conditonal_mia_aug_v_m_random.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import logging\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from utils.load_dataset import load_dataset\n",
    "\n",
    "with open(\"./config.json\") as f:\n",
    "    config = json.load(f)\n",
    "\n",
    "logger = logging.getLogger(\"\")\n",
    "dataset_name = 'cifar100'\n",
    "arch = 'resnet18'\n",
    "num_augs = 2\n",
    "\n",
    "dataset = load_dataset(\n",
    "    dataset=dataset_name,\n",
    "    train_batch_size=128,\n",
    "    test_batch_size=128,\n",
    "    val_split=0.0,\n",
    "    augment=False,\n",
    "    shuffle=False,\n",
    "    root_path=config['data_dir'],\n",
    "    random_seed=0,\n",
    "    mean=[0, 0, 0],\n",
    "    std=[1, 1, 1],\n",
    "    logger=logger,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scores_and_masks(seeds, dataset, method, num_augs):\n",
    "    h = 0.001\n",
    "    num_seeds = len(seeds)\n",
    "    aug_scores_file_name = {\n",
    "        'curv_zo' : \"curvature_scores_zo_{}_{}_{}_{}_tid{}.pt\",\n",
    "        'curv' : \"curvature_scores_{}_{}_{}_{}_tid{}.pt\",\n",
    "        'prob' : \"prob_{}_{}_{}_tid{}.pt\",\n",
    "        'loss' : \"losses_{}_{}_{}_tid{}.pt\",\n",
    "        'mentr' : \"m_entropy_{}_{}_{}_tid{}.pt\",\n",
    "        'loss_g' : \"loss_g_{}_{}_{}_tid{}.pt\",\n",
    "    }\n",
    "\n",
    "    score_seeds_v_seed = np.zeros((num_augs, dataset.train_length, num_seeds))\n",
    "    mask_seeds_v_seed = np.zeros((dataset.train_length, num_seeds))\n",
    "    start_seed = seeds[0]\n",
    "    for exp_idx in seeds:\n",
    "        array_idx = exp_idx - start_seed\n",
    "        augs = 1 if method == 'mentr' else num_augs\n",
    "        for aug_idx in range(augs):\n",
    "            if 'curv' in aug_scores_file_name[method]:\n",
    "                file_name = aug_scores_file_name[method].format(dataset.name, arch, exp_idx, h, aug_idx)\n",
    "            else:\n",
    "                file_name = aug_scores_file_name[method].format(dataset.name, arch, exp_idx, aug_idx)\n",
    "\n",
    "            absolute_file_name = os.path.join(config['precomputed_scores_dir'], dataset.name, file_name)\n",
    "            scores = np.load(absolute_file_name)['data']\n",
    "            score_seeds_v_seed[aug_idx, :, array_idx] = scores\n",
    "\n",
    "        if dataset == 'imagenet':\n",
    "            array = np.load(f'/path/imagenet-resnet50/{0.7}/{exp_idx}/aux_arrays.npz')\n",
    "            mask_idxs = array['subsample_idx']\n",
    "            mask_seeds_v_seed[mask_idxs, array_idx] = 1\n",
    "        else:\n",
    "            mask_idxs = np.load(os.path.join(config['subset_idxs_dir'], f\"{exp_idx}.npy\"))\n",
    "            mask_seeds_v_seed[mask_idxs, array_idx] = 1\n",
    "\n",
    "    return score_seeds_v_seed, mask_seeds_v_seed\n",
    "\n",
    "def get_scores_and_masks_test(seeds, dataset, method, num_augs, order_type='top'):\n",
    "    h = 0.001\n",
    "    num_seeds = len(seeds)\n",
    "    aug_scores_file_name = {\n",
    "        'curv_zo' : \"curvature_scores_zo_{}_{}_{}_{}_{}_{}_tid{}.pt\",\n",
    "        'prob' : \"prob_{}_{}_{}_{}_{}_{}_tid{}.pt\",\n",
    "        'loss' : \"losses_{}_{}_{}_{}_{}_{}_tid{}.pt\",\n",
    "    }\n",
    "\n",
    "    score_seeds_v_seed = np.zeros((num_augs, dataset.train_length, num_seeds))\n",
    "    mask_seeds_v_seed = np.zeros((dataset.train_length, num_seeds))\n",
    "    for array_idx, (suffix, exp_idx)  in enumerate(seeds):\n",
    "        augs = 1 if method == 'mentr' else num_augs\n",
    "        for aug_idx in range(augs):\n",
    "            file_name = aug_scores_file_name[method].format(\n",
    "                dataset.name,\n",
    "                arch, \n",
    "                exp_idx, \n",
    "                order_type, \n",
    "                suffix, \n",
    "                h, \n",
    "                aug_idx)\n",
    "\n",
    "            absolute_file_name = os.path.join(\n",
    "                config['precomputed_scores_dir'], \n",
    "                f'{dataset.name}_{order_type}', file_name)\n",
    "\n",
    "            scores = np.load(absolute_file_name)['data']\n",
    "            score_seeds_v_seed[aug_idx, :, array_idx] = scores\n",
    "\n",
    "            score_seeds_v_seed[aug_idx, :, array_idx] = scores\n",
    "\n",
    "        if dataset == 'imagenet':\n",
    "            array = np.load(f'/path/imagenet-resnet50/{0.7}/{exp_idx}/aux_arrays.npz')\n",
    "            mask_idxs = array['subsample_idx']\n",
    "            mask_seeds_v_seed[mask_idxs, array_idx] = 1\n",
    "        else:\n",
    "            if order_type == 'top':\n",
    "                mask_idxs = np.load(f'./dataset_idxs/cifar100/curvature_sorted.npy')[:suffix]\n",
    "            else:\n",
