5_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\n",
    "        \"adult\",\n",
    "        \"german\",\n",
    "        \"compas\",\n",
    "        \"diabetes\",\n",
    "        \"breast_cancer\",\n",
    "        ]\n",
    "\n",
    "attack_list = [\n",
    "        'deepfool', \n",
    "        'carlini_l_2', \n",
    "        # 'carlini_l_inf', \n",
    "        'lowprofool_l_2', \n",
    "        # 'lowprofool_l_inf', \n",
    "        # 'fgsm_l_1',\n",
    "        # 'fgsm_l_2',\n",
    "        'fgsm_l_inf',\n",
    "        # 'bim',\n",
    "        # 'mim',\n",
    "        # 'pgd_l_1',\n",
    "        # 'pgd_l_2',\n",
    "        'pgd_l_inf',\n",
    "\n",
    "        # 'boundary', \n",
    "        # 'hopskipjump_l_2', 'hopskipjump_l_inf'\n",
    "        ]\n",
    "\n",
    "def compare_cols(row):\n",
    "    if row['ground_truth'] != row['adv_prediction']:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['model'] = 'LR'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['model'] = 'SVC'\n",
      "C:\\Users\\n10599070\\AppData\\Local\\Temp\\ipykernel_14920\\4221973197.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['model'] = 'MLP'\n"
     ]
    }
   ],
   "source": [
    "data_list_list=[]\n",
    "for dataset in datasets:\n",
    "    data_list = []\n",
    "    for attack in attack_list:\n",
    "        folder_name = f'{attack}_{dataset}'\n",
    "        file_name_lr = f'eval_{folder_name}_lr_result_0.csv'\n",
    "        result_path_lr = f'./results/{folder_name}/{file_name_lr}'\n",
    "        file_name_svc = f'eval_{folder_name}_svc_result_0.csv'\n",
    "        result_path_svc = f'./results/{folder_name}/{file_name_svc}'\n",
    "        file_name_nn = f'eval_{folder_name}_nn_2_result_0.csv'\n",
    "        result_path_nn = f'./results/{folder_name}/{file_name_nn}'\n",
    "\n",
    "        try:\n",
    "            df1 = pd.read_csv(result_path_lr)\n",
    "            df2 = pd.read_csv(result_path_svc)\n",
    "            df3 = pd.read_csv(result_path_nn)\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "        data1 = df1[['eval_L2', 'eval_Sen', 'eval_Mahalanobis', 'eval_Sparsity', 'ground_truth', 'adv_prediction']]\n",
    "        data1['model'] = 'LR'\n",
    "\n",
    "        data2 = df2[['eval_L2', 'eval_Sen', 'eval_Mahalanobis', 'eval_Sparsity', 'ground_truth', 'adv_prediction']]\n",
    "        data2['model'] = 'SVC'\n",
    "\n",
    "        data3 = df3[['eval_L2', 'eval_Sen', 'eval_Mahalanobis', 'eval_Sparsity', 'ground_truth', 'adv_prediction']]\n",
    "        data3['model'] = 'MLP'\n",
    "\n",
    "\n",
    "        data = pd.concat([data1, data2 , data3], axis=0) # data2\n",
    "        data['attack'] = attack \n",
    "        data['dataset'] = dataset\n",
    "        data_list.append(data)\n",
    "    \n",
    "    data_list_df = pd.concat(data_list, axis=0)\n",
    "\n",
    "    data_list_df['attack_success'] = data_list_df.apply(compare_cols, axis=1)\n",
    "    # data_list_df.to_csv(f'{dataset}_l2_success.csv')\n",
    "\n",
    "    data_list_list.append(data_list_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list_list_df = pd.concat(data_list_list, axis=0)\n",
