notebook/KnockoffDB.ipynb

{
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    "KnockoffDB\n",
    "---\n",
    "\n",
    "This class is responsible for building the tables and inserting the data into the database. It accomplishes this using a **KnockoffDatabaseService** provided to its \\_\\_init\\_\\_ to interact with the database for getting table definitions and uploading knockoff data. The **DefaultDatabaseService** is an implmentation of the **KnockoffDatabaseService** that can be used out of the box to interact with any sqlalchemy compatible database.\n",
    "\n",
    "There are various patterns that can leveraged for configuring a **KnockoffDB** instance with a **KnockoffDatabaseService** and **KnockoffTable** instances, but in this example we'll use the classes and objects directly from the sdk. The other patterns (e.g. using a **Blueprint**) will be show cased in other examples, but are to support dependency injection and composability for pytest fixtures and for the CLI.\n",
    "\n",
    "\n",
    "## Example\n",
    "\n",
    "Preliminary knowledge of the [KnockoffTable](KnockoffTable.ipynb) is recommended for this example. For this example we'll create a basic data model with the following tables: Product, Location, Transaction.\n",
    "\n",
    "* [Product Table](#Product-Table)\n",
    "* [Location Table](#Location-Table)\n",
    "* [Transaction Table](#Transaction-Table)\n",
    "* [KnockoffDB](#KnockoffDB-ex)\n",
    "\n",
    "\n",
    "### Product Table\n",
    "\n",
    "For this example we want our product table to satisfy a hierachy to resemble more realistic data. To do so, we'll first need to generate a hiearchy DataFrame."
   ]
  },
  {
   "cell_type": "code",
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       "    }\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>division</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Lifestyle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Basketball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Jordan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Training &amp; Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Soccer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Golf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Cross Country</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Skateboarding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Tennis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Baseball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Sandals &amp; Slides</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Tops &amp; T-Shirts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Shorts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Hoodies &amp; Sweatshirts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Pants &amp; Tights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Matching Sets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Jackets &amp; Vests</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Swimwear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Polos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Yoga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Socks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Underwear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Big &amp; Tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Sustainable Materials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   division               category\n",
       "0     Shoes              Lifestyle\n",
       "1     Shoes                Running\n",
       "2     Shoes             Basketball\n",
       "3     Shoes                 Jordan\n",
       "4     Shoes         Training & Gym\n",
       "5     Shoes                 Soccer\n",
       "6     Shoes                   Golf\n",
       "7     Shoes          Cross Country\n",
       "8     Shoes          Skateboarding\n",
       "9     Shoes                 Tennis\n",
       "10    Shoes               Baseball\n",
       "11    Shoes       Sandals & Slides\n",
       "0   Apparel        Tops & T-Shirts\n",
       "1   Apparel                 Shorts\n",
       "2   Apparel  Hoodies & Sweatshirts\n",
       "3   Apparel         Pants & Tights\n",
       "4   Apparel          Matching Sets\n",
       "5   Apparel        Jackets & Vests\n",
       "6   Apparel               Swimwear\n",
       "7   Apparel                  Polos\n",
       "8   Apparel                   Yoga\n",
       "9   Apparel                  Socks\n",
       "10  Apparel              Underwear\n",
       "11  Apparel             Big & Tall\n",
       "12  Apparel  Sustainable Materials"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "shoes_categories = [\n",
    "    \"Lifestyle\",\n",
    "    \"Running\",\n",
    "    \"Basketball\",\n",
    "    \"Jordan\",\n",
    "    \"Training & Gym\",\n",
    "    \"Soccer\",\n",
    "    \"Golf\",\n",
    "    \"Cross Country\",\n",
    "    \"Skateboarding\",\n",
    "    \"Tennis\",\n",
    "    \"Baseball\",\n",
    "    \"Sandals & Slides\"\n",
    "]\n",
    "\n",
    "apparel_categories = [\n",
    "    \"Tops & T-Shirts\",\n",
    "    \"Shorts\",\n",
    "    \"Hoodies & Sweatshirts\",\n",
    "    \"Pants & Tights\",\n",
    "    \"Matching Sets\",\n",
    "    \"Jackets & Vests\",\n",
    "    \"Swimwear\",\n",
    "    \"Polos\",\n",
    "    \"Yoga\",\n",
    "    \"Socks\",\n",
    "    \"Underwear\",\n",
    "    \"Big & Tall\",\n",
    "    \"Sustainable Materials\"\n",
    "]\n",
    "\n",
    "\n",
    "def cartesian_product(values, names):\n",
    "    return pd.DataFrame(\n",
    "        index=pd.MultiIndex.from_product(\n",