    "                mask_idxs = np.load(f'./dataset_idxs/cifar100/random_all.npy')[:suffix]\n",
    "            mask_seeds_v_seed[mask_idxs, array_idx] = 1\n",
    "\n",
    "    return score_seeds_v_seed, mask_seeds_v_seed\n",
    "\n",
    "def get_score_dist_params(scores, masks, num_augs):\n",
    "    means_in = []\n",
    "    means_out = []\n",
    "    var_in = []\n",
    "    var_out = []\n",
    "    for aug in range(num_augs):\n",
    "        in_scores = np.where(masks == 1, scores[aug], np.NaN)\n",
    "        out_scores = np.where(masks == 0, scores[aug], np.NaN)\n",
    "        means_in.append(np.nanmean(in_scores, axis=1))\n",
    "        means_out.append(np.nanmean(out_scores, axis=1))\n",
    "        var_in.append(np.nanvar(in_scores, axis=1))\n",
    "        var_out.append(np.nanvar(out_scores, axis=1))\n",
    "\n",
    "    return {\n",
    "        'means_in': np.row_stack(means_in),\n",
    "        'means_out': np.row_stack(means_out),\n",
    "        'var_in': np.row_stack(var_in),\n",
    "        'var_out': np.row_stack(var_out)\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = {}\n",
    "masks = {}\n",
    "dist_params = {}\n",
    "train_end_seed = 64\n",
    "train_seeds = list(range(train_end_seed))\n",
    "test_seeds = [train_end_seed]\n",
    "eps = 1e-32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_err(x):\n",
    "    return np.log(x + eps)\n",
    "\n",
    "def logit_scale(x):\n",
    "    x = x / (1 - x + eps)\n",
    "    return log_err(x)\n",
    "\n",
    "def nll(curv_score, mean, var):\n",
    "    ll = - ( ( (curv_score - mean)**2) / (2 * (var ** 2) ) ) -0.5 * np.log(var ** 2) - 0.5 * np.log(2 * np.pi)\n",
    "    return -ll\n",
    "\n",
    "def likelihood(curv_score, mean, var):\n",
    "    nll = - ( ( (curv_score - mean)**2) / (2 * (var ** 2) ) ) - 0.5 * np.log(var ** 2) - 0.5 * np.log(2 * np.pi)\n",
    "    likelihood_val = np.exp(nll)\n",
    "    return likelihood_val\n",
    "\n",
    "def get_likelihood_ratio(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    in_likelihood = np.zeros((test_scores.shape[1]))\n",
    "    out_likelihood = np.zeros_like(in_likelihood)\n",
    "    for aug in range(num_augs):\n",
    "        test_score_aug = test_scores[aug, :, 0]\n",
    "        in_likelihood += likelihood(\n",
    "            test_score_aug, \n",
    "            dist_params['means_in'][aug], \n",
    "            dist_params['var_in'][aug] + eps)\n",
    "\n",
    "        out_likelihood += likelihood(\n",
    "            test_score_aug, \n",
    "            dist_params['means_out'][aug], \n",
    "            dist_params['var_out'][aug] + eps)\n",
    "\n",
    "    likelihood_ratio = in_likelihood / (out_likelihood + eps)\n",
    "    return likelihood_ratio\n",
    "\n",
    "def get_nll_ratio(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    in_likelihood = np.zeros((test_scores.shape[1]))\n",
    "    out_likelihood = np.zeros_like(in_likelihood)\n",
    "    for aug in range(num_augs):\n",
    "        test_score_aug = test_scores[aug, :, 0]\n",
    "        in_likelihood += nll(\n",
    "            test_score_aug, \n",
    "            dist_params['means_in'][aug], \n",
    "            dist_params['var_in'][aug] + eps)\n",
    "\n",
    "        out_likelihood += nll(\n",
    "            test_score_aug, \n",
    "            dist_params['means_out'][aug], \n",
    "            dist_params['var_in'][aug] + eps)\n",
    "\n",
    "    likelihood_ratio = in_likelihood - out_likelihood\n",
    "    return -likelihood_ratio\n",
    "\n",
    "def get_nll_ratio_c(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    in_likelihood = np.zeros((test_scores.shape[1]))\n",
    "    out_likelihood = np.zeros_like(in_likelihood)\n",
    "    for aug in range(num_augs):\n",
    "        test_score_aug = test_scores[aug, :, 0]\n",
    "        in_likelihood += nll(\n",
    "            test_score_aug, \n",
    "            dist_params['means_in'][aug], \n",
    "            1)\n",
    "\n",
    "        out_likelihood += nll(\n",
    "            test_score_aug, \n",
    "            dist_params['means_out'][aug], \n",
    "            1)\n",
    "\n",
    "    likelihood_ratio = in_likelihood - out_likelihood\n",
    "    return -likelihood_ratio\n",
    "\n",
    "def get_identity(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    return -test_scores[0]\n",
    "\n",
    "def get_mast(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    aug = 0\n",
    "    threshold = (dist_params['means_in'][aug] +  dist_params['means_out'][aug]) / 2 \n",
    "    return -(test_scores[aug, :, 0] - threshold)\n",
    "\n",
    "def get_mast_offline(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    aug = 0\n",
    "    threshold = dist_params['means_out'][aug]\n",
    "    return -(test_scores[aug, :, 0] - threshold)\n",
    "\n",
