{
 "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 We compare the results with the [`lifetimes`](https://github.com/CamDavidsonPilon/lifetimes) package (no longer maintained and last meaningful update was July 2020).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": 1,
   "id": "813aa3e6",
   "metadata": {},
   "outputs": [],
   "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": 2,
   "id": "4039ce96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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": 2,
     "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": 3,
   "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": 3,
     "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": 4,
   "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": 4,
     "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": 5,
   "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": 5,
     "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": 6,
   "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": 6,
     "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": 7,
   "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": 7,
     "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": 8,
   "id": "f0986b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.25295817604993"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "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": 9,
     "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": 10,
   "id": "e39da004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22437de0e1a64cee9910ed327dae9b0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<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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      ],
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       "\n"
      ]
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    {
     "data": {
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       "<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(method=\"map\").posterior.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8f11643",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>6.248787</td>\n",
       "      <td>3.744591</td>\n",
       "      <td>15.447813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   p         q          v\n",
       "chain draw                               \n",
       "0     0     6.248787  3.744591  15.447813"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "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 2 jobs)\n",
      "NUTS: [p, q, v]\n"
     ]
    },
    {
     "data": {
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       "model_id": "baf13be2ccca49c0922b236c2028c56f",
       "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\"></pre>\n"
      ],
      "text/plain": []
     },
     "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"
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      "text/plain": [
       "\n"
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     "metadata": {},
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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 449 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": 13,
   "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_version:  5.14.0\n",
       "    sampling_time:              448.93818640708923\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-a4c5aa0d-cce4-4869-bdb7-4c188d657502' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4c5aa0d-cce4-4869-bdb7-4c188d657502' 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-cdeba45d-a74e-466b-a87c-c4d82e7d49db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cdeba45d-a74e-466b-a87c-c4d82e7d49db' 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-83f13810-271d-4636-a392-59bbc2efc21a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83f13810-271d-4636-a392-59bbc2efc21a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d74d5161-3cc6-4f41-9ebc-b33c5426e194' class='xr-var-data-in' type='checkbox'><label for='data-d74d5161-3cc6-4f41-9ebc-b33c5426e194' 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-3b8f284b-6bf8-4b87-b500-bd511ed963d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b8f284b-6bf8-4b87-b500-bd511ed963d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c95b1529-e453-4204-bfc6-f4a0c3b5c797' class='xr-var-data-in' type='checkbox'><label for='data-c95b1529-e453-4204-bfc6-f4a0c3b5c797' 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-15622360-14fc-42b4-a7be-dec4014b5041' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15622360-14fc-42b4-a7be-dec4014b5041' 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'>4.713 4.829 7.353 ... 