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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
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   "source": "data = pd.read_csv(r'C:\\Users\\alepa\\PycharmProjects\\pythonProject\\Optimization\\Project\\Attacks Dataset - MNIST.csv')",
   "outputs": [],
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       "     epsilon attack_type  norm_type  targeted        step_rule  accuracy  \\\n",
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       "\n",
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       "          epsilon   norm_type    accuracy  total_queries\n",
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   "source": "print(data.groupby('attack_type')[['accuracy', 'total_queries']].mean())",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             accuracy  total_queries\n",
      "attack_type                         \n",
      "fw           0.620250    1052.333333\n",
      "fw_away      0.621083    2002.000000\n",
      "fw_pair      0.619583    1056.633333\n"
     ]
    }
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   "execution_count": 47
  },
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   "source": "print(data.groupby('epsilon')['accuracy'].mean())",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon\n",
      "0.005    0.702917\n",
      "0.010    0.701250\n",
      "0.050    0.690278\n",
      "0.100    0.634028\n",
      "0.250    0.373056\n",
      "Name: accuracy, dtype: float64\n"
     ]
    }
   ],
   "execution_count": 48
  },
  {
   "metadata": {
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     "start_time": "2024-07-05T10:00:39.845144Z"
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   "cell_type": "code",
   "source": "print(data.groupby('norm_type')['accuracy'].mean())",
   "id": "3ff32ea5ba11215e",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "norm_type\n",
      "-1    0.774917\n",
      " 1    0.109167\n",
      " 2    0.976833\n",
      "Name: accuracy, dtype: float64\n"
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   "execution_count": 49
  },
  {
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   "cell_type": "code",
   "source": "print(data.groupby('targeted')['accuracy'].mean())",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "targeted\n",
      "False    0.597000\n",
      "True     0.643611\n",
      "Name: accuracy, dtype: float64\n"
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    }
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   "execution_count": 50
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   "cell_type": "code",
   "source": "print(data.groupby('step_rule')[['accuracy', 'total_queries']].mean())",
   "id": "608a884c74003aee",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 accuracy  total_queries\n",
      "step_rule                               \n",
      "amjo             0.609111    1365.366667\n",
      "decay            0.606778    1365.177778\n",
      "lipschitz_mnist  0.630333    1383.777778\n",
      "ls               0.635000    1366.966667\n"
     ]
    }
   ],
   "execution_count": 51
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    "threshold = 0.1\n",
    "nice_data = pd.DataFrame(data[data['accuracy'] < threshold])\n",
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       "     epsilon attack_type  norm_type  targeted        step_rule  accuracy  \\\n",
       "8      0.005          fw          1      True  lipschitz_mnist      0.09   \n",
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       "351    0.250     fw_pair          1     False               ls      0.09   \n",
       "\n",
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       "8              202  \n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>0.250</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>amjo</td>\n",
       "      <td>0.09</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.250</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>lipschitz_mnist</td>\n",
       "      <td>0.09</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>0.250</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>decay</td>\n",
       "      <td>0.09</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0.250</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>amjo</td>\n",
       "      <td>0.09</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>0.250</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>ls</td>\n",
       "      <td>0.09</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 7 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-05T10:01:01.526552Z",
     "start_time": "2024-07-05T10:01:01.516631Z"
    }
   },
   "cell_type": "code",
   "source": [
    "nice_data = pd.DataFrame(data[data['epsilon'] == 0.25])\n",
    "nice_data"
   ],
   "id": "457c7a104b6e2217",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     epsilon attack_type  norm_type  targeted        step_rule  accuracy  \\\n",
       "288     0.25          fw         -1      True  lipschitz_mnist      0.51   \n",
       "289     0.25          fw         -1      True            decay      0.05   \n",
       "290     0.25          fw         -1      True             amjo      0.04   \n",
       "291     0.25          fw         -1      True               ls      0.04   \n",
       "292     0.25          fw         -1     False  lipschitz_mnist      0.05   \n",
       "..       ...         ...        ...       ...              ...       ...   \n",
       "355     0.25     fw_pair          2      True               ls      0.95   \n",
       "356     0.25     fw_pair          2     False  lipschitz_mnist      0.88   \n",
       "357     0.25     fw_pair          2     False            decay      0.85   \n",
       "358     0.25     fw_pair          2     False             amjo      0.85   \n",
       "359     0.25     fw_pair          2     False               ls      0.85   \n",
       "\n",
       "     total_queries  \n",
       "288           1458  \n",
       "289            871  \n",
       "290            871  \n",
       "291            851  \n",
       "292            558  \n",
       "..             ...  \n",
       "355           2002  \n",
       "356           1928  \n",
       "357           1859  \n",
       "358           1856  \n",
       "359           1855  \n",
       "\n",
       "[72 rows x 7 columns]"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epsilon</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>norm_type</th>\n",
       "      <th>targeted</th>\n",
       "      <th>step_rule</th>\n",
       "      <th>accuracy</th>\n",
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       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "      <td>lipschitz_mnist</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1458</td>\n",
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       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "      <td>decay</td>\n",
       "      <td>0.05</td>\n",
       "      <td>871</td>\n",
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       "      <th>290</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "      <td>amjo</td>\n",
       "      <td>0.04</td>\n",
       "      <td>871</td>\n",
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       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "      <td>ls</td>\n",
       "      <td>0.04</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>lipschitz_mnist</td>\n",
       "      <td>0.05</td>\n",
       "      <td>558</td>\n",
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       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>ls</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2002</td>\n",
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       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>lipschitz_mnist</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1928</td>\n",
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       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>decay</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>amjo</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>0.25</td>\n",
       "      <td>fw_pair</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>ls</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 7 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 53
  }
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