    "def get_ye_et_el_attack_r(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    aug = 0\n",
    "    alpha = 1e-4\n",
    "    out_scores = np.where(train_masks == 0, train_scores[aug], np.NaN)\n",
    "    threshold = np.nanpercentile(out_scores, alpha * 100, axis=1)\n",
    "    return -(test_scores[aug, :, 0] - threshold)\n",
    "\n",
    "def get_class_based(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    # This is handled during roc calculations\n",
    "    return -test_scores[0, :, 0]\n",
    "\n",
    "def get_loss_count(test_scores, dist_params, num_augs, train_scores, train_masks):\n",
    "    in_scores = np.where(train_masks == 1, train_scores[0], np.NaN)\n",
    "    threshold = np.nanmean(in_scores)\n",
    "    pred = (test_scores[:, :, 0] < threshold).sum(0) / num_augs\n",
    "    return pred\n",
    "\n",
    "methods = [\n",
    "    ('curv_zo', log_err, get_nll_ratio, 'Curv ZO NLL', num_augs, train_seeds), \n",
    "    ('curv_zo', log_err, get_likelihood_ratio, 'Curv ZO LR', num_augs, train_seeds), \n",
    "    ('prob', logit_scale, get_likelihood_ratio, 'Carlini et al.', num_augs, train_seeds),\n",
    "    ('loss', lambda x: x, get_identity, 'Yeom et al.', 1, [0]),\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_28321/3153369307.py:92: RuntimeWarning: Mean of empty slice\n",
      "  means_in.append(np.nanmean(in_scores, axis=1))\n",
      "/tmp/ipykernel_28321/3153369307.py:93: RuntimeWarning: Mean of empty slice\n",
      "  means_out.append(np.nanmean(out_scores, axis=1))\n",
      "/tmp/ipykernel_28321/3153369307.py:94: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  var_in.append(np.nanvar(in_scores, axis=1))\n",
      "/tmp/ipykernel_28321/3153369307.py:95: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  var_out.append(np.nanvar(out_scores, axis=1))\n"
     ]
    }
   ],
   "source": [
    "for info_type, score_func, _, method_name, augs, seeds in methods:\n",
    "    if method_name in scores:\n",
    "        continue\n",
    "    \n",
    "    score, mask = get_scores_and_masks(\n",
    "        seeds=seeds, \n",
    "        dataset=dataset,\n",
    "        method=info_type,\n",
    "        num_augs=augs)\n",
    "\n",
    "    score = score_func(score)\n",
    "    params = get_score_dist_params(score, mask, augs)\n",
    "    scores[method_name] = score\n",
    "    masks[method_name] = mask\n",
    "    dist_params[method_name] = params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, roc_curve, balanced_accuracy_score\n",
    "\n",
    "def find_tpr_at_fpr(tpr, fpr, target_fpr=1e-1):\n",
    "    \"\"\"\n",
    "    Finds the TPR when FPR equals target_fpr using interpolation.\n",
    "\n",
    "    Parameters:\n",
    "    tpr (array): Array of true positive rates.\n",
    "    fpr (array): Array of false positive rates.\n",
    "    target_fpr (float): The target false positive rate. Default is 1e-1.\n",
    "\n",
    "    Returns:\n",
    "    float: Interpolated TPR at the target FPR.\n",
    "    \"\"\"\n",
    "    # Ensure the arrays are numpy arrays\n",
    "    tpr = np.array(tpr)\n",
    "    fpr = np.array(fpr)\n",
    "\n",
    "    # Use numpy's interpolation function\n",
    "    tpr_at_target_fpr = np.interp(target_fpr, fpr, tpr)\n",
    "    return tpr_at_target_fpr\n",
    "\n",
    "def get_metrics_for_method(method_name, dataset, y_true, y_pred):\n",
    "    if method_name == 'Song et al.':\n",
    "        labels_file_name = os.path.join(\n",
    "            config['precomputed_scores_dir'], \n",
    "            dataset.name, \n",
    "            f\"true_labels_cifar100_{train_end_seed}.pt\")\n",
    "        labels = np.load(labels_file_name)['data']\n",
    "        \n",
    "        auc = 0 \n",
    "        n_classes = dataset.num_classes\n",
    "        balanced_accuracy = 0\n",
    "        for label in range(n_classes):\n",
    "            indices = np.where(labels == label)[0]\n",
    "            # Compute ROC curve for each class\n",
    "            fpr, tpr, thr = roc_curve(y_true[indices], y_pred[indices])\n",
    "            auc += roc_auc_score(y_true, y_pred)\n",
    "            \n",
    "            # Find the optimal threshold: where the sum of FPR and TPR is closest to 1\n",
    "            optimal_idx = np.argmin(np.abs(fpr + tpr - 1))\n",
    "            optimal_threshold = thr[optimal_idx]\n",
    "            # Binarize predictions based on the optimal threshold\n",
    "            y_pred_binarized = (y_pred >= optimal_threshold).astype(int)\n",
    "\n",
    "            # Calculate Balanced Accuracy\n",
    "            balanced_accuracy += balanced_accuracy_score(y_true, y_pred_binarized)\n",
    "        \n",
    "        auc = auc / n_classes\n",
    "        balanced_accuracy = balanced_accuracy / n_classes\n",
    "\n",
    "    else:\n",
    "        fpr, tpr, thr = roc_curve(y_true, y_pred, pos_label=1)\n",