6.317 5.654</div><input id='attrs-6637d56f-664b-4002-ac6a-613d1bc5c192' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6637d56f-664b-4002-ac6a-613d1bc5c192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6d8be4-d182-4fff-9691-d368935888d3' class='xr-var-data-in' type='checkbox'><label for='data-8f6d8be4-d182-4fff-9691-d368935888d3' 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([[4.71278591, 4.82949361, 7.35313977, ..., 6.49129938, 5.98404134,\n",
       "        6.57773885],\n",
       "       [5.85624248, 5.94799155, 6.78333667, ..., 6.10810083, 6.39251412,\n",
       "        9.04262561],\n",
       "       [5.60712309, 7.32725116, 7.60589313, ..., 7.82559522, 8.67127993,\n",
       "        8.92248246],\n",
       "       [5.92647516, 5.04103944, 5.15064289, ..., 5.57756909, 6.31742909,\n",
       "        5.65412111]])</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'>4.289 4.288 3.402 ... 3.863 3.836</div><input id='attrs-f751ea7c-5ddf-40f8-9ab1-8bf43669e157' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f751ea7c-5ddf-40f8-9ab1-8bf43669e157' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9fd2650-e8f1-44f1-aab1-dc6520b1d7ab' class='xr-var-data-in' type='checkbox'><label for='data-a9fd2650-e8f1-44f1-aab1-dc6520b1d7ab' 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([[4.28880268, 4.28763369, 3.40189015, ..., 3.78078447, 4.00280848,\n",
       "        3.44641363],\n",
       "       [4.27381858, 4.29211538, 3.30433529, ..., 3.91239388, 4.04071984,\n",
       "        3.34318637],\n",
       "       [3.71504279, 3.50624373, 3.27294273, ..., 3.81401076, 3.62673096,\n",
       "        3.53376889],\n",
       "       [3.85383344, 3.83008644, 3.79294056, ..., 4.17562191, 3.86349613,\n",
       "        3.83608094]])</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'>24.09 23.53 11.76 ... 16.49 17.34</div><input id='attrs-b535c7ee-3c68-4c2c-9097-2f0815c4fa08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b535c7ee-3c68-4c2c-9097-2f0815c4fa08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-016b1c5d-d7dc-46fe-9413-78232c1205b5' class='xr-var-data-in' type='checkbox'><label for='data-016b1c5d-d7dc-46fe-9413-78232c1205b5' 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([[24.09226482, 23.52760816, 11.76464558, ..., 15.51930362,\n",
       "        17.43004074, 13.47428107],\n",
       "       [18.83778404, 18.55765954, 12.46628414, ..., 16.16020759,\n",
       "        16.16693255,  8.93437771],\n",
       "       [16.82990522, 11.8648299 , 10.59038996, ..., 12.04182128,\n",
       "        10.79404284,  9.83045259],\n",
       "       [17.01617607, 19.74048264, 19.62294477, ..., 20.2153804 ,\n",
       "        16.48701549, 17.34027873]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c0f8cc8-20f4-4c61-81d3-2edd59c4c4d7' class='xr-section-summary-in' type='checkbox'  ><label for='section-6c0f8cc8-20f4-4c61-81d3-2edd59c4c4d7' 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-975a4898-fb49-4bfc-a751-dda89bea82fb' class='xr-index-data-in' type='checkbox'/><label for='index-975a4898-fb49-4bfc-a751-dda89bea82fb' 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-607ce4ff-8348-4075-9c45-5b83bcb8200d' class='xr-index-data-in' type='checkbox'/><label for='index-607ce4ff-8348-4075-9c45-5b83bcb8200d' 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-ce905632-48a1-4f79-a5d9-fb926eefcf36' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce905632-48a1-4f79-a5d9-fb926eefcf36' 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>2024-07-01T15:48:01.851699</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.14.0</dd><dt><span>sampling_time :</span></dt><dd>448.93818640708923</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",
       "    n_steps                (chain, draw) float64 64kB 47.0 11.0 ... 35.0 23.0\n",
       "    acceptance_rate        (chain, draw) float64 64kB 0.9899 0.9385 ... 1.0\n",
