{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9de8716",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "# Gamma-gamma Model\n",
    "\n",
    "In this notebook we show how to fit a Gamma-Gamma model in PyMC-Marketing. We compare the results with the [`lifetimes`](https://github.com/CamDavidsonPilon/lifetimes) package (no longer maintained and last meaningful update was July 2020). The model is presented in the paper: Fader, P. S., & Hardie, B. G. (2013). [The Gamma-Gamma model of monetary value](http://www.brucehardie.com/notes/025/gamma_gamma.pdf). February, 2, 1-9."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a579696d",
   "metadata": {},
   "source": [
    "## Prepare Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "813aa3e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from lifetimes import GammaGammaFitter\n",
    "\n",
    "from pymc_marketing import clv\n",
    "\n",
    "# Plotting configuration\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 6]\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = \"retina\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e9df33",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "We start by loading the `CDNOW` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4039ce96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>30.43</td>\n",
       "      <td>38.86</td>\n",
       "      <td>22.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.71</td>\n",
       "      <td>38.86</td>\n",
       "      <td>11.77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frequency  recency      T  monetary_value  customer_id\n",
       "0          2    30.43  38.86           22.35            0\n",
       "1          1     1.71  38.86           11.77            1\n",
       "2          0     0.00  38.86            0.00            2\n",
       "3          0     0.00  38.86            0.00            3\n",
       "4          0     0.00  38.86            0.00            4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/clv_quickstart.csv\"\n",
    "\n",
    "summary_with_money_value = pd.read_csv(data_path)\n",
    "summary_with_money_value[\"customer_id\"] = summary_with_money_value.index\n",
    "summary_with_money_value.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d244cc1a",
   "metadata": {},
   "source": [
    "For the Gamma-Gamma model, we need to filter out customers who have made only one purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bae5c34c",
   "metadata": {
    "id": "a374e74d"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>30.43</td>\n",
       "      <td>38.86</td>\n",
       "      <td>22.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.71</td>\n",
       "      <td>38.86</td>\n",
       "      <td>11.77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>29.43</td>\n",
       "      <td>38.86</td>\n",
       "      <td>73.74</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>11.77</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>35.71</td>\n",
       "      <td>38.86</td>\n",
       "      <td>25.55</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frequency  recency      T  monetary_value  customer_id\n",
       "0          2    30.43  38.86           22.35            0\n",
       "1          1     1.71  38.86           11.77            1\n",
       "5          7    29.43  38.86           73.74            5\n",
       "6          1     5.00  38.86           11.77            6\n",
       "8          2    35.71  38.86           25.55            8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returning_customers_summary = summary_with_money_value.query(\"frequency > 0\")\n",
    "\n",
    "returning_customers_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16374e35",
   "metadata": {},
   "source": [
    "## Model Specification\n",
    "\n",
    "Here we briefly describe the assumptions and the parametrization of the Gamma-Gamma model from the paper above.\n",
    "\n",
    "The model of spend per transaction is based on the following three general assumptions:\n",
    "\n",
    "- The monetary value of a customer’s given transaction varies randomly around their average transaction value.\n",
    "- Average transaction values vary across customers but do not vary over time for any given individual.\n",
    "- The distribution of average transaction values across customers is independent of the transaction process.\n",
    "  \n",
    "For a customer with x transactions, let $z_1, z_2, \\ldots, z_x$ denote the value of each transaction. The customer’s observed average transaction value by\n",
    "\n",
    "$$\n",
    "\\bar{z} = \\frac{1}{x} \\sum_{i=1}^{x} z_i\n",
    "$$\n",
    "\n",
    "Now let's describe the parametrization: \n",
    "\n",
    "1. We assume that $z_i \\sim \\text{Gamma}(p, ν)$, with $E(Z_i| p, ν) = \\xi = p/ν$.\n",
    "\n",
    "    – Given the convolution properties of the gamma, it follows that total spend across x transactions is distributed $\\text{Gamma}(px, ν)$.\n",
    "\n",
    "    – Given the scaling property of the gamma distribution, it follows that $\\bar{z} \\sim \\text{Gamma}(px, νx)$.\n",
    "\n",
    "2.  We assume $ν \\sim \\text{Gamma}(q, \\gamma)$.\n",
    "\n",
    "We are interested in estimating the parameters $p$, $q$ and $ν$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8696dc0a",
   "metadata": {},
   "source": [
    "```{note}\n",
    "The Gamma-Gamma model assumes that there is no relationship between the monetary value and the purchase frequency. We can check this assumption by calculating the correlation between the average spend and the frequency of purchases.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c413718e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>monetary_value</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.113884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>0.113884</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                monetary_value  frequency\n",
       "monetary_value        1.000000   0.113884\n",
       "frequency             0.113884   1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returning_customers_summary[[\"monetary_value\", \"frequency\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4bea0a",
   "metadata": {},
   "source": [
    "The value of this correlation is close to $0.11$, which in practice is considered low enough to proceed with the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df93d769",
   "metadata": {},
   "source": [
    "## Lifetimes Implementation\n",
    "\n",
    "First, we fit the model using the `lifetimes` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e511391",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a0dbb204",
    "outputId": "743601c8-48c5-46a7-993f-4bd19f083ea7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.GammaGammaFitter: fitted with 946 subjects, p: 6.25, q: 3.74, v: 15.45>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggf = GammaGammaFitter()\n",
    "ggf.fit(\n",
    "    returning_customers_summary[\"frequency\"],\n",
    "    returning_customers_summary[\"monetary_value\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b65b7690",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>lower 95% bound</th>\n",
       "      <th>upper 95% bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>6.248802</td>\n",
       "      <td>1.189687</td>\n",
       "      <td>3.917016</td>\n",
       "      <td>8.580589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>3.744588</td>\n",
       "      <td>0.290166</td>\n",
       "      <td>3.175864</td>\n",
       "      <td>4.313313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>15.447748</td>\n",
       "      <td>4.159994</td>\n",
       "      <td>7.294160</td>\n",
       "      <td>23.601336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        coef  se(coef)  lower 95% bound  upper 95% bound\n",
       "p   6.248802  1.189687         3.917016         8.580589\n",
       "q   3.744588  0.290166         3.175864         4.313313\n",
       "v  15.447748  4.159994         7.294160        23.601336"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggf.summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cace007a",
   "metadata": {},
   "source": [
    "Once the model is fitted we can use the following method to compute the conditional expectation of the average profit per transaction for a group of one or more customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "12dbdd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.658616\n",
