{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f18d338",
   "metadata": {},
   "source": [
    "# Lomas Client Side: Using Smartnoise-Synth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1582a2ae",
   "metadata": {},
   "source": [
    "This notebook showcases how researcher could use the Secure Data Disclosure system. It explains the different functionnalities provided by the `lomas-client` client library to interact with the secure server.\n",
    "\n",
    "The secure data are never visible by researchers. They can only access to differentially private responses via queries to the server.\n",
    "\n",
    "Each user has access to one or multiple projects and for each dataset has a limited budget with $\\epsilon$ and $\\delta$ values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ae30d2",
   "metadata": {},
   "source": [
    "## Step 1: Install the library\n",
    "To interact with the secure server on which the data is stored, Dr.Antartica first needs to install the library `lomas-client` on her local developping environment. \n",
    "\n",
    "It can be installed via the pip command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc563050-fcc0-4c11-9e63-46eaefa63ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install lomas_client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5df0c8f-ca9c-4af1-8c60-fb1d30d6283d",
   "metadata": {},
   "source": [
    "Or using a local version of the client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36d508bf-6cc3-4034-8e11-fffe858552f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join('..')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9535e92e-620e-4df4-92dd-4ea2c653e4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lomas_client import Client\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c63718b",
   "metadata": {},
   "source": [
    "## Step 2: Initialise the client\n",
    "\n",
    "Once the library is installed, a Client object must be created. It is responsible for sending sending requests to the server and processing responses in the local environment. It enables a seamless interaction with the server. \n",
    "\n",
    "To create the client, Dr. Antartica needs to give it a few parameters:\n",
    "- a url: the root application endpoint to the remote secure server.\n",
    "- user_name: her name as registered in the database (Dr. Alice Antartica)\n",
    "- dataset_name: the name of the dataset that she wants to query (PENGUIN)\n",
    "\n",
    "She will only be able to query on the real dataset if the queen Icergina has previously made her an account in the database, given her access to the PENGUIN dataset and has given her some epsilon and delta credit (as is done in the Admin Notebook for Users and Datasets management)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4c18a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_NAME = \"PENGUIN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38b3eb04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following would usually be set in the environment by a system administrator\n",
    "# and be tranparent to lomas users.\n",
    "APP_URL = \"http://localhost:48080\"                                    # For local devenv setup\n",
    "# APP_URL = \"http://lomas_server:48080\"                               # For local docker compose setup\n",
    "# APP_URL = \"http://lomas-server.example.com:80\"                 # For Kubernetes deployment\n",
    "USER_NAME = \"Dr.Antartica\"\n",
    "\n",
    "import os\n",
    "os.environ[\"LOMAS_CLIENT_ID\"] = USER_NAME\n",
    "os.environ[\"LOMAS_CLIENT_SECRET\"] = USER_NAME.lower()\n",
    "os.environ[\"LOMAS_KEYCLOAK_ADDRESS\"] = \"localhost\"                    # For local devenv setup\n",
    "# os.environ[\"LOMAS_KEYCLOAK_ADDRESS\"] = \"keycloak\"                   # For local docker compose setup\n",
    "# os.environ[\"LOMAS_KEYCLOAK_ADDRESS\"] = \"lomas-keycloak.example.com\" # For Kubernetes deployment \n",
    "os.environ[\"LOMAS_KEYCLOAK_PORT\"] = \"80\"                              # For local deployments\n",
    "# os.environ[\"LOMAS_KEYCLOAK_PORT\"] = \"443\"                           # For Kubernetes deployment\n",
    "os.environ[\"LOMAS_KEYCLOAK_USE_TLS\"] = \"0\"                            # For local deployments\n",
    "# os.environ[\"LOMAS_KEYCLOAK_USE_TLS\"] = \"1\"                          # For Kubernetes deployments\n",
    "os.environ[\"LOMAS_REALM\"] = \"lomas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d11725be",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(url=APP_URL, dataset_name=DATASET_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec400c8",
   "metadata": {},
   "source": [
    "And that's it for the preparation. She is now ready to use the various functionnalities offered by `lomas-client`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9a5f13",
   "metadata": {},
   "source": [
    "## Step 3: Metadata and dummy dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7cb5531",
   "metadata": {},
   "source": [
    "### Getting dataset metadata\n",
    "\n",
    "Dr. Antartica has never seen the data and as a first step to understand what is available to her, she would like to check the metadata of the dataset. Therefore, she just needs to call the `get_dataset_metadata()` function of the client. As this is public information, this does not cost any budget.\n",
    "\n",
    "This function returns metadata information in a format based on [SmartnoiseSQL dictionary format](https://docs.smartnoise.org/sql/metadata.html#dictionary-format), where among other, there is information about all the available columns, their type, bound values (see Smartnoise page for more details). Any metadata is required for Smartnoise-SQL is also required here and additional information such that the different categories in a string type column column can be added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0fdebac9-57fc-4410-878b-5a77425af634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_ids': 1,\n",
       " 'rows': 344,\n",
       " 'row_privacy': True,\n",
       " 'censor_dims': False,\n",
       " 'columns': {'species': {'private_id': False,\n",
       "   'nullable': False,\n",