    "data_list_list_df = data_list_list_df.astype({'eval_L2':float, 'eval_Sen':float, 'eval_Mahalanobis':float, 'attack_success': float, 'eval_Sparsity':float})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>eval_L2</th>\n",
       "      <th>eval_Sen</th>\n",
       "      <th>eval_Mahalanobis</th>\n",
       "      <th>eval_Sparsity</th>\n",
       "      <th>ground_truth</th>\n",
       "      <th>adv_prediction</th>\n",
       "      <th>model</th>\n",
       "      <th>attack</th>\n",
       "      <th>dataset</th>\n",
       "      <th>attack_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.832862</td>\n",
       "      <td>3.445641</td>\n",
       "      <td>0.067658</td>\n",
       "      <td>4.0</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>LR</td>\n",
       "      <td>deepfool</td>\n",
       "      <td>adult</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002511</td>\n",
       "      <td>0.010387</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>4.0</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>LR</td>\n",
       "      <td>deepfool</td>\n",
       "      <td>adult</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.061329</td>\n",
       "      <td>0.146191</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>2.0</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>LR</td>\n",
       "      <td>deepfool</td>\n",
       "      <td>adult</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.665483</td>\n",
       "      <td>2.753176</td>\n",
       "      <td>0.054061</td>\n",
       "      <td>4.0</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>LR</td>\n",
       "      <td>deepfool</td>\n",
       "      <td>adult</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.245437</td>\n",
       "      <td>1.015401</td>\n",
       "      <td>0.019938</td>\n",
       "      <td>4.0</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>LR</td>\n",
       "      <td>deepfool</td>\n",
       "      <td>adult</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.402326</td>\n",
       "      <td>1.783665</td>\n",
       "      <td>0.393426</td>\n",
       "      <td>30.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>MLP</td>\n",
       "      <td>pgd_l_inf</td>\n",
       "      <td>breast_cancer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1.464652</td>\n",
       "      <td>1.894572</td>\n",
       "      <td>0.478344</td>\n",
       "      <td>30.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>MLP</td>\n",
       "      <td>pgd_l_inf</td>\n",
       "      <td>breast_cancer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1.544936</td>\n",
       "      <td>2.043933</td>\n",
       "      <td>0.163271</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>MLP</td>\n",
       "      <td>pgd_l_inf</td>\n",
       "      <td>breast_cancer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1.557057</td>\n",
       "      <td>2.098848</td>\n",
       "      <td>0.406677</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>MLP</td>\n",
       "      <td>pgd_l_inf</td>\n",
       "      <td>breast_cancer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1.457735</td>\n",