       "    reached_max_treedepth  (chain, draw) bool 8kB False False ... False False\n",
       "    lp                     (chain, draw) float64 64kB -4.051e+03 ... -4.051e+03\n",
       "    process_time_diff      (chain, draw) float64 64kB 0.02783 ... 0.03698\n",
       "    diverging              (chain, draw) bool 8kB False False ... False False\n",
       "    ...                     ...\n",
       "    tree_depth             (chain, draw) int64 64kB 6 4 6 6 5 2 ... 3 5 5 2 6 5\n",
       "    energy                 (chain, draw) float64 64kB 4.052e+03 ... 4.052e+03\n",
       "    perf_counter_start     (chain, draw) float64 64kB 7.433e+04 ... 7.471e+04\n",
       "    index_in_trajectory    (chain, draw) int64 64kB -19 5 -50 10 ... -27 1 16 -9\n",
       "    step_size_bar          (chain, draw) float64 64kB 0.05982 ... 0.06508\n",
       "    perf_counter_diff      (chain, draw) float64 64kB 0.04164 0.00677 ... 0.0375\n",
       "Attributes:\n",
       "    created_at:                 2024-07-01T15:48:01.937737\n",
       "    arviz_version:              0.17.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.14.0\n",
       "    sampling_time:              448.93818640708923\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-b9405104-b679-47cc-a781-53944d81b063' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b9405104-b679-47cc-a781-53944d81b063' 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-d420b898-740e-4bb3-bb2d-e2a37ed5db7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d420b898-740e-4bb3-bb2d-e2a37ed5db7f' 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-75b896f1-232f-4609-9a9a-90280079de07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75b896f1-232f-4609-9a9a-90280079de07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a017ee9-6146-4ea1-97e8-377b6be9ea97' class='xr-var-data-in' type='checkbox'><label for='data-1a017ee9-6146-4ea1-97e8-377b6be9ea97' 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-6346e351-c3e2-4149-a790-9ace8c8fc3f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6346e351-c3e2-4149-a790-9ace8c8fc3f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96c9d0af-11bb-417e-9fd3-9f0afcaf34f9' class='xr-var-data-in' type='checkbox'><label for='data-96c9d0af-11bb-417e-9fd3-9f0afcaf34f9' 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-f650ded8-f800-4fcc-9cdc-290f10adc039' class='xr-section-summary-in' type='checkbox'  ><label for='section-f650ded8-f800-4fcc-9cdc-290f10adc039' 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>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'>47.0 11.0 63.0 ... 3.0 35.0 23.0</div><input id='attrs-ac58482f-910d-4ef8-8ec2-e600f0ed3613' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac58482f-910d-4ef8-8ec2-e600f0ed3613' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-392c56e4-65f7-4a51-9fe4-66d6cebb2b66' class='xr-var-data-in' type='checkbox'><label for='data-392c56e4-65f7-4a51-9fe4-66d6cebb2b66' 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([[47., 11., 63., ..., 27., 43., 63.],\n",
       "       [11.,  7., 79., ...,  3., 17., 63.],\n",
       "       [35., 63., 31., ...,  9., 31., 23.],\n",
       "       [31., 63., 15., ...,  3., 35., 23.]])</pre></div></li><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'>0.9899 0.9385 0.9847 ... 0.8103 1.0</div><input id='attrs-d7e55336-25c9-4890-8e0d-cd2880ef408f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7e55336-25c9-4890-8e0d-cd2880ef408f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95ee6d36-6e6f-48d7-a5f9-63fc1ca72dfa' class='xr-var-data-in' type='checkbox'><label for='data-95ee6d36-6e6f-48d7-a5f9-63fc1ca72dfa' 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([[0.98991261, 0.93852068, 0.98473159, ..., 0.70408623, 0.97325347,\n",
       "        0.96729984],\n",
       "       [0.96753701, 0.7350514 , 0.99958638, ..., 0.81616812, 0.78319998,\n",
       "        0.83473034],\n",
       "       [0.7600182 , 0.99679219, 0.99898933, ..., 0.39617396, 0.99390848,\n",
       "        0.90203233],\n",
       "       [0.99505205, 0.98536473, 0.98956565, ..., 0.76961917, 0.81031776,\n",