       "   'max_partition_length': None,\n",
       "   'max_influenced_partitions': None,\n",
       "   'max_partition_contributions': None,\n",
       "   'type': 'string',\n",
       "   'cardinality': 3,\n",
       "   'categories': ['Adelie', 'Chinstrap', 'Gentoo']},\n",
       "  'island': {'private_id': False,\n",
       "   'nullable': False,\n",
       "   'max_partition_length': None,\n",
       "   'max_influenced_partitions': None,\n",
       "   'max_partition_contributions': None,\n",
       "   'type': 'string',\n",
       "   'cardinality': 3,\n",
       "   'categories': ['Torgersen', 'Biscoe', 'Dream']},\n",
       "  'bill_length_mm': {'private_id': False,\n",
       "   'nullable': False,\n",
       "   'max_partition_length': None,\n",
       "   'max_influenced_partitions': None,\n",
       "   'max_partition_contributions': None,\n",
       "   'type': 'float',\n",
       "   'precision': 64,\n",
       "   'lower': 30.0,\n",
       "   'upper': 65.0},\n",
       "  'bill_depth_mm': {'private_id': False,\n",
       "   'nullable': False,\n",
       "   'max_partition_length': None,\n",
       "   'max_influenced_partitions': None,\n",
       "   'max_partition_contributions': None,\n",
       "   'type': 'float',\n",
       "   'precision': 64,\n",
       "   'lower': 13.0,\n",
       "   'upper': 23.0},\n",
       "  'flipper_length_mm': {'private_id': False,\n",
       "   'nullable': False,\n",
       "   'max_partition_length': None,\n",
       "   'max_influenced_partitions': None,\n",
       "   'max_partition_contributions': None,\n",
       "   'type': 'float',\n",
       "   'precision': 64,\n",
       "   'lower': 150.0,\n",
       "   'upper': 250.0},\n",
       "  'body_mass_g': {'private_id': False,\n",
       "   'nullable': False,\n",
       "   'max_partition_length': None,\n",
       "   'max_influenced_partitions': None,\n",
       "   'max_partition_contributions': None,\n",
       "   'type': 'float',\n",
       "   'precision': 64,\n",
       "   'lower': 2000.0,\n",
       "   'upper': 7000.0},\n",
       "  'sex': {'private_id': False,\n",
       "   'nullable': False,\n",
       "   'max_partition_length': None,\n",
       "   'max_influenced_partitions': None,\n",
       "   'max_partition_contributions': None,\n",
       "   'type': 'string',\n",
       "   'cardinality': 2,\n",
       "   'categories': ['MALE', 'FEMALE']}}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguin_metadata = client.get_dataset_metadata()\n",
    "penguin_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7ca7ae-bf17-40c8-aa75-2d72fcdd3088",
   "metadata": {},
   "source": [
    "## Step 3: Create a Synthetic Dataset keeping all default parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de1389c-53a7-4098-bc3c-397c12a4b869",
   "metadata": {},
   "source": [
    "We want to get a synthetic model to represent the private data.\n",
    "\n",
    "Therefore, we use a Smartnoise Synth Synthesizers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3423d410-2501-4eaa-bea4-6b31fba8c869",
   "metadata": {},
   "source": [
    "Let's list the potential options. There respective paramaters are then available in Smarntoise Synth documentation [here](https://docs.smartnoise.org/synth/synthesizers/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf6dd9f4-a9ca-4805-a597-553a26604430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mwem', 'dpctgan', 'patectgan', 'mst', 'pacsynth', 'dpgan', 'pategan', 'aim']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from snsynth import Synthesizer\n",
    "Synthesizer.list_synthesizers()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06365e9-4076-4592-871a-31af91d6a05d",
   "metadata": {},
   "source": [
    "### AIM: Adaptive Iterative Mechanism"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f83dffe-f5b6-42fc-a74c-f3f00dc6c257",
   "metadata": {},
   "source": [
    "We start by executing a query on the dummy dataset without specifying any special parameters for AIM (all optional kept as default).\n",
    "Also only works on categorical columns so we select \"species\" and \"island\" columns to create a synthetic dataset of these two columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a17ef2a7-1a70-440a-b11f-9867e1e9dd70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Torgersen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Torgersen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       species     island\n",
       "0       Gentoo     Biscoe\n",
       "1       Adelie     Biscoe\n",
       "2       Gentoo      Dream\n",
       "3    Chinstrap      Dream\n",
       "4       Gentoo     Biscoe\n",
       "..         ...        ...\n",
       "195     Gentoo  Torgersen\n",
       "196  Chinstrap           \n",
       "197  Chinstrap  Torgersen\n",
       "198     Adelie     Biscoe\n",
       "199     Gentoo      Dream\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"aim\",\n",
    "    epsilon=1.0,\n",
    "    delta=0.0001,\n",
    "    select_cols = [\"species\", \"island\"],\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12ed311-3622-4cb8-b5e5-585cf20c91a8",
   "metadata": {},
   "source": [
    "The algorithm works and returned a synthetic dataset. We now estimate the cost of running this command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51063e79-0809-49ee-b7f0-c19b190571c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CostResponse(epsilon=1.0, delta=0.0001)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_cost = client.smartnoise_synth.cost(\n",
    "    synth_name=\"aim\",\n",
    "    epsilon=1.0,\n",
    "    delta=0.0001,\n",
    "    select_cols = [\"species\", \"island\"],\n",
    ")\n",
    "res_cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f582e93-ca3b-4a9d-b24a-8c26996cab64",
   "metadata": {},
   "source": [
    "Executing such a query on the private dataset would cost 1.0 epsilon and 0.0001 delta. Dr. Antartica decides to do it with now the flag `dummmy` to False and specifiying that the wants the aim synthesizer model in return (with `return_model = True`).\n",
    "\n",
