Robustness_Testing_Generate_Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd55657d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f15fe159",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = pd.read_csv(\"logs/resnet_perturbation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6af9d9d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Perturbation</th>\n",
       "      <th>Value</th>\n",
       "      <th>Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1-Macro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brightness Decrease</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.101683</td>\n",
       "      <td>0.669623</td>\n",
       "      <td>0.669508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brightness Decrease</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.101935</td>\n",
       "      <td>0.662971</td>\n",
       "      <td>0.663016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brightness Decrease</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.107042</td>\n",
       "      <td>0.665188</td>\n",
       "      <td>0.665148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brightness Decrease</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.112217</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>0.658313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brightness Decrease</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.114886</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>0.658450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Perturbation  Value      Loss  Accuracy  F1-Macro\n",
       "0  Brightness Decrease    0.0  1.101683  0.669623  0.669508\n",
       "1  Brightness Decrease    5.0  1.101935  0.662971  0.663016\n",
       "2  Brightness Decrease   10.0  1.107042  0.665188  0.665148\n",
       "3  Brightness Decrease   15.0  1.112217  0.658537  0.658313\n",
       "4  Brightness Decrease   20.0  1.114886  0.658537  0.658450"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b15e76d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brightness Decrease     10\n",
       "Brightness Increase     10\n",
       "Contrast Decrease       10\n",
       "Contrast Increase       10\n",
       "Gaussian Blur           10\n",
       "Gaussian Pixel Noise    10\n",
       "Occlusion               10\n",
       "Salt And Pepper         10\n",
       "Name: Perturbation, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl[\"Perturbation\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25330625",
   "metadata": {},
   "source": [
    "# Gaussian pixel noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c1ef6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_GPN = dl[dl[\"Perturbation\"]==\"Gaussian Pixel Noise\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a430280",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Perturbation</th>\n",
       "      <th>Value</th>\n",
       "      <th>Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1-Macro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.101683</td>\n",
       "      <td>0.669623</td>\n",
       "      <td>0.669508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.095302</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>0.657018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.105464</td>\n",
       "      <td>0.625277</td>\n",
       "      <td>0.623516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.116534</td>\n",
       "      <td>0.629712</td>\n",
       "      <td>0.626201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.127140</td>\n",
       "      <td>0.629712</td>\n",
       "      <td>0.626251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.159359</td>\n",
       "      <td>0.598670</td>\n",
       "      <td>0.592961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.203127</td>\n",
       "      <td>0.607539</td>\n",
       "      <td>0.599946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.207544</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.576064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.262335</td>\n",
       "      <td>0.558758</td>\n",
       "      <td>0.548524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Gaussian Pixel Noise</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.305009</td>\n",
       "      <td>0.558758</td>\n",
       "      <td>0.547650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Perturbation  Value      Loss  Accuracy  F1-Macro\n",
       "50  Gaussian Pixel Noise    0.0  1.101683  0.669623  0.669508\n",
       "51  Gaussian Pixel Noise    2.0  1.095302  0.658537  0.657018\n",
       "52  Gaussian Pixel Noise    4.0  1.105464  0.625277  0.623516\n",
       "53  Gaussian Pixel Noise    6.0  1.116534  0.629712  0.626201\n",
       "54  Gaussian Pixel Noise    8.0  1.127140  0.629712  0.626251\n",
       "55  Gaussian Pixel Noise   10.0  1.159359  0.598670  0.592961\n",
       "56  Gaussian Pixel Noise   12.0  1.203127  0.607539  0.599946\n",
       "57  Gaussian Pixel Noise   14.0  1.207544  0.585366  0.576064\n",
       "58  Gaussian Pixel Noise   16.0  1.262335  0.558758  0.548524\n",
       "59  Gaussian Pixel Noise   18.0  1.305009  0.558758  0.547650"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl_GPN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "17646ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_GPN = pd.read_csv(\"Robustness_Testing_Results_SVM/Gaussian pixel noise.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1d2d00c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>f-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.278206</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.229133</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.259396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.243009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0.229431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>0.250595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>0.246675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>0.242613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>0.247777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>0.249761</td>\n",
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       "   Unnamed: 0       f-1\n",
       "0           0  0.278206\n",
       "1           2  0.229133\n",
       "2           4  0.259396\n",
       "3           6  0.243009\n",
       "4           8  0.229431\n",
       "5          10  0.250595\n",
       "6          12  0.246675\n",
       "7          14  0.242613\n",
       "8          16  0.247777\n",
       "9          18  0.249761"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_GPN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6176d77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0]"
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dl_GPN[\"Value\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9538a868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6695076474348219,\n",
       " 0.6570179907444058,\n",
       " 0.6235157706893529,\n",
       " 0.6262008167278008,\n",