       "1    18.911480\n",
       "2    35.171002\n",
       "3    35.171002\n",
       "4    35.171002\n",
       "5    71.462851\n",
       "6    18.911480\n",
       "7    35.171002\n",
       "8    27.282408\n",
       "9    35.171002\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_profit = ggf.conditional_expected_average_profit(\n",
    "    summary_with_money_value[\"frequency\"], summary_with_money_value[\"monetary_value\"]\n",
    ")\n",
    "avg_profit.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f0986b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.25295817604995"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_profit.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff387fd",
   "metadata": {
    "id": "a2z_ZcC74wPI"
   },
   "source": [
    "## PyMC Marketing Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d153908d",
   "metadata": {},
   "source": [
    "We can use the pre-built PyMC Marketing implementation of the Gamma-Gamma model, which also provides nice plotting and prediction methods:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e27387",
   "metadata": {},
   "source": [
    "We can *build* the model so that we can see the model specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6c912da",
   "metadata": {
    "id": "eoQmmIrj43NV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gamma-Gamma Model (Mean Transactions)\n",
       "         p ~ HalfFlat()\n",
       "         q ~ HalfFlat()\n",
       "         v ~ HalfFlat()\n",
       "likelihood ~ Potential(f(q, p, v))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = clv.GammaGammaModel(data=returning_customers_summary)\n",
    "model.build_model()\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1826502a",
   "metadata": {},
   "source": [
    "```{note}\n",
    "It is not necessary to build the model before fitting it. We can fit the model directly.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21608cf4",
   "metadata": {},
   "source": [
    "### Using MAP\n",
    "\n",
    "To begin with, lets use a numerical optimizer (`L-BFGS-B`) from `scipy.optimize` to find the maximum a posteriori (MAP) estimate of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e39da004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "712fb13d4aac41749a8e2767231d2d79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idata_map = model.fit(fit_method=\"map\").posterior.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b8f11643",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chain</th>\n",
       "      <th>draw</th>\n",
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       "      <td>6.248787</td>\n",
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       "      <td>15.447813</td>\n",
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      ],
      "text/plain": [
       "                   p         q          v\n",
       "chain draw                               \n",
       "0     0     6.248787  3.744591  15.447813"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b140c648",
   "metadata": {},
   "source": [
    "These values are very close to the ones obtained by the `lifetimes` package."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756eb4d5",
   "metadata": {},
   "source": [
    "### MCMC\n",
    "\n",
    "We can also use MCMC to sample from the posterior distribution of the parameters. MCMC is a more robust method than MAP and provides uncertainty estimates for the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed88b572",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p, q, v]\n"
     ]
    },
    {
     "data": {
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       "model_id": "a9f2a1d48dd74e0394cb66ec19a33b8f",
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       "version_minor": 0
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      "text/plain": [
       "Output()"
      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 9 seconds.\n"
     ]
    }
   ],
   "source": [
    "sampler_kwargs = {\n",
    "    \"draws\": 2_000,\n",
    "    \"target_accept\": 0.9,\n",
    "    \"chains\": 4,\n",
    "    \"random_seed\": 42,\n",
    "}\n",
    "\n",
    "idata_mcmc = model.fit(**sampler_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "52c3b00e",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 208kB\n",
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       "    inference_library:          pymc\n",
       "    inference_library_version:  5.13.0\n",
       "    sampling_time:              8.762557983398438\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-77d0cb29-2a5e-4587-83be-137c48aa663d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-77d0cb29-2a5e-4587-83be-137c48aa663d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b03cc059-52df-4af5-a288-e04cd4c5a8a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b03cc059-52df-4af5-a288-e04cd4c5a8a8' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-4f472b78-634a-4bf6-95bf-0c7113a49a7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f472b78-634a-4bf6-95bf-0c7113a49a7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1b14d95-02e6-4bb5-a7e2-fd6317df966f' class='xr-var-data-in' type='checkbox'><label for='data-b1b14d95-02e6-4bb5-a7e2-fd6317df966f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-f43048d3-31c0-4acc-9df4-4978765a3c9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f43048d3-31c0-4acc-9df4-4978765a3c9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853fc271-2cd7-4acd-80e2-dd4b2459eb2f' class='xr-var-data-in' type='checkbox'><label for='data-853fc271-2cd7-4acd-80e2-dd4b2459eb2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff384be3-5521-4d5a-b6d6-e9352be73461' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff384be3-5521-4d5a-b6d6-e9352be73461' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.714 5.668 6.494 ... 6.557 5.684</div><input id='attrs-a04a7fcb-a8ce-43c6-84d8-ac09c5d8b99c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a04a7fcb-a8ce-43c6-84d8-ac09c5d8b99c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fb47694-fabb-4038-ae62-5471f76cb1ea' class='xr-var-data-in' type='checkbox'><label for='data-1fb47694-fabb-4038-ae62-5471f76cb1ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 5.71416122,  5.6676733 ,  6.49384126, ...,  6.87374347,\n",
       "         6.03032606,  5.37106178],\n",
       "       [ 6.12496266,  6.39719508,  6.20416749, ...,  8.39504671,\n",
       "         5.99743133,  6.01075277],\n",
       "       [10.99376248, 11.01915285,  5.77446903, ...,  6.14409996,\n",
       "         6.18666532,  6.64637051],\n",
       "       [ 7.57734926,  7.61722397,  8.00414706, ...,  5.19566766,\n",
       "         6.55673272,  5.68353917]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.1 4.145 3.842 ... 3.729 3.962</div><input id='attrs-7c7a0e01-99df-4475-8e12-b5c99deebfb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c7a0e01-99df-4475-8e12-b5c99deebfb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28e5a9da-21f2-4d96-8e7e-1b676ead172f' class='xr-var-data-in' type='checkbox'><label for='data-28e5a9da-21f2-4d96-8e7e-1b676ead172f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.09950143, 4.14456137, 3.8422203 , ..., 3.87007165, 3.87240126,\n",
       "        4.06013384],\n",
       "       [4.07837883, 4.12453789, 4.03936959, ..., 3.49935494, 3.74801628,\n",
       "        3.83114109],\n",
       "       [3.09648783, 3.09013564, 3.93685735, ..., 3.67908406, 3.6794061 ,\n",
       "        3.72178106],\n",
       "       [3.89744363, 3.92892377, 3.76107785, ..., 4.03248252, 3.72853974,\n",
       "        3.96238228]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.86 18.47 15.19 ... 14.82 18.38</div><input id='attrs-a0993a57-b029-496c-b4a1-9077cd329221' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0993a57-b029-496c-b4a1-9077cd329221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee1dc9c8-5a70-45d5-a28c-99fd305b5342' class='xr-var-data-in' type='checkbox'><label for='data-ee1dc9c8-5a70-45d5-a28c-99fd305b5342' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[18.86358538, 18.47125165, 15.19011131, ..., 14.42345846,\n",
       "        16.72387479, 20.00269094],\n",
       "       [17.17316369, 16.84187742, 16.93941708, ..., 10.53226281,\n",
       "        16.09625255, 16.1122605 ],\n",
       "       [ 7.00465044,  6.9194529 , 17.96394339, ..., 16.01018821,\n",
       "        15.20800729, 14.18201342],\n",
       "       [14.0497322 , 13.49744465, 11.86675149, ..., 20.82900548,\n",
