Experiments_DB_MT.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "c0081a55",
   "metadata": {},
   "source": [
    "## References\n",
    "- [1] Erlingsson, Pihur, and Korolova (2014) \"RAPPOR: Randomized aggregatable privacy-preserving ordinal response\" (ACM CCS).\n",
    "- [2] Arcolezi et al (2022) \"Improving the Utility of Locally Differentially Private Protocols for Longitudinal and Multidimensional Frequency Estimates\" (Digital Communications and Networks).\n",
    "- [3] Ding et al (2017) \"Collecting telemetry data privately\" (NIPS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a2294ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "frac = 0.25 # (reduced dataset) frac=1 for results in the paper\n",
    "nb_seed = 5 # nb_seed=20 for results in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "682c75eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "params = {'axes.titlesize':'20','xtick.labelsize':'20','ytick.labelsize':'20','font.size':'18', 'legend.fontsize':'medium', 'lines.linewidth':'2.5', 'font.weight':'normal','lines.markersize':'16', 'text.latex.preamble': r'\\usepackage{amsfonts}', 'lines.markerfacecolor':'none'}\n",
    "matplotlib.rcParams.update(params)\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"cm\"\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "from sys import maxsize\n",
    "import time\n",
    "import numpy as np\n",
    "import xxhash\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9630fe15",
   "metadata": {},
   "source": [
    "## Importing protocols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e10b1132",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seeding function to ensure reproducibility\n",
    "from LDP.protocols import setting_seed\n",
    "# Our protocols\n",
    "from LDP.protocols import LOLOHA_Client, LOLOHA_Aggregator\n",
    "# Competitors\n",
    "from LDP.protocols import RAPPOR_Client, RAPPOR_Aggregator # [1]\n",
    "from LDP.protocols import L_OSUE_Client, L_OSUE_Aggregator # [2]\n",
    "from LDP.protocols import L_GRR_Client, L_GRR_Aggregator # [2]\n",
    "from LDP.protocols import dBitFlipPM_Client, dBitFlipPM_Aggregator # [3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d2dee8",
   "metadata": {},
   "source": [
    "## Reading PWGTP_Montana dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b31d9af9",
   "metadata": {},
   "outputs": [
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       "...    ...  ...  ...  ...  ...  ...  ...  \n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/PWGTP_Montana.csv')\n",
    "df.fillna(-99999, inplace=True)\n",
    "df.columns = range(df.shape[1])   \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23137e61",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "806fa24b",
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <td>110</td>\n",
       "      <td>87</td>\n",
       "      <td>109</td>\n",
       "      <td>42</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>379</td>\n",
       "      <td>224</td>\n",
       "      <td>78</td>\n",
       "      <td>322</td>\n",
       "      <td>284</td>\n",
       "      <td>393</td>\n",
       "      <td>297</td>\n",
       "      <td>92</td>\n",
       "      <td>219</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>117</td>\n",
       "      <td>181</td>\n",
       "      <td>389</td>\n",
       "      <td>366</td>\n",
       "      <td>211</td>\n",
       "      <td>301</td>\n",
       "      <td>298</td>\n",
       "      <td>176</td>\n",
       "      <td>212</td>\n",
       "      <td>428</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>49</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>60</td>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2580</th>\n",
       "      <td>159</td>\n",
       "      <td>87</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>29</td>\n",
       "      <td>158</td>\n",
       "      <td>66</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>156</td>\n",
       "      <td>174</td>\n",
       "      <td>118</td>\n",
       "      <td>30</td>\n",
       "      <td>93</td>\n",
       "      <td>82</td>\n",
       "      <td>142</td>\n",
       "      <td>26</td>\n",
       "      <td>127</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>96</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>102</td>\n",
       "      <td>105</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>160</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>69</td>\n",
       "      <td>56</td>\n",
       "      <td>77</td>\n",
       "      <td>89</td>\n",
       "      <td>84</td>\n",
       "      <td>107</td>\n",
       "      <td>66</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2582</th>\n",
       "      <td>497</td>\n",
       "      <td>287</td>\n",
       "      <td>133</td>\n",
       "      <td>305</td>\n",
       "      <td>334</td>\n",
       "      <td>392</td>\n",
       "      <td>247</td>\n",