       "      <td>1.849604</td>\n",
       "      <td>0.581779</td>\n",
       "      <td>30.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>MLP</td>\n",
       "      <td>pgd_l_inf</td>\n",
       "      <td>breast_cancer</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99648 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     eval_L2  eval_Sen  eval_Mahalanobis  eval_Sparsity ground_truth  \\\n",
       "0   0.832862  3.445641          0.067658            4.0        <=50K   \n",
       "1   0.002511  0.010387          0.000204            4.0        <=50K   \n",
       "2   0.061329  0.146191          0.009033            2.0         >50K   \n",
       "3   0.665483  2.753176          0.054061            4.0        <=50K   \n",
       "4   0.245437  1.015401          0.019938            4.0        <=50K   \n",
       "..       ...       ...               ...            ...          ...   \n",
       "59  1.402326  1.783665          0.393426           30.0            N   \n",
       "60  1.464652  1.894572          0.478344           30.0            N   \n",
       "61  1.544936  2.043933          0.163271           30.0            Y   \n",
       "62  1.557057  2.098848          0.406677           30.0            Y   \n",
       "63  1.457735  1.849604          0.581779           30.0            N   \n",
       "\n",
       "   adv_prediction model     attack        dataset  attack_success  \n",
       "0            >50K    LR   deepfool          adult             1.0  \n",
       "1            >50K    LR   deepfool          adult             1.0  \n",
       "2            >50K    LR   deepfool          adult             0.0  \n",
       "3            >50K    LR   deepfool          adult             1.0  \n",
       "4            >50K    LR   deepfool          adult             1.0  \n",
       "..            ...   ...        ...            ...             ...  \n",
       "59              Y   MLP  pgd_l_inf  breast_cancer             1.0  \n",
       "60              Y   MLP  pgd_l_inf  breast_cancer             1.0  \n",
       "61              N   MLP  pgd_l_inf  breast_cancer             1.0  \n",
       "62              N   MLP  pgd_l_inf  breast_cancer             1.0  \n",
       "63              Y   MLP  pgd_l_inf  breast_cancer             1.0  \n",
       "\n",
       "[99648 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_list_list_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace cell values\n",
    "data_list_list_df['attack'] = data_list_list_df['attack'].replace('fgsm_l_inf', 'FGSM')\n",
    "data_list_list_df['attack'] = data_list_list_df['attack'].replace('pgd_l_inf', 'PGD')\n",
    "data_list_list_df['attack'] = data_list_list_df['attack'].replace('carlini_l_2', 'C&W')\n",
    "data_list_list_df['attack'] = data_list_list_df['attack'].replace('lowprofool_l_2', 'LowProFool')\n",
    "data_list_list_df['attack'] = data_list_list_df['attack'].replace('deepfool', 'DeepFool')\n",
    "\n",
    "data_list_list_df['dataset'] = data_list_list_df['dataset'].replace('adult', 'Adult')\n",
    "data_list_list_df['dataset'] = data_list_list_df['dataset'].replace('german', 'German')\n",