    "            values, \n",
    "            names=names\n",
    "        )\n",
    "    ).reset_index()\n",
    "\n",
    "shoes = cartesian_product(\n",
    "    [[\"Shoes\"], shoes_categories], \n",
    "    names=[\"division\", \"category\"]\n",
    ")\n",
    "\n",
    "apparel = cartesian_product(\n",
    "    [[\"Apparel\"], apparel_categories], \n",
    "    names=[\"division\", \"category\"]\n",
    ")\n",
    "\n",
    "hierarchy = pd.concat([shoes, apparel])\n",
    "display(hierarchy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5220c0",
   "metadata": {},
   "source": [
    "We'll also want valid size values based on the division so here we'll define a **ColumnFactory** with a dependency on **division**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c3496b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from knockoff.sdk.factory.column import ColumnFactory, FakerFactory, ChoiceFactory\n",
    "\n",
    "def size_factory(division):\n",
    "    return {\n",
    "        \"Apparel\": ChoiceFactory([\"XS\", \"S\", \"M\", \"L\", \"XL\", \"XXL\"]),\n",
    "        \"Shoes\": FakerFactory(\"pyint\", min_value=4, max_value=16),\n",
    "    }[division]()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa427f1",
   "metadata": {},
   "source": [
    "Now we can define our Product KnockoffTable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a82bf6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from knockoff.sdk.table import KnockoffTable\n",
    "from knockoff.sdk.factory.collections import KnockoffDataFrameFactory\n",
    "from knockoff.sdk.constraints import KnockoffUniqueConstraint\n",
    "\n",
    "product_table = KnockoffTable(\n",
    "    \"product\",\n",
    "    columns=[\n",
    "        \"division\",\n",
    "        \"category\",\n",
    "        \"gender\",\n",
    "        \"color\",\n",
    "        \"size\",\n",
    "        \"sku\",\n",
    "        \"price\"\n",
    "    ],\n",
    "    factories=[\n",
    "        KnockoffDataFrameFactory(hierarchy),\n",
    "        ColumnFactory(\"gender\", ChoiceFactory([\"Mens\", \"Womens\"])),\n",
    "        ColumnFactory(\"color\", FakerFactory(\"color_name\")),\n",
    "        ColumnFactory(\"size\", size_factory, depends_on=[\"division\"]),\n",
    "        ColumnFactory(\"sku\", FakerFactory(\"numerify\", text=\"%#########\")),\n",
    "        ColumnFactory(\"price\", FakerFactory(\"pyfloat\", right_digits=2, min_value=5, max_value=300)),\n",
    "    ],\n",
    "    constraints=[KnockoffUniqueConstraint([\"sku\"])],\n",
    "    size=50\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f953c0a",
   "metadata": {},
   "source": [
    "### Location Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a695f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_table = KnockoffTable(\n",
    "    \"location\",\n",
    "    columns=[\n",
    "        \"location_id\",\n",
    "        \"address\",\n",
    "    ],\n",
    "    factories=[\n",
    "        ColumnFactory(\"location_id\", FakerFactory(\"numerify\", text=\"%###\")),\n",
    "        ColumnFactory(\"address\", FakerFactory(\"address\")),\n",
    "    ],\n",
    "    constraints=[KnockoffUniqueConstraint([\"location_id\"])],\n",
    "    size=10\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3dc342",
   "metadata": {},
   "source": [
    "### Transaction Table\n",
    "\n",
    "For the transaction table we'll need to use the KnockoffTableFactory to model foreign key relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab77157a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from knockoff.sdk.factory.collections import KnockoffTableFactory\n",
    "\n",
    "# create a demand function\n",
    "def sales_func():\n",
    "    return max(np.random.poisson(1), 1)\n",
    "\n",
    "# create a revenue function based on price and units\n",
    "def revenue_func(price, units):\n",
    "    return price*float(units)\n",
    "\n",
    "\n",
    "transaction_table = KnockoffTable(\n",
    "    \"transaction\",\n",
    "    columns=[\n",
    "        \"location_id\",\n",
    "        \"sku\",\n",
    "        \"units\",\n",
    "        \"revenue\",\n",
    "        \"date\"\n",
    "    ],\n",
    "    factories=[\n",
    "        KnockoffTableFactory(product_table, columns=[\"sku\", \"price\"]),\n",
    "        KnockoffTableFactory(location_table, columns=[\"location_id\"]),\n",
    "        ColumnFactory(\"units\", sales_func),\n",
    "        ColumnFactory(\"revenue\", revenue_func, depends_on=[\"price\", \"units\"]),\n",
    "        ColumnFactory(\"date\", FakerFactory(\"date_between\", start_date=\"-2y\", end_date=\"today\")),\n",
    "    ],\n",
    "    size=200\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004278b0",
   "metadata": {},
   "source": [
    "### <a name=\"KnockoffDB-ex\"></a>KnockoffDB\n",
    "\n",
    "Now that we've defined our tables, we can provide them to the KnockoffDB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd7620db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "from knockoff.sdk.db import KnockoffDB, DefaultDatabaseService\n",
    "\n",
    "# The database service is actually only required\n",
    "# if we want to autoload the table definitions or\n",
    "# if we want to insert the data. For this example\n",
    "# we'll simply build the DataFrames that would be\n",
    "# inserted.\n",
    "database_service = None \n",
    "\n",
    "# Below is an example of configuring the DefaultDatabaseService\n",
    "# url = .. # connection url to database\n",
    "# database_service = DefaultDatabaseService(create_engine(url))\n",
    "\n",
    "\n",
    "# Instantiate KnockoffDB instance\n",
