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    "(mmm_budget_allocation_example)=\n",
    "# Budget Allocation with PyMC-Marketing\n",
    "\n",
    "The purpose of this notebook is to explore the recently included function in the PyMC-Marketing library that focuses on budget allocation. This function's underpinnings are based on the methodologies inspired by Bolt work in his article, [\"Budgeting with Bayesian Models\"](https://bolt.eu/en/blog/budgeting-with-bayesian-models-pymc-marketing/).\n",
    "\n",
    "## Prerequisite Knowledge\n",
    "The notebook assumes the reader has knowledge of the essential functionalities of PyMC-Marketing. If one is unfamiliar, the [\"MMM Example Notebook\"](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html) serves as an excellent starting point, offering a comprehensive introduction to media mix models in this context.\n",
    "\n",
    "## Context\n",
    "The emphasis of this notebook is on enhancing marketing budgets. Contrarily to broader issues addressed in prior notebooks, our primary aim here is to unravel specialized knowledge on budget allocation tactics using the functionality.\n",
    "\n",
    "## Objectives\n",
    "To elucidate more efficient ways of resource allocation across diverse media channels.\n",
    "To deliver data-driven, actionable insights for budgeting decisions.\n",
    "\n",
    "## Introducing the budget allocator\n",
    "This notebook instigates an examination of the function within the PyMC-Marketing library, which addresses these challenges using Bayesian models. The function intends to provide:\n",
    "\n",
    "1. Quantitative measures of the effectiveness of different media channels.\n",
    "2. Probabilistic ROI estimates under a range of budget scenarios.\n",
    "\n",
    "## What to Anticipate\n",
    "Upon completing this notebook, readers should get a comprehensive understanding of the budget allocation function. They will then be equipped to incorporate this analytic tool into their marketing analytics routines for data-driven decision-making.\n",
    "\n",
    "## Installing PyMC-Marketing\n",
    "Before delving into the specifics of budget allocation, the initial step is to install the PyMC-Marketing library and ascertain its version. This step will confirm support for the budget allocation function. The following pip command can be run on your Jupyter Notebook:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vWMGdRlmyjcI"
   },
   "source": [
    "## Basic Setup\n",
    "Like previous notebooks revolving around PyMC-Marketing, this relies on a specific library set. Here are the requisite imports necessary for executing the provided code snippets subsequently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "28_D6j7jjQTy"
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   "source": [
    "import warnings\n",
    "\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from pymc_marketing.mmm import MMM\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = [12, 7]\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = \"retina\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9AlvbqZ6yqhs"
   },
   "source": [
    "These imports and configurations form the fundamental setup necessary for the entire span of this notebook.\n",
    "\n",
    "The expectation is that a model has already been trained using the functionalities provided in prior versions of the PyMC-Marketing library. Thus, the data generation and training processes will be replicated in a different notebook. Those unfamiliar with these procedures are advised to refer to the [\"MMM Example Notebook.\"](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html)\n",
