diff --git a/inspect_mi.ipynb b/inspect_mi.ipynb
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index 0000000..bc19749
--- /dev/null
+++ b/inspect_mi.ipynb
@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7624f659",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
+    "import math\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.font_manager as fm\n",
+    "\n",
+    "mpl.rcParams[\"figure.dpi\"] = 300\n",
+    "mpl.rcParams['pdf.fonttype'] = 42\n",
+    "mpl.rcParams['ps.fonttype'] = 42\n",
+    "mpl.rcParams[\"font.family\"] = \"Times New Roman\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f18f952b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_focus = 'ref-stablelm-base-alpha-3b-v2'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c05b74da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"./niloofar/results_for_niloofar/stable_lm_ref/EleutherAI_pythia-12b-deduped--bert-temp/fp32-0.3-1-the_pile-the_pile-1000200100_plen30_--tok_false-wikipedia_(en)_ngram_13_<0.8_truncated/{attack_focus}_results.json\", 'r') as f:\n",
+    "    og_data = json.load(f)\n",
+    "\n",
+    "member_scores = og_data['predictions']['member']\n",
+    "nonmember_scores = og_data['predictions']['nonmember']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6e50dca1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['nonmember', 'member'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "og_data['predictions'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6a089015",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filter_out_nan(x):\n",
+    "    return [element for element in x if not math.isnan(element)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dd97043c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_auc_from_thresholds(preds_member, preds_nonmember):\n",
+    "    \"\"\"\n",
+    "    Compute FPRs and TPRs corresponding to given thresholds\n",
+    "    \"\"\"\n",
+    "    tpr, fpr = [], []\n",
+    "    for threshold in thresholds:\n",
+    "        tp = np.sum(preds_member >= threshold)\n",
+    "        tn = np.sum(preds_nonmember < threshold)\n",
+    "        fp = np.sum(preds_nonmember >= threshold)\n",
+    "        fn = np.sum(preds_member < threshold)\n",
+    "\n",
+    "        tpr.append(tp / (tp + fn))\n",
+    "        fpr.append(fp / (fp + tn))\n",
+    "    \n",
+    "    tpr = np.array(tpr)\n",
+    "    fpr = np.array(fpr)\n",
+    "    return auc(fpr, tpr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f17e17d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds_member_ = -np.array(member_scores)\n",
+    "preds_nonmember_ = -np.array(nonmember_scores)\n",
+    "total_preds = np.concatenate((preds_member_ , preds_nonmember_))\n",
+    "\n",
+    "total_labels = [1] * len(preds_member_) + [0] * len(preds_nonmember_)\n",
+    "fpr, tpr, thresholds = roc_curve(total_labels, total_preds)\n",
+    "roc_auc = auc(fpr, tpr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7e508567",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.578991"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6b6ee259",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.578991"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_auc_from_thresholds(preds_member_, preds_nonmember_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f3eb6855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./newmia_results_all/wikipedia_(en).json\", 'r') as f:\n",
+    "    d = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8df212e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "care = d[attack_focus]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86579d4b",
+   "metadata": {},
+   "source": [
+    "## Consider edited-members as members"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0328d0ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = ['edit distance', 'AUC']\n",
+    "raw_data = []\n",
+    "raw_data.append((0, roc_auc))\n",
+    "\n",
+    "for n, v in care.items():\n",
+    "    for score in v.values():\n",
+    "        pm = -np.array(score)\n",
+    "        raw_data.append((int(n), get_auc_from_thresholds(pm, preds_nonmember_)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "627bf8c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(raw_data, columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93ceef87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.lineplot(data=df, x='edit distance', y='AUC', markers=True, dashes=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4e0edfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.groupby('edit distance').mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da26a95",
+   "metadata": {},
+   "source": [
+    "### Consider edited-members as non-members"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9030c99d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = ['edit distance', 'AUC']\n",
+    "raw_data = []\n",
+    "raw_data.append((0, 0.5))\n",
+    "\n",
+    "for n, v in care.items():\n",
+    "    for score in v.values():\n",
+    "        # Consider \"members\" as members\n",
+    "        preds_member_ = -np.array(member_scores)\n",
+    "        # And edited members as \"non members\"\n",
+    "        preds_nonmember_ = -np.array(score)\n",
+    "        total_preds = np.concatenate((preds_member_ , preds_nonmember_))\n",
+    "\n",
+    "        total_labels = [1] * len(preds_member_) + [0] * len(preds_nonmember_)\n",
+    "        fpr, tpr, thresholds = roc_curve(total_labels, total_preds)\n",
+    "        roc_auc = auc(fpr, tpr)\n",
+    "        \n",
+    "        raw_data.append((int(n), roc_auc))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "3fc70513",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(raw_data, columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0a967bb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: xlabel='edit distance', ylabel='AUC'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data=df, x='edit distance', y='AUC', markers=True, dashes=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8ec375f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>AUC</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>edit distance</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.504326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.534676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.590877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.742131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.909689</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    AUC\n",
+       "edit distance          \n",
+       "0              0.500000\n",
+       "1              0.504326\n",
+       "5              0.534676\n",
+       "10             0.590877\n",
+       "25             0.742131\n",
+       "100            0.909689"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('edit distance').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c6c7d9a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "phd9",
+   "language": "python",
+   "name": "phd9"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}