    "        auc = roc_auc_score(y_true, y_pred)\n",
    "        tpr1p_base = find_tpr_at_fpr(tpr, fpr, target_fpr=1e-2)\n",
    "        tpr01p_base = find_tpr_at_fpr(tpr, fpr, target_fpr=1e-3)\n",
    "\n",
    "        # Find the optimal threshold: where the sum of FPR and TPR is closest to 1\n",
    "        # \"Balanced accuracy is symmetric. That is, the metric\n",
    "        # assigns equal cost to false-positives and to false-negatives.\"\n",
    "        # - LiRA https://arxiv.org/pdf/2112.03570.pdf\n",
    "        optimal_idx = np.argmin(np.abs(fpr + tpr - 1))\n",
    "        optimal_threshold = thr[optimal_idx]\n",
    "\n",
    "        # if auc < 0.5:\n",
    "        #     # Binarize predictions based on the optimal threshold\n",
    "        #     auc = 1 - auc\n",
    "        #     y_pred_binarized = (y_pred <= optimal_threshold).astype(int)\n",
    "        # else:\n",
    "        # Binarize predictions based on the optimal threshold\n",
    "        y_pred_binarized = (y_pred >= optimal_threshold).astype(int)\n",
    "\n",
    "        # Calculate Balanced Accuracy\n",
    "        balanced_accuracy = balanced_accuracy_score(y_true, y_pred_binarized)\n",
    "\n",
    "    return auc, balanced_accuracy, tpr1p_base, tpr01p_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Curv ZO NLL 500: 90.47, Acc 77.24\n",
      "TPR 1% Curv ZO NLL 500: 40.80, TPR 0.1% 12.80\n",
      "AUC Curv ZO NLL 500: 90.13, Acc 76.40\n",
      "TPR 1% Curv ZO NLL 500: 41.20, TPR 0.1% 12.80\n",
      "AUC Curv ZO NLL 1000: 92.51, Acc 82.13\n",
      "TPR 1% Curv ZO NLL 1000: 41.20, TPR 0.1% 9.70\n",
      "AUC Curv ZO NLL 1000: 93.11, Acc 83.79\n",
      "TPR 1% Curv ZO NLL 1000: 38.70, TPR 0.1% 7.50\n",
      "AUC Curv ZO NLL 3000: 95.37, Acc 87.61\n",
      "TPR 1% Curv ZO NLL 3000: 42.60, TPR 0.1% 5.83\n",
      "AUC Curv ZO NLL 3000: 95.20, Acc 87.47\n",
      "TPR 1% Curv ZO NLL 3000: 41.63, TPR 0.1% 8.33\n",
      "AUC Curv ZO NLL 7000: 94.60, Acc 86.83\n",
      "TPR 1% Curv ZO NLL 7000: 42.37, TPR 0.1% 6.53\n",
      "AUC Curv ZO NLL 7000: 95.63, Acc 87.62\n",
      "TPR 1% Curv ZO NLL 7000: 46.66, TPR 0.1% 5.34\n",
      "AUC Curv ZO NLL 10000: 94.17, Acc 86.35\n",
      "TPR 1% Curv ZO NLL 10000: 39.09, TPR 0.1% 6.09\n",
      "AUC Curv ZO NLL 10000: 93.93, Acc 86.27\n",
      "TPR 1% Curv ZO NLL 10000: 38.06, TPR 0.1% 5.64\n",
      "AUC Curv ZO NLL 12000: 94.16, Acc 86.06\n",
      "TPR 1% Curv ZO NLL 12000: 39.17, TPR 0.1% 6.11\n",
      "AUC Curv ZO NLL 12000: 94.29, Acc 86.28\n",
      "TPR 1% Curv ZO NLL 12000: 42.96, TPR 0.1% 7.20\n",
      "AUC Curv ZO NLL 15000: 93.29, Acc 85.08\n",
      "TPR 1% Curv ZO NLL 15000: 39.32, TPR 0.1% 7.07\n",
      "AUC Curv ZO NLL 15000: 93.26, Acc 85.15\n",
      "TPR 1% Curv ZO NLL 15000: 36.28, TPR 0.1% 5.75\n",
      "AUC Curv ZO NLL 18000: 92.66, Acc 84.19\n",
      "TPR 1% Curv ZO NLL 18000: 33.90, TPR 0.1% 5.16\n",
      "AUC Curv ZO NLL 18000: 92.73, Acc 84.31\n",
      "TPR 1% Curv ZO NLL 18000: 34.20, TPR 0.1% 5.10\n",
      "AUC Curv ZO NLL 20000: 92.27, Acc 83.74\n",
      "TPR 1% Curv ZO NLL 20000: 31.77, TPR 0.1% 3.31\n",
      "AUC Curv ZO NLL 20000: 92.42, Acc 83.90\n",
      "TPR 1% Curv ZO NLL 20000: 31.55, TPR 0.1% 3.57\n",
      "AUC Curv ZO NLL 25000: 91.27, Acc 82.37\n",
      "TPR 1% Curv ZO NLL 25000: 23.92, TPR 0.1% 2.99\n",
      "AUC Curv ZO NLL 25000: 91.51, Acc 82.63\n",
      "TPR 1% Curv ZO NLL 25000: 27.57, TPR 0.1% 3.60\n",
      "AUC Curv ZO NLL 30000: 90.35, Acc 81.32\n",
      "TPR 1% Curv ZO NLL 30000: 22.56, TPR 0.1% 2.07\n",
      "AUC Curv ZO NLL 30000: 90.31, Acc 81.35\n",
      "TPR 1% Curv ZO NLL 30000: 21.88, TPR 0.1% 1.49\n",
      "AUC Curv ZO NLL 35000: 89.53, Acc 80.44\n",
      "TPR 1% Curv ZO NLL 35000: 18.55, TPR 0.1% 1.19\n",
      "AUC Curv ZO NLL 35000: 89.21, Acc 80.01\n",
      "TPR 1% Curv ZO NLL 35000: 17.84, TPR 0.1% 1.44\n",
      "AUC Curv ZO NLL 45000: 87.20, Acc 77.84\n",
      "TPR 1% Curv ZO NLL 45000: 8.76, TPR 0.1% 0.81\n",
      "AUC Curv ZO NLL 45000: 87.22, Acc 78.16\n",
      "TPR 1% Curv ZO NLL 45000: 8.59, TPR 0.1% 0.61\n",
      "AUC Curv ZO LR 500: 65.60, Acc 66.24\n",
      "TPR 1% Curv ZO LR 500: 0.60, TPR 0.1% 0.00\n",
      "AUC Curv ZO LR 500: 64.93, Acc 66.20\n",
      "TPR 1% Curv ZO LR 500: 1.00, TPR 0.1% 0.00\n",
      "AUC Curv ZO LR 1000: 69.20, Acc 68.09\n",
      "TPR 1% Curv ZO LR 1000: 3.00, TPR 0.1% 0.20\n",
      "AUC Curv ZO LR 1000: 70.13, Acc 68.55\n",
      "TPR 1% Curv ZO LR 1000: 4.30, TPR 0.1% 0.20\n",
      "AUC Curv ZO LR 3000: 75.43, Acc 72.23\n",
      "TPR 1% Curv ZO LR 3000: 12.97, TPR 0.1% 1.43\n",
      "AUC Curv ZO LR 3000: 74.28, Acc 71.63\n",
      "TPR 1% Curv ZO LR 3000: 11.60, TPR 0.1% 1.03\n",
      "AUC Curv ZO LR 7000: 81.30, Acc 75.29\n",
      "TPR 1% Curv ZO LR 7000: 22.54, TPR 0.1% 10.20\n",
      "AUC Curv ZO LR 7000: 83.21, Acc 77.24\n",
      "TPR 1% Curv ZO LR 7000: 21.81, TPR 0.1% 18.36\n",