    "\n",
    "### Employing ModelBuilder: A Feature in PyMC-Marketing\n",
    "The ModelBuilder feature, introduced in version 0.2.0 of PyMC-Marketing, empowers users to easily save and load pre-trained models. The capability to load a pre-existing model is especially advantageous for accelerating analyses, mainly when dealing with expansive data sets or intricate models.\n",
    "\n",
    "### Saving Model\n",
    "Once the model has been trained, it is easy to save for later use. An example of the \".save\" method is demonstrated below to store the model at a designated [location](https://github.com/pymc-labs/pymc-marketing/tree/main/data).\n",
    "\n",
    "## Loading a Pre-Trained Model\n",
    "To utilize a saved model, load it into a new instance of the MMM class using the load method below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "6eM1cTtMys1O"
   },
   "outputs": [],
   "source": [
    "mmm = MMM.load(\"model.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X3NUoJ1bzBvK"
   },
   "source": [
    "For more details on the `save()` and `load()` methods, consult the [pymc-marketing documentation](https://www.pymc-marketing.io/en/stable/guide/benefits/model_deployment.html) on Model Deployment.\n",
    "\n",
    "## Problem Statement\n",
    "Before jumping into the data, let’s first define the business problem we are trying to solve.In a progressively competitive scenario, marketers are tasked with distributing a predetermined marketing budget across various channels for optimizing Return on Investment (ROI). Consider a forthcoming quarter wherein a marketing team must decide the division of its operations between two advertising channels, represented as x1 and x2. These could effectively symbolize any medium, such as TV, digital advertising, print, etc.\n",
    "\n",
    "The task lies in making decisions that invoke data, comply with factual evidence, and align with business logic. For instance, how can one incorporate prior information like budget restrictions, platform trends, constraints, or even distinctive features of each channel into the decision-making process?\n",
    "\n",
    "## Introducing Budget Allocation Function\n",
    "The updated budget allocation function in PyMC-Marketing aims to tackle this issue by offering a Bayesian framework for optimal allocation. This enables marketers to:\n",
    "\n",
    "* Integrate the outcomes of Media Mix Modeling (MMM), quantifying each channel's effectiveness in metrics like ROI, incremental sales, etc.\n",
    "* Merge this empirical data with prior business knowledge and logic for making holistic and robust decisions.\n",
    "\n",
    "By utilizing this function, marketers can guarantee that the budget spread not only obeys the mathematical rigor furnished by the MMM outcomes but also incorporates business-specific factors, thereby achieving a balanced and optimized budget plan.\n",
    "\n",
    "## Getting started\n",
    "Media Mix Modeling (MMM) acts as a dependable method to estimate the contribution of each channel (e.g., x1, x2) to a target variable like sales or any variable. The function `plot_direct_contribution_curves()` allows for visualization of this direct channel impact. However, it is crucial to remember that this only unveils the \"observable space\" for values of X (spend) and Y (contribution)."
   ]
  },
  {
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    "id": "_QF9nHcqyycl",
    "outputId": "af09b2a1-1c30-429e-ab1f-a4af0c163434"
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   "outputs": [