       "        14.81826667, 18.37967578]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da9418e1-045d-4e52-8191-0932769df193' class='xr-section-summary-in' type='checkbox'  ><label for='section-da9418e1-045d-4e52-8191-0932769df193' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bc23154e-598c-400e-a766-5f47b823b34f' class='xr-index-data-in' type='checkbox'/><label for='index-bc23154e-598c-400e-a766-5f47b823b34f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cc4aa0db-3f49-486c-a713-c64110107655' class='xr-index-data-in' type='checkbox'/><label for='index-cc4aa0db-3f49-486c-a713-c64110107655' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed050c11-22f9-4658-bdfa-608f194dd67e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed050c11-22f9-4658-bdfa-608f194dd67e' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-26T12:36:52.420774+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.13.0</dd><dt><span>sampling_time :</span></dt><dd>8.762557983398438</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
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       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 992kB\n",
       "Dimensions:                (chain: 4, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 32B 0 1 2 3\n",
       "  * draw                   (draw) int64 16kB 0 1 2 3 4 ... 1996 1997 1998 1999\n",
       "Data variables: (12/17)\n",
       "    acceptance_rate        (chain, draw) float64 64kB 1.0 0.3785 ... 0.9421\n",
       "    diverging              (chain, draw) bool 8kB False False ... False False\n",
       "    energy                 (chain, draw) float64 64kB 4.051e+03 ... 4.052e+03\n",
       "    energy_error           (chain, draw) float64 64kB -0.03456 ... -0.0594\n",
       "    index_in_trajectory    (chain, draw) int64 64kB -1 -2 -16 -2 ... 27 19 19 23\n",
       "    largest_eigval         (chain, draw) float64 64kB nan nan nan ... nan nan\n",
       "    ...                     ...\n",
       "    process_time_diff      (chain, draw) float64 64kB 0.000166 ... 0.003844\n",
       "    reached_max_treedepth  (chain, draw) bool 8kB False False ... False False\n",
       "    smallest_eigval        (chain, draw) float64 64kB nan nan nan ... nan nan\n",
       "    step_size              (chain, draw) float64 64kB 0.0635 0.0635 ... 0.06346\n",
       "    step_size_bar          (chain, draw) float64 64kB 0.06791 ... 0.05799\n",
       "    tree_depth             (chain, draw) int64 64kB 1 2 6 3 3 6 ... 4 5 7 5 6 6\n",
       "Attributes:\n",
       "    created_at:                 2024-06-26T12:36:52.430582+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.13.0\n",
       "    sampling_time:              8.762557983398438\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7dd6caf6-9ee8-42b0-bc96-20da4ee1c50a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7dd6caf6-9ee8-42b0-bc96-20da4ee1c50a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7262413d-1ca0-489f-a74d-453acb7d21fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7262413d-1ca0-489f-a74d-453acb7d21fc' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-5d701f86-ad38-4d3b-877f-f3547ab3cc6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d701f86-ad38-4d3b-877f-f3547ab3cc6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-908a4a6e-7519-4c48-841f-1ac9070a95b1' class='xr-var-data-in' type='checkbox'><label for='data-908a4a6e-7519-4c48-841f-1ac9070a95b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-935c1c2d-7d9b-4928-ba17-7d0a1d49f722' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-935c1c2d-7d9b-4928-ba17-7d0a1d49f722' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1df27d8-bdfd-4c83-9d42-491c9bb8f3f8' class='xr-var-data-in' type='checkbox'><label for='data-b1df27d8-bdfd-4c83-9d42-491c9bb8f3f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54242bbf-7a0b-4503-94ea-3f2998d51219' class='xr-section-summary-in' type='checkbox'  ><label for='section-54242bbf-7a0b-4503-94ea-3f2998d51219' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.3785 0.999 ... 0.9941 0.9421</div><input id='attrs-e6448a8b-d75b-499e-8c24-76b9a0a32f97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6448a8b-d75b-499e-8c24-76b9a0a32f97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea5ed18d-3c37-499b-afa2-0263c2742d16' class='xr-var-data-in' type='checkbox'><label for='data-ea5ed18d-3c37-499b-afa2-0263c2742d16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.        , 0.37848189, 0.99895683, ..., 0.93393673, 0.98027604,\n",
       "        0.87811434],\n",
       "       [0.91250908, 0.99890145, 0.95613024, ..., 0.83258785, 0.97063941,\n",
       "        0.87733911],\n",
       "       [0.99812208, 0.77432905, 0.94681861, ..., 0.99922698, 0.84549597,\n",
       "        0.55729299],\n",
       "       [0.16750555, 1.        , 0.99221886, ..., 0.97599777, 0.99407752,\n",
       "        0.94214495]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bbcacc0b-649c-4dc5-a4d8-9a80cafdde99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bbcacc0b-649c-4dc5-a4d8-9a80cafdde99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6a47d11-3525-4a3b-b16d-f94589717e28' class='xr-var-data-in' type='checkbox'><label for='data-a6a47d11-3525-4a3b-b16d-f94589717e28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.051e+03 4.054e+03 ... 4.052e+03</div><input id='attrs-2d866992-02d0-4d78-b864-d7551ed8c20b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d866992-02d0-4d78-b864-d7551ed8c20b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe990a03-ff7c-4221-b1b0-59b8051f0146' class='xr-var-data-in' type='checkbox'><label for='data-fe990a03-ff7c-4221-b1b0-59b8051f0146' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4050.6761275 , 4054.09439627, 4053.18408611, ..., 4053.17317907,\n",
       "        4051.35879693, 4051.40972439],\n",
       "       [4052.33996217, 4053.33240143, 4051.84228673, ..., 4052.49817861,\n",
       "        4053.78287981, 4050.93627427],\n",
       "       [4057.53115741, 4055.42893915, 4056.56816099, ..., 4052.36590808,\n",
       "        4052.43809492, 4053.56013977],\n",
       "       [4058.7892016 , 4057.78341917, 4053.99234852, ..., 4052.10766013,\n",
       "        4051.63713829, 4052.29583911]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.03456 0.5863 ... -0.0594</div><input id='attrs-58099f5c-aa54-43af-b855-3ae42408edbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58099f5c-aa54-43af-b855-3ae42408edbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f323c346-4d53-4dbf-96f3-6a4be9dcdd39' class='xr-var-data-in' type='checkbox'><label for='data-f323c346-4d53-4dbf-96f3-6a4be9dcdd39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.45552076e-02,  5.86273027e-01, -5.73236759e-01, ...,\n",
       "        -5.90447482e-03,  6.14789772e-03,  9.98639512e-03],\n",
       "       [-9.86323262e-02,  3.95725665e-03, -2.62612398e-02, ...,\n",
       "        -2.89915205e-02, -1.07959047e-02,  2.47854688e-01],\n",
       "       [-6.97735021e-02, -7.05029659e-02,  1.66720265e-02, ...,\n",
       "         8.82793944e-04, -4.99788157e-01,  9.55992054e-03],\n",
       "       [ 1.78673882e+00, -2.31430736e+00, -3.87362385e-01, ...,\n",
       "         7.89056376e-03, -5.52399931e-02, -5.94043165e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-1 -2 -16 -2 -3 ... -15 27 19 19 23</div><input id='attrs-052da328-ec16-4302-8525-9c9007c01d6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-052da328-ec16-4302-8525-9c9007c01d6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdef8731-c56b-4aa2-814c-9bf4b923b672' class='xr-var-data-in' type='checkbox'><label for='data-fdef8731-c56b-4aa2-814c-9bf4b923b672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -1,  -2, -16, ...,  -5, -17,  20],\n",
       "       [-27,  -2,   5, ...,  -2,  28,   9],\n",
       "       [-26,  -2, -28, ...,   5,  -3,   5],\n",
       "       [  1,  -1,  12, ...,  19,  19,  23]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-974a4b6a-a5fb-4e2e-a9fe-f95be049b540' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-974a4b6a-a5fb-4e2e-a9fe-f95be049b540' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3cb93fb-1429-4830-bc3e-e44f923a4941' class='xr-var-data-in' type='checkbox'><label for='data-c3cb93fb-1429-4830-bc3e-e44f923a4941' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.051e+03 -4.052e+03 ... -4.05e+03</div><input id='attrs-b1a45fb4-0e15-4e5e-a524-958a53e0302d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1a45fb4-0e15-4e5e-a524-958a53e0302d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6589d104-574a-4726-b8ba-591b4458c947' class='xr-var-data-in' type='checkbox'><label for='data-6589d104-574a-4726-b8ba-591b4458c947' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-4050.60629227, -4052.42137327, -4050.21922716, ...,\n",
