                0.871425               0.871441   \n",
       "4                   0.421726                0.871456               0.871472   \n",
       "...                      ...                     ...                    ...   \n",
       "19995               0.519089                0.827502               0.862966   \n",
       "19996               0.519124                0.827376               0.862867   \n",
       "19997               0.519111                0.827376               0.862873   \n",
       "19998               0.519111                0.827376               0.862873   \n",
       "19999               0.519150                0.827282               0.862893   \n",
       "\n",
       "       accuracy-unseen-target  loss-before-target  loss-after-target  \\\n",
       "0                    0.818776            0.295165           0.295165   \n",
       "1                    0.818826            0.295161           0.295158   \n",
       "2                    0.818850            0.295155           0.295140   \n",
       "3                    0.818727            0.295154           0.295133   \n",
       "4                    0.818652            0.295147           0.295126   \n",
       "...                       ...                 ...                ...   \n",
       "19995                0.769817            0.385148           0.345447   \n",
       "19996                0.769867            0.385135           0.345436   \n",
       "19997                0.769718            0.385143           0.345440   \n",
       "19998                0.769718            0.385143           0.345440   \n",
       "19999                0.769817            0.385156           0.345445   \n",
       "\n",
       "       loss-unseen-target  auc-before-surrogate  auc-after-surrogate  \\\n",
       "0                0.419914              0.947351             0.947351   \n",
       "1                0.419942              0.947349             0.947351   \n",
       "2                0.419923              0.947348             0.947354   \n",
       "3                0.419926              0.947344             0.947354   \n",
       "4                0.419938              0.947335             0.947344   \n",
       "...                   ...                   ...                  ...   \n",
       "19995            0.514448              0.906635             0.943540   \n",
       "19996            0.514471              0.906642             0.943537   \n",
       "19997            0.514509              0.906635             0.943535   \n",
       "19998            0.514509              0.906635             0.943535   \n",
       "19999            0.514500              0.906635             0.943541   \n",
       "\n",
       "       auc-unseen-surrogate  auc-before-target  auc-after-target  \\\n",
       "0                  0.897315           0.947068          0.947068   \n",
       "1                  0.897312           0.947072          0.947073   \n",
       "2                  0.897304           0.947076          0.947083   \n",
       "3                  0.897304           0.947077          0.947087   \n",
       "4                  0.897303           0.947080          0.947089   \n",
       "...                     ...                ...               ...   \n",
       "19995              0.863775           0.906237          0.943765   \n",
       "19996              0.863752           0.906244          0.943770   \n",
       "19997              0.863771           0.906240          0.943770   \n",
       "19998              0.863771           0.906240          0.943770   \n",
       "19999              0.863745           0.906229          0.943769   \n",
       "\n",
       "       auc-unseen-target  \n",
       "0               0.896990  \n",
       "1               0.896986  \n",
       "2               0.896996  \n",
       "3               0.896993  \n",
       "4               0.896985  \n",
       "...                  ...  \n",
       "19995           0.864213  \n",
       "19996           0.864216  \n",
       "19997           0.864199  \n",
       "19998           0.864199  \n",
       "19999           0.864204  \n",
       "\n",
       "[20000 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  extra-CFD(16,adam)-CFD(16,adam)-base-D=20000-T(50,50)-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()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "6cc16854982e999868884e9acf9c225c9c7fd7a24892daed95faad6ec1058b96"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}