    "NOTE: if she does not set the parameter `return_model = True`, then it is False by default and she will get a synthetic dataframe as response directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8160a5ab-dd53-4d0d-9f6d-ff31c39831c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/site-packages/mbi/__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n",
      "  warnings.warn('MixtureInference disabled, please install jax and jaxlib')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<snsynth.aim.aim.AIMSynthesizer at 0x71b3586e6a80>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = client.smartnoise_synth.query(\n",
    "    synth_name=\"aim\",\n",
    "    epsilon=1.0,\n",
    "    delta=0.0001,\n",
    "    select_cols = [\"species\", \"island\"],\n",
    "    dummy=True,\n",
    "    return_model = True\n",
    ")\n",
    "res.result.model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d8db1d-6fe2-4bf0-9b12-e9e25a9df235",
   "metadata": {},
   "source": [
    "She can now get the model and sample results with it. She choose to sample 10 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1add6713-1906-4d57-bd90-9e54b0a883d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Torgersen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Torgersen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     island\n",
       "0     Gentoo  Torgersen\n",
       "1     Gentoo     Biscoe\n",
       "2  Chinstrap     Biscoe\n",
       "3     Gentoo      Dream\n",
       "4     Adelie      Dream\n",
       "5  Chinstrap     Biscoe\n",
       "6     Gentoo      Dream\n",
       "7     Adelie  Torgersen\n",
       "8  Chinstrap      Dream\n",
       "9     Gentoo  Torgersen"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synth = res.result.model\n",
    "synth.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9837d3-11a5-49d9-aaaf-0637061cf2f5",
   "metadata": {},
   "source": [
    "She now wants to specify some specific parameters to the AIM model. Therefore, she needs to set some parameters in `synth_params` based on the Smartnoise-Synth documentation [here](https://docs.smartnoise.org/synth/synthesizers/aim.html#parameters). She decides that she wants to modify the `max_model_size` to 50 (the default was 80) and tries on the dummy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8be9943c-4cb3-41ef-bb31-02cbb6e773c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<snsynth.aim.aim.AIMSynthesizer at 0x71b321d293d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"aim\",\n",
    "    epsilon=1.0,\n",
    "    delta=0.0001,\n",
    "    select_cols = [\"species\", \"island\"],\n",
    "    dummy=True,\n",
    "    return_model = True,\n",
    "    synth_params = {\"max_model_size\": 50}\n",
    ")\n",
    "res_dummy.result.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0e958c9-76de-499c-bdeb-658f7052d0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Torgersen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     island\n",
       "0     Gentoo  Torgersen\n",
       "1  Chinstrap      Dream\n",
       "2     Gentoo     Biscoe\n",
       "3  Chinstrap     Biscoe\n",
       "4     Adelie      Dream"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synth = res_dummy.result.model\n",
    "synth.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b656a6c-2199-465e-b109-818c369b2798",
   "metadata": {},
   "source": [
    "Now that the workflow is understood for AIM, she wants to experiment with various synthesizer on the dummy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69cab29c-a882-4821-a0ef-eeb863e03071",
   "metadata": {},
   "source": [
    "### MWEM: Multiplicative Weights Exponential Mechanism "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "036bb9fe-29e1-42c1-bf7b-e684c7c37336",
   "metadata": {},
   "source": [
    "She tries MWEM on all columns with all default parameters. As `return_model` is not specified she will directly receive a synthetic dataframe back. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "002a9a17-3e75-427b-8293-0fbd5188f762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>56.25</td>\n",
       "      <td>22.5</td>\n",
       "      <td>165.0</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "      <td>35.25</td>\n",
       "      <td>18.5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.25</td>\n",
       "      <td>13.5</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>35.25</td>\n",
       "      <td>13.5</td>\n",
       "      <td>225.0</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>56.25</td>\n",
       "      <td>22.5</td>\n",
       "      <td>165.0</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0     Adelie   Dream           56.25           22.5              165.0   \n",
       "1     Gentoo   Dream           35.25           18.5              205.0   \n",
       "2  Chinstrap  Biscoe           49.25           13.5              215.0   \n",
       "3     Adelie  Biscoe           35.25           13.5              225.0   \n",
       "4     Adelie   Dream           56.25           22.5              165.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       4750.0  FEMALE  \n",
       "1       2750.0  FEMALE  \n",
       "2       2250.0  FEMALE  \n",
       "3       4750.0    MALE  \n",
       "4       4750.0  FEMALE  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"mwem\",\n",
    "    epsilon=1.0,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf112c1d-2313-451e-8107-966a7b731283",
   "metadata": {},
   "source": [
    "She now specifies 3 columns and some parameters explained [here](https://docs.smartnoise.org/synth/synthesizers/mwem.html#snsynth.mwem.MWEMSynthesizer)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f7303b7-77e0-4023-8f59-6b30e503567b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     island     sex\n",
       "0  Chinstrap  Torgersen  FEMALE\n",
       "1     Adelie  Torgersen  FEMALE\n",
       "2     Adelie  Torgersen  FEMALE\n",
       "3  Chinstrap  Torgersen  FEMALE\n",
       "4     Gentoo     Biscoe    MALE"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"mwem\",\n",
    "    epsilon=1.0,\n",
    "    select_cols = [\"species\", \"island\", \"sex\"],\n",
    "    synth_params = {\"measure_only\": False, \"max_retries_exp_mechanism\": 5},\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184114e9-1cf0-4b53-a2e4-5a14d787562a",