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_curve_fig = mmm.plot_direct_contribution_curves();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iG_KsGWPzsrp"
   },
   "source": [
    "The observable space only encompasses our data points and does not illustrate what transpires beyond those points. As a result, it is not assured that the maximum contribution point for each channel lies within this observable range.\n",
    "\n",
    "By using the `xlim_max` parameter, we can predict the shape of the model fitting curve for the amount spent that was not previously observed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 828
    },
    "id": "47RuJ_epzm2U",
    "outputId": "5df16f8a-ef66-447b-8e7d-a093ca7c1504"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XTz/9tCTvPfjSSy/VsGHD1KNHD8XFxcnj8Wj37t2aPXu2/5xLL71UCxcu9G9PmzZNd911V1TBtuBA2Hnnnac6depEHD9hwgS9/fbbpn0tWrTQddddp/79+/v/nyN5K3bNmjVL7733nj/UmZ+fr3vuuUfTp09X48aNw15jz549Gjt2rGlfq1at9Le//U39+vVTs2bNTKGu/fv3a/PmzVq6dKnmzp1bpmqHAAAAAAAcCyyGYRjVvQgAAAAAAKraQw89ZPrl9mmnnaaPPvqowvOuX79ew4cPN+375z//qZEjR5ZpHS+88EKJrbHGjh2rN998M2R/hw4dNGHCBH+AqDS33Xab5s6d699u3LixXn/9dfXs2bPE86ZPn65HHnnEH0CyWCz67LPP1L1794jnfP7553r00UdN+0466SS99dZbatKkSalr3b9/v77//nvddNNNYY/v3r1b55xzjn+7WbNmR7UF2/XXX28Ku/hceOGFevHFF8NWsgl25MgRXXLJJaaAUu/evTVmzBg1atQo4nlut1vPPfecpkyZ4t9Xv359zZ492x/KCbRmzRpdeeWV/u20tDR99tlnat26dalrlLxhoD179uiMM86IOKZjx45h91977bUltvLcunWrbrjhBlOFr/r162vGjBlKT0+PeL3HH388JAR166236p577olY0Wrfvn3629/+ph07dvj3xcbG6vPPP1eXLl0iXiv4+9XnL3/5i/7973+HraomeUNet99+uymQU79+fc2fPz9ihbQnnnjCFBQ977zz9Nprr4VUcgrHMAz9/PPP6ty5c8R7gsPh0NVXX63ffvvNv699+/Z6/fXXdeKJJ5Y4/7vvvqsxY8b4t+Pj4zVz5swyfT9Hq6rvH9Heg4MtXbpUI0aM8G+X9v+S4PE+qampevvtt3XqqaeWek273a5+/fopOzvbv++DDz4o8fsykMPhUP/+/U0VuCZOnKgzzzwz7PjVq1fr2muvNQVPr732Wj300EMl3t8yMzN15513auXKlf59Z511lt55552w44O/rnr27Kn3338/4vdVsOXLl6tu3bqlfv0CAAAAAFDb0ZYRAAAAAIAKCFf55M8//6zSa6alpUUV7Prll19Mwa7U1FS9//77pQa7JOmSSy4xVeoyDEPvvfdexPF2u91UVUuSOnfurMmTJ5c5CNKoUaOIwYya6uSTT9Yrr7xSpmCXJL3//vumYNdJJ52kd999t8Rgl+St9PPYY4/p7LPP9u87dOiQvvzyy7DjA4M8knTVVVeVOdglSS1btixzgCTQWWedpccee6zEVmonnnii3nnnHdOYQ4cO6bPPPot4zv79+/XVV1+Z9g0fPlz33Xdfia0KmzRpovfff9/UbtDlcoVUJiqLrl276o033igxgGKz2fT888+bvh4OHTpUYiW09evXm7bvu+++MgW7JG/oMrBaXDjTp083fT00a9ZMH3zwQZmCMbfccouuv/56/7bdbteHH35YprVF43i6f4wZM6ZMwS7JG6a78MILTfuiac04d+5cU7CrSZMmJX5fv/zyy6Zg11VXXaXHH3+81PtbvXr19J///EfNmjXz75s3b542bdoUdnzw1/zo0aPLHOySpFNPPZVgFwAAAADguEC4CwAAAACACggX7srPz6/Sa95xxx1lDnZJCgljjR49Wm3bti3z+ddff71atmzp3/7pp5+UmZkZduyXX35pCrfFxsZqzJgxUf3CvjZ6/PHHSwwXBcrPz9cnn3zi37ZYLHr22WfL/DGyWCx66KGHTKGo4EpWPoGVfiT52wdWpdjYWD366KNlahl30kkn6eqrrzbtmzp1qjweT9jxn3zyianFZFpamh566KEyratp06YhbRFnzZoV0t6xNGUJuUjekNFZZ51l2hfYqjFYVX6uDMPQxIkTTfseeeQRNWjQoMxz3H333UpNTfVvf/HFF3K73ZW2Run4uX+cffbZEVupRhJcXey///2vqb1mScIFIiPdr1atWqUVK1b4txs1alTm7zHJ2yZ39OjRpn2ff/552LHVcX8CAAAAAKA2ItwFAAAAAEAFhAsdBIZPKltcXJyGDRtW5vGZmZlatGiRfzshIUFXXHFFVNeMjY3V4MGD/dsej8f0y/9AM2fONG2fd955x3xllZNOOqnE1n7BFixYYKqic8opp0R1viS1atVKXbt29W///vvvOnz4cMi4wDCOJP36669RXac8+vXrpxYtWpR5/F//+lfT9p49e/T777+HHfvzzz+bti+66KISWzgGu/zyy03fs26329Q6sTSdO3dWjx49yjy+V69epu3t27dHHFuVn6vffvtN27Zt8283bdrU1Nq0LOrUqaP+/fv7t7OzsyNWZCqv4+X+Edgqtay6d++uDh06+LcLCgr0ww8/lHrewYMHTV/jFoulxDaU3333nWn7sssuizpcN2jQIFPVuXBtbKXquT8BAAAAAFAbEe4CAAAAAKAC8vLyQvaVtTVfeXTs2DGqMMvy5ctlGIZ/u1evXkpOTo76usHho1WrVoWMcTgcWr16tWnfxRdfHPW1aps+ffpENX7ZsmWm7X79+pXruoGfE8MwtHbt2pAx3bt3N21/+eWXmjJlSsTKWJUh2tBQu3bt1KpVK9O+cO8lPz8/JEx03nnnRXWt5ORkU0BJCv+1HEnv3r2jul5wyC24UlGg4M/Vww8/rI0bN0Z1vUiCv+bOPPPMMlVWC1aW+0B5HS/3D4vFEvXXkU9wKCu4Ilc406dPN7VY7N27d4nhy+AgVnnuTykpKabv6d9//z3s/yuDv+ZfeOEFLV26NOrrAQAAAABwrIstfQgAAAAAAIgkJycnZF9iYmKVXS+wcktZrFmzxrTtcDhMLQHLauvWrabtgwcPhozZtm2bCgsLTft69uwZ9bVqm44dO0Y1Pvhzsnv37nJ9Tnbv3m3aPnDgQMiYbt26qWvXrv6KOG63W08//bQmTJigCy+8UAMGDFCPHj0qNZAYbRUy3zl//PGHfztcqGnLli2mkIrFYtFJJ50U9bW6du1qqhAVTfWpZs2aRXWt4CBlSW30rrzySn388cf+MOaOHTs0bNgw9enTR4MGDdIZZ5wRVTvVQMFfc4cPHy7X11zwxyrcfaC8jpf7R7NmzZSSklKucy+++GKNGTPGXx1y5cqV2rFjh1q3bh3xnGnTppm2L7vssohj8/PzQ6rmrVy5Ups3b456rQ6Hw//a4/Ho0KFDId8Pl1xyicaOHauCggJJ0qFDhzRixAh1795dQ4YM0ZlnnqkOHTqUueUtAAAAAADHKsJdAAAAAABUwJEjR0L2nXDCCVV2vWiqdknSn3/+adpevny5li9fXuF1hHvfmZmZpu3U1FTVq1evwteq6dLS0qIaH/w5+eKLL/TFF19UeB3hPieS9NJLL+mvf/2r6fiePXv07rvv6t1335XNZlOXLl3Uq1cv9enTR3369KlQQLFp06YVPiewbaVP8PtLS0sLaetWFs2bNy9x3pJEe73gUEpJFdM6deqke++9V//+97/9+wzD0JIlS7RkyRJJUr169dSzZ0/17t1bp59+epmDdMFfc7NmzdKsWbPK+jYiiuZjV5rj5f4R7f0iUP369TVgwADNnj3bv+/rr7/WvffeG3b82rVrTWGt5ORkDRo0KOL8hw4dMlV6lKRXXnml3OsNlJWVpZYtW5r2NWjQQE8//bQefPBB0/fG2rVr/dX76tSpo+7du+vUU09Vnz591LNnT8JeAAAAAIDjDn8TBgAAAACgAsJVGCpPuKWskpKSohpfmeGLQL5KK4GCAzl16tSpkmvXNNF+TsIFlypDcNUjn3bt2unzzz/XaaedFva4rx3exIkTdeutt6pv3776xz/+EVVFq0DlqUoUHJoK93Ub3NKwPO1FpdD1RfM9Up5WhtG49dZb9X//939q0KBB2OOZmZmaM2eOXnrpJQ0fPlznnXee3n777RIrgklVdx+I9DVXHsfL/aO8X7c+wa0Zp02bFjE0GNy28fzzzy/xflVVXydS5K+Viy++WO+++25I8MsnOztbCxcu1GuvvaZrrrlGAwcO1CuvvKJDhw5V2VoBAAAAAKhpCHcBAAAAAFABwe3OJKlz587VsJLwfO27UHNU1eckuOJOoFatWumjjz7SlClTdOWVV6pJkyYRx+bn5+ubb77RJZdcoueff/6ofA2VtPZIKitoVdWBrWhdfPHFmjVrlp5//nn169evxDDOzp079eqrr+q8887T/PnzI44LbJFXmcrzeUPFnHXWWabwX0ZGhhYvXhwyzuFwaMaMGaZ9JbVklKr2/xclfa30799fM2bM0GuvvabzzjuvxGDfgQMHNH78eJ177rkh4TUAAAAAAI5VtGUEAAAAAKACli5datquX79+xAok1SG4Bdjf//533XHHHVVyreCWkcGVluCVnp6ugwcP+renTJmiU0899ahc+9RTT/Vfa/fu3Vq+fLlWrlyp5cuXa+vWraaxhmFo0qRJysrK0ssvv1zma+Tm5kbdTi+48lS41nXBgY/SqlWV9Vo1sUJUYmKiLrvsMl122WVyOp1av369li9frhUrVmjFihUhVa4yMzN1xx136J133lG/fv1C5gv+3nz55Zd1ySWXVOE7iF5tuX+U1FrzaIiNjdXFF1+siRMn+vd9/fXXIZ/3WbNmmSpxtW7dWr169Spx7uDvu7i4OK1du/aotEGMi4vT+eefr/PPP18ej0ebNm3yf70vW7bMdM+UvCHUhx9+WB6PR5dffnmVrw8AAAAAgOpE5S4AAAAAAMpp0aJF2rFjh2nfwIEDa1QloOCQza5du47atXJycpSZmVll16ut6tata9revXt3tayjefPmGjZsmJ5++mnNmDFDc+bM0b333huyvunTp+uXX34p87x79+6Nei3B5wQHfcLtO3LkiHJycqK+VvDHO1yQrCaJi4tTjx49NHLkSI0bN06LFy/W5MmTNXToUNO9xuVy6YknnpDL5QqZI/hzWpX3gfI6WveP4Puz2+2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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mmm.plot_direct_contribution_curves(show_fit=True, xlim_max=1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit of the model comes from the saturation function selected at the time of training. You can verify this by directly accessing the model method and plotting the class name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model was train using the LogisticSaturation function\n",
      "and the GeometricAdstock function\n"
     ]
    }
   ],
   "source": [
    "print(f\"Model was train using the {mmm.saturation.__class__.__name__} function\")\n",
    "print(f\"and the {mmm.adstock.__class__.__name__} function\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "--4LSceFihbn"
   },
   "source": [
    "Within PyMC-Marketing we have different saturation functions, you can observe all in the [transformer module](https://www.pymc-marketing.io/en/stable/api/generated/pymc_marketing.mmm.transformers.html).\n",
    "\n",
    "Let's understand a few saturation functions with a few examples!\n",
    "\n",
    "## Understanding Saturation Functions: Sigmoid and Michaelis-Menten\n",
    "What do we mean by saturation functions? We assume the effect of spend on sales is not linear and saturates at some point. Two prevalent functions deployed to comprehend and estimate the saturation effects in advertising channels are the sigmoid and the Michaelis-Menten functions.\n",
    "\n",