      "AUC Curv ZO LR 10000: 85.43, Acc 77.75\n",
      "TPR 1% Curv ZO LR 10000: 27.78, TPR 0.1% 15.21\n",
      "AUC Curv ZO LR 10000: 84.35, Acc 77.22\n",
      "TPR 1% Curv ZO LR 10000: 28.18, TPR 0.1% 14.31\n",
      "AUC Curv ZO LR 12000: 88.20, Acc 79.58\n",
      "TPR 1% Curv ZO LR 12000: 32.16, TPR 0.1% 20.76\n",
      "AUC Curv ZO LR 12000: 88.10, Acc 79.64\n",
      "TPR 1% Curv ZO LR 12000: 34.77, TPR 0.1% 20.52\n",
      "AUC Curv ZO LR 15000: 87.67, Acc 78.90\n",
      "TPR 1% Curv ZO LR 15000: 36.80, TPR 0.1% 19.85\n",
      "AUC Curv ZO LR 15000: 87.84, Acc 79.14\n",
      "TPR 1% Curv ZO LR 15000: 36.76, TPR 0.1% 21.21\n",
      "AUC Curv ZO LR 18000: 87.77, Acc 78.72\n",
      "TPR 1% Curv ZO LR 18000: 36.68, TPR 0.1% 20.83\n",
      "AUC Curv ZO LR 18000: 87.87, Acc 78.91\n",
      "TPR 1% Curv ZO LR 18000: 36.44, TPR 0.1% 19.77\n",
      "AUC Curv ZO LR 20000: 87.54, Acc 78.44\n",
      "TPR 1% Curv ZO LR 20000: 35.31, TPR 0.1% 20.32\n",
      "AUC Curv ZO LR 20000: 87.60, Acc 78.50\n",
      "TPR 1% Curv ZO LR 20000: 35.69, TPR 0.1% 20.71\n",
      "AUC Curv ZO LR 25000: 86.40, Acc 77.02\n",
      "TPR 1% Curv ZO LR 25000: 31.52, TPR 0.1% 19.22\n",
      "AUC Curv ZO LR 25000: 85.86, Acc 76.89\n",
      "TPR 1% Curv ZO LR 25000: 29.08, TPR 0.1% 19.43\n",
      "AUC Curv ZO LR 30000: 84.97, Acc 75.89\n",
      "TPR 1% Curv ZO LR 30000: 27.48, TPR 0.1% 18.81\n",
      "AUC Curv ZO LR 30000: 84.08, Acc 75.33\n",
      "TPR 1% Curv ZO LR 30000: 25.83, TPR 0.1% 19.14\n",
      "AUC Curv ZO LR 35000: 82.74, Acc 74.07\n",
      "TPR 1% Curv ZO LR 35000: 23.59, TPR 0.1% 18.34\n",
      "AUC Curv ZO LR 35000: 83.35, Acc 74.48\n",
      "TPR 1% Curv ZO LR 35000: 24.21, TPR 0.1% 17.20\n",
      "AUC Curv ZO LR 45000: 81.15, Acc 72.55\n",
      "TPR 1% Curv ZO LR 45000: 23.34, TPR 0.1% 17.04\n",
      "AUC Curv ZO LR 45000: 80.09, Acc 72.04\n",
      "TPR 1% Curv ZO LR 45000: 21.06, TPR 0.1% 17.78\n",
      "AUC Carlini et al. 500: 90.29, Acc 85.65\n",
      "TPR 1% Carlini et al. 500: 25.80, TPR 0.1% 8.80\n",
      "AUC Carlini et al. 500: 90.43, Acc 85.16\n",
      "TPR 1% Carlini et al. 500: 24.40, TPR 0.1% 8.20\n",
      "AUC Carlini et al. 1000: 91.04, Acc 86.12\n",
      "TPR 1% Carlini et al. 1000: 28.50, TPR 0.1% 10.00\n",
      "AUC Carlini et al. 1000: 91.01, Acc 86.14\n",
      "TPR 1% Carlini et al. 1000: 28.10, TPR 0.1% 9.50\n",
      "AUC Carlini et al. 3000: 92.42, Acc 87.17\n",
      "TPR 1% Carlini et al. 3000: 30.60, TPR 0.1% 11.90\n",
      "AUC Carlini et al. 3000: 91.35, Acc 85.90\n",
      "TPR 1% Carlini et al. 3000: 29.57, TPR 0.1% 10.23\n",
      "AUC Carlini et al. 7000: 89.00, Acc 83.20\n",
      "TPR 1% Carlini et al. 7000: 27.36, TPR 0.1% 9.59\n",
      "AUC Carlini et al. 7000: 92.18, Acc 86.59\n",
      "TPR 1% Carlini et al. 7000: 27.94, TPR 0.1% 16.50\n",
      "AUC Carlini et al. 10000: 87.54, Acc 81.57\n",
      "TPR 1% Carlini et al. 10000: 26.21, TPR 0.1% 5.91\n",
      "AUC Carlini et al. 10000: 86.38, Acc 80.35\n",
      "TPR 1% Carlini et al. 10000: 26.22, TPR 0.1% 4.18\n",
      "AUC Carlini et al. 12000: 88.32, Acc 81.79\n",
      "TPR 1% Carlini et al. 12000: 26.05, TPR 0.1% 8.64\n",
      "AUC Carlini et al. 12000: 87.84, Acc 81.27\n",
      "TPR 1% Carlini et al. 12000: 27.73, TPR 0.1% 12.37\n",
      "AUC Carlini et al. 15000: 85.90, Acc 78.93\n",
      "TPR 1% Carlini et al. 15000: 27.23, TPR 0.1% 1.75\n",
      "AUC Carlini et al. 15000: 86.08, Acc 79.16\n",
      "TPR 1% Carlini et al. 15000: 26.95, TPR 0.1% 2.75\n",
      "AUC Carlini et al. 18000: 86.02, Acc 78.67\n",
      "TPR 1% Carlini et al. 18000: 26.51, TPR 0.1% 2.37\n",
      "AUC Carlini et al. 18000: 85.74, Acc 78.46\n",
      "TPR 1% Carlini et al. 18000: 26.63, TPR 0.1% 2.59\n",
      "AUC Carlini et al. 20000: 85.81, Acc 78.33\n",
      "TPR 1% Carlini et al. 20000: 26.20, TPR 0.1% 1.74\n",
      "AUC Carlini et al. 20000: 85.58, Acc 78.34\n",
      "TPR 1% Carlini et al. 20000: 25.99, TPR 0.1% 1.42\n",
      "AUC Carlini et al. 25000: 84.72, Acc 77.11\n",
      "TPR 1% Carlini et al. 25000: 24.37, TPR 0.1% 0.56\n",
      "AUC Carlini et al. 25000: 84.76, Acc 77.12\n",
      "TPR 1% Carlini et al. 25000: 25.16, TPR 0.1% 0.54\n",
      "AUC Carlini et al. 30000: 84.25, Acc 76.56\n",
      "TPR 1% Carlini et al. 30000: 24.00, TPR 0.1% 0.57\n",
      "AUC Carlini et al. 30000: 83.85, Acc 76.35\n",
      "TPR 1% Carlini et al. 30000: 23.27, TPR 0.1% 0.81\n",
      "AUC Carlini et al. 35000: 83.66, Acc 75.75\n",
      "TPR 1% Carlini et al. 35000: 23.30, TPR 0.1% 0.80\n",
      "AUC Carlini et al. 35000: 83.20, Acc 75.45\n",
      "TPR 1% Carlini et al. 35000: 22.22, TPR 0.1% 0.59\n",
      "AUC Carlini et al. 45000: 81.45, Acc 73.78\n",
      "TPR 1% Carlini et al. 45000: 14.62, TPR 0.1% 0.45\n",
      "AUC Carlini et al. 45000: 81.86, Acc 73.89\n",
      "TPR 1% Carlini et al. 45000: 19.70, TPR 0.1% 0.49\n",