   "metadata": {},
   "source": [
    "Finally it MWEM, she wants to go more in depth and create her own data preparation pipeline. Therefore, she can use Smartnoise-Synth \"Data Transformers\" explained [here](https://docs.smartnoise.org/synth/transforms/index.html) and send her own constraints dictionnary for specific steps. This is more for advanced user.\n",
    "\n",
    "By default, if no constraints are specified, the server creates its automatically a data transformer based on selected columns, synthesizer and metadata.\n",
    "\n",
    "Here she wants to add a clamping transformation on the continuous columns before training the synthesizer. She add the bounds based on metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c3bf0ec-ca04-4c91-8742-c31f79633191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'private_id': False,\n",
       "  'nullable': False,\n",
       "  'max_partition_length': None,\n",
       "  'max_influenced_partitions': None,\n",
       "  'max_partition_contributions': None,\n",
       "  'type': 'float',\n",
       "  'precision': 64,\n",
       "  'lower': 30.0,\n",
       "  'upper': 65.0},\n",
       " {'private_id': False,\n",
       "  'nullable': False,\n",
       "  'max_partition_length': None,\n",
       "  'max_influenced_partitions': None,\n",
       "  'max_partition_contributions': None,\n",
       "  'type': 'float',\n",
       "  'precision': 64,\n",
       "  'lower': 13.0,\n",
       "  'upper': 23.0})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bl_bounds = penguin_metadata[\"columns\"][\"bill_length_mm\"]\n",
    "bd_bounds = penguin_metadata[\"columns\"][\"bill_depth_mm\"]\n",
    "bl_bounds, bd_bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c3215ff4-aafb-4c1e-adf0-50dc383cd133",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snsynth.transform import BinTransformer, ClampTransformer, ChainTransformer, LabelTransformer\n",
    "\n",
    "my_own_constraints = {\n",
    "    \"bill_length_mm\": ChainTransformer(\n",
    "        [\n",
    "            ClampTransformer(lower = bl_bounds[\"lower\"] + 10, upper = bl_bounds[\"upper\"] - 10),\n",
    "            BinTransformer(bins = 20, lower = bl_bounds[\"lower\"] + 10, upper = bl_bounds[\"upper\"] - 10),\n",
    "        ]\n",
    "    ),\n",
    "    \"bill_depth_mm\": ChainTransformer(\n",
    "        [\n",
    "            ClampTransformer(lower = bd_bounds[\"lower\"] + 2, upper = bd_bounds[\"upper\"] - 2),\n",
    "            BinTransformer(bins=20, lower = bd_bounds[\"lower\"] + 2, upper = bd_bounds[\"upper\"] - 2),\n",
    "        ]\n",
    "    ),\n",
    "    \"species\": LabelTransformer(nullable=True)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ffba8bcd-0b6b-4cc0-98a3-b25e8bdd1786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47.875</td>\n",
       "      <td>15.15</td>\n",
       "      <td>Chinstrap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.625</td>\n",
       "      <td>15.45</td>\n",
       "      <td>Gentoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.000</td>\n",
       "      <td>20.85</td>\n",
       "      <td>Adelie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.875</td>\n",
       "      <td>15.15</td>\n",
       "      <td>Chinstrap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47.875</td>\n",
       "      <td>15.15</td>\n",
       "      <td>Chinstrap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bill_length_mm  bill_depth_mm    species\n",
       "0          47.875          15.15  Chinstrap\n",
       "1          48.625          15.45     Gentoo\n",
       "2          55.000          20.85     Adelie\n",
       "3          47.875          15.15  Chinstrap\n",
       "4          47.875          15.15  Chinstrap"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"mwem\",\n",
    "    epsilon=1.0,\n",
    "    select_cols = [\"bill_length_mm\", \"bill_depth_mm\", \"species\"],\n",
    "    constraints = my_own_constraints,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d680bdc-e99f-4a40-a9ff-c2d1d053a573",
   "metadata": {},
   "source": [
    "Also a subset of constraints can be specified for certain columns and the server will automatically generate those for the missing columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "21cc6269-5a6f-40c6-b081-52e59a62d903",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_own_constraints = {\n",
    "    \"bill_length_mm\": ChainTransformer(\n",
    "        [\n",
    "            ClampTransformer(lower = bl_bounds[\"lower\"] + 10, upper = bl_bounds[\"upper\"] - 10),\n",
    "            BinTransformer(bins = 20, lower = bl_bounds[\"lower\"] + 10, upper = bl_bounds[\"upper\"] - 10),\n",
    "        ]\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b590d77-3cde-42ef-b55b-107cd253aad4",
   "metadata": {},
   "source": [
    "In this case, only the bill_length will be clamped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "432e076e-8411-49f0-8250-100e0940313a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.625</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Adelie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.375</td>\n",
       "      <td>13.5</td>\n",
       "      <td>Gentoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.625</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Adelie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.625</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Adelie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.625</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Adelie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bill_length_mm  bill_depth_mm species\n",
       "0          54.625           14.5  Adelie\n",
       "1          40.375           13.5  Gentoo\n",
       "2          54.625           14.5  Adelie\n",
       "3          54.625           14.5  Adelie\n",
       "4          54.625           14.5  Adelie"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"mwem\",\n",
    "    epsilon=1.0,\n",
    "    select_cols = [\"bill_length_mm\", \"bill_depth_mm\", \"species\"],\n",