       "        -4050.63117075, -4050.08541594, -4050.45891355],\n",
       "       [-4050.9061146 , -4051.53933602, -4050.70774529, ...,\n",
       "        -4051.22452027, -4050.0925731 , -4050.76528526],\n",
       "       [-4054.43360394, -4054.30382855, -4050.28069055, ...,\n",
       "        -4051.69218551, -4050.20586265, -4050.31859432],\n",
       "       [-4057.99826179, -4052.92507719, -4051.51453843, ...,\n",
       "        -4050.88448738, -4050.33993068, -4050.28945679]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.03456 1.327 ... -0.1561 0.1826</div><input id='attrs-f47e4296-67d2-4c9e-a314-2f008df7275b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f47e4296-67d2-4c9e-a314-2f008df7275b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79e6b2e-f678-463b-80c0-5f42b5d82d78' class='xr-var-data-in' type='checkbox'><label for='data-b79e6b2e-f678-463b-80c0-5f42b5d82d78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.03455521,  1.32655358, -0.5786979 , ...,  0.14138593,\n",
       "         0.03973771,  0.28070218],\n",
       "       [ 0.25235497, -0.02800269,  0.09576345, ...,  0.41820351,\n",
       "         0.07923848,  0.25973355],\n",
       "       [-0.18109995,  0.49567592,  0.13263414, ..., -0.56201984,\n",
       "         0.50780183,  1.28991441],\n",
       "       [ 1.78673882, -2.61560581, -0.38845666, ..., -0.15338265,\n",
       "        -0.15612987,  0.18256741]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 3.0 35.0 7.0 ... 31.0 63.0 47.0</div><input id='attrs-f3f18d61-693c-4ee3-ac1f-9810f0ffbb67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3f18d61-693c-4ee3-ac1f-9810f0ffbb67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a6a649-42fd-4e46-8d5e-056085c9302b' class='xr-var-data-in' type='checkbox'><label for='data-00a6a649-42fd-4e46-8d5e-056085c9302b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.,  3., 35., ..., 19., 31., 35.],\n",
       "       [35., 31., 11., ..., 27., 63., 19.],\n",
       "       [55.,  3., 63., ..., 67.,  7., 23.],\n",
       "       [ 1.,  7., 15., ..., 31., 63., 47.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001662 0.0003218 ... 0.003852</div><input id='attrs-dcea0b54-f4e1-4189-823a-792b00d93af8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcea0b54-f4e1-4189-823a-792b00d93af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a75f97ca-d9f5-456a-8a3a-d87124039f4b' class='xr-var-data-in' type='checkbox'><label for='data-a75f97ca-d9f5-456a-8a3a-d87124039f4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00016621, 0.00032183, 0.0028225 , ..., 0.00171087, 0.00255975,\n",
       "        0.00282483],\n",
       "       [0.003132  , 0.00259321, 0.00096608, ..., 0.00218021, 0.00493433,\n",
       "        0.00154242],\n",
       "       [0.00447462, 0.00034854, 0.00515108, ..., 0.00536704, 0.00064367,\n",
       "        0.00188192],\n",
       "       [0.00017317, 0.00066071, 0.00131058, ..., 0.00242658, 0.00481387,\n",
       "        0.00385158]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.857e+05 7.857e+05 ... 7.857e+05</div><input id='attrs-0ecd05a1-ded4-4907-9a0f-8e2029d83924' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ecd05a1-ded4-4907-9a0f-8e2029d83924' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb007fe-82a7-4ec7-8e32-34b08aec0bb6' class='xr-var-data-in' type='checkbox'><label for='data-fcb007fe-82a7-4ec7-8e32-34b08aec0bb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[785678.39490096, 785678.39511404, 785678.395485  , ...,\n",
       "        785683.77364521, 785683.77542263, 785683.77803692],\n",
       "       [785678.50682746, 785678.51004387, 785678.51269092, ...,\n",
       "        785684.30593758, 785684.30817192, 785684.31315804],\n",
       "       [785678.5307645 , 785678.53530358, 785678.53569942, ...,\n",
       "        785684.07131733, 785684.07672975, 785684.07741775],\n",
       "       [785678.60619267, 785678.60642946, 785678.60714354, ...,\n",
       "        785683.91439867, 785683.91686988, 785683.92173129]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.000166 0.000322 ... 0.003844</div><input id='attrs-1c99f305-ffe0-407d-a197-0365b3aec48c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c99f305-ffe0-407d-a197-0365b3aec48c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44209d1b-8e8f-4e9c-af0f-0421bd245613' class='xr-var-data-in' type='checkbox'><label for='data-44209d1b-8e8f-4e9c-af0f-0421bd245613' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.000166, 0.000322, 0.002822, ..., 0.001701, 0.002558, 0.002826],\n",
       "       [0.003083, 0.002594, 0.000966, ..., 0.002131, 0.004871, 0.001528],\n",
       "       [0.004475, 0.000343, 0.005143, ..., 0.005363, 0.000645, 0.001881],\n",
       "       [0.000173, 0.000661, 0.001311, ..., 0.002411, 0.004768, 0.003844]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5b0cc06f-ae54-4b45-91ed-97b67a5e3a71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b0cc06f-ae54-4b45-91ed-97b67a5e3a71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c37149cf-d226-4a49-87e4-81f1573e1fe0' class='xr-var-data-in' type='checkbox'><label for='data-c37149cf-d226-4a49-87e4-81f1573e1fe0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-92b6316c-16b5-4be6-8055-d01b120adde1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92b6316c-16b5-4be6-8055-d01b120adde1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c79fd4af-e50b-441e-8a04-0bf6a2add6c6' class='xr-var-data-in' type='checkbox'><label for='data-c79fd4af-e50b-441e-8a04-0bf6a2add6c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0635 0.0635 ... 0.06346 0.06346</div><input id='attrs-b2a4af7f-e506-4c1f-af59-059b0fd28ce2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2a4af7f-e506-4c1f-af59-059b0fd28ce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c29a12b2-cc53-4257-8136-a8579480242d' class='xr-var-data-in' type='checkbox'><label for='data-c29a12b2-cc53-4257-8136-a8579480242d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.06350208, 0.06350208, 0.06350208, ..., 0.06350208, 0.06350208,\n",
       "        0.06350208],\n",
       "       [0.07259119, 0.07259119, 0.07259119, ..., 0.07259119, 0.07259119,\n",
       "        0.07259119],\n",
       "       [0.06660675, 0.06660675, 0.06660675, ..., 0.06660675, 0.06660675,\n",
       "        0.06660675],\n",
       "       [0.06345515, 0.06345515, 0.06345515, ..., 0.06345515, 0.06345515,\n",
       "        0.06345515]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06791 0.06791 ... 0.05799 0.05799</div><input id='attrs-d1bf1fa9-fe2b-455c-9c87-b0683b2cec2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1bf1fa9-fe2b-455c-9c87-b0683b2cec2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1be7447e-ef85-496f-8d4d-2114f3d27d83' class='xr-var-data-in' type='checkbox'><label for='data-1be7447e-ef85-496f-8d4d-2114f3d27d83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.06791141, 0.06791141, 0.06791141, ..., 0.06791141, 0.06791141,\n",
       "        0.06791141],\n",
       "       [0.0584074 , 0.0584074 , 0.0584074 , ..., 0.0584074 , 0.0584074 ,\n",
       "        0.0584074 ],\n",
       "       [0.06908522, 0.06908522, 0.06908522, ..., 0.06908522, 0.06908522,\n",
       "        0.06908522],\n",
       "       [0.05799451, 0.05799451, 0.05799451, ..., 0.05799451, 0.05799451,\n",
       "        0.05799451]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 6 3 3 6 6 5 ... 6 2 4 5 7 5 6 6</div><input id='attrs-ec1acb3f-ace6-448f-9d84-2c52261791f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec1acb3f-ace6-448f-9d84-2c52261791f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1f0a3fd-ed3f-45f8-9105-c0bab0835500' class='xr-var-data-in' type='checkbox'><label for='data-b1f0a3fd-ed3f-45f8-9105-c0bab0835500' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1, 2, 6, ..., 5, 5, 6],\n",
       "       [6, 5, 4, ..., 5, 6, 5],\n",
       "       [6, 2, 6, ..., 7, 3, 5],\n",