    "### Sigmoid Function\n",
    "The sigmoid function is formulated as:\n",
    "\n",
    "$$\n",
    "\\beta \\cdot \\frac{\\exp(-\\lambda x)}{1 + \\exp(-\\lambda x)}\n",
    "$$\n",
    "\n",
    "Key Elements:\n",
    "* β (beta): Denotes the Asymptotic Maximum or Ceiling Value. It is the point that the function approaches as the input x becomes immense.\n",
    "* λ (Lambda): Influences the steepness of the curve. A more substantial value of λ renders the curve steeper, while a lesser value makes it more even-paced.\n",
    "\n",
    "### Michaelis-Menten Function\n",
    "The Michaelis-Menten function is formulated as:\n",
    "\n",
    "$$\n",
    "\\frac{\\alpha \\times x}{\\lambda + x}\n",
    "$$\n",
    "\n",
    "Key Elements:\n",
    "* α (Alpha or Vmax): It represents the maximum contribution (y) a channel can make, also recognized as the plateau point.\n",
    "* λ (k): Denotes the elbow on the function in x, signifying the moment when the curve adjusts its direction.\n",
    "\n",
    "## Which Function to Use?\n",
    "The preference between the sigmoid and Michaelis-Menten functions ought to be steered by the data's goodness of fit. But it really comes down to your assumptions about where the peak might be and the speed at which it saturates the curve.\n",
    "\n",
    "> **Tip**: When you're choosing a saturation function, it's helpful to consider the data you already have. While saturation functions may have different parameterizations, they all have the same effect. If you already know about any relationship between spending and response, it's a good idea to pick a function with the same parameter values. This will help you understand and analyze its operation and outcomes better, which is essential within the Bayesian framework.\n",
    "\n",
    "Once these parameters are obtained, you can visualize it using the `arviz.summary` function (each parameter has the prefix `saturation` or `adstock` respectively) and, if desired, you can recreate the curves for each channel independently based on them. More crucially, these parameter values are indispensable when using the `budget_allocator` function, which leverages this information to optimize your marketing budget across distinct channels. This section is fundamental to budget optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "      <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>saturation_beta[x1]</th>\n",
       "      <td>0.362</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2204.0</td>\n",
       "      <td>2268.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_beta[x2]</th>\n",
       "      <td>0.271</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>895.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x1]</th>\n",
       "      <td>3.966</td>\n",
       "      <td>0.386</td>\n",
       "      <td>3.260</td>\n",
       "      <td>4.691</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x2]</th>\n",
       "      <td>3.085</td>\n",
       "      <td>1.163</td>\n",
       "      <td>1.038</td>\n",
       "      <td>5.157</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1059.0</td>\n",
       "      <td>901.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x1]</th>\n",
       "      <td>0.401</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>2876.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x2]</th>\n",
       "      <td>0.189</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "saturation_beta[x1]  0.362  0.020   0.326    0.399      0.000    0.000   \n",
       "saturation_beta[x2]  0.271  0.078   0.191    0.387      0.003    0.002   \n",