       "      <td>75</td>\n",
       "      <td>269</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>358</td>\n",
       "      <td>484</td>\n",
       "      <td>524</td>\n",
       "      <td>364</td>\n",
       "      <td>278</td>\n",
       "      <td>299</td>\n",
       "      <td>308</td>\n",
       "      <td>339</td>\n",
       "      <td>487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2583</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2584 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9   ...   70   71   72  \\\n",
       "0      22   77  103   53   52  105   51   18   21   19  ...   57   75   75   \n",
       "1      25   85   17   88  132   69  111   18   64  103  ...  142   62   66   \n",
       "2      76  113   67   24   71   78   26   78  134  126  ...   25   86   83   \n",
       "3     153   83   53  121   86   72   30  110   26  133  ...   29  125   27   \n",
       "4     379  224   78  322  284  393  297   92  219   58  ...  117  181  389   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2579   28    9   49   41   39   60   42   32   31   30  ...   25    6   10   \n",
       "2580  159   87   33   25   64  170   29  158   66   94  ...  156  174  118   \n",
       "2581   96   72   66   20   17  102  105   18   76  160  ...   24   16   69   \n",
       "2582  497  287  133  305  334  392  247   75  269   88  ...  102  358  484   \n",
       "2583   10    1    9   18   12   16   12    7    9   15  ...   11   20   15   \n",
       "\n",
       "       73   74   75   76   77   78   79  \n",
       "0      59   63  119   80   88   41   25  \n",
       "1      74   64   57   21   88  156   21  \n",
       "2      71   85   84  131  118  106  114  \n",
       "3     141  242  110   87  109   42   36  \n",
       "4     366  211  301  298  176  212  428  \n",
       "...   ...  ...  ...  ...  ...  ...  ...  \n",
       "2579    5   17   49   37    7   23   96  \n",
       "2580   30   93   82  142   26  127   31  \n",
       "2581   56   77   89   84  107   66  111  \n",
       "2582  524  364  278  299  308  339  487  \n",
       "2583   14    8    2    9   18   11   17  \n",
       "\n",
       "[2584 rows x 80 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LE = LabelEncoder()\n",
    "LE.fit(np.array([val for col in df.columns for val in df[col]])) # considering all unique values\n",
    "\n",
    "for col in df.columns:\n",
    "    df[col] = LE.transform(df[col])\n",
    "\n",
    "df = df.sample(frac=frac) # to decrease execution time, we'll use frac% of the dataset\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4246ca21",
   "metadata": {},
   "source": [
    "## Static Parameteres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48e8a5f2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Users = 2584\n",
      "Number of data collections =  80\n",
      "\n",
      "Attribute's domain size = 1412\n",
      "\n",
      "dBitFlipPM parameters\n",
      "Number of bukects b: 353\n",
      "Bucket size: 4.0\n",
      "\n",
      "Privacy guarantees:\n",
      "List of epsilon_perm = [0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5. ]\n",
      "alpha = 0.4\n"
     ]
    }
   ],
   "source": [
    "# number of users (n)\n",
    "n = df.shape[0]\n",
    "print('Number of Users =',n)\n",
    "\n",
    "# number of data collections (tau)\n",
    "tau = df.shape[1]\n",
    "print('Number of data collections = ', tau)\n",
    "\n",
    "# attribute's domain size\n",
    "k = len(LE.classes_)\n",
    "print(\"\\nAttribute's domain size =\", k)\n",
    "\n",
    "print('\\ndBitFlipPM parameters')\n",
    "b = k // 4 # Number of buckets < k\n",
    "print(\"Number of bukects b:\", b)\n",
    "\n",
    "bulk_size = k / b\n",
    "print(\"Bucket size:\", bulk_size)\n",
    "\n",
    "print(\"\\nPrivacy guarantees:\")\n",
    "\n",
    "# upper bound (infinity reports)\n",
    "lst_eps_perm = np.arange(0.5, 5.1, 0.5)\n",
    "print(\"List of epsilon_perm =\", lst_eps_perm)\n",
    "\n",
    "# alpha value\n",
    "alpha = 0.4\n",
    "print(\"alpha =\", alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709d61cd",
   "metadata": {},
   "source": [
    "## Real frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f40b5087",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       " 38: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 39: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 42: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 43: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 44: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 45: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 46: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 47: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 48: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 49: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 50: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 58: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 60: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       "        0.        ]),\n",