    "    constraints = my_own_constraints,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035b69c3-4819-4fc1-b4ac-1c2bac1b31fc",
   "metadata": {},
   "source": [
    "### MST: Maximum Spanning Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21190b72-3089-4783-addd-09590814b94f",
   "metadata": {},
   "source": [
    "She now experiments with MST. As the synthesizer is very needy in terms of computation, she selects a subset of column for it. See MST [here](https://docs.smartnoise.org/synth/synthesizers/mst.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "08d3f738-7dd0-4c73-9db6-ea91ee188968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     sex\n",
       "0  Chinstrap  FEMALE\n",
       "1                   \n",
       "2  Chinstrap        \n",
       "3                   \n",
       "4     Gentoo    MALE"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"mst\",\n",
    "    epsilon=1.0,\n",
    "    select_cols = [\"species\", \"sex\"],\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249615d7-14e6-4f9c-8368-3fc00b4832c9",
   "metadata": {},
   "source": [
    "She can also specify a specific number of samples to get (if return_model is not True):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7ea90e9d-7190-43d7-8ab8-c58214cd4198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     sex\n",
       "0             FEMALE\n",
       "1     Gentoo    MALE\n",
       "2     Gentoo  FEMALE\n",
       "3  Chinstrap    MALE"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"mst\",\n",
    "    epsilon=1.0,\n",
    "    select_cols = [\"species\", \"sex\"],\n",
    "    nb_samples = 4,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b4c876-11a5-4466-8831-d94786debe00",
   "metadata": {},
   "source": [
    "And a condition on these samples. For instance, here, she only wants female samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1d50939-1fc7-4fcb-84d2-d7611435800b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>Gentoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>Gentoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>Gentoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>Gentoo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex species\n",
       "0      Gentoo\n",
       "1      Gentoo\n",
       "2      Gentoo\n",
       "3      Gentoo"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"mst\",\n",
    "    epsilon=1.0,\n",
    "    select_cols = [\"sex\", \"species\"],\n",
    "    nb_samples = 4,\n",
    "    condition = \"sex = FEMALE\",\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54809d01-9dd4-4bef-a575-3bc61d52b842",
   "metadata": {},
   "source": [
    "## DPCTGAN: Differentially Private Conditional Tabular GAN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e0cb6b4-f539-4613-86f1-031b646e2376",
   "metadata": {},
   "source": [
    "She now tries DPCTGAN. A first warning let her know that the random noise generation for this model is not cryptographically secure and if it is not ok for her, she can decode to stop using this synthesizer. Then she does not get a response but an error 422 with an explanation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7b508f33-7294-4928-8a25-27e0f0c702d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/code/lomas_client/utils.py:62: UserWarning: Warning:dpctgan synthesizer random generator for noise and shuffling is not cryptographically secure. (pseudo-rng in vanilla PyTorch).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ExternalLibraryException",
     "evalue": "('smartnoise_synth', 'Error fitting model: sample_rate=5.0 is not a valid value. Please provide a float between 0 and 1. Try decreasing batch_size in synth_params (default batch_size=500).')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mExternalLibraryException\u001b[0m                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m res_dummy \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msmartnoise_synth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43msynth_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdpctgan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepsilon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdummy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m res_dummy\n",
      "File \u001b[0;32m/code/lomas_client/libraries/smartnoise_synth.py:203\u001b[0m, in \u001b[0;36mSmartnoiseSynthClient.query\u001b[0;34m(self, synth_name, epsilon, delta, select_cols, synth_params, nullable, constraints, dummy, return_model, condition, nb_samples, nb_rows, seed)\u001b[0m\n\u001b[1;32m    200\u001b[0m     r_model \u001b[38;5;241m=\u001b[39m QueryResponse\u001b[38;5;241m.\u001b[39mmodel_validate_json(data)\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m r_model\n\u001b[0;32m--> 203\u001b[0m \u001b[43mraise_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mres\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/code/lomas_client/utils.py:38\u001b[0m, in \u001b[0;36mraise_error\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m InvalidQueryException(error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalidQueryException\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m status\u001b[38;5;241m.\u001b[39mHTTP_422_UNPROCESSABLE_ENTITY:\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ExternalLibraryException(\n\u001b[1;32m     39\u001b[0m         error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlibrary\u001b[39m\u001b[38;5;124m\"\u001b[39m], error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExternalLibraryException\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     40\u001b[0m     )\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m status\u001b[38;5;241m.\u001b[39mHTTP_403_FORBIDDEN:\n\u001b[1;32m     42\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m UnauthorizedAccessException(error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnauthorizedAccessException\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
      "\u001b[0;31mExternalLibraryException\u001b[0m: ('smartnoise_synth', 'Error fitting model: sample_rate=5.0 is not a valid value. Please provide a float between 0 and 1. Try decreasing batch_size in synth_params (default batch_size=500).')"
