Data Analysis.ipynb

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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": [],
   "execution_count": 43
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   "cell_type": "code",
   "source": "data = pd.DataFrame(data)",
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   "execution_count": 44
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       "     epsilon attack_type  norm_type  targeted        step_rule  accuracy  \\\n",
       "0      0.005          fw         -1      True  lipschitz_mnist      1.00   \n",
       "1      0.005          fw         -1      True            decay      1.00   \n",
       "2      0.005          fw         -1      True             amjo      1.00   \n",
       "3      0.005          fw         -1      True               ls      1.00   \n",
       "4      0.005          fw         -1     False  lipschitz_mnist      1.00   \n",
       "..       ...         ...        ...       ...              ...       ...   \n",
       "355    0.250     fw_pair          2      True               ls      0.95   \n",
       "356    0.250     fw_pair          2     False  lipschitz_mnist      0.88   \n",
       "357    0.250     fw_pair          2     False            decay      0.85   \n",
       "358    0.250     fw_pair          2     False             amjo      0.85   \n",
       "359    0.250     fw_pair          2     False               ls      0.85   \n",
       "\n",
       "     total_queries  \n",
       "0             1948  \n",
       "1             1948  \n",
       "2             1948  \n",
       "3             1948  \n",
       "4             1876  \n",
       "..             ...  \n",
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       "356           1928  \n",
       "357           1859  \n",
       "358           1856  \n",
       "359           1855  \n",
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       "[360 rows x 7 columns]"
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   "source": "data.describe()",
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       "          epsilon   norm_type    accuracy  total_queries\n",
       "count  360.000000  360.000000  360.000000     360.000000\n",
       "mean     0.083000    0.666667    0.620306    1370.322222\n",
       "std      0.090325    1.248955    0.426050     831.701901\n",
       "min      0.005000   -1.000000    0.010000     124.000000\n",
       "25%      0.010000   -1.000000    0.090000     202.000000\n",
       "50%      0.050000    1.000000    0.930000    1984.000000\n",
       "75%      0.100000    2.000000    1.000000    2002.000000\n",
       "max      0.250000    2.000000    1.000000    2002.000000"
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       "      <td>0.083000</td>\n",
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   "cell_type": "code",
   "source": "print(data.groupby('attack_type')[['accuracy', 'total_queries']].mean())",
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     "name": "stdout",
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     "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"
     ]
    }
   ],
   "execution_count": 47
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   },
   "cell_type": "code",
   "source": "print(data.groupby('epsilon')['accuracy'].mean())",
   "id": "98b0dc78de43c13",
   "outputs": [
    {
     "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"
     ]
    }
   ],
   "execution_count": 49
  },
  {
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     "start_time": "2024-07-05T10:00:39.856486Z"
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   },
   "cell_type": "code",
   "source": "print(data.groupby('targeted')['accuracy'].mean())",
   "id": "706907346e0e9426",
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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"
     ]
    }
   ],
   "execution_count": 50
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   },
   "cell_type": "code",
   "source": "print(data.groupby('step_rule')[['accuracy', 'total_queries']].mean())",
   "id": "608a884c74003aee",
   "outputs": [
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     "name": "stdout",
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     "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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   },
   "cell_type": "code",
   "source": [
    "threshold = 0.1\n",
    "nice_data = pd.DataFrame(data[data['accuracy'] < threshold])\n",
    "nice_data"
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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",
       "9      0.005          fw          1      True            decay      0.09   \n",
       "10     0.005          fw          1      True             amjo      0.09   \n",
       "12     0.005          fw          1     False  lipschitz_mnist      0.09   \n",
       "13     0.005          fw          1     False            decay      0.09   \n",
       "..       ...         ...        ...       ...              ...       ...   \n",
       "346    0.250     fw_pair          1      True             amjo      0.09   \n",
       "348    0.250     fw_pair          1     False  lipschitz_mnist      0.09   \n",
       "349    0.250     fw_pair          1     False            decay      0.09   \n",
       "350    0.250     fw_pair          1     False             amjo      0.09   \n",
       "351    0.250     fw_pair          1     False               ls      0.09   \n",
       "\n",
       "     total_queries  \n",
       "8              202  \n",
       "9              202  \n",
       "10             202  \n",
       "12             202  \n",
       "13             202  \n",
       "..             ...  \n",
       "346            202  \n",
       "348            202  \n",
       "349            202  \n",
       "350            202  \n",
       "351            202  \n",
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       "[126 rows x 7 columns]"
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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]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>total_queries</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <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",
       "    </tr>\n",
       "    <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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       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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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