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       "        0.        ]),\n",
       " 63: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 64: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 65: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 66: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 67: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 68: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 69: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 70: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 71: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 72: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 73: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 74: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 75: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
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       " 76: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 77: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 78: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " 79: array([0.00154799, 0.        , 0.00232198, ..., 0.        , 0.        ,\n",
       "        0.        ])}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_real = {t:0 for t in range(tau)}\n",
    "for t in range(tau):\n",
    "    real_freq = np.zeros(k)\n",
    "    for val in df[col].values:\n",
    "        real_freq[val]+=1\n",
    "    real_freq = real_freq / sum(real_freq)\n",
    "    dic_real[t] = real_freq\n",
    "dic_real"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cfec53",
   "metadata": {},
   "source": [
    "## Take average results due to randomness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ded5a6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save MSE per seed\n",
    "dic_mse = {seed: \n",
    "               {\"OLOLOHA\": [],\n",
    "                \"BiLOLOHA\": [],\n",
    "                \"L_OSUE\": [],\n",
    "                \"RAPPOR\": [],\n",
    "                \"L_GRR\": [],\n",
    "                \"1BitFlipPM\": [],\n",
    "                \"bBitFlipPM\": []\n",
    "               } \n",
    "               for seed in range(nb_seed)\n",
    "          }\n",
    "# Save budget consumption per seed\n",
    "dic_bdg = {seed: \n",
    "               {\"OLOLOHA\": -1,\n",
    "                \"BiLOLOHA\": -1,\n",
    "                \"L_OSUE\": -1,\n",
    "                \"RAPPOR\": -1,\n",
    "                \"L_GRR\": -1,\n",
    "                \"1BitFlipPM\": -1,\n",
    "                \"bBitFlipPM\": -1\n",
    "               } \n",
    "               for seed in range(nb_seed)\n",
    "          }\n",
    "\n",
    "# Save data change detection per seed\n",
    "dic_chg = {seed: \n",
    "               {\n",
    "                \"1BitFlipPM\": -1,\n",
    "                \"bBitFlipPM\": -1\n",
    "               } \n",
    "               for seed in range(nb_seed)\n",
    "          }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d6178e",
   "metadata": {},
   "source": [
    "## OLOLOLHA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "322719b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 59121.01716184616 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = LOLOHA_Client(row.values, k, eps_perm, eps_1, alpha, optimal=True)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = LOLOHA_Aggregator(reports[:, t], k, eps_perm, eps_1, alpha, optimal=True)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "    \n",
    "    dic_mse[seed][\"OLOLOHA\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"OLOLOHA\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c12702",
   "metadata": {},
   "source": [
    "## BiLOLOHA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e774569e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 26335.60682606697 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = LOLOHA_Client(row.values, k, eps_perm, eps_1, alpha, optimal=False)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = LOLOHA_Aggregator(reports[:, t], k, eps_perm, eps_1, alpha, optimal=False)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"BiLOLOHA\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"BiLOLOHA\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1dc3dd",
   "metadata": {},
   "source": [
    "## L-OSUE [2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2628f17d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 697.7018685340881 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = L_OSUE_Client(row.values, k, eps_perm, eps_1)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = L_OSUE_Aggregator(reports[:, t], eps_perm, eps_1)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"L_OSUE\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"L_OSUE\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2c7fe1",