    "data_list_list_df['dataset'] = data_list_list_df['dataset'].replace('compas', 'Compas')\n",
    "data_list_list_df['dataset'] = data_list_list_df['dataset'].replace('diabetes', 'Diabetes')\n",
    "data_list_list_df['dataset'] = data_list_list_df['dataset'].replace('breast_cancer', 'Breast Cancer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x1600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot violin plot, for each chart include two class (attack success or not) of all attacks for each dataset and model\n",
    "# three models: LR, SVC, MLP\n",
    "# five datasets: Adult, German, Compas, Diabetes, Breast Cancer\n",
    "\n",
    "# L2 distance\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "# sns.set(font_scale=1.5)\n",
    "g = sns.catplot(x=\"attack\", y=\"eval_L2\", hue=\"attack_success\", col=\"model\", row=\"dataset\", \n",
    "                data=data_list_list_df, kind=\"box\", height=4, aspect=1.5, margin_titles=True,\n",
    "                sharex=False, sharey=False, legend=False, showfliers=False, width=0.5)\n",
    "g.set_axis_labels(\"\", \"L2 distance\")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "g.add_legend(title='Attack Success')\n",
    "g.fig.subplots_adjust(top=0.94)\n",
    "g.fig.suptitle('L2 Distance')\n",
    "g.fig.set_size_inches(13, 16)\n",
    "g.savefig('Visualisation/l2_distance_boxplot.png')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x1600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sensitivity\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "# sns.set(font_scale=1.5)\n",
    "g = sns.catplot(x=\"attack\", y=\"eval_Sen\", hue=\"attack_success\", col=\"model\", row=\"dataset\", \n",
    "                data=data_list_list_df, kind=\"box\", height=4, aspect=1.5, margin_titles=True,\n",
    "                sharex=False, sharey=False, legend=False, showfliers=False, width=0.5)\n",
    "# , palette=\"Set1\"\n",
    "g.set_axis_labels(\"\", \"Sensitivity\")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "g.add_legend(title='Attack Success')\n",
    "g.fig.subplots_adjust(top=0.94)\n",
    "g.fig.suptitle('Sensitivity')\n",
    "g.fig.set_size_inches(13, 16)\n",
    "g.savefig('Visualisation/sensitivity_boxplot.png')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I+Y7lbNeKmE/MrwxnifkScb8sxHx4CpKTVSAwMFANGjSwvG7WrJlat26tAQMG6OOPP9bEiROrtT3BwcE27XEVCxcuVGZmpjZv3mxz0zV16lRlZ2dr1qxZio2NVc2aNXX06FH99re/VYsWLTR48GAtW7ZM586ds9y0dO7c2fLhVHDTcvjwYd24cUMJCQlatGiRTCaTvLzudCbOyMhQt27dLK+dTe3atW1+p40aNdLkyZM1atQofffdd3rwwQfv6vh169Z1mveMv79/iW05dOiQ3nvvPU2ZMsXmyWZISIgiIyOVm5urefPm6bPPPpMkrVu3Tj179tRzzz1n2fbee++V0WjUzJkzNWXKFNWpU0eS1LZtW508eVLbt2/X4MGDLdtnZ2dr586d6tSpU1WcbqUUjjkNGzbUjBkz1KtXL33zzTcKCQnRqlWrtGTJEvXt29eyXZMmTdSlSxeNGjVKixYt0sKFC0s8pqPjmDNztmtFzHe/mC95Ttwn5peNmO9YznatiPnE/MpwlpgvEffLQsyHp3DeiOxmmjRpooceekhffvmlJOn69et688031a1bN3Xq1ElPP/20Dh8+bLPPjh07NHToULVr104PPfSQFixYoLy8PMv6sLAw/eEPf9Dw4cMVERGhQYMGadu2bRVu28mTJ/XCCy+oa9eu6tSpkxISEvTvf//bsv727dtasWKF+vXrp4iICPXr109r166t5JUon/z8fP3xj3/U0KFDi30aPGHCBC1btkz+/v6SpB49euiTTz7Rjz/+qAULFli6qReoVauWIiIidODAAcuynTt3KjIyUg8++KCuXLmi77//XtKdp5MHDhxQTEyM3c7niy++0ODBg9WuXTvFxsZqyZIlun37tkwmk7p3765