       "        1.        ]])</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-a8a1e806-9889-4aa7-a090-5c5adde103cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8a1e806-9889-4aa7-a090-5c5adde103cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4faf918f-4943-4993-97bd-0ba514a600f9' class='xr-var-data-in' type='checkbox'><label for='data-4faf918f-4943-4993-97bd-0ba514a600f9' 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>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 ... -4.051e+03</div><input id='attrs-a54172f1-463f-4d09-abca-0a608b39832c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a54172f1-463f-4d09-abca-0a608b39832c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d7a0283-8390-4b63-b74e-ef69d8bda14a' class='xr-var-data-in' type='checkbox'><label for='data-4d7a0283-8390-4b63-b74e-ef69d8bda14a' 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([[-4051.47478478, -4051.30521228, -4051.07463511, ...,\n",
       "        -4051.58489208, -4050.36553322, -4051.04830651],\n",
       "       [-4052.01323521, -4052.42007671, -4052.12705756, ...,\n",
       "        -4051.06172453, -4051.12126471, -4052.97254573],\n",
       "       [-4051.0445905 , -4051.19048141, -4052.53573355, ...,\n",
       "        -4052.51584773, -4052.63434163, -4051.77109323],\n",
       "       [-4050.11788622, -4051.08626533, -4051.14084452, ...,\n",
       "        -4051.82583904, -4052.41109982, -4050.54407542]])</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'>0.02783 0.006771 ... 0.03698</div><input id='attrs-6bbef15e-1607-47ce-b073-1774d33cafb2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6bbef15e-1607-47ce-b073-1774d33cafb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d57bfc3-5d1e-4111-8519-32bb6b879285' class='xr-var-data-in' type='checkbox'><label for='data-1d57bfc3-5d1e-4111-8519-32bb6b879285' 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([[0.0278278, 0.006771 , 0.0427258, ..., 0.022541 , 0.0358441,\n",
       "        0.0564235],\n",
       "       [0.0072548, 0.0050663, 0.0471627, ..., 0.0058644, 0.0137624,\n",
       "        0.0528905],\n",
       "       [0.0244019, 0.0449321, 0.0301198, ..., 0.007756 , 0.0319213,\n",
       "        0.0235691],\n",
       "       [0.0252459, 0.053352 , 0.0108423, ..., 0.0026548, 0.0449496,\n",
       "        0.0369828]])</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-a5d0d193-d277-45fb-abc2-24834b078615' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5d0d193-d277-45fb-abc2-24834b078615' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6be41d6-8c7e-40c9-bd45-576dcf960534' class='xr-var-data-in' type='checkbox'><label for='data-f6be41d6-8c7e-40c9-bd45-576dcf960534' 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_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'>0.01026 -0.02338 ... 0.4311 -0.568</div><input id='attrs-7f2d36fc-6a59-4362-aa99-c9616f629bfc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f2d36fc-6a59-4362-aa99-c9616f629bfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2d6fe0d-0a29-41ac-9257-013763896742' class='xr-var-data-in' type='checkbox'><label for='data-e2d6fe0d-0a29-41ac-9257-013763896742' 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([[ 1.02562746e-02, -2.33762149e-02,  4.37919690e-04, ...,\n",
       "         5.17735183e-01, -5.58287735e-01,  2.48355928e-02],\n",
       "       [-2.18617334e-01,  3.35909728e-02, -8.42919322e-02, ...,\n",
       "         1.63575258e-01, -2.47814736e-01,  2.81963127e-01],\n",
       "       [ 2.88262705e-01, -2.93833777e-02, -7.84260891e-03, ...,\n",
       "         3.29424848e-01,  4.31483726e-02, -3.92502550e-01],\n",
       "       [ 9.98103121e-03,  3.67105617e-02,  1.99760122e-02, ...,\n",
       "         2.99058046e-01,  4.31106064e-01, -5.68005721e-01]])</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'>0.03088 0.1457 ... 0.6261 -0.7185</div><input id='attrs-56a14dc6-f854-40be-83da-306e0cef0282' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56a14dc6-f854-40be-83da-306e0cef0282' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3afdf6c-f780-4140-8815-aa4d69e1abc1' class='xr-var-data-in' type='checkbox'><label for='data-d3afdf6c-f780-4140-8815-aa4d69e1abc1' 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([[ 0.03087958,  0.14573675,  0.04063234, ...,  0.73446327,\n",
       "        -0.55828774,  0.0671384 ],\n",
       "       [-0.3049404 ,  0.69226781, -0.11911223, ...,  0.5118232 ,\n",
       "         0.64566427,  0.41515742],\n",