   "metadata": {},
   "source": [
    "## RAPPOR [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2ffe554",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 874.9823417663574 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = RAPPOR_Client(row.values, k, eps_perm, eps_1)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = RAPPOR_Aggregator(reports[:, t], eps_perm, eps_1)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"RAPPOR\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"RAPPOR\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a74382",
   "metadata": {},
   "source": [
    "## L-GRR [2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a25202a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 122.5985472202301 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    setting_seed(seed) # Ensure reproducibility\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        eps_1 = alpha * eps_perm # First report privacy guarantee (lower bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget = L_GRR_Client(row.values, k, eps_perm, eps_1)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = dic_real[t] # for Adult dataset, the real frequency is the same for all t \\in [\\tau]\n",
    "            est_freq = L_GRR_Aggregator(reports[:, t], k, eps_perm, eps_1)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        \n",
    "    dic_mse[seed][\"L_GRR\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"L_GRR\"] = lst_bdg_avg\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db482602",
   "metadata": {},
   "source": [
    "## 1BitFlipPM [3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49c27858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 2729.3789484500885 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "# Number of bits each user sample/report\n",
    "d = 1\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    lst_detect_change = [] # List of 100% detected change\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        change_tracking = [] # List of Boolean value indicating data change detection for all values\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget, detect_change = dBitFlipPM_Client(row.values, k, b, d, eps_perm)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "            change_tracking.append(detect_change)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = np.zeros(b)\n",
    "            for val in df[t].values:\n",
    "                real_freq[int(val / bulk_size)]+=1\n",
    "            real_freq = real_freq/sum(real_freq)\n",
    "            \n",
    "            est_freq = dBitFlipPM_Aggregator(reports[:, t], b, d, eps_perm)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        lst_detect_change.append(100 * sum(change_tracking) / n) # Averaged data change detection in %\n",
    "        \n",
    "    dic_mse[seed][\"1BitFlipPM\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"1BitFlipPM\"] = lst_bdg_avg\n",
    "    dic_chg[seed][\"1BitFlipPM\"] = lst_detect_change\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c08141",
   "metadata": {},
   "source": [
    "## bBitFlipPM [3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2fbf5e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "That took 52189.2732758522 seconds\n"
     ]
    }
   ],
   "source": [
    "starttime = time.time()\n",
    "\n",
    "# Number of bits each user sample/report\n",
    "d = b\n",
    "\n",
    "for seed in range(nb_seed):\n",
    "    print(seed)\n",
    "    np.random.seed(seed) # Ensure reproducibility\n",
    "    \n",
    "    lst_mse_avg = [] # List of averaged MSE per eps_perm\n",
    "    lst_bdg_avg = [] # List of averaged budget consumption per eps_perm\n",
    "    lst_detect_change = [] # List of 100% detected change\n",
    "    \n",
    "    for eps_perm in lst_eps_perm: # Longitudinal privacy guarantee (upper bound)\n",
    "        \n",
    "        # Client-Side\n",
    "        reports = [] # List of reports of all n users\n",
    "        budget_tracking = [] # List of consumed privacy budget of all n users\n",
    "        change_tracking = [] # List of Boolean value indicating data change detection for all values\n",
    "        for ind, row in df.iterrows():\n",
    "            \n",
    "            sanitized_reports, final_budget, detect_change = dBitFlipPM_Client(row.values, k, b, d, eps_perm)\n",
    "            reports.append(sanitized_reports)\n",
    "            budget_tracking.append(final_budget)\n",
    "            change_tracking.append(detect_change)\n",
    "        \n",
    "        reports = np.array(reports)\n",
    "        \n",
    "        # Server-Side\n",
    "        lst_mse = [] # List of all MSE per data collection\n",
    "        for t in range(tau): # For each data collection\n",
    "            \n",
    "            real_freq = np.zeros(b)\n",
    "            for val in df[t].values:\n",