PPvnEsu3KlSsVFhZm83545ZVXin2iaK1GjTvPGnx9fSXdeX8sWrRIffr0UUxMjE6dOiWj0agFCxaob9++ioiI0JAhQ/SXv/ylwudz7tw5TZo0SdHR0erQoYOee+45HTt2rFznbE+rV69W06ZNFRcXV+z6adOmafny5ZbXBoNBx44d0/nz5222e+yxx7R582abXgY+Pj7q27ev/vznP9tsu3XrVoWFhalZs2Z2PBP7s34/rFy5Ul27drW5YSlgMBi0cOFCvfvuu2Ues3AcQ8mI+RXjbjFfIu5XRdwn5peMmO9YxPyKIeYT88uLuF88Yj7cEcnJanT//ffrzJkzys7O1rhx43TmzBktXbpU69evV4cOHTRq1CgdOXJEkvTtt99qwoQJGj58uDZv3qy33npLX3/9tRITE22OOW/ePA0ZMkR/+tOf9MADD+jll1+2+WAuy7///W+NGDHCEtjS0tJ08eJFxcXFKTs7W5I0Z84cLVmyRC+//LI2bdqkp556Su+8845WrFhht2tT2JkzZ3TlyhV17Nix2PWNGjVSu3bt5O3tLemXp6yPPvqocnNzlZaWZgnaBXr06KGDBw9aXu/atUvR0dFq1KiR7r//fn377beSpGPHjik7O1s9evSwy7msWLFCb775pkaMGKGNGzdq/PjxWr58uebMmSMvLy898MAD2r17t2X7v/3tbzIYDJYaLfn5+dq9e3exHzjSne79p06dUnJysho2bGhzzdasWaNFixYpNTVVLVq00KuvvqovvvhCb775pjZu3KgHH3xQ48eP19atW8t9PtnZ2Ro1apTOnz+vDz74QJ9++qn8/f0VFxdnudkt7Zztae/everWrVuJwy6Cg4NVt25dy+sxY8bo559/VmxsrMaMGaPU1FTt3btXPj4+atmyZZH3zKOPPqpdu3ZZ/hakO8M8BgwYYNfzsLfz589r5syZCgwM1AMPPKADBw6U+n5u1KhRuYcHFcQxe9WycmfE/PJzp5gvEferKu4T84tHzHcOxPzyI+YT88uLuF8UMR/uiuRkNSroQr59+3b9/e9/14IFC9S+fXu1bNlSr776qjp06KDf//73kqQPP/xQw4cP18iRI9W8eXPFxMRoxowZ+vOf/2xTkHro0KF66qmn9Ktf/UqTJk1SRESEVq9ebfNzBw4cqMjISJuvjIwMSXc+0AIDAzVv3jyFh4erffv2WrRokX7++Wf96U9/UnZ2ttauXauEhAQNGjRILVq00NNPP60nn3xSH330kcxmc5Vcq6tXr0qSzYdNSX744QfFx8erQYMG8vPzU506dVS/fn0ZjUabp3fdu3fXiRMndO3aNV29elWHDh2yPDWNiYnRrl27JEn79u1TixYt1LRp07s+D7PZrGXLlikuLk5PPfWUWrRooSFDhighIUFr167V9evXFRsbq4yMDOXl5Sk/P1979+5V3759LTcsGRkZMplMNh86b731luV3GRERoUceeUQ5OTl6//33bWqmDBkyRBEREerQoYNOnjypbdu26a233lLv3r1133336ZVXXlHfvn314Ycf2rR73LhxRd4zGzdulCRt3LhRly9f1sKFC9WuXTuFh4frvffek7+/v/7whz+U65wrYtOmTUXaEh8fL0n66aefLEN6CixbtqzI9v/5z38kSR07dtSGDRv02GOP6eTJk1q8eLFGjx6tPn36FHvT1qNHDwUEBGjHjh2S7rwvv/vuOz366KMVOoeqtnTpUpv3Q69evXTixAktWLBANWrUkMlkKnKdZs6cWeQ6lUdBHLO+iUPxiPnl5y4xXyLu323cJ+aXjZjvnIj55UfMJ+ZbI+6XjpgPT0HNyWpUEKjPnDkjs9msPn362KzPy8tTbm6uJOnIkSM6dOiQpX6GJMsNwsmTJxUaGipJ6tq1q80xIiMjbZ7MSdJHH32kRo0a2SwreH38+HG1bdvWMjxAkho0aKD77rtPx48f1w8//KD8/PwiNTe6dOmilStX6ueff67YRSinggB75cqVUrfLycnRM888o4iICKWkpOibb77R//zP/6hNmza6ePGitmzZoh07dshgMKh9+/by9/fXwYMHlZOTozp16ujXv/61JCk6OlqrVq1STk6OMjIyip3xrzIuXbqkn376qdjrl5+frx9++EHR0dG6ffu29u/frxo1aqhmzZoaMWKExo8fr1u3bumvf/2roqOjLUNbJCkhIUEPP/ywJMnb21v16tUr9onYvffea/m+YHa/wm2JiopSSkqKzbJZs2apffv2NsvuueceSXfeMy1atLD5EPT391e7du10/Pjxcp1zwbHKIzY2VpMmTbJZVnAt6tWrV+Q9Mnz4cMu1+cc//qHExESZTCbL+latWumdd96RdOdvaefOnVq9erXGjx+vDRs22AwTKhju8Ze//EWDBg3Sli1b1KFDhyJ/T442cuRISx0eLy8vBQUFWQpZ5+XlyWAwFLlOL7/8ssaMGSNJ2rJli+bNm1eun1UQxwoXDkdRxPzyc5eYLxH3Szrn8sZ9Yn7ZiPnOiZhffsR8Yr414n7piPnwFCQnq9E///lPtWjRQj4+PqpVq5Y2bNhQZJuCmweTyaT4+HibmeUKWBevLdw1/fbt20WKOzdp0sRyk1NYSU9ETSaTfHx8Sl1f3M+3l2bNmql+/fo6cOCAZaY6aydPntQ777yjBx54QOfPn9fnn38uPz8/DRw4UCdOnND8+fMVEBCgIUOGWIYB+Pr6qlOnTvr73/+uixcvKjo62rIuKipK3t7eOnjwoPbv36/f/e53djmP8ly/mjVrqmvXrtq9e7d8fHzUtWtXde7cWfn5+Tp8+LD++te/6r//+79t9r/nnntsbkZKYn2TU1obC/8eGzVqVOLxSzunGjVq2P09U7NmzRLb0qlTJ+3du9dmWd26dS1P4rOysizLc3JylJKSoscff1ytW7eWJLVs2VItW7bU4MGD1adPH+3atcvmhkWS+vfvr9/+9re6ceOGvv7662Lfj45Wt27dEq+Rr6+vIiIitHfvXj3//POW5cHBwZabzorcQBbEsbudJdITEPPLz11ivkTcL7xeqtj7hphfNmK+cyLmlx8xn5hvjbhfOmI+PAXDuqtJVlaWtm3bpkGDBun+++9Xdna28vPzde+991q+li1bZil0/V//9V/68ccfbdZnZWVp7ty5NjUgChfXPnjwoOUpYXkUFGO