       "       [ 0.57135425, -0.27965959, -0.2510317 , ...,  2.04649469,\n",
       "        -0.39911295, -0.41250973],\n",
       "       [ 0.01483767,  0.04423364, -0.05737037, ...,  0.42035113,\n",
       "         0.62611456, -0.71848156]])</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-dd8dc5ea-909a-47d7-8896-de1aac69d3ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd8dc5ea-909a-47d7-8896-de1aac69d3ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df18499a-3e3c-46b2-9bc9-49149fd8b807' class='xr-var-data-in' type='checkbox'><label for='data-df18499a-3e3c-46b2-9bc9-49149fd8b807' 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'>0.05476 0.05476 ... 0.09085 0.09085</div><input id='attrs-43222d50-9f34-4826-9c4a-5708a60ca5b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43222d50-9f34-4826-9c4a-5708a60ca5b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-516d6655-6d83-48fd-9ca0-2706eb8582b3' class='xr-var-data-in' type='checkbox'><label for='data-516d6655-6d83-48fd-9ca0-2706eb8582b3' 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([[0.05476336, 0.05476336, 0.05476336, ..., 0.05476336, 0.05476336,\n",
       "        0.05476336],\n",
       "       [0.07551869, 0.07551869, 0.07551869, ..., 0.07551869, 0.07551869,\n",
       "        0.07551869],\n",
       "       [0.08776161, 0.08776161, 0.08776161, ..., 0.08776161, 0.08776161,\n",
       "        0.08776161],\n",
       "       [0.09085116, 0.09085116, 0.09085116, ..., 0.09085116, 0.09085116,\n",
       "        0.09085116]])</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-5d99e434-d1ed-4088-af8c-870ba019bc2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d99e434-d1ed-4088-af8c-870ba019bc2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda821b8-ee68-4e0c-80a8-2ee02d65f0cf' class='xr-var-data-in' type='checkbox'><label for='data-eda821b8-ee68-4e0c-80a8-2ee02d65f0cf' 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>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'>6 4 6 6 5 2 4 5 ... 2 7 3 5 5 2 6 5</div><input id='attrs-2e389ed6-e2e2-4d0b-971f-ab6551e9512c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e389ed6-e2e2-4d0b-971f-ab6551e9512c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2e2901d-7da3-44d4-b471-2735670ffdb6' class='xr-var-data-in' type='checkbox'><label for='data-e2e2901d-7da3-44d4-b471-2735670ffdb6' 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([[6, 4, 6, ..., 5, 6, 6],\n",
       "       [4, 3, 7, ..., 2, 5, 6],\n",
       "       [6, 6, 5, ..., 4, 5, 5],\n",
       "       [5, 6, 4, ..., 2, 6, 5]])</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'>4.052e+03 4.052e+03 ... 4.052e+03</div><input id='attrs-5f3e2bf2-968b-4fb1-835e-7840c284dbb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f3e2bf2-968b-4fb1-835e-7840c284dbb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95669ca3-45e5-4127-92aa-a14bc72b1763' class='xr-var-data-in' type='checkbox'><label for='data-95669ca3-45e5-4127-92aa-a14bc72b1763' 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([[4052.35179023, 4051.76541251, 4052.18212857, ..., 4052.33127752,\n",
       "        4052.0498843 , 4051.77701675],\n",
       "       [4053.07360463, 4054.08459625, 4053.84243191, ..., 4051.93702249,\n",
       "        4054.05258538, 4054.52578421],\n",
       "       [4051.57963873, 4053.08494107, 4052.90234693, ..., 4055.49794362,\n",
       "        4054.74883491, 4053.59934878],\n",
       "       [4050.55482849, 4052.05123832, 4051.38059262, ..., 4052.13534265,\n",
       "        4054.45520717, 4052.28157803]])</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'>7.433e+04 7.433e+04 ... 7.471e+04</div><input id='attrs-ba562ab8-5efb-478b-91ec-ff89da5da6d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba562ab8-5efb-478b-91ec-ff89da5da6d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d01f61b-0c6c-487a-955f-759230c796a4' class='xr-var-data-in' type='checkbox'><label for='data-6d01f61b-0c6c-487a-955f-759230c796a4' 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([[74328.11177576, 74328.17681317, 74328.18455017, ...,\n",
       "        74507.97760621, 74508.06744942, 74508.14650443],\n",
       "       [74327.46858898, 74327.47689868, 74327.48346318, ...,\n",
       "        74520.72070925, 74520.72704735, 74520.77549485],\n",