       "saturation_lam[x1]   3.966  0.386   3.260    4.691      0.008    0.006   \n",
       "saturation_lam[x2]   3.085  1.163   1.038    5.157      0.034    0.024   \n",
       "adstock_alpha[x1]    0.401  0.030   0.343    0.458      0.001    0.000   \n",
       "adstock_alpha[x2]    0.189  0.041   0.113    0.266      0.001    0.001   \n",
       "\n",
       "                     ess_bulk  ess_tail  r_hat  \n",
       "saturation_beta[x1]    2204.0    2268.0    1.0  \n",
       "saturation_beta[x2]    1066.0     895.0    1.0  \n",
       "saturation_lam[x1]     2243.0    2225.0    1.0  \n",
       "saturation_lam[x2]     1059.0     901.0    1.0  \n",
       "adstock_alpha[x1]      2460.0    2876.0    1.0  \n",
       "adstock_alpha[x2]      1720.0    2106.0    1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    data=mmm.fit_result,\n",
    "    var_names=[\n",
    "        \"saturation_beta\",\n",
    "        \"saturation_lam\",\n",
    "        \"adstock_alpha\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GEGsiGlf142o"
   },
   "source": [
    "## Example Use-Cases\n",
    "The `budget_allocator` function within PyMC-Marketing boasts a myriad of applications that can solve various business predicaments. Here, we present five critical use cases that exemplify its utility in real-world marketing scenarios.\n",
    "\n",
    "### What are we optimizing?\n",
    "Before jumping into the examples, we need to understand the basis of our optimizer.\n",
    "\n",
    "We aim to optimize the allocation of budgets across multiple channels to maximize the overall contribution to key performance indicators (KPIs), such as sales or conversions. Each channel has its own sigmoid or michaelis-menten curve, representing the relationship between the amount spent and the resultant performance.\n",
    "\n",
    "These curves vary in characteristics: some channels saturate quickly, meaning that additional spending yields diminishing returns, while others may offer more linear growth in contribution with increased spending.\n",
    "\n",
    "To solve this optimization problem, we employ the Sequential Least Squares Quadratic Programming ([SLSQP](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-slsqp.html#optimize-minimize-slsqp)) algorithm, a gradient-based optimization technique. SLSQP is well-suited for this application as it allows for the imposition of both equality and inequality constraints, ensuring that the budget allocation adheres to business rules or limitations.\n",
    "\n",
    "The algorithm works by iteratively approximating the objective function and constraints using quadratic functions and solving the resulting sub-problems to find a local minimum. This enables us to effectively navigate the multidimensional space of budget allocations to find the most efficient distribution of resources.\n",
    "\n",
    "The optimizer aims to maximize the total contribution from all channels while adhering to the following constraints:\n",
    "\n",
    "1. Budget Limitations: The total spending across all channels should not exceed the overall marketing budget.\n",
    "2. Channel-specific Constraints: Some channels may have minimum or maximum spending limits.\n",
    "\n",
    "By leveraging the SLSQP algorithm, we can optimize the multi-channel budget allocation in a rigorous, mathematically sound manner, ensuring that we get the highest possible return on investment.\n",
    "\n",
    "### Maximizing Contribution\n",
    "Assume you're managing the marketing for a retail company with a substantial budget to allocate for advertising across multiple channels. Suppose you're already apportioning funds to channels x1 and x2. Still, you're contemplating ways to optimize the forthcoming quarter's outlay to maximize the overall contribution.\n",