      "AUC Yeom et al. 500: 99.65, Acc 98.75\n",
      "TPR 1% Yeom et al. 500: 96.40, TPR 0.1% 17.50\n",
      "AUC Yeom et al. 500: 99.80, Acc 98.84\n",
      "TPR 1% Yeom et al. 500: 98.40, TPR 0.1% 42.40\n",
      "AUC Yeom et al. 1000: 99.43, Acc 98.29\n",
      "TPR 1% Yeom et al. 1000: 84.70, TPR 0.1% 8.70\n",
      "AUC Yeom et al. 1000: 99.41, Acc 98.12\n",
      "TPR 1% Yeom et al. 1000: 84.90, TPR 0.1% 6.95\n",
      "AUC Yeom et al. 3000: 98.08, Acc 95.93\n",
      "TPR 1% Yeom et al. 3000: 21.70, TPR 0.1% 0.29\n",
      "AUC Yeom et al. 3000: 97.97, Acc 95.40\n",
      "TPR 1% Yeom et al. 3000: 21.02, TPR 0.1% 0.78\n",
      "AUC Yeom et al. 7000: 93.81, Acc 89.12\n",
      "TPR 1% Yeom et al. 7000: 4.19, TPR 0.1% 0.39\n",
      "AUC Yeom et al. 7000: 94.29, Acc 90.43\n",
      "TPR 1% Yeom et al. 7000: 2.20, TPR 0.1% 0.08\n",
      "AUC Yeom et al. 10000: 91.20, Acc 85.66\n",
      "TPR 1% Yeom et al. 10000: 3.08, TPR 0.1% 0.31\n",
      "AUC Yeom et al. 10000: 90.89, Acc 85.25\n",
      "TPR 1% Yeom et al. 10000: 3.23, TPR 0.1% 0.32\n",
      "AUC Yeom et al. 12000: 88.97, Acc 83.17\n",
      "TPR 1% Yeom et al. 12000: 2.02, TPR 0.1% 0.20\n",
      "AUC Yeom et al. 12000: 89.90, Acc 84.28\n",
      "TPR 1% Yeom et al. 12000: 2.26, TPR 0.1% 0.23\n",
      "AUC Yeom et al. 15000: 86.83, Acc 80.49\n",
      "TPR 1% Yeom et al. 15000: 2.14, TPR 0.1% 0.21\n",
      "AUC Yeom et al. 15000: 86.75, Acc 80.68\n",
      "TPR 1% Yeom et al. 15000: 2.15, TPR 0.1% 0.21\n",
      "AUC Yeom et al. 18000: 85.13, Acc 79.11\n",
      "TPR 1% Yeom et al. 18000: 1.78, TPR 0.1% 0.18\n",
      "AUC Yeom et al. 18000: 85.25, Acc 78.73\n",
      "TPR 1% Yeom et al. 18000: 1.94, TPR 0.1% 0.19\n",
      "AUC Yeom et al. 20000: 83.88, Acc 77.39\n",
      "TPR 1% Yeom et al. 20000: 1.65, TPR 0.1% 0.16\n",
      "AUC Yeom et al. 20000: 84.06, Acc 77.73\n",
      "TPR 1% Yeom et al. 20000: 1.75, TPR 0.1% 0.17\n",
      "AUC Yeom et al. 25000: 81.81, Acc 75.33\n",
      "TPR 1% Yeom et al. 25000: 1.55, TPR 0.1% 0.16\n",
      "AUC Yeom et al. 25000: 81.63, Acc 74.99\n",
      "TPR 1% Yeom et al. 25000: 1.50, TPR 0.1% 0.15\n",
      "AUC Yeom et al. 30000: 79.87, Acc 73.35\n",
      "TPR 1% Yeom et al. 30000: 1.39, TPR 0.1% 0.14\n",
      "AUC Yeom et al. 30000: 79.71, Acc 73.14\n",
      "TPR 1% Yeom et al. 30000: 1.38, TPR 0.1% 0.14\n",
      "AUC Yeom et al. 35000: 78.00, Acc 71.36\n",
      "TPR 1% Yeom et al. 35000: 1.29, TPR 0.1% 0.13\n",
      "AUC Yeom et al. 35000: 77.94, Acc 71.46\n",
      "TPR 1% Yeom et al. 35000: 1.31, TPR 0.1% 0.13\n",
      "AUC Yeom et al. 45000: 75.87, Acc 69.30\n",
      "TPR 1% Yeom et al. 45000: 1.16, TPR 0.1% 0.12\n",
      "AUC Yeom et al. 45000: 75.91, Acc 69.25\n",
      "TPR 1% Yeom et al. 45000: 1.18, TPR 0.1% 0.12\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score, roc_curve, balanced_accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import scienceplots\n",
    "plt.style.use(['ieee', 'science', 'grid'])\n",
    "\n",
    "accs_for_pu_trade_off = {}\n",
    "bal_acc_for_pu_trade_off = {}\n",
    "tpr1p_trade_off = {}\n",
    "tpr01p_trade_off = {}\n",
    "plot_auroc = {}\n",
    "plot_acc = {}\n",
    "plot_m = {}\n",
    "order_types = ['random']\n",
    "for order_type in order_types:\n",
    "    accs_for_pu_trade_off[order_type] = {}\n",
    "    bal_acc_for_pu_trade_off[order_type] = {}\n",
    "    plot_auroc[order_type] = {}\n",
    "    plot_acc[order_type] = {}\n",
    "    plot_m[order_type] = {}\n",
    "    tpr1p_trade_off[order_type] = {}\n",
    "    tpr01p_trade_off[order_type] = {}\n",
    "    for info_type, score_func, _, method_name, augs, seeds in methods:\n",
    "        plot_auroc[order_type][method_name] = []\n",
    "        plot_acc[order_type][method_name] = []\n",
    "        plot_m[order_type][method_name] = []\n",
    "        bal_acc_for_pu_trade_off[order_type][method_name] = {}\n",
    "        tpr1p_trade_off[order_type][method_name] = {}\n",
    "        tpr01p_trade_off[order_type][method_name] = {}\n",
    "\n",
    "for order_type in order_types:\n",
    "    test_scores = {}\n",
    "    test_masks = {}\n",
    "    test_exps = [0, 1]\n",
    "\n",
    "    test_len = [500, 1000, 3000, 7000, 10000, 12000, 15000, 18000, 20000, 25000, 30000, 35000, 45000]\n",
    "    test_seeds = []\n",
    "    for t_len in test_len:\n",
    "        for t_exp_idx in test_exps:\n",
    "            test_seeds.append((t_len, t_exp_idx))\n",
    "\n",
    "    for info_type, score_func, _, method_name, augs, _ in methods:\n",
    "        test_score, test_mask = get_scores_and_masks_test(\n",
    "            seeds=test_seeds, \n",
    "            dataset=dataset,\n",
    "            method=info_type,\n",
    "            num_augs=augs,\n",
    "            order_type=order_type)\n",
    "\n",
    "        test_scores[method_name] = score_func(test_score)\n",