     ]
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"dpctgan\",\n",
    "    epsilon=1.0,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240b3ad1-bc17-48cd-877e-19a05dc18b67",
   "metadata": {},
   "source": [
    "The default parameters of DPCTGAN do not work for PENGUIN dataset. Hence, as advised in the error message, she decreases the batch_size (also she checks the documentation [here](https://docs.smartnoise.org/synth/synthesizers/dpctgan.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8b122e7f-9cd4-42ca-b175-f26d34609646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>43.414108</td>\n",
       "      <td>17.841402</td>\n",
       "      <td>180.284642</td>\n",
       "      <td>5016.072102</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>43.298852</td>\n",
       "      <td>16.777365</td>\n",
       "      <td>222.225340</td>\n",
       "      <td>5162.192479</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>50.622394</td>\n",
       "      <td>19.280649</td>\n",
       "      <td>209.893867</td>\n",
       "      <td>5275.184557</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>41.493216</td>\n",
       "      <td>17.206660</td>\n",
       "      <td>233.323157</td>\n",
       "      <td>2938.070863</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.749278</td>\n",
       "      <td>17.139504</td>\n",
       "      <td>204.060608</td>\n",
       "      <td>5795.609772</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0     Adelie   Dream       43.414108      17.841402         180.284642   \n",
       "1     Gentoo  Biscoe       43.298852      16.777365         222.225340   \n",
       "2     Adelie   Dream       50.622394      19.280649         209.893867   \n",
       "3  Chinstrap  Biscoe       41.493216      17.206660         233.323157   \n",
       "4     Adelie  Biscoe       46.749278      17.139504         204.060608   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0  5016.072102  FEMALE  \n",
       "1  5162.192479    MALE  \n",
       "2  5275.184557  FEMALE  \n",
       "3  2938.070863  FEMALE  \n",
       "4  5795.609772    MALE  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"dpctgan\",\n",
    "    epsilon=1.0,\n",
    "    synth_params = {\"batch_size\": 50},\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0275fe1-7af3-4c1a-b54c-f054b9fc9658",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6372e6e4-26d7-401d-b11b-46d1313a9b1f",
   "metadata": {},
   "source": [
    "## PATEGAN: Private Aggregation of Teacher Ensembles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547e950a-0070-42b4-819a-f8527b4e24f1",
   "metadata": {},
   "source": [
    "Unfortunatelly, she is not able to train the pategan synthetizer on the PENGUIN dataset. Hence, she must try another one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a9859d6-5bd7-4300-9232-95314bee37f6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ExternalLibraryException",
     "evalue": "('smartnoise_synth', 'pategan not reliable with this dataset.')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mExternalLibraryException\u001b[0m                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m res_dummy \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msmartnoise_synth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43msynth_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpategan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepsilon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdummy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m res_dummy\n",
      "File \u001b[0;32m/code/lomas_client/libraries/smartnoise_synth.py:203\u001b[0m, in \u001b[0;36mSmartnoiseSynthClient.query\u001b[0;34m(self, synth_name, epsilon, delta, select_cols, synth_params, nullable, constraints, dummy, return_model, condition, nb_samples, nb_rows, seed)\u001b[0m\n\u001b[1;32m    200\u001b[0m     r_model \u001b[38;5;241m=\u001b[39m QueryResponse\u001b[38;5;241m.\u001b[39mmodel_validate_json(data)\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m r_model\n\u001b[0;32m--> 203\u001b[0m \u001b[43mraise_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mres\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/code/lomas_client/utils.py:38\u001b[0m, in \u001b[0;36mraise_error\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m InvalidQueryException(error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalidQueryException\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m status\u001b[38;5;241m.\u001b[39mHTTP_422_UNPROCESSABLE_ENTITY:\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ExternalLibraryException(\n\u001b[1;32m     39\u001b[0m         error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlibrary\u001b[39m\u001b[38;5;124m\"\u001b[39m], error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExternalLibraryException\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     40\u001b[0m     )\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m status\u001b[38;5;241m.\u001b[39mHTTP_403_FORBIDDEN:\n\u001b[1;32m     42\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m UnauthorizedAccessException(error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnauthorizedAccessException\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
      "\u001b[0;31mExternalLibraryException\u001b[0m: ('smartnoise_synth', 'pategan not reliable with this dataset.')"