       "       [74585.46986475, 74585.51948755, 74585.56505085, ...,\n",
       "        74691.96996683, 74691.99011894, 74692.05319303],\n",
       "       [74595.96372917, 74596.01206826, 74596.14817296, ...,\n",
       "        74709.62766239, 74709.63440439, 74709.68924299]])</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'>-19 5 -50 10 12 ... -4 -27 1 16 -9</div><input id='attrs-75c44129-6123-4a3f-a0c2-8ca5c4c4e3b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75c44129-6123-4a3f-a0c2-8ca5c4c4e3b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0ea4959-953e-4ee6-9d0d-6e10c2e223df' class='xr-var-data-in' type='checkbox'><label for='data-d0ea4959-953e-4ee6-9d0d-6e10c2e223df' 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([[-19,   5, -50, ...,  13, -16,  23],\n",
       "       [  4,   2, -35, ...,   1,   5,  50],\n",
       "       [-15, -14,  -9, ...,   2,   6,  -5],\n",
       "       [-22,   9,  -8, ...,   1,  16,  -9]])</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'>0.05982 0.05982 ... 0.06508 0.06508</div><input id='attrs-6168251f-44c3-45d4-bb76-821cea6a6288' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6168251f-44c3-45d4-bb76-821cea6a6288' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de1f6a8f-b99d-4698-9085-9bf9ed615d0e' class='xr-var-data-in' type='checkbox'><label for='data-de1f6a8f-b99d-4698-9085-9bf9ed615d0e' 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([[0.05981998, 0.05981998, 0.05981998, ..., 0.05981998, 0.05981998,\n",
       "        0.05981998],\n",
       "       [0.06440369, 0.06440369, 0.06440369, ..., 0.06440369, 0.06440369,\n",
       "        0.06440369],\n",
       "       [0.07248637, 0.07248637, 0.07248637, ..., 0.07248637, 0.07248637,\n",
       "        0.07248637],\n",
       "       [0.06508213, 0.06508213, 0.06508213, ..., 0.06508213, 0.06508213,\n",
       "        0.06508213]])</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'>0.04164 0.00677 ... 0.0538 0.0375</div><input id='attrs-5c2f6e99-a6d3-4e96-a475-27eef73d4b27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c2f6e99-a6d3-4e96-a475-27eef73d4b27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89447a21-5119-4bc1-9526-40ebd22f4181' class='xr-var-data-in' type='checkbox'><label for='data-89447a21-5119-4bc1-9526-40ebd22f4181' 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([[0.0416367 , 0.0067699 , 0.09428671, ..., 0.08890311, 0.07860451,\n",
       "        0.15317861],\n",
       "       [0.0072882 , 0.0055949 , 0.08385851, ..., 0.0058625 , 0.0479695 ,\n",
       "        0.1454977 ],\n",
       "       [0.0491513 , 0.0450964 , 0.030244  , ..., 0.0084469 , 0.0608978 ,\n",
       "        0.0452538 ],\n",
       "       [0.0478594 , 0.13561839, 0.0208688 , ..., 0.0026557 , 0.0538009 ,\n",
       "        0.0374977 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-05c42899-b497-4aef-904c-092fab833f67' class='xr-section-summary-in' type='checkbox'  ><label for='section-05c42899-b497-4aef-904c-092fab833f67' 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-6e74928b-ddd0-47a5-baa7-1fb03561ad20' class='xr-index-data-in' type='checkbox'/><label for='index-6e74928b-ddd0-47a5-baa7-1fb03561ad20' 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-3a601931-d5a7-4c24-a3ff-7188b3d4c603' class='xr-index-data-in' type='checkbox'/><label for='index-3a601931-d5a7-4c24-a3ff-7188b3d4c603' 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-a943776f-fd21-40c1-badb-46f57e78cd8f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a943776f-fd21-40c1-badb-46f57e78cd8f' 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>2024-07-01T15:48:01.937737</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.14.0</dd><dt><span>sampling_time :</span></dt><dd>448.93818640708923</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_databb6cb131-3b89-45e2-8400-2e7a93351297\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_fit_databb6cb131-3b89-45e2-8400-2e7a93351297\" 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-1e1491ac-2f52-4592-99ad-b831c9e5558b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1e1491ac-2f52-4592-99ad-b831c9e5558b' 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-a42feea8-e5da-446e-89a8-b62f271384b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a42feea8-e5da-446e-89a8-b62f271384b1' 