       " 0.6262509264721815,\n",
       " 0.5929612841848866,\n",
       " 0.5999456852049591,\n",
       " 0.5760639264647067,\n",
       " 0.5485239110175695,\n",
       " 0.5476503496357281]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl_GPN[\"F1-Macro\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dbef6521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_GPN[\"Value\"].tolist(), dl_GPN[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_GPN[\"Value\"].tolist(), svm_GPN[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Standard Deviation\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.savefig(\"Gaussian pixel noise.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fb9bd5",
   "metadata": {},
   "source": [
    "# Gaussian blurring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2a3de49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_GB = dl[dl[\"Perturbation\"]==\"Gaussian Blur\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4c72e874",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_GB = pd.read_csv(\"Robustness_Testing_Results_SVM/Gaussian blurring.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7664aa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "91d872e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_GB[\"Value\"].tolist(), dl_GB[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_GB[\"Value\"].tolist(), svm_GB[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Standard Deviation\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.savefig(\"Gaussian blurring.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e464b9",
   "metadata": {},
   "source": [
    "# Image Contrast Increase "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b4e7f58a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_ICI = dl[dl[\"Perturbation\"]==\"Contrast Increase\"]\n",
    "svm_ICI = pd.read_csv(\"Robustness_Testing_Results_SVM/Image Contrast Increase.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "58137437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_ICI[\"Value\"].tolist(), dl_ICI[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_ICI[\"Value\"].tolist(), svm_ICI[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Multiplication Value for each Pixel\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.savefig(\"Image Contrast Increase.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9506243c",
   "metadata": {},
   "source": [
    "# Image Contrast Decrease "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7f60936f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_ICD = dl[dl[\"Perturbation\"]==\"Contrast Decrease\"]\n",
    "svm_ICD = pd.read_csv(\"Robustness_Testing_Results_SVM/Image Contrast Decrease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "06e5bbb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6695076474348219,\n",
       " 0.6696665954775196,\n",
       " 0.6599123285219513,\n",
       " 0.6419483937441124,\n",
       " 0.6387585013286836,\n",
       " 0.6298089492233571,\n",
       " 0.5898649477587268,\n",
       " 0.5528156350087943,\n",
       " 0.4822511878478533,\n",
       " 0.24330587901691]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = dl_ICD[\"Value\"].tolist()\n",
    "l = l[::-1]\n",
    "d = dl_ICD[\"F1-Macro\"].tolist()\n",
    "d = d[::-1]\n",
    "svm_ICD[\"f-1\"].tolist()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6a412b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_ICD[\"Value\"].tolist(), dl_ICD[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_ICD[\"Value\"].tolist(), svm_ICD[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Multiplication Value for each Pixel\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.xlim(max(dl_ICD[\"Value\"].tolist()), 0.1)\n",
    "plt.savefig(\"Image Contrast Decrease.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a3567f",
   "metadata": {},
   "source": [
    "# Image Brightness Increase "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c75d17da",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_IBI = dl[dl[\"Perturbation\"]==\"Brightness Increase\"]\n",
    "svm_IBI = pd.read_csv(\"Robustness_Testing_Results_SVM/Image Brightness Increase.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6375e304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_IBI[\"Value\"].tolist(), dl_IBI[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_IBI[\"Value\"].tolist(), svm_IBI[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Addition Value for each Pixel\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.savefig(\"Image Brightness Increase.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a238f7a5",
   "metadata": {},
   "source": [
    "# Image Brightness Decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "98d50e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_IBD = dl[dl[\"Perturbation\"]==\"Brightness Decrease\"]\n",
    "svm_IBD = pd.read_csv(\"Robustness_Testing_Results_SVM/Image Brightness Decrease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c01c7720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_IBD[\"Value\"].tolist(), dl_IBD[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_IBD[\"Value\"].tolist(), svm_IBD[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Subtraction Value for each Pixel\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.savefig(\"Image Brightness Decrease.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d8c603",
   "metadata": {},
   "source": [
    "# Occlusion of the Image Increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d358d594",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_IBD = dl[dl[\"Perturbation\"]==\"Occlusion\"]\n",
    "svm_IBD = pd.read_csv(\"Robustness_Testing_Results_SVM/Occlusion of the Image Increase.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b3df81b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_IBD[\"Value\"].tolist(), dl_IBD[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_IBD[\"Value\"].tolist(), svm_IBD[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Square Edge length\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.savefig(\"Occlusion.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1390fe0b",
   "metadata": {},
   "source": [
    "# Salt and Pepper Noise "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "246bbbdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_IBD = dl[dl[\"Perturbation\"]==\"Salt And Pepper\"]\n",
    "svm_IBD = pd.read_csv(\"Robustness_Testing_Results_SVM/Salt and Pepper Noise.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6ed5c411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dl_IBD[\"Value\"].tolist(), dl_IBD[\"F1-Macro\"].tolist(), 'b', marker = 'x')\n",
    "plt.plot(dl_IBD[\"Value\"].tolist(), svm_IBD[\"f-1\"].tolist(), 'r', marker = 'x')\n",
    "plt.xlabel(\"Strength\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.legend(['ResNet-26D', 'SVM with SIFT'])\n",
    "plt.savefig(\"Salt and Pepper Noise.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf3bccc5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}