2LsD9008/6fTp05Zg7uPjo/3799vsl5GRoQYNGpRrOEZleHl56YknntCGDRt07ty5Ius//vhjHT582BKoL168aFk3YcIE3XfffcrOzlbv3r1t9uvRo4cOHTqkffv22Tw19fPzU+fOnfXll1/q6tWrRZ5UV1b9+vVVv379Yq+fj4+PZUbBPn36aPfu3dqzZ4+6d++uwMBAdejQQevWrdOZM2eKnEdlFHwQF9eWVq1aVeg4p06dsnmanpubq++//16tWrUq9znbw9NPP61Tp05p/fr1xa63fu/4+/tr06ZN+vTTT4tsV7NmTXl7exf74d2tWzcFBgbqiy++UEZGhvr162e39leXZ555Rrt27dLOnTuLXV/c31hxrOMYSkfMrxh3ifkScb/wz7Fn3Cfmlw8xv/oR8yuGmE/MLy/iftmI+XAX9JysAjk5OZYPUaPRqMzMTC1YsEChoaF69tln5e/vr9atW2vixImaOnWqQkJCtGbNGm3YsMEy29i4ceM0YcIEpaamasCAAcrKytLUqVMVGhpq80R15cqV+tWvfqW2bdtq/fr1yszMtHRjL49Ro0Zp7dq1SkxM1Isvvqi8vDwlJSWpXr16GjBggGrVqqURI0Zo0aJFCgoKUkREhHbt2qU1a9bo1VdfLbE4sT288MIL2rlzp5588kmNHz9eHTt21JUrV7R27Vp98cUXmj9/vmJiYhQeHq7Jkyfr7bffVu3atbV69WqdPXtW4eHheuONN7R06VLLjVz37t2VmpqqmzdvFhnSERMTo0WLFql9+/aV6sp++vRpS7HtAv7+/nruuec0f/58NWvWTNHR0Tp06JBSU1M1YsQIS5f82NhYzZo1S97e3kpOTpZ054Py/fffV8eOHYvUEamMli1bqk+fPpoxY4YMBoPuvfdeffnll9q2bZsWLFhQ7uMMGjRIS5cu1YQJE5SYmChfX1+9//77ysnJ0YgRIySpzHMuqDV0tzp27KjXX39dM2fO1Pfff6/BgwcrJCRE586d08aNG/XZZ5+pTZs2CgoKkpeXlyZNmqRp06ZJkh5//HEFBwfrX//6l9LS0hQSEqJHHnmkyM+oUaOGHnroIaWkpCgqKsouv4vqNmDAAH3//fd68cUXNWbMGPXr10/33HOPTp8+rfXr1+vrr79Wt27dbPYpK47hF8R8+3C1mC8R96s77hPzy4eYX7WI+fZBzCfmlwdxv2zEfLgLkpNVIC0tTWlpaZLu1LEICQlR//79NXbsWEsX6bS0NCUnJ2vChAm6efOmWrZsqdTUVHXv3l2S9Mgjj2j+/PlaunSpPvzwQwUFBRVbj2PkyJFasWKFjh8/rvDwcC1fvlzh4eHlbmtoaKhWr16t5ORky2x+0dHRSk5OthTEnTJliurVq6d58+bpp59+UosWLTR9+nQNHz7cHperRAEBAVq9erXS0tK0bNky/ec//5G/v7/atGmjVatWqXPnzpLuFEV+9913FR8fL5PJpMjISK1Zs0bNmzfXSy+9pMOHD1tuWlq3bi1fX1+FhoYWqSUSExOjOXPmVHr2vk2bNmnTpk02y5o2bart27dbZkl899131bhxY40bN07PPfecZbuQkBCFhYUpOzvbUqC7R48eWrx4cYkz91VGSkqKUlJSNHXqVF27dk3333+/Fi9erIceeqjcxyi4MZwzZ46eeeYZSXeGXKxdu1bNmjWTJI0dO7bMc7aXMWPGKDIyUqtXr1ZiYqIuXryoWrVqqW3btpozZ4769+9vGV4ybNgwNWjQQCtXrtS4ceN048YN1a9fX3379tXcuXNLHBrTv39/rV+/3qln7ivL5MmTFRMTo08//VQvvfSSLl++rKCgIHXo0EEffPCBYmNjbbYvTxzDHcR8+3C1mC8R9x0R94n55UPMrzrEfPsg5hPzy4u4XzZiPtyBwVxV07ChyoWFhWn27NkaOnSoo5vitMxmc5U+9QWA6kLMLxsxH4C7IOaXjZgPAO6DmpNwa9ywAIDnIOYDgOcg5gOA+yA5CQAAAAAAAMAhGNYNAAAAAAAAwCHoOQkAAAAAAADAIUhOAgAAAAAAAHAIkpMAAAAAAAAAHILkJAAAAAAAAACHIDkJAAAAAAAAwCFITgIAAAAAAABwCJKTAAAAAAAAAByC5CQAAAAAAAAAh/h/JzkaH8OVoEMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1300x1600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Deviation\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "# sns.set(font_scale=1.5)\n",
    "g = sns.catplot(x=\"attack\", y=\"eval_Mahalanobis\", hue=\"attack_success\", col=\"model\", row=\"dataset\", \n",
    "                data=data_list_list_df, kind=\"box\", height=4, aspect=1.5, margin_titles=True,\n",
    "                sharex=False, sharey=False, legend=False, showfliers=False, width=0.5)\n",
    "g.set_axis_labels(\"\", \"Deviation\")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "g.add_legend(title='Attack Success')\n",
    "g.fig.subplots_adjust(top=0.94)\n",
    "g.fig.suptitle('Deviation')\n",
    "g.fig.set_size_inches(13, 16)\n",
    "g.savefig('Visualisation/deviation_boxplot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_list_list_df.to_csv(f'all_imp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sns.set_theme()\n",
    "sns.set(font_scale=1.9)\n",
    "\n",