    "                real_freq[int(val / bulk_size)]+=1\n",
    "            real_freq = real_freq/sum(real_freq)\n",
    "            \n",
    "            est_freq = dBitFlipPM_Aggregator(reports[:, t], b, d, eps_perm)\n",
    "            lst_mse.append(mean_squared_error(real_freq, est_freq))\n",
    "        \n",
    "        lst_mse_avg.append(np.mean(lst_mse)) # Averaged MSE\n",
    "        lst_bdg_avg.append(np.mean(budget_tracking)) # Averaged privacy budget consumption\n",
    "        lst_detect_change.append(100 * sum(change_tracking) / n) # Averaged data change detection in %\n",
    "        \n",
    "    dic_mse[seed][\"bBitFlipPM\"] = lst_mse_avg\n",
    "    dic_bdg[seed][\"bBitFlipPM\"] = lst_bdg_avg\n",
    "    dic_chg[seed][\"bBitFlipPM\"] = lst_detect_change\n",
    "    \n",
    "print('That took {} seconds'.format(time.time() - starttime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7e5b32",
   "metadata": {},
   "source": [
    "## Plotting MSE_avg metric results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "822aa3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.grid(color='grey', linestyle='dashdot', linewidth=0.5)\n",
    "# plt.plot(np.mean([dic_mse[seed][\"bBitFlipPM\"] for seed in range(nb_seed)], axis=0), label='bBitFlipPM', marker='*')\n",
    "plt.plot(np.mean([dic_mse[seed][\"L_OSUE\"] for seed in range(nb_seed)], axis=0), label='L-OSUE',marker='s')\n",
    "plt.plot(np.mean([dic_mse[seed][\"OLOLOHA\"] for seed in range(nb_seed)], axis=0), label='OLOLOHA', marker='o')\n",
    "plt.plot(np.mean([dic_mse[seed][\"RAPPOR\"] for seed in range(nb_seed)], axis=0), label='RAPPOR',marker='X')\n",
    "plt.plot(np.mean([dic_mse[seed][\"BiLOLOHA\"] for seed in range(nb_seed)], axis=0), label='BiLOLOHA',marker='>')\n",
    "# plt.plot(np.mean([dic_mse[seed][\"1BitFlipPM\"] for seed in range(nb_seed)], axis=0), label='1BitFlipPM', marker='P')\n",
    "plt.plot(np.mean([dic_mse[seed][\"L_GRR\"] for seed in range(nb_seed)], axis=0), label='L-GRR', marker='D')\n",
    "\n",
    "plt.yscale('log')\n",
    "plt.xlabel('$\\epsilon_{\\infty}$')\n",
    "plt.ylabel('$MSE_{avg}$')\n",
    "plt.xticks(range(len(lst_eps_perm)), lst_eps_perm)\n",
    "plt.title('$\\epsilon_1$ $=$' +f'${alpha}$'+ '$\\epsilon_{\\infty}$', loc='center')\n",
    "plt.legend(ncol=5, loc='upper center', bbox_to_anchor=(0.5, 1.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2033826",
   "metadata": {},
   "source": [
    "## Plotting privacy budget consumption metric results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "979f3515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.grid(color='grey', linestyle='dashdot', linewidth=0.5)\n",
    "plt.plot(np.mean([dic_bdg[seed][\"bBitFlipPM\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='bBitFlipPM', marker='P')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"L_OSUE\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='L-OSUE',marker='s')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"OLOLOHA\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='OLOLOHA', marker='o')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"RAPPOR\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='RAPPOR',marker='X')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"BiLOLOHA\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='BiLOLOHA',marker='>')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"1BitFlipPM\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='1BitFlipPM', marker='P')\n",
    "plt.plot(np.mean([dic_bdg[seed][\"L_GRR\"] for seed in range(nb_seed)], axis=0)*lst_eps_perm, label='L-GRR', marker='D', color='tab:pink')\n",
    "\n",
    "\n",
    "ticks = np.arange(0,271,50)\n",
    "plt.yticks(ticks,ticks)\n",
    "# plt.set_yticklabels()\n",
    "plt.xlabel('$\\epsilon_{\\infty}$')\n",
    "plt.ylabel('$\\check{\\epsilon}_{avg}$')\n",
    "plt.xticks(range(len(lst_eps_perm)), lst_eps_perm)\n",
    "plt.title('$\\epsilon_1$ $=$' +f'${alpha}$'+ '$\\epsilon_{\\infty}$', loc='center')\n",
    "plt.legend(ncol=4, loc='upper center', bbox_to_anchor=(0.5, 1.35))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0e1ff4",
   "metadata": {},
   "source": [
    "## Plotting data change detection for dBitFlipPM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e08b84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.grid(color='grey', linestyle='dashdot', linewidth=0.5)\n",
    "plt.plot(np.mean([dic_chg[seed][\"bBitFlipPM\"] for seed in range(nb_seed)], axis=0), label='bBitFlipPM', marker='*')\n",
    "plt.plot(np.mean([dic_chg[seed][\"1BitFlipPM\"] for seed in range(nb_seed)], axis=0), label='1BitFlipPM', marker='P')\n",
    "# plt.yscale('log')\n",
    "plt.xlabel('$\\epsilon_{\\infty}$')\n",
    "plt.ylabel('\\% of Users')\n",
    "plt.xticks(range(len(lst_eps_perm)), lst_eps_perm)\n",
    "plt.legend(ncol=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cf88e6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}