       "       [1, 3, 4, ..., 5, 6, 6]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15784d36-3a96-4473-b97b-2b7cc8d77fd1' class='xr-section-summary-in' type='checkbox'  ><label for='section-15784d36-3a96-4473-b97b-2b7cc8d77fd1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2ddddac2-184f-409d-a328-cb98a50adec3' class='xr-index-data-in' type='checkbox'/><label for='index-2ddddac2-184f-409d-a328-cb98a50adec3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f75d3dbd-8e82-422b-939e-32d370d3530a' class='xr-index-data-in' type='checkbox'/><label for='index-f75d3dbd-8e82-422b-939e-32d370d3530a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5a8e92a-54b5-4863-a405-8c4202343f19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5a8e92a-54b5-4863-a405-8c4202343f19' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-26T12:36:52.430582+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.13.0</dd><dt><span>sampling_time :</span></dt><dd>8.762557983398438</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_fit_data49c38c73-2909-4da9-926d-09cef3d87680\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_fit_data49c38c73-2909-4da9-926d-09cef3d87680\" class = \"xr-section-summary\">fit_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 45kB\n",
       "Dimensions:         (index: 946)\n",
       "Coordinates:\n",
       "  * index           (index) int64 8kB 0 1 5 6 8 10 ... 2347 2348 2349 2353 2355\n",
       "Data variables:\n",
       "    frequency       (index) int64 8kB 2 1 7 1 2 5 10 1 3 2 ... 1 2 1 2 7 1 2 5 4\n",
       "    recency         (index) float64 8kB 30.43 1.71 29.43 ... 21.86 24.29 26.57\n",
       "    T               (index) float64 8kB 38.86 38.86 38.86 ... 27.0 27.0 27.0\n",
       "    monetary_value  (index) float64 8kB 22.35 11.77 73.74 ... 18.56 44.93 33.32\n",
       "    customer_id     (index) int64 8kB 0 1 5 6 8 10 ... 2347 2348 2349 2353 2355</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e8e85a52-ea7b-4f4f-baf6-256e1f0458a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e8e85a52-ea7b-4f4f-baf6-256e1f0458a1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 946</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-03c80150-a3da-49ad-92cd-eb9730446d62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03c80150-a3da-49ad-92cd-eb9730446d62' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 5 6 8 ... 2348 2349 2353 2355</div><input id='attrs-e7969c30-8d9d-453a-abf5-e925c6b6b654' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7969c30-8d9d-453a-abf5-e925c6b6b654' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a474d7d-255a-4d8d-a415-1f63bfa333e5' class='xr-var-data-in' type='checkbox'><label for='data-7a474d7d-255a-4d8d-a415-1f63bfa333e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    5, ..., 2349, 2353, 2355])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48fa3b32-4176-44f1-9588-f22037a57c63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48fa3b32-4176-44f1-9588-f22037a57c63' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 1 7 1 2 5 10 1 ... 1 2 7 1 2 5 4</div><input id='attrs-98132422-cf12-40da-b297-31d1cddac844' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98132422-cf12-40da-b297-31d1cddac844' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3735b59-ff9d-4869-885b-02114281a034' class='xr-var-data-in' type='checkbox'><label for='data-a3735b59-ff9d-4869-885b-02114281a034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 2,  1,  7,  1,  2,  5, 10,  1,  3,  2,  1,  1,  1,  1,  1,  1, 12,\n",
       "        1,  2,  1,  1,  4,  1,  2,  3,  1,  1,  3,  1,  7, 10,  1,  6,  2,\n",
       "        4,  1,  5,  1,  1,  1,  1,  1,  1,  1,  1,  4,  3,  1,  2,  1,  2,\n",
       "        2,  1,  1,  5,  2,  7,  2,  4, 13,  4,  4,  1,  1, 29,  3,  1, 10,\n",
       "       13,  1,  6,  8,  1,  4,  2,  3,  3,  6,  1,  2,  2,  2,  1,  1,  7,\n",
       "        2,  4,  1,  1,  2,  1,  1,  2,  3,  4,  1,  5,  2,  1,  2,  1,  1,\n",
       "        5,  1,  2,  1,  1,  2,  1,  1,  1,  3,  1, 13,  3,  3,  1,  3,  5,\n",
       "        2,  1,  1,  1,  1,  3,  1,  1,  1,  4,  2,  3,  1,  1,  2,  1,  3,\n",
       "       25,  5,  2,  5,  1,  2,  1,  2,  1,  1,  2,  2,  4,  1,  3,  1,  1,\n",
       "        1,  1, 11,  4,  1,  2,  1,  1,  3,  1,  2,  8,  1,  2,  1,  1,  2,\n",
       "        3,  2,  2,  1,  2,  1,  1,  2,  4,  2,  6,  1,  1,  1,  1,  2,  1,\n",
       "        7,  4,  7,  1,  1,  2,  1,  4,  1,  2,  4,  2,  1,  1, 11,  4,  4,\n",
       "        3,  1,  1,  6,  3,  2,  2, 18,  1,  1,  5,  1,  6,  2,  2,  1,  1,\n",
       "        1,  1,  2,  1,  2,  2,  1, 10,  1,  3,  3,  2, 12,  1,  2,  2,  5,\n",
       "        2,  5,  1,  1,  1,  1,  2,  1,  7,  2,  2,  4,  3,  1,  2,  2,  1,\n",
       "        1,  2,  2,  1,  1,  3,  1,  1,  3,  4,  2,  1,  1,  5,  1,  1,  2,\n",
       "        1,  1,  8,  1,  6,  1,  2,  3,  6,  1,  2,  5,  1,  1,  1, 13,  1,\n",
       "        3,  5,  2,  1,  1,  1,  1,  4,  6,  1,  2,  2,  7,  3,  5,  2,  1,\n",
       "        3,  1,  1,  2,  1,  1, 10,  3,  3,  2,  5,  4,  1,  1,  1,  3,  1,\n",
       "        6,  2,  1,  6,  1,  2,  7,  2,  2,  1, 13,  7,  3,  1,  1,  1,  2,\n",
       "...\n",
       "        1,  4, 26,  1,  6,  1,  2,  2,  1,  2,  1, 14,  1,  2,  4,  1,  6,\n",
       "        1,  3,  1,  1,  1,  8,  3,  1,  3,  1,  1,  1,  1,  1,  9,  3,  1,\n",
       "        1,  2,  4,  1,  2,  1,  1,  1,  3,  2,  2,  5,  1,  1,  1,  1,  5,\n",
       "        1,  1,  4,  4,  3,  1,  1,  2,  2,  4,  2,  1,  1,  1,  3,  3,  2,\n",
       "        1,  1,  5,  1,  3,  2,  1,  1,  1,  2,  2,  3,  5,  2,  1,  1,  9,\n",
       "        2,  1,  1,  1,  1,  2,  3,  1,  2,  4,  4,  3,  3,  2,  4,  5,  3,\n",
       "        2,  2,  3,  2,  4,  7,  1,  2,  2,  2,  3,  1,  1,  3,  1,  6,  1,\n",
       "        2,  3,  3,  1,  5,  1,  9,  4,  2,  1,  4,  1,  1,  1,  1,  1,  1,\n",
       "        1,  5,  1,  1,  1,  1,  1,  1,  7,  1,  1,  1,  1,  1,  2,  1,  1,\n",
       "       15,  1,  2,  3,  1, 21,  2,  3,  3,  2,  1,  1,  1,  1,  1,  2,  2,\n",
       "        1,  4,  2,  1,  1,  1,  2,  2,  2,  1,  6, 11,  1,  1,  1,  1,  2,\n",
       "        2,  3,  1,  1, 17,  2,  2,  2,  2,  1,  3,  1,  1,  2,  1,  1,  7,\n",
       "        1,  3,  2,  4,  1,  2,  1,  4,  2,  1,  3,  1,  1,  1,  1,  2,  3,\n",
       "        1,  1,  1,  3,  3,  1,  2,  1,  1,  1,  2,  2,  3,  4,  1,  1,  1,\n",
       "        3,  1,  1,  1,  1,  3,  6,  3,  2,  5,  1,  1,  1,  2,  1,  2,  7,\n",
       "        2,  2,  2,  2,  8,  5,  1,  1,  1,  1,  3,  2,  2,  1,  1,  2,  2,\n",
       "        1,  1,  2,  1,  2,  3,  1,  1,  9,  3,  3,  2,  1,  1,  1,  2,  6,\n",
       "        1,  1,  1,  1,  4,  2,  1,  1,  1,  2,  1,  6,  1,  2,  6,  1,  2,\n",
       "        7,  1,  2,  1,  1,  1,  1,  5,  1,  2,  1,  1,  1,  5,  1,  1,  2,\n",
       "        1,  2,  1,  2,  1,  2,  7,  1,  2,  5,  4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.43 1.71 29.43 ... 24.29 26.57</div><input id='attrs-0032025f-5490-49f8-9c15-29f88ffd274c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0032025f-5490-49f8-9c15-29f88ffd274c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a81982b-5929-4d9a-b650-10317058a924' class='xr-var-data-in' type='checkbox'><label for='data-1a81982b-5929-4d9a-b650-10317058a924' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([30.43,  1.71, 29.43,  5.  , 35.71, 24.43, 34.14,  4.86, 28.29,\n",
       "       14.57,  1.57, 29.43,  5.86, 27.14,  2.14, 24.43, 34.43,  1.57,\n",
       "       33.14,  4.29, 38.43, 29.43, 16.14, 21.14, 29.29,  5.86,  5.14,\n",
       "       16.86,  7.43, 26.43, 35.43, 33.29, 37.43, 31.86, 37.57, 23.86,\n",
       "       36.14, 24.14,  6.14,  0.29,  0.29, 24.14,  5.29,  3.  ,  7.14,\n",
       "        7.57, 26.  , 24.  , 28.43, 23.43,  7.43, 16.86,  2.14, 15.71,\n",
       "        6.86, 28.57, 34.14, 35.71, 29.29, 37.86, 33.  , 35.29,  2.86,\n",
       "        6.43, 37.71, 24.71,  2.  , 37.  , 28.14, 37.43, 32.86, 36.14,\n",
       "        5.  , 36.14,  7.86, 21.14,  6.  , 27.  ,  1.57, 14.29,  7.  ,\n",
       "       31.  , 18.  ,  0.29, 17.86, 29.  , 24.29, 14.86, 27.43, 11.43,\n",
       "        2.  ,  1.86, 33.  , 36.57, 31.43,  8.29,  5.14, 24.  ,  1.86,\n",
       "       20.43, 32.  ,  0.43, 16.14,  5.86, 28.57, 36.86,  0.14, 35.57,\n",
       "        5.71,  2.  ,  3.86, 24.29, 34.  , 30.71, 19.43, 32.86,  3.  ,\n",
       "       31.57, 36.43, 35.43,  0.43,  0.71, 15.29,  1.14, 34.43, 36.29,\n",
       "       17.86,  3.43,  4.29, 34.57, 27.71, 19.86, 16.57, 32.  ,  8.86,\n",
       "        2.71, 27.29, 22.57, 14.14, 36.  , 22.43, 22.57, 22.57, 22.57,\n",
       "        9.57,  2.57, 23.71, 10.57, 28.71, 11.71, 17.43, 14.14,  5.71,\n",
       "       10.43, 22.  , 36.43, 31.14, 15.29, 10.14, 25.86,  4.43, 21.29,\n",
       "        8.  , 21.86, 31.14,  6.14, 15.86, 31.86, 21.71,  3.43, 20.86,\n",