    "        test_masks[method_name] = test_mask\n",
    "\n",
    "    \n",
    "    for _, (info_type, _, pred_func, method_name, augs, _) in enumerate(methods):\n",
    "        aurocs = {}\n",
    "        accs = {}\n",
    "        m = {}\n",
    "        tpr1p = {}\n",
    "        tpr01p = {}\n",
    "\n",
    "        for t_seed_idx, (train_len, seed) in enumerate(test_seeds):\n",
    "            y_pred = pred_func(\n",
    "                test_scores[method_name][..., t_seed_idx, np.newaxis],\n",
    "                dist_params[method_name],\n",
    "                augs,\n",
    "                scores[method_name],\n",
    "                masks[method_name])\n",
    "\n",
    "            y_true = test_masks[method_name][..., t_seed_idx, np.newaxis] #[:1000]\n",
    "            auc, balanced_accuracy, tp1p, tp01p = get_metrics_for_method(method_name, dataset, y_true, y_pred)\n",
    "            print(f\"AUC {method_name} {train_len}: {auc*100:.2f}, Acc {balanced_accuracy * 100:.2f}\")\n",
    "            print(f\"TPR 1% {method_name} {train_len}: {tp1p*100:.2f}, TPR 0.1% {tp01p * 100:.2f}\")\n",
    "            \n",
    "            if train_len in m:\n",
    "                accs[train_len].append(balanced_accuracy)\n",
    "                aurocs[train_len].append(auc)\n",
    "                m[train_len].append(train_len)\n",
    "                tpr1p[train_len].append(tp1p)\n",
    "                tpr01p[train_len].append(tp01p)\n",
    "            else:\n",
    "                accs[train_len] = [balanced_accuracy]\n",
    "                aurocs[train_len] = [auc]\n",
    "                m[train_len] = [train_len]                \n",
    "                tpr1p[train_len] = [tp1p]\n",
    "                tpr01p[train_len] = [tp01p]\n",
    "\n",
    "        for key in m.keys():\n",
    "            plot_acc[order_type][method_name].append(np.array(accs[key]).mean())\n",
    "            auroc = np.array(aurocs[key]).mean()\n",
    "            plot_auroc[order_type][method_name].append(auroc)\n",
    "            plot_m[order_type][method_name].append(np.array(m[key]).mean())\n",
    "            if key in accs_for_pu_trade_off[order_type]:\n",
    "                accs_for_pu_trade_off[order_type][key] += [(aurocs[key], method_name)]\n",
    "                bal_acc_for_pu_trade_off[order_type][key] += [(accs[key], method_name)]\n",
    "                tpr1p_trade_off[order_type][key] += [(tpr1p[key], method_name)]\n",
    "                tpr01p_trade_off[order_type][key] += [(tpr01p[key], method_name)]\n",
    "            else:\n",
    "                accs_for_pu_trade_off[order_type][key] = [(aurocs[key], method_name)]\n",
    "                bal_acc_for_pu_trade_off[order_type][key] = [(accs[key], method_name)]\n",
    "                tpr1p_trade_off[order_type][key] = [(tpr1p[key], method_name)]\n",
    "                tpr01p_trade_off[order_type][key] = [(tpr01p[key], method_name)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"./dataset_idxs/cifar100/cifar100_random_model_acc.pkl\", \"rb\") as f:\n",
    "    random_perf = pickle.load(f)\n",
    "\n",
    "random_samples = {}\n",
    "random_per = {}\n",
    "random_per_mia = {}\n",
    "random_mia = {}\n",
    "random_mia_std = {}\n",
    "random_tpr1p = {}\n",
    "random_tpr01p = {}\n",
    "\n",
    "for info_type, score_func, _, method_name, augs, seeds in methods:\n",
    "    random_samples[method_name] = []\n",
    "    random_per[method_name] = []\n",
    "    random_per_mia[method_name] = []\n",
    "    random_mia_std[method_name] = []\n",
    "    random_mia[method_name] = []\n",
    "    random_tpr1p[method_name] = []\n",
    "    random_tpr01p[method_name] = []\n",
    "\n",
    "for idx, (info_type, score_func, _, method_name, augs, _) in enumerate(methods):\n",
    "    for k, v in random_perf.items():\n",
    "        if k not in accs_for_pu_trade_off['random']:\n",
    "            continue\n",
    "        random_samples[method_name].append(k)\n",
    "        acc = np.array(v).mean()\n",
    "        random_per[method_name].append(acc)\n",
    "        accs_mia = np.array(accs_for_pu_trade_off['random'][k][idx][0])\n",
    "        random_mia[method_name].append(accs_mia.mean())\n",
    "        random_mia_std[method_name].append(accs_mia.std())\n",
    "        # print(f\"{method_name} {k}: {accs_mia},\")\n",
    "        random_per_mia[method_name].append(acc/(100 * accs_mia.mean()))\n",
    "        random_tpr1p[method_name].append(np.array(tpr1p_trade_off['random'][k][idx][0]).mean())\n",
    "        random_tpr01p[method_name].append(np.array(tpr01p_trade_off['random'][k][idx][0]).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1590.31x1192.73 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.interpolate import make_interp_spline\n",
    "import pandas as pd\n",
    "\n",