    "\n",
    "Without, you might have considered scattering your money linearly without an MMM model - equal investments in each channel. However, you wish to explore better alternatives now that you possess an MMM model. Given that you lack prior knowledge, you impose the same restrictions on both channels. They must each expend a minimum of 1 million euros and no more than 5 million, equating to your total budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "iAtdq9uU10xr"
   },
   "outputs": [],
   "source": [
    "total_budget = 5  # Imagine is 5K or 5M\n",
    "# Define your channels\n",
    "channels = [\"x1\", \"x2\"]\n",
    "# The initial split per channel\n",
    "budget_per_channel = total_budget / len(channels)\n",
    "# Initial budget per channel as dictionary.\n",
    "initial_budget_dict = {channel: budget_per_channel for channel in channels}\n",
    "# bounds for each channel\n",
    "min_budget, max_budget = 1, 5\n",
    "budget_bounds = {channel: [min_budget, max_budget] for channel in channels}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nd5hRZu92MRi"
   },
   "source": [
    "We can use our function and see the results with this information saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "g6BD5BhY2Dcm",
    "outputId": "f546dc05-ef6d-4af2-d210-28c271d928eb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    }
   ],
   "source": [
    "response = mmm.allocate_budget_to_maximize_response(\n",
    "    budget=total_budget,\n",
    "    num_periods=8,\n",
    "    time_granularity=\"weekly\",\n",
    "    budget_bounds=budget_bounds,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x1': 3.1152460638531667, 'x2': 1.8847539361468335}\n"
     ]
    }
   ],
   "source": [
    "print(mmm.optimal_allocation_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mmm.plot_budget_allocation(samples=response, figsize=(12, 8))\n",
    "fig.suptitle(\"Response vs spent per channel\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = mmm.plot_allocated_contribution_by_channel(samples=response)\n",
    "fig.suptitle(\"Estimated Contribution per channel over time\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 611,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.suptitle(\"Optimal total marketing contribution\")\n",
    "response[\"y\"].mean(dim=\"sample\").plot()\n",
    "plt.fill_between(\n",
    "    x=response.y.date.values,\n",
    "    y1=response.y.quantile(0.025, dim=\"sample\"),\n",
    "    y2=response.y.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ");"
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    "These results are expected based on the estimates from the curve and our estimated average contribution from the posterior distribution.\n",
    "\n",
    "However, based on our main assumptions, how can we ensure this result is optimal? How can we compare this outcome to what we would initially have if we followed our first setup?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 352kB\n",
       "Dimensions:  (date: 8, sample: 4000)\n",
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       "  * date     (date) datetime64[ns] 64B 2021-09-06 2021-09-13 ... 2021-10-25\n",
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       "Data variables:\n",
       "    y        (date, sample) float64 256kB 7.936 6.92 7.254 ... 8.583 9.119 8.292\n",
       "Attributes:\n",
       "    created_at:                 2024-08-17T12:10:33.755948+00:00\n",