       "       12.43, 19.71,  4.43, 30.  ,  8.43, 18.14, 26.14, 26.71,  3.14,\n",
       "...\n",
       "       20.57,  2.14,  0.43, 17.14,  3.29, 25.14, 18.29,  3.71,  5.14,\n",
       "       28.57, 27.86, 22.71, 19.14, 11.14, 27.71, 20.14,  4.  , 15.14,\n",
       "       21.29, 23.29,  3.86,  1.57, 28.  , 18.29, 10.86, 25.43,  1.43,\n",
       "        8.86,  1.71, 28.43, 23.71, 11.86,  3.  , 21.29, 16.71, 15.86,\n",
       "       22.71, 15.14,  8.43, 15.57, 13.  , 24.29,  8.71, 15.14, 25.29,\n",
       "       21.71,  2.29,  9.57,  2.14, 25.14,  5.71, 14.86, 15.  , 11.  ,\n",
       "       13.14,  8.43, 25.43, 27.71, 27.71, 13.86,  4.14, 21.14,  7.29,\n",
       "       27.71,  1.43,  8.43,  0.86, 28.29,  1.29,  5.43, 22.14, 26.  ,\n",
       "       11.14, 11.  ,  7.57, 18.86, 21.14, 13.29,  1.57, 19.  ,  4.29,\n",
       "       27.71, 12.43, 24.43, 25.  ,  9.57,  3.57,  2.86,  7.14, 17.  ,\n",
       "        5.43, 24.57, 26.86, 14.29, 21.86,  2.86,  8.43, 27.57, 18.43,\n",
       "        8.43,  1.43,  3.57, 11.29,  8.  , 20.57, 12.57, 19.57,  0.14,\n",
       "       25.71, 23.71,  7.43, 23.57, 24.43,  1.71,  7.57, 25.71, 21.57,\n",
       "       14.  , 25.29, 20.14, 19.86, 26.43, 27.29, 21.43,  7.71,  6.43,\n",
       "       27.57,  9.71,  8.43,  3.71,  9.  , 23.  , 27.  , 16.29,  4.86,\n",
       "       22.14, 20.71,  0.71, 13.29, 15.86, 15.43, 21.14, 12.14, 19.  ,\n",
       "       23.43, 24.43,  8.29,  6.14,  1.57,  0.43, 25.86, 18.  ,  7.86,\n",
       "       13.29,  2.57, 21.43, 13.71, 13.  , 20.43,  2.57,  6.86, 11.71,\n",
       "       17.57,  6.29, 12.  ,  7.57,  9.86, 24.14,  9.29, 21.86, 24.29,\n",
       "       26.57])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.86 38.86 38.86 ... 27.0 27.0</div><input id='attrs-508e0a51-3671-404c-8aaf-cdddaa83e84d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-508e0a51-3671-404c-8aaf-cdddaa83e84d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6695e5ff-8bc1-4f80-9d89-d471f8478efe' class='xr-var-data-in' type='checkbox'><label for='data-6695e5ff-8bc1-4f80-9d89-d471f8478efe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([38.86, 38.86, 38.86, 38.86, 38.86, 38.86, 38.86, 38.86, 38.71,\n",
       "       38.71, 38.71, 38.71, 38.71, 38.71, 38.71, 38.57, 38.57, 38.57,\n",
       "       38.57, 38.57, 38.43, 38.43, 38.43, 38.43, 38.43, 38.43, 38.43,\n",
       "       38.29, 38.29, 38.29, 38.29, 38.29, 38.29, 38.29, 38.29, 38.29,\n",
       "       38.14, 38.14, 38.14, 38.14, 38.14, 38.14, 38.14, 38.14, 38.14,\n",
       "       38.  , 38.  , 38.  , 38.  , 38.  , 38.  , 38.  , 38.  , 38.  ,\n",
       "       38.  , 38.  , 38.  , 38.  , 38.  , 38.  , 38.  , 38.  , 38.  ,\n",
       "       38.  , 38.  , 37.86, 37.86, 37.86, 37.86, 37.86, 37.71, 37.71,\n",
       "       37.71, 37.71, 37.71, 37.71, 37.71, 37.71, 37.71, 37.57, 37.57,\n",
       "       37.57, 37.57, 37.57, 37.57, 37.57, 37.57, 37.43, 37.43, 37.43,\n",
       "       37.43, 37.43, 37.43, 37.43, 37.43, 37.29, 37.29, 37.29, 37.29,\n",
       "       37.29, 37.29, 37.29, 37.29, 37.29, 37.29, 37.29, 37.29, 37.29,\n",
       "       37.29, 37.14, 37.14, 37.14, 37.14, 37.14, 37.14, 37.14, 37.14,\n",
       "       37.14, 37.14, 37.14, 37.14, 37.14, 37.  , 37.  , 37.  , 37.  ,\n",
       "       37.  , 37.  , 37.  , 37.  , 37.  , 37.  , 37.  , 37.  , 36.86,\n",
       "       36.86, 36.86, 36.86, 36.86, 36.86, 36.86, 36.71, 36.71, 36.71,\n",
       "       36.71, 36.71, 36.71, 36.71, 36.71, 36.57, 36.57, 36.57, 36.57,\n",
       "       36.57, 36.57, 36.57, 36.57, 36.43, 36.43, 36.43, 36.43, 36.43,\n",
       "       36.43, 36.43, 36.43, 36.43, 36.43, 36.43, 36.43, 36.43, 36.29,\n",
       "       36.29, 36.29, 36.29, 36.29, 36.29, 36.29, 36.29, 36.29, 36.14,\n",
       "...\n",
       "       29.29, 29.29, 29.29, 29.14, 29.14, 29.14, 29.14, 29.14, 29.14,\n",
       "       29.14, 29.14, 29.14, 29.14, 29.14, 29.14, 29.14, 29.  , 29.  ,\n",
       "       29.  , 29.  , 29.  , 29.  , 29.  , 29.  , 29.  , 29.  , 29.  ,\n",
       "       28.86, 28.86, 28.86, 28.86, 28.86, 28.86, 28.86, 28.86, 28.71,\n",
       "       28.71, 28.71, 28.71, 28.71, 28.71, 28.71, 28.71, 28.57, 28.57,\n",
       "       28.57, 28.57, 28.57, 28.57, 28.57, 28.57, 28.57, 28.57, 28.57,\n",
       "       28.57, 28.57, 28.57, 28.43, 28.43, 28.43, 28.43, 28.43, 28.43,\n",
       "       28.43, 28.43, 28.43, 28.43, 28.29, 28.29, 28.29, 28.29, 28.29,\n",
       "       28.29, 28.29, 28.29, 28.29, 28.29, 28.29, 28.29, 28.29, 28.29,\n",
       "       28.14, 28.14, 28.14, 28.14, 28.14, 28.14, 28.14, 28.14, 28.14,\n",
       "       28.14, 28.14, 28.  , 28.  , 28.  , 28.  , 28.  , 28.  , 28.  ,\n",
       "       28.  , 28.  , 28.  , 28.  , 28.  , 27.86, 27.86, 27.86, 27.86,\n",
       "       27.86, 27.86, 27.86, 27.86, 27.86, 27.86, 27.71, 27.71, 27.71,\n",
       "       27.71, 27.71, 27.71, 27.57, 27.57, 27.57, 27.57, 27.57, 27.57,\n",
       "       27.57, 27.43, 27.43, 27.43, 27.43, 27.43, 27.43, 27.43, 27.29,\n",
       "       27.29, 27.29, 27.29, 27.29, 27.29, 27.29, 27.29, 27.14, 27.14,\n",
       "       27.14, 27.14, 27.14, 27.14, 27.14, 27.14, 27.14, 27.14, 27.14,\n",
       "       27.14, 27.14, 27.  , 27.  , 27.  , 27.  , 27.  , 27.  , 27.  ,\n",
       "       27.  , 27.  , 27.  , 27.  , 27.  , 27.  , 27.  , 27.  , 27.  ,\n",
       "       27.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monetary_value</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.35 11.77 73.74 ... 44.93 33.32</div><input id='attrs-b7ff41aa-38b6-416f-82d9-132a025ce7b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7ff41aa-38b6-416f-82d9-132a025ce7b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65ca5140-d2b8-4542-ba11-ce09b9f192db' class='xr-var-data-in' type='checkbox'><label for='data-65ca5140-d2b8-4542-ba11-ce09b9f192db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 22.35,  11.77,  73.74,  11.77,  25.55,  29.44,  24.24,   9.99,\n",
       "        25.08,  32.3 , 227.14,  80.46,  40.47,  31.14,  13.97,  14.96,\n",
       "        29.54,  14.37,  28.03,  15.36,   6.49,  53.4 ,  43.13, 133.48,\n",
       "        42.63,  69.23,  20.77,  20.22,  42.09,  27.63,  13.66,  14.99,\n",
       "        50.87,  33.42,  20.68,  26.22,  17.47,  92.8 ,  52.3 ,  39.09,\n",
       "        12.97,  24.99,  28.31,  13.77,  36.71,  13.62,  19.36,  71.66,\n",
       "        13.87,  19.54,  23.48,  51.8 ,  29.33,  23.54,  35.58,  15.16,\n",
       "        22.92,  18.98,  29.86,  29.34,  28.53,  24.94,  16.7 ,  34.32,\n",
       "        26.26,  46.59,  46.28,  62.44,  25.46,  14.49,  35.18,  49.25,\n",
       "        15.76,  33.91,  11.87,  37.99,  28.46,  28.77,  27.13,  41.48,\n",
       "        18.88,  21.87,  14.96,  34.27,  71.81,  14.47,  37.26,  14.99,\n",
       "        21.74,  20.35,  13.97,   9.77,  28.65,  22.29,  24.09,  55.28,\n",
       "       194.86,  36.11,  30.32,  26.94,  16.36,  11.77,  32.12,  38.77,\n",
       "        52.94,  64.45,  11.77,  21.36,  90.83,  79.72,  41.82,  20.5 ,\n",
       "        25.74,  17.56,  87.43,  19.62,  36.67,  33.97,  36.98,  65.07,\n",
       "        20.75,  18.99,  76.41,  12.97,  20.08,  31.48,  69.84,  14.37,\n",
       "        48.81,  25.61,  93.4 ,  29.75,  48.94,  60.75,  41.77,  15.89,\n",
       "        37.21,  59.42,  10.88,  45.07,   9.77,  15.7 ,   4.79,  79.02,\n",
       "        12.77, 242.98,  36.22,   9.77,  41.81, 176.45,  26.42,  24.74,\n",
       "        13.77,  13.97,  16.7 ,  37.11,  22.86, 100.59,  22.08, 189.64,\n",
       "...\n",
       "        38.25,  30.04,  14.96,  13.97,  11.49,  38.97,  49.97,  25.66,\n",
       "        37.45,  22.77,  60.25,  48.23,  15.16,  35.86,  26.54,  20.65,\n",
       "        13.97,  62.95,  21.7 ,  19.99,  20.1 ,  11.77,  27.94,  16.6 ,\n",
       "        10.77,  29.2 ,  34.9 ,  26.56,  13.77,  13.07,  66.43,  37.66,\n",
       "        30.94,  14.79,  43.7 ,  12.25,  50.48,  14.96, 122.37,  39.34,\n",
       "        19.47,   8.97,  35.91,  28.74,  11.6 ,  27.09,   4.79,  44.99,\n",
       "        14.96,  12.99,  16.36,  21.66,  25.85,  45.64,  77.06,  31.76,\n",
       "        54.51,  48.91,  18.82,  15.76,  42.29,  14.37, 176.86,  33.47,\n",
       "        37.81,  53.57,  19.64,  40.54,  47.28,  16.7 ,  24.99,  41.83,\n",
       "        11.77,  13.57,  37.83,  48.68,  22.55,  15.37,  38.13,  51.97,\n",
       "        18.63,  14.37,  15.36,  19.54,  29.6 ,  12.57,  54.59,  62.23,\n",
       "        26.33,  14.79,  59.66,  12.02,   9.98,  37.95,  87.9 , 100.37,\n",