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-d96a7ece-6776-483f-b0bb-a34d72c311ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d96a7ece-6776-483f-b0bb-a34d72c311ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2f7d84d-248d-4e68-a545-6b1d136e92ed' class='xr-var-data-in' type='checkbox'><label for='data-b2f7d84d-248d-4e68-a545-6b1d136e92ed' 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-287ac809-36b2-4901-8832-20cafb60e27c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-287ac809-36b2-4901-8832-20cafb60e27c' 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-12427eeb-8c45-43d1-8063-756e0257678a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12427eeb-8c45-43d1-8063-756e0257678a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62d0abe8-9c88-4ae6-9b61-ae9f22a79aae' class='xr-var-data-in' type='checkbox'><label for='data-62d0abe8-9c88-4ae6-9b61-ae9f22a79aae' 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-766ccbb0-51b8-4456-a9cf-3dc593211dd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-766ccbb0-51b8-4456-a9cf-3dc593211dd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07820578-c87c-4cb3-ba43-c4c0de1e91a7' class='xr-var-data-in' type='checkbox'><label for='data-07820578-c87c-4cb3-ba43-c4c0de1e91a7' 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-a3c196cb-a321-49e3-93be-8f46001f7de6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3c196cb-a321-49e3-93be-8f46001f7de6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c577189a-3b4c-460f-ba84-81ea9ab3a792' class='xr-var-data-in' type='checkbox'><label for='data-c577189a-3b4c-460f-ba84-81ea9ab3a792' 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-7fca0ed0-e5d2-46d7-a1f7-20c43a3e97bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fca0ed0-e5d2-46d7-a1f7-20c43a3e97bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4653f6dc-2381-481a-93bc-bde14018da25' class='xr-var-data-in' type='checkbox'><label for='data-4653f6dc-2381-481a-93bc-bde14018da25' 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-7ca1b10c-50c2-40cb-9849-7c8d64a197af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ca1b10c-50c2-40cb-9849-7c8d64a197af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21fb0e99-5e83-4566-8461-08b137b07dad' class='xr-var-data-in' type='checkbox'><label for='data-21fb0e99-5e83-4566-8461-08b137b07dad' 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-967063b6-939d-423b-859d-178ffdd69895' class='xr-section-summary-in' type='checkbox'  ><label for='section-967063b6-939d-423b-859d-178ffdd69895' 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-0189a8a1-1e87-41a9-902f-2c18d4f5772c' class='xr-index-data-in' type='checkbox'/><label for='index-0189a8a1-1e87-41a9-902f-2c18d4f5772c' 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-2d381907-25b5-40cf-8355-271fafb73cfc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d381907-25b5-40cf-8355-271fafb73cfc' 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",
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      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> fit_data"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "id": "58a103ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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>6.403</td>\n",
       "      <td>1.303</td>\n",
       "      <td>4.217</td>\n",
       "      <td>8.710</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.023</td>\n",
       "      <td>1686.0</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>3.781</td>\n",
       "      <td>0.290</td>\n",
       "      <td>3.238</td>\n",
       "      <td>4.338</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>2187.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>16.051</td>\n",
       "      <td>4.243</td>\n",
       "      <td>8.050</td>\n",
       "      <td>23.907</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.076</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1746.0</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   6.403  1.303   4.217    8.710      0.032    0.023    1686.0    1783.0   \n",
       "q   3.781  0.290   3.238    4.338      0.007    0.005    1753.0    2187.0   \n",
       "v  16.051  4.243   8.050   23.907      0.107    0.076    1563.0    1746.0   \n",
       "\n",
       "   r_hat  \n",