    "knockoff_db = KnockoffDB(database_service)\n",
    "\n",
    "# Add tables with dependencies\n",
    "knockoff_db.add(transaction_table, \n",
    "                depends_on=[\"product\", \"location\"])\n",
    "knockoff_db.add(product_table) # these can be added in any order\n",
    "knockoff_db.add(location_table)\n",
    "\n",
    "dfs = knockoff_db.build() # if we had provided an actual database service, \n",
    "                          # calling insert() would load these into the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8639e3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['location', 'product', 'transaction'])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_id</th>\n",
       "      <th>address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3985</td>\n",
       "      <td>6973 Wong Radial Suite 361\\nNorth Brandon, CA ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3125</td>\n",
       "      <td>8242 Jacob Crossing\\nSouth Andre, MI 71867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9949</td>\n",
       "      <td>274 Ryan Forge Suite 432\\nLake Rodney, AL 73237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4546</td>\n",
       "      <td>239 Michaela Spring Suite 435\\nJonathonberg, K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5596</td>\n",
       "      <td>USS Montgomery\\nFPO AP 36767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location_id                                            address\n",
       "0        3985  6973 Wong Radial Suite 361\\nNorth Brandon, CA ...\n",
       "1        3125         8242 Jacob Crossing\\nSouth Andre, MI 71867\n",
       "2        9949    274 Ryan Forge Suite 432\\nLake Rodney, AL 73237\n",
       "3        4546  239 Michaela Spring Suite 435\\nJonathonberg, K...\n",
       "4        5596                       USS Montgomery\\nFPO AP 36767"
      ]
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      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>division</th>\n",
       "      <th>category</th>\n",
       "      <th>gender</th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>sku</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Socks</td>\n",
       "      <td>Womens</td>\n",
       "      <td>DarkViolet</td>\n",
       "      <td>XL</td>\n",
       "      <td>2778986833</td>\n",
       "      <td>71.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Big &amp; Tall</td>\n",
       "      <td>Womens</td>\n",
       "      <td>Lavender</td>\n",
       "      <td>M</td>\n",
       "      <td>6425191615</td>\n",
       "      <td>29.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>Mens</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>5</td>\n",
       "      <td>3418806930</td>\n",
       "      <td>108.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>Big &amp; Tall</td>\n",
       "      <td>Mens</td>\n",
       "      <td>PapayaWhip</td>\n",
       "      <td>M</td>\n",
       "      <td>9215866886</td>\n",
       "      <td>122.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>Sandals &amp; Slides</td>\n",
       "      <td>Womens</td>\n",
       "      <td>NavajoWhite</td>\n",
       "      <td>10</td>\n",
       "      <td>2546345047</td>\n",
       "      <td>131.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  division          category  gender        color size         sku   price\n",
       "0  Apparel             Socks  Womens   DarkViolet   XL  2778986833   71.44\n",
       "1  Apparel        Big & Tall  Womens     Lavender    M  6425191615   29.36\n",
       "2    Shoes         Lifestyle    Mens       Yellow    5  3418806930  108.36\n",
       "3  Apparel        Big & Tall    Mens   PapayaWhip    M  9215866886  122.71\n",
       "4    Shoes  Sandals & Slides  Womens  NavajoWhite   10  2546345047  131.96"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>location_id</th>\n",
       "      <th>sku</th>\n",
       "      <th>units</th>\n",
       "      <th>revenue</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7723</td>\n",
       "      <td>4154009234</td>\n",
       "      <td>1</td>\n",
       "      <td>299.32</td>\n",
       "      <td>2021-04-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9949</td>\n",
       "      <td>6584917951</td>\n",
       "      <td>1</td>\n",
       "      <td>280.11</td>\n",
       "      <td>2021-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4396</td>\n",
       "      <td>7777086793</td>\n",
       "      <td>1</td>\n",
       "      <td>267.46</td>\n",
       "      <td>2020-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3125</td>\n",
       "      <td>8116513548</td>\n",
       "      <td>1</td>\n",
       "      <td>198.37</td>\n",
       "      <td>2019-10-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6537</td>\n",
       "      <td>4339201699</td>\n",
       "      <td>2</td>\n",
       "      <td>361.98</td>\n",
       "      <td>2021-03-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location_id         sku  units  revenue        date\n",
       "0        7723  4154009234      1   299.32  2021-04-21\n",
       "1        9949  6584917951      1   280.11  2021-01-27\n",
       "2        4396  7777086793      1   267.46  2020-07-25\n",
       "3        3125  8116513548      1   198.37  2019-10-27\n",
       "4        6537  4339201699      2   361.98  2021-03-10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dfs.keys())\n",
    "\n",
    "display(dfs['location'].head())\n",
    "display(dfs['product'].head())\n",
    "display(dfs['transaction'].head())"
   ]
  }
 ],
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