     ]
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"pategan\",\n",
    "    epsilon=1.0,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248f8cdc-a7d0-46e2-9b3a-61b78da606b0",
   "metadata": {},
   "source": [
    "## PATECTGAN: Conditional tabular GAN using Private Aggregation of Teacher Ensembles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "87dcdd69-8bf0-4eb4-88c1-f6bc9c87ea09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>44.965223</td>\n",
       "      <td>21.050138</td>\n",
       "      <td>197.011286</td>\n",
       "      <td>3798.269078</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>54.784711</td>\n",
       "      <td>18.795483</td>\n",
       "      <td>189.339603</td>\n",
       "      <td>4936.383002</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>58.836415</td>\n",
       "      <td>14.854715</td>\n",
       "      <td>201.541473</td>\n",
       "      <td>4849.516831</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "      <td>49.260641</td>\n",
       "      <td>19.661433</td>\n",
       "      <td>245.845395</td>\n",
       "      <td>4142.061740</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>48.662708</td>\n",
       "      <td>17.788002</td>\n",
       "      <td>177.374248</td>\n",
       "      <td>5917.481452</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0     Adelie  Torgersen       44.965223      21.050138         197.011286   \n",
       "1  Chinstrap     Biscoe       54.784711      18.795483         189.339603   \n",
       "2  Chinstrap     Biscoe       58.836415      14.854715         201.541473   \n",
       "3     Gentoo      Dream       49.260641      19.661433         245.845395   \n",
       "4     Gentoo  Torgersen       48.662708      17.788002         177.374248   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0  3798.269078    MALE  \n",
       "1  4936.383002    MALE  \n",
       "2  4849.516831    MALE  \n",
       "3  4142.061740  FEMALE  \n",
       "4  5917.481452  FEMALE  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"patectgan\",\n",
    "    epsilon=1.0,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "06eb0b95-1265-422c-9087-121b7091f37c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dream</td>\n",
       "      <td>51.295550</td>\n",
       "      <td>4649.196619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Biscoe</td>\n",
       "      <td>38.369172</td>\n",
       "      <td>4301.166393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Biscoe</td>\n",
       "      <td>52.136779</td>\n",
       "      <td>4498.011571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Torgersen</td>\n",
       "      <td>58.900825</td>\n",
       "      <td>4223.040946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dream</td>\n",
       "      <td>40.492166</td>\n",
       "      <td>3707.417592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      island  bill_length_mm  body_mass_g\n",
       "0      Dream       51.295550  4649.196619\n",
       "1     Biscoe       38.369172  4301.166393\n",
       "2     Biscoe       52.136779  4498.011571\n",
       "3  Torgersen       58.900825  4223.040946\n",
       "4      Dream       40.492166  3707.417592"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"patectgan\",\n",
    "    epsilon=1.0,\n",
    "    select_cols = [\"island\", \"bill_length_mm\", \"body_mass_g\"],\n",
    "    synth_params = {\n",
    "        \"embedding_dim\": 256, \n",
    "        \"generator_dim\": (128, 128), \n",
    "        \"discriminator_dim\": (256, 256),\n",
    "        \"generator_lr\": 0.0003, \n",
    "        \"generator_decay\": 1e-05, \n",
    "        \"discriminator_lr\": 0.0003, \n",
    "        \"discriminator_decay\": 1e-05, \n",
    "        \"batch_size\": 500\n",
    "    },\n",
    "    nb_samples = 100,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fcda9d1-4137-4ff3-9af7-eead85057dd5",
   "metadata": {},
   "source": [
    "## DPGAN: DIfferentially Private GAN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084ea436-f47e-4da2-95ea-7a068b9f1510",
   "metadata": {},
   "source": [
    "For DPGAN, there is the same warning as for DPCTGAN with the cryptographically secure random noise generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "03c70909-e5f6-4c34-a787-37f5615ed600",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/code/lomas_client/utils.py:62: UserWarning: Warning:dpgan synthesizer random generator for noise and shuffling is not cryptographically secure. (pseudo-rng in vanilla PyTorch).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>61.084300</td>\n",
       "      <td>17.778250</td>\n",
       "      <td>202.404261</td>\n",
       "      <td>4074.338235</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>45.143127</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>4078.621872</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>63.310050</td>\n",
       "      <td>16.944589</td>\n",
       "      <td>215.155567</td>\n",
       "      <td>3999.723613</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>22.198413</td>\n",
       "      <td>218.926238</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>191.299780</td>\n",
       "      <td>4249.239404</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Gentoo  Biscoe       61.084300      17.778250         202.404261   \n",
       "1  Adelie   Dream       45.143127      23.000000         250.000000   \n",
       "2  Gentoo  Biscoe       63.310050      16.944589         215.155567   \n",
       "3  Gentoo   Dream       65.000000      22.198413         218.926238   \n",
       "4  Adelie   Dream       65.000000      23.000000         191.299780   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0  4074.338235    MALE  \n",
       "1  4078.621872    MALE  \n",
       "2  3999.723613  FEMALE  \n",
       "3  7000.000000    MALE  \n",
       "4  4249.239404    MALE  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"dpgan\",\n",
    "    epsilon=1.0,\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5329b4-6a21-4b38-abeb-68a68195109e",
   "metadata": {},
   "source": [
    "One final time she samples with conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "17651dfc-d3b7-4030-a4be-1767de3767d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/code/lomas_client/utils.py:62: UserWarning: Warning:dpgan synthesizer random generator for noise and shuffling is not cryptographically secure. (pseudo-rng in vanilla PyTorch).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>48.614252</td>\n",
       "      <td>17.252423</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>62.443527</td>\n",
       "      <td>17.991540</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Dream</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>226.908019</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Dream</td>\n",
       "      <td>60.141646</td>\n",
       "      <td>16.770572</td>\n",
       "      <td>246.724272</td>\n",
       "      <td>5726.566434</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>46.260255</td>\n",
       "      <td>16.974378</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>6849.641472</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0     Gentoo  Torgersen       48.614252      17.252423         250.000000   \n",