    "# Plot sepal width as a function of sepal_length across days\n",
    "g = sns.lmplot(\n",
    "    data=data_list_list_df,\n",
    "    x=\"eval_L2\", y=\"attack_success\", \n",
    "    hue=\"model\", col='attack', row='dataset',\n",
    "    # height=5,\n",
    "    logistic=True,\n",
    "    facet_kws=dict(sharex=False, sharey=True, margin_titles=True),\n",
    ")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "# Use more informative axis labels than are provided by default\n",
    "g.set_axis_labels(\"Proximity: L2\", \"Attack Success\")\n",
    "sns.move_legend(\n",
    "    g, \"lower center\",\n",
    "    bbox_to_anchor=(.5, 1), ncol=3, title=None, frameon=False,\n",
    ")\n",
    "\n",
    "# g.set_titles(\"Dataset: Diabetes  Attack: FGSM Linf\")\n",
    "# g.set(title=\"Dataset: Diabetes  Attack: FGSM Linf\")\n",
    "# sns.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sns.set_theme()\n",
    "sns.set(font_scale=1.9)\n",
    "\n",
    "# Plot sepal width as a function of sepal_length across days\n",
    "g = sns.lmplot(\n",
    "    data=data_list_list_df,\n",
    "    x=\"eval_Mahalanobis\", y=\"attack_success\", \n",
    "    hue=\"model\", col='attack', row='dataset',\n",
    "    # height=5,\n",
    "    logistic=True,\n",
    "    facet_kws=dict(sharex=False, sharey=True, margin_titles=True),\n",
    ")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "# Use more informative axis labels than are provided by default\n",
    "g.set_axis_labels(\"Deviation\", \"Attack Success\")\n",
    "sns.move_legend(\n",
    "    g, \"lower center\",\n",
    "    bbox_to_anchor=(.5, 1), ncol=3, title=None, frameon=False,\n",
    ")\n",
    "# g.set_titles(\"Dataset: Diabetes  Attack: FGSM Linf\")\n",
    "# g.set(title=\"Dataset: Diabetes  Attack: FGSM Linf\")\n",
    "# sns.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sns.set_theme()\n",
    "sns.set(font_scale=1.9)\n",
    "\n",
    "# Plot sepal width as a function of sepal_length across days\n",
    "g = sns.lmplot(\n",
    "    data=data_list_list_df,\n",
    "    x=\"eval_Sen\", y=\"attack_success\", \n",
    "    hue=\"model\", col='attack', row='dataset',\n",
    "    # height=5,\n",
    "    logistic=True,\n",
    "    facet_kws=dict(sharex=False, sharey=True, margin_titles=True),\n",
    ")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "# Use more informative axis labels than are provided by default\n",
    "g.set_axis_labels(\"Sensitivity\", \"Attack Success\")\n",
    "sns.move_legend(\n",
    "    g, \"lower center\",\n",
    "    bbox_to_anchor=(.5, 1), ncol=3, title=None, frameon=False,\n",
    ")\n",
    "# g.set_titles(\"Dataset: Diabetes  Attack: FGSM Linf\")\n",
    "# g.set(title=\"Dataset: Diabetes  Attack: FGSM Linf\")\n",
    "# sns.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = sns.FacetGrid(data=data_list_list_df, col='dataset')\n",
    "m.map_dataframe(sns.barplot, x=\"attack\", y=\"eval_L2\", hue=\"model\",)\n",
    "\n",
    "# astype({'eval_L2':float, 'eval_Sen':float, 'eval_Mahalanobis':float, 'attack_success': float})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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