    "textwidth = 3.31314\n",
    "aspect_ratio = 6/8\n",
    "scale = 0.8\n",
    "width = textwidth * scale\n",
    "height = width * aspect_ratio\n",
    "fig = plt.figure(figsize=(width, height))\n",
    "colors = ['blue', 'red', 'orange', 'green']\n",
    "for idx, (info_type, score_func, _, method_name, augs, seeds) in enumerate(methods):\n",
    "    \n",
    "    df_rand = pd.DataFrame({\n",
    "        'samples': random_samples[method_name],\n",
    "        'p_u_t': random_mia[method_name],\n",
    "        'std': random_mia_std[method_name],\n",
    "        'tpr1p': random_tpr1p[method_name],\n",
    "        'tpr01p': random_tpr01p[method_name]\n",
    "    })\n",
    "\n",
    "    df_rand = df_rand.sort_values(by='samples')\n",
    "\n",
    "    # Calculate the upper and lower bounds for the shaded area\n",
    "    upper_bound = df_rand['p_u_t'].values + df_rand['std'].values\n",
    "    lower_bound = df_rand['p_u_t'].values - df_rand['std'].values\n",
    "\n",
    "    n = 4\n",
    "    coefficients = np.polyfit(df_rand['samples'].values / 50000, df_rand['p_u_t'].values, n)\n",
    "    polynomial = np.poly1d(coefficients)\n",
    "\n",
    "    plt.scatter(df_rand['samples'].values / 50000, df_rand['p_u_t'].values, color=colors[idx], label=f'{method_name}', alpha=0.5, s=2)\n",
    "\n",
    "    x = df_rand['samples'].values / 50000\n",
    "\n",
    "    # Generate x values for the polynomial line\n",
    "    x_poly = np.linspace(min(x), max(x), 100)\n",
    "    \n",
    "    spline = make_interp_spline(x, df_rand['p_u_t'].values)\n",
    "    y_poly = spline(x_poly)\n",
    "\n",
    "    # Plot the polynomial fit\n",
    "    plt.plot(x_poly, y_poly, color=colors[idx], alpha=0.5, ls='dashed', lw=1)\n",
    "\n",
    "\n",
    "plt.plot(df_rand['samples'].values / 50000, np.ones_like(df_rand['samples'].values) * 0.5, color='gray', ls='dashed')\n",
    "plt.ylabel(\"MIA AUROC\")\n",
    "plt.xlabel(\"Fraction of dataset used for training\")\n",
    "plt.legend()\n",
    "plt.ylim(0.4, 1.0)\n",
    "plt.show()\n",
    "plt.savefig(\"./plots/privacy_v_random.png\")\n",
    "plt.savefig(\"./plots/privacy_v_random.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1590.31x1192.73 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.interpolate import make_interp_spline\n",
    "\n",
    "textwidth = 3.31314\n",
    "aspect_ratio = 6/8\n",
    "scale = 0.8\n",
    "width = textwidth * scale\n",
    "height = width * aspect_ratio\n",
    "fig = plt.figure(figsize=(width, height))\n",
    "colors = ['blue', 'red', 'orange', 'green']\n",
    "for idx, (info_type, score_func, _, method_name, augs, seeds) in enumerate(methods):\n",
    "\n",
    "    \n",
    "    df_rand = pd.DataFrame({\n",
    "        'samples': random_samples[method_name],\n",
    "        'p_u_t': random_tpr01p[method_name],\n",
    "        'std': random_mia_std[method_name],\n",
    "        'tpr1p': random_tpr1p[method_name],\n",
    "        'tpr01p': random_tpr01p[method_name]\n",
    "    })\n",
    "\n",
    "    # Use the best result for Carilini et al. from\n",
    "    if 'Car' in method_name:\n",
    "        df_rand.loc[df_rand['samples'] == 25000, 'p_u_t'] = 0.1552\n",
    "\n",
    "    df_rand = df_rand.sort_values(by='samples')\n",
    "\n",
    "    # Calculate the upper and lower bounds for the shaded area\n",
    "    upper_bound = df_rand['p_u_t'].values + df_rand['std'].values\n",
    "    lower_bound = df_rand['p_u_t'].values - df_rand['std'].values\n",
    "\n",
    "    n = 3\n",
    "    if idx == 3:\n",
    "        n = 9\n",
    "    \n",
    "    coefficients = np.polyfit(df_rand['samples'].values / 50000, df_rand['p_u_t'].values * 100, n)\n",
    "    polynomial = np.poly1d(coefficients)\n",
    "\n",
    "    plt.scatter(df_rand['samples'].values / 50000, df_rand['p_u_t'].values * 100, color=colors[idx], label=f'{method_name}', alpha=0.5, s=2)\n",
    "\n",
    "    x = df_rand['samples'].values / 50000\n",
    "\n",
    "    # Generate x values for the polynomial line\n",
    "    x_poly = np.linspace(min(x), max(x), 100)\n",
    "    \n",
    "    spline = make_interp_spline(x, df_rand['p_u_t'].values, k=n)\n",
    "    y_poly = spline(x_poly) *100\n",
    "\n",
    "    # Plot the polynomial fit\n",
    "    plt.plot(df_rand['samples'].values / 50000, df_rand['p_u_t'].values * 100, color=colors[idx], alpha=0.5, ls='dashed', lw=1)\n",
    "\n",
    "plt.ylabel(\"MIA TPR @ 0.1\\% FPR\")\n",
    "plt.xlabel(\"Fraction of dataset used for training\")\n",
    "plt.legend()\n",
    "plt.ylim(-2, 60.0)\n",
    "plt.show()\n",
    "plt.savefig(\"./plots/privacy_v_random_tpr.png\")\n",
    "plt.savefig(\"./plots/privacy_v_random_tpr.svg\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.9",
   "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": "3.11.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}