       "        20.96,  18.43,   5.96,  11.77,  15.81, 125.6 ,  28.39,  41.44,\n",
       "        38.97,  71.5 ,  39.98,  45.61,  31.1 ,  18.76,  13.77,  61.24,\n",
       "        95.95,  79.86,  77.29,  14.96,  26.99,  45.29,  29.65,  14.37,\n",
       "        41.79,  29.33,  22.15,  34.67,  25.34,  38.37,  38.57,  27.98,\n",
       "        40.41,  14.96,  28.93,  15.96,  12.49,  20.68,  50.87,  23.66,\n",
       "        22.99,  34.97,  15.36, 146.96,  68.63,  32.91,  21.85,  21.  ,\n",
       "        43.8 ,  52.69,  15.24,  11.77,  32.64,  16.36,  13.97,  18.56,\n",
       "        44.93,  33.32])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>customer_id</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 5 6 8 ... 2348 2349 2353 2355</div><input id='attrs-f8b7cf9c-aad6-4090-a58f-6c76e416c825' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8b7cf9c-aad6-4090-a58f-6c76e416c825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37c57254-5fcf-4a4e-8ac9-218be5ea7559' class='xr-var-data-in' type='checkbox'><label for='data-37c57254-5fcf-4a4e-8ac9-218be5ea7559' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    5,    6,    8,   10,   16,   17,   18,   20,   25,\n",
       "         27,   29,   34,   37,   44,   45,   48,   55,   56,   58,   60,\n",
       "         62,   66,   69,   72,   74,   78,   81,   85,   87,   90,   91,\n",
       "         97,   98,   99,  100,  104,  105,  108,  110,  113,  115,  120,\n",
       "        124,  128,  129,  131,  132,  134,  135,  137,  138,  139,  141,\n",
       "        142,  143,  144,  146,  147,  149,  150,  153,  155,  156,  158,\n",
       "        161,  163,  166,  169,  177,  178,  182,  184,  186,  187,  188,\n",
       "        194,  196,  199,  201,  202,  205,  207,  210,  214,  216,  218,\n",
       "        219,  225,  227,  228,  229,  231,  241,  242,  243,  246,  248,\n",
       "        249,  252,  254,  256,  259,  260,  262,  265,  266,  267,  269,\n",
       "        271,  273,  274,  275,  281,  282,  283,  286,  288,  290,  291,\n",
       "        293,  296,  297,  301,  302,  303,  305,  309,  311,  313,  316,\n",
       "        318,  320,  322,  323,  324,  329,  331,  335,  338,  343,  347,\n",
       "        349,  350,  356,  357,  362,  363,  366,  367,  369,  375,  376,\n",
       "        377,  381,  384,  387,  388,  393,  394,  396,  397,  400,  402,\n",
       "        403,  405,  406,  409,  411,  414,  421,  422,  426,  427,  433,\n",
       "        438,  439,  440,  443,  445,  447,  449,  450,  451,  452,  453,\n",
       "        455,  456,  460,  462,  463,  465,  466,  468,  470,  473,  476,\n",
       "        479,  480,  482,  484,  486,  490,  492,  494,  495,  498,  499,\n",
       "        504,  506,  508,  510,  512,  516,  518,  520,  522,  525,  527,\n",
       "...\n",
       "       1782, 1785, 1788, 1789, 1791, 1793, 1794, 1797, 1799, 1807, 1811,\n",
       "       1819, 1820, 1821, 1824, 1825, 1826, 1828, 1830, 1833, 1835, 1839,\n",
       "       1841, 1842, 1844, 1848, 1850, 1852, 1854, 1855, 1859, 1862, 1864,\n",
       "       1871, 1872, 1875, 1877, 1881, 1885, 1886, 1888, 1890, 1893, 1898,\n",
       "       1900, 1901, 1905, 1908, 1909, 1910, 1913, 1914, 1915, 1918, 1919,\n",
       "       1920, 1921, 1924, 1928, 1931, 1933, 1934, 1938, 1942, 1945, 1949,\n",
       "       1952, 1954, 1957, 1958, 1960, 1961, 1962, 1963, 1965, 1974, 1979,\n",
       "       1980, 1981, 1982, 1984, 1986, 1991, 2004, 2006, 2009, 2011, 2016,\n",
       "       2021, 2023, 2026, 2029, 2032, 2033, 2035, 2040, 2041, 2043, 2044,\n",
       "       2047, 2048, 2051, 2054, 2056, 2057, 2067, 2068, 2070, 2072, 2073,\n",
       "       2074, 2075, 2080, 2082, 2084, 2088, 2089, 2091, 2093, 2095, 2097,\n",
       "       2099, 2100, 2103, 2105, 2109, 2110, 2116, 2118, 2120, 2122, 2127,\n",
       "       2131, 2132, 2134, 2137, 2139, 2142, 2144, 2145, 2148, 2151, 2153,\n",
       "       2154, 2160, 2162, 2165, 2167, 2169, 2171, 2174, 2176, 2178, 2182,\n",
       "       2183, 2188, 2190, 2195, 2198, 2200, 2201, 2204, 2206, 2209, 2213,\n",
       "       2217, 2219, 2220, 2227, 2230, 2232, 2233, 2234, 2238, 2239, 2249,\n",
       "       2251, 2253, 2259, 2260, 2263, 2269, 2273, 2277, 2279, 2281, 2284,\n",
       "       2289, 2292, 2293, 2295, 2300, 2302, 2304, 2305, 2306, 2307, 2308,\n",
       "       2311, 2312, 2314, 2317, 2324, 2327, 2329, 2331, 2332, 2334, 2335,\n",
       "       2338, 2339, 2341, 2343, 2345, 2346, 2347, 2348, 2349, 2353, 2355])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-812f7d30-1176-4029-9cc4-8bb4a4ae7226' class='xr-section-summary-in' type='checkbox'  ><label for='section-812f7d30-1176-4029-9cc4-8bb4a4ae7226' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2777b838-52c5-41e6-b0b7-f8438c5eb1d1' class='xr-index-data-in' type='checkbox'/><label for='index-2777b838-52c5-41e6-b0b7-f8438c5eb1d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    5,    6,    8,   10,   16,   17,   18,   20,\n",
       "       ...\n",
       "       2339, 2341, 2343, 2345, 2346, 2347, 2348, 2349, 2353, 2355],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;index&#x27;, length=946))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c633cfe0-f6a7-4d2d-850d-fbbf1ed650c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c633cfe0-f6a7-4d2d-850d-fbbf1ed650c6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> fit_data"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_mcmc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf03827",
   "metadata": {},
   "source": [
    "We can see some statistics of the posterior distribution of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "58a103ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>6.448</td>\n",
       "      <td>1.340</td>\n",
       "      <td>4.248</td>\n",
       "      <td>9.027</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>3.775</td>\n",
       "      <td>0.298</td>\n",
       "      <td>3.238</td>\n",
       "      <td>4.347</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>15.928</td>\n",
       "      <td>4.292</td>\n",
       "      <td>8.306</td>\n",
       "      <td>24.053</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.080</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "p   6.448  1.340   4.248    9.027      0.034    0.024    1559.0    1871.0   \n",
       "q   3.775  0.298   3.238    4.347      0.007    0.005    1610.0    2231.0   \n",
       "v  15.928  4.292   8.306   24.053      0.113    0.080    1450.0    1775.0   \n",
       "\n",
       "   r_hat  \n",
       "p    1.0  \n",
       "q    1.0  \n",
       "v    1.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee948a75",
   "metadata": {},
   "source": [
    "Let's visualize the posterior distributions and the rank plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "63137cf5",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1200x900 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 911,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = az.plot_trace(\n",
    "    data=model.idata,\n",
    "    compact=True,\n",
    "    kind=\"rank_bars\",\n",
    "    backend_kwargs={\"figsize\": (12, 9), \"layout\": \"constrained\"},\n",
    ")\n",
    "plt.gcf().suptitle(\"Gamma-Gamma Model Trace\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0b065a",
   "metadata": {},
   "source": [