       "1     Gentoo  Torgersen       62.443527      17.991540         250.000000   \n",
       "2  Chinstrap      Dream       65.000000      23.000000         226.908019   \n",
       "3     Gentoo      Dream       60.141646      16.770572         246.724272   \n",
       "4     Adelie  Torgersen       46.260255      16.974378         250.000000   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0  7000.000000  FEMALE  \n",
       "1  7000.000000  FEMALE  \n",
       "2  7000.000000    MALE  \n",
       "3  5726.566434    MALE  \n",
       "4  6849.641472    MALE  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"dpgan\",\n",
    "    epsilon=1.0,\n",
    "    condition = \"body_mass_g > 5000\",\n",
    "    dummy=True,\n",
    ")\n",
    "res_dummy.result.df_samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177c8a4b-4eb7-444c-836d-cce81327e5c6",
   "metadata": {},
   "source": [
    "And now on the real dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "799c7574-0eb9-49c1-bc01-b051782d7b62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/code/lomas_client/utils.py:62: UserWarning: Warning:dpgan synthesizer random generator for noise and shuffling is not cryptographically secure. (pseudo-rng in vanilla PyTorch).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>44.275917</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>194.386986</td>\n",
       "      <td>5710.911572</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.761536</td>\n",
       "      <td>19.180464</td>\n",
       "      <td>190.228606</td>\n",
       "      <td>5585.222661</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>51.918343</td>\n",
       "      <td>20.711846</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>5547.108099</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>193.761142</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Dream</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>244.220206</td>\n",
       "      <td>6518.389255</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>61.533132</td>\n",
       "      <td>20.927101</td>\n",
       "      <td>186.077987</td>\n",
       "      <td>5242.271543</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Dream</td>\n",
       "      <td>46.066000</td>\n",
       "      <td>20.364783</td>\n",
       "      <td>198.249876</td>\n",
       "      <td>5248.364478</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>63.791512</td>\n",
       "      <td>17.969750</td>\n",
       "      <td>199.137564</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>16.838814</td>\n",
       "      <td>180.955905</td>\n",
       "      <td>6145.199358</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.727768</td>\n",
       "      <td>183.611000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0     Adelie     Biscoe       44.275917      23.000000         194.386986   \n",
       "1  Chinstrap     Biscoe       45.761536      19.180464         190.228606   \n",
       "2  Chinstrap     Biscoe       51.918343      20.711846         250.000000   \n",
       "3     Adelie      Dream       65.000000      23.000000         193.761142   \n",
       "4  Chinstrap      Dream       65.000000      23.000000         244.220206   \n",
       "5     Adelie  Torgersen       61.533132      20.927101         186.077987   \n",
       "6  Chinstrap      Dream       46.066000      20.364783         198.249876   \n",
       "7     Adelie  Torgersen       63.791512      17.969750         199.137564   \n",
       "8     Adelie      Dream       65.000000      16.838814         180.955905   \n",
       "9     Adelie     Biscoe       65.000000      19.727768         183.611000   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0  5710.911572          \n",
       "1  5585.222661  FEMALE  \n",
       "2  5547.108099          \n",
       "3  7000.000000          \n",
       "4  6518.389255    MALE  \n",
       "5  5242.271543    MALE  \n",
       "6  5248.364478          \n",
       "7  7000.000000          \n",
       "8  6145.199358          \n",
       "9  7000.000000          "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dummy = client.smartnoise_synth.query(\n",
    "    synth_name=\"dpgan\",\n",
    "    epsilon=1.0,\n",
    "    condition = \"body_mass_g > 5000\",\n",
    "    nb_samples = 10,\n",
    "    dummy=False,\n",
    ")\n",
    "res_dummy.result.df_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94eaf59b-c108-424c-8978-b1c86e141ccb",
   "metadata": {},
   "source": [
    "## Step 6: See archives of queries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64003c53-de56-4bdc-a3c2-0c3e40031919",
   "metadata": {},
   "source": [
    "She now wants to verify all the queries that she did on the real data. It is possible because an archive of all queries is kept in a secure database. With a function call she can see her queries, budget and associated responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "008fd230-cdfd-4e03-91ce-5a60b06c106d",
   "metadata": {},
   "outputs": [],
   "source": [
    "previous_queries = client.get_previous_queries()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a34bc3-d1a5-4124-983f-ddc09dd1af7b",
   "metadata": {},
   "source": [
    "Let's check the last query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1795a54b-d04e-4687-8649-93982c84ad30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_name': 'Dr. Antartica',\n",
       " 'dataset_name': 'PENGUIN',\n",
       " 'dp_library': 'smartnoise_synth',\n",
       " 'client_input': {'dataset_name': 'PENGUIN',\n",
       "  'synth_name': 'dpgan',\n",
       "  'epsilon': 1.0,\n",
       "  'delta': None,\n",
       "  'select_cols': [],\n",
       "  'synth_params': {},\n",
       "  'nullable': True,\n",
       "  'constraints': '',\n",
       "  'return_model': False,\n",
       "  'condition': 'body_mass_g > 5000',\n",
       "  'nb_samples': 10},\n",
       " 'response': {'epsilon': 1.0,\n",
       "  'delta': 0.00015673368198174188,\n",
       "  'requested_by': 'Dr. Antartica',\n",
       "  'result':                       res_type  \\\n",
       "  index         sn_synth_samples   \n",
       "  columns       sn_synth_samples   \n",
       "  data          sn_synth_samples   \n",
       "  index_names   sn_synth_samples   \n",
       "  column_names  sn_synth_samples   \n",
       "  \n",
       "                                                       df_samples  \n",
       "  index                            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]  \n",
       "  columns       [species, island, bill_length_mm, bill_depth_m...  \n",
       "  data          [[Adelie, Biscoe, 44.27591737359762, 23.0, 194...  \n",
       "  index_names                                              [None]  \n",
       "  column_names                                             [None]  },\n",
       " 'timestamp': 1728461702.0089684}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_query = previous_queries[-1]\n",
    "last_query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08253456-fae7-424d-ac63-52dfc539c1e4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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