       "p    1.0  \n",
       "q    1.0  \n",
       "v    1.0  "
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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": 16,
   "id": "21ef284f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAAAAAIAERLgLAAAAAAAAAAAAAAAAABIQ4S4AAAAAAAAAAAAAAAAASECEuwAAAAAAAAAAAAAAAAAgARHuAgAAAAAAAAAAAAAAAIAERLgLAAAAAAAAAAAAAAAAABIQ4S4AAAAAAAAAAAAAAAAASECEuwAAAAAAAAAAAAAAAAAgARHuAgAAAAAAAAAAAAAAAIAERLgLAAAAAAAAAAAAAAAAABIQ4S4AAAAAAAAAAAAAAAAASECEuwAAAAAAAAAAAAAAAAAgARHuAgAAAAAAAAAAAAAAAIAERLgLAAAAAAAAAAAAAAAAABIQ4S4AAAAAAAAAAAAAAAAASECEuwAAAAAAAAAAAAAAAAAgARHuAgAAAAAAAAAAAAAAAIAERLgLAAAAAAAAAAAAAAAAABIQ4S4AAAAAAAAAAAAAAAAASECEuwAAAAAAAAAAAAAAAAAgAf1/PruqvyDGCrYAAAAASUVORK5CYII=",
      "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": 17,
   "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": 18,
   "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>24.706</td>\n",
       "      <td>0.512</td>\n",
       "      <td>23.839</td>\n",
       "      <td>25.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[1]</th>\n",
       "      <td>18.994</td>\n",
       "      <td>1.311</td>\n",
       "      <td>16.641</td>\n",
       "      <td>21.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[2]</th>\n",
       "      <td>35.195</td>\n",
       "      <td>0.924</td>\n",
       "      <td>33.447</td>\n",
       "      <td>36.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[3]</th>\n",
       "      <td>35.195</td>\n",
       "      <td>0.924</td>\n",
       "      <td>33.447</td>\n",
       "      <td>36.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[4]</th>\n",
       "      <td>35.195</td>\n",
       "      <td>0.924</td>\n",
       "      <td>33.447</td>\n",
       "      <td>36.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[5]</th>\n",
       "      <td>71.387</td>\n",
       "      <td>0.599</td>\n",
       "      <td>70.165</td>\n",
       "      <td>72.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[6]</th>\n",
       "      <td>18.994</td>\n",
       "      <td>1.311</td>\n",
       "      <td>16.641</td>\n",
       "      <td>21.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[7]</th>\n",
       "      <td>35.195</td>\n",
       "      <td>0.924</td>\n",
       "      <td>33.447</td>\n",
       "      <td>36.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[8]</th>\n",
       "      <td>27.318</td>\n",
       "      <td>0.394</td>\n",
       "      <td>26.627</td>\n",
       "      <td>28.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[9]</th>\n",
       "      <td>35.195</td>\n",
       "      <td>0.924</td>\n",
       "      <td>33.447</td>\n",
       "      <td>36.887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%\n",
       "x[0]  24.706  0.512  23.839   25.762\n",
       "x[1]  18.994  1.311  16.641   21.547\n",
       "x[2]  35.195  0.924  33.447   36.887\n",
       "x[3]  35.195  0.924  33.447   36.887\n",
       "x[4]  35.195  0.924  33.447   36.887\n",
       "x[5]  71.387  0.599  70.165   72.409\n",
       "x[6]  18.994  1.311  16.641   21.547\n",
       "x[7]  35.195  0.924  33.447   36.887\n",
       "x[8]  27.318  0.394  26.627   28.107\n",
       "x[9]  35.195  0.924  33.447   36.887"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(expected_spend.isel(customer_id=range(10)), kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "17e3de45",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 20,
   "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>35.268</td>\n",
       "      <td>0.629</td>\n",
       "      <td>34.107</td>\n",
       "      <td>36.442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%\n",
       "x  35.268  0.629  34.107   36.442"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(expected_spend.mean(\"customer_id\"), kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "id": "b72a8110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Mon Jul 01 2024\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.13\n",
      "IPython version      : 8.22.2\n",
      "\n",
      "pymc    : 5.14.0\n",
      "pytensor: 2.20.0\n",
      "\n",
      "arviz         : 0.17.1\n",
      "pymc_marketing: 0.6.0\n",
      "matplotlib    : 3.8.3\n",
      "pandas        : 2.2.1\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
}