    "We can compare the results with the ones obtained by the `lifetimes` package and the MAP estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "21ef284f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6z8MPPxzd3d2Z9vnnnz/u8wIoN040AFNFW1tbfOYzn8m6pczChQvjn//5n6OxsXGfjq12BcrVRK6thers7Ix77703q+/DH/7wpM4BoBja2trixz/+cVbfmWeemXOsuhUgP4WsrYVSt0J5E+4qAffcc08kSZJpn3XWWZFKpYo4I4D917nnnhsLFy7MtP/93/89nn/++WHHv/HGG3Hfffdl2rNnz47LLrtsQucIMNU50QBMFV1dXfHXf/3XWfXkUUcdFQ888EDMmDFjn4+vdgXK0USurdu2bYvm5ua8x/f09MRXvvKVrC/ezp49Oy644IJ9mgfAZOro6Ih169YVtE9PT0/83d/9Xfz2t7/N9H3wgx+M+fPn5xyvbgXKzUSvrepWoFDCXeOoo6Mj6zLi+fj+978f3/ve9zLtVCoVV1xxxXhPDaBspFKpuP766zPtJEni2muvjdWrVw8Zu2bNmlixYkW0t7dn+j772c9O+u0fAEqZEw1AuUqSJG688cZ48sknM32HHnpoPPjggzFr1qxxeQ+1K1BuJnptffXVV+Occ86JO+64Y8jtcAZbt25drFixIh599NGs/uuvvz7q6+v3eS4Ak6WtrS0++clPxuc+97n4+c9/Hh0dHSOOf/nll+OKK66IH/7wh5m+ioqK+Nu//dth91G3AuVmotdWdStQqFQy8JJR7JNdu3bF6aefHueff35cfPHFcfrpp0dVVVXOse+8807cc889Qy5du3z58rj99tsnY7oARbN58+b4vd/7vZzbBl6qOyKisrIy57gHH3wwPvShDw37HnfdddeQq8accsopceKJJ0ZFRUWsX78+fvnLX2ZdOfHCCy+MO++8M98fA2C/cc4558TmzZsjIv3h2RNPPJH3vo8//njccMMN8Ud/9Efxh3/4h3HccccNO3bdunVx6623xq9+9aus/ttuuy0uvvjisU0eoEg2b94c55xzTlZfKpWKiorCvid36KGHxmOPPTbiGLUrUC4mem19/PHH46/+6q8y7dmzZ8dxxx0Xhx9+eDQ1NUWSJLFz5854+eWXY/369UP2v+qqq+KGG24oaC4AxbZr165YunRppl1XVxeLFy+OD3zgAzFjxoyoq6uLvXv3xtatW+PVV1+Nt99+O2v/VCoVt9xyS1xyySWjvpe6FSgXE722qluBQuVOHjFm3d3dsXLlyli5cmU0NjbGscceG/Pnz48ZM2ZEdXV17Nq1K9atWxcvvfRSdHZ2Zu172mmnxc0331ykmQNMniRJhoS4hjPcuNGyyddff320tbXFQw89lOl74YUX4oUXXsg5/hOf+ER87Wtfy2tOAKUo3+Ds5s2bhw1oDRec3bt3b9x///1x//33j+lEg2AXsD/KVW8WUsf2yWe82hUoF5O5tkakv2D71FNPjTqutrY2brzxxvj0pz9d0DwASlFra2u8+OKL8eKLL446ds6cOfHVr341fvd3fzevY6tbgXI1kWtrhLoVGJ1w1wTas2dPPP/88yPed7zPpz/96bjxxhtj2rRpkzAzgKmvoqIivvSlL8VHPvKRuOeee+Kll17KOW7hwoVx9dVXx0UXXTS5EwQYZ5MRnI1wogFgIqhdAcbHokWL4o//+I/jueeei40bN0ZPT8+I42fPnh3Lly+Pyy+/PObOnTtJswQYXw0NDXHHHXfEL37xi3j++edj27Zto+5z3HHHxfLly+Piiy+OxsbGvN9L3QqUi4leW9WtQKHclnEcdXR0xDe+8Y341a9+Fa+99tqo996tr6+P8847L6644oo44YQTJmmWAOXpzTffjNdeey22b98e3d3dMWfOnFiwYEEsWrSo2FMDGBebNm2Kc889d5+O8Z3vfCd+53d+J6vv7bffjvvvv9+JBoBJpHYF2HctLS3x61//OjZt2hQ7duyIlpaWSKVS0dTUFAceeGAsWbIkDj/88GJPE2Dcbd++PV5//fXYtGlTvP/++9He3h719fXR2NgYhx12WCxZsiSmT58+Lu+lbgXKxUSurepWIB/CXROkq6sr3njjjXjrrbdi69atsXfv3ujq6oqmpqaYPn16pritrKws9lQBACAvTjQAAAAAAABMLuEuAAAAAAAAAACAElRR7AkAAAAAAAAAAAAwlHAXAAAAAAAAAABACRLuAgAAAAAAAAAAKEHCXQAAAAAAAAAAACVIuAsAAAAAAAAAAKAECXcBAAAAAAAAAACUIOEuAAAAAAAAAACAEiTcBQAAAAAAAAAAUIKEuwAAAAAAAAAAAEqQcBcAAAAAAAAAAEAJEu4CAAAAAAAAAAAoQcJdAAAAAAAAAAAAJUi4CwAAAAAAAAAAoAQJdwEAAAAAAAAAAJQg4S4AAAAAAAAAAIASJNwFAAAAAAAAAABQgoS7AAAAAAAAAAAASpBwFwAAAAAAAAAAQAkS7gIAAAAAAAAAAChBwl0AAAAAAAAAAAAlSLgLAAAAAAAAAACgBAl3AQAAAAAAAAAAlCDhLgAAAAAAAAAAgBIk3AUAAAAAAAAAAFCChLsAAAAAAAAAAABK0P8Pt1mXjWoUY9oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x1000 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    nrows=3, ncols=1, figsize=(12, 10), sharex=False, sharey=False, layout=\"constrained\"\n",
    ")\n",
    "\n",
    "for i, var_name in enumerate([\"p\", \"q\", \"v\"]):\n",
    "    ax = axes[i]\n",
    "    az.plot_posterior(\n",
    "        idata_mcmc.posterior[var_name].values.flatten(),\n",
    "        color=\"C0\",\n",
    "        point_estimate=\"mean\",\n",
    "        hdi_prob=0.95,\n",
    "        ref_val=ggf.summary[\"coef\"][var_name],\n",
    "        ax=ax,\n",
    "        label=\"MCMC\",\n",
    "    )\n",
    "    ax.axvline(\n",
    "        x=ggf.summary[\"lower 95% bound\"][var_name],\n",
    "        color=\"C1\",\n",
    "        linestyle=\"--\",\n",
    "        label=\"lifetimes 95% CI\",\n",
    "    )\n",
    "    ax.axvline(\n",
    "        x=ggf.summary[\"upper 95% bound\"][var_name],\n",
    "        color=\"C1\",\n",
    "        linestyle=\"--\",\n",
    "    )\n",
    "    ax.axvline(x=idata_map[var_name].item(), color=\"C2\", linestyle=\"-.\", label=\"MAP\")\n",
    "    ax.legend(loc=\"upper right\")\n",
    "\n",
    "plt.gcf().suptitle(\"Gamma-Gamma Model Parameters\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d6855dd",
   "metadata": {},
   "source": [
    "We see that the `lifetimes` and MAP estimates are essentially the same. Both of them are close to the mean of the posterior distribution obtained by MCMC."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772fe770",
   "metadata": {},
   "source": [
    "## Expected Customer Spend\n",
    "\n",
    "Once we have the posterior distribution of the parameters, we can use the `expected_average_profit` method to compute the conditional expectation of the average profit per transaction for a group of one or more customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e6c61b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_spend = model.expected_customer_spend(data=summary_with_money_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a2805e",
   "metadata": {},
   "source": [
    "Let's see how it looks for a subset of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "57706dfe",
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x[0]</th>\n",
       "      <td>24.688</td>\n",
       "      <td>0.516</td>\n",
       "      <td>23.718</td>\n",
       "      <td>25.651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[1]</th>\n",
       "      <td>18.949</td>\n",
       "      <td>1.331</td>\n",
       "      <td>16.300</td>\n",
       "      <td>21.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[2]</th>\n",
       "      <td>35.183</td>\n",
       "      <td>0.912</td>\n",
       "      <td>33.600</td>\n",
       "      <td>37.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[3]</th>\n",
       "      <td>35.183</td>\n",
       "      <td>0.912</td>\n",
       "      <td>33.600</td>\n",
       "      <td>37.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[4]</th>\n",
       "      <td>35.183</td>\n",
       "      <td>0.912</td>\n",
       "      <td>33.600</td>\n",
       "      <td>37.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[5]</th>\n",
       "      <td>71.402</td>\n",
       "      <td>0.610</td>\n",
       "      <td>70.245</td>\n",
       "      <td>72.517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[6]</th>\n",
       "      <td>18.949</td>\n",
       "      <td>1.331</td>\n",
       "      <td>16.300</td>\n",
       "      <td>21.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[7]</th>\n",
       "      <td>35.183</td>\n",
       "      <td>0.912</td>\n",
       "      <td>33.600</td>\n",
       "      <td>37.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[8]</th>\n",
       "      <td>27.304</td>\n",
       "      <td>0.395</td>\n",
       "      <td>26.545</td>\n",
       "      <td>28.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[9]</th>\n",
       "      <td>35.183</td>\n",
       "      <td>0.912</td>\n",
       "      <td>33.600</td>\n",
       "      <td>37.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%\n",
       "x[0]  24.688  0.516  23.718   25.651\n",
       "x[1]  18.949  1.331  16.300   21.284\n",
       "x[2]  35.183  0.912  33.600   37.005\n",
       "x[3]  35.183  0.912  33.600   37.005\n",
       "x[4]  35.183  0.912  33.600   37.005\n",
       "x[5]  71.402  0.610  70.245   72.517\n",
       "x[6]  18.949  1.331  16.300   21.284\n",
       "x[7]  35.183  0.912  33.600   37.005\n",
       "x[8]  27.304  0.395  26.545   28.035\n",
       "x[9]  35.183  0.912  33.600   37.005"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(expected_spend.isel(customer_id=range(10)), kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "17e3de45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 811
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, *_ = az.plot_forest(\n",
    "    data=expected_spend.isel(customer_id=(range(10))), combined=True, figsize=(8, 7)\n",
    ")\n",
    "ax.set(xlabel=\"Expected Spend (10 Customers)\", ylabel=\"Customer ID\")\n",
    "ax.set_title(\"Expected Spend\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d29614",
   "metadata": {},
   "source": [
    "Finally, lets look at some statistics and the distribution for the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f700b627",
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>35.258</td>\n",
       "      <td>0.619</td>\n",
       "      <td>34.154</td>\n",
       "      <td>36.469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%\n",
       "x  35.258  0.619  34.154   36.469"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(expected_spend.mean(\"customer_id\"), kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bb63ea2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 611,
       "width": 1011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "az.plot_dist(expected_spend.mean(\"customer_id\"), label=\"Mean over Customer ID\", ax=ax)\n",
    "ax.axvline(x=expected_spend.mean(), color=\"black\", ls=\"--\", label=\"Overall Mean\")\n",
    "ax.legend(loc=\"upper right\")\n",
    "ax.set(xlabel=\"Expected Spend\", ylabel=\"Density\")\n",
    "ax.set_title(\"Expected Spend\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b72a8110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Wed Jun 26 2024\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.14\n",
      "IPython version      : 8.22.2\n",
      "\n",
      "pymc    : 5.13.0\n",
      "pytensor: 2.20.0\n",
      "\n",
      "arviz         : 0.18.0\n",
      "pymc_marketing: 0.6.0\n",
      "matplotlib    : 3.8.4\n",
      "pandas        : 2.2.2\n",
      "\n",
      "Watermark: 2.4.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pymc,pytensor"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "gamma-gamma model",
   "provenance": []
  },
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}