       "    arviz_version:              0.19.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1</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-dd324b8c-ebe7-4666-baa4-1965afd0f83c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd324b8c-ebe7-4666-baa4-1965afd0f83c' 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'>date</span>: 8</li><li><span class='xr-has-index'>sample</span>: 4000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-20213779-5a6f-478b-9737-b376160ec0f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-20213779-5a6f-478b-9737-b376160ec0f4' class='xr-section-summary' >Coordinates: <span>(4)</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'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-09-06 ... 2021-10-25</div><input id='attrs-54b5ce81-338b-4a1f-b3d8-39d5261a2888' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54b5ce81-338b-4a1f-b3d8-39d5261a2888' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-271494e1-f421-4e48-adf6-9561396e018d' class='xr-var-data-in' type='checkbox'><label for='data-271494e1-f421-4e48-adf6-9561396e018d' 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([&#x27;2021-09-06T00:00:00.000000000&#x27;, &#x27;2021-09-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-20T00:00:00.000000000&#x27;, &#x27;2021-09-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-10-04T00:00:00.000000000&#x27;, &#x27;2021-10-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-10-18T00:00:00.000000000&#x27;, &#x27;2021-10-25T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-9e7b2b05-189e-4d27-b2b8-c08cffde34b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e7b2b05-189e-4d27-b2b8-c08cffde34b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f404942e-5167-4872-9c5e-5ef84c42bdf0' class='xr-var-data-in' type='checkbox'><label for='data-f404942e-5167-4872-9c5e-5ef84c42bdf0' 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, 0), (0, 1), (0, 2), ..., (3, 997), (3, 998), (3, 999)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3</div><input id='attrs-f502c7d6-9519-4a64-9208-bb88e513e379' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f502c7d6-9519-4a64-9208-bb88e513e379' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e144fc3-1dfd-4e3d-89d1-e559e72c9588' class='xr-var-data-in' type='checkbox'><label for='data-6e144fc3-1dfd-4e3d-89d1-e559e72c9588' 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, 0, 0, ..., 3, 3, 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'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-ffbaf254-0810-4d93-8a14-e308b54ba531' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ffbaf254-0810-4d93-8a14-e308b54ba531' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18e11a63-2c7f-4f1e-8e41-984f66fcbc35' class='xr-var-data-in' type='checkbox'><label for='data-18e11a63-2c7f-4f1e-8e41-984f66fcbc35' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c49d65f9-94ec-4a1b-ab06-f45691c15361' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c49d65f9-94ec-4a1b-ab06-f45691c15361' class='xr-section-summary' >Data variables: <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>y</span></div><div class='xr-var-dims'>(date, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.936 6.92 7.254 ... 9.119 8.292</div><input id='attrs-1d68de2a-7974-4913-ac0f-524893a66f20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d68de2a-7974-4913-ac0f-524893a66f20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9a8045c-bc2c-4da1-933e-f1911506d23c' class='xr-var-data-in' type='checkbox'><label for='data-a9a8045c-bc2c-4da1-933e-f1911506d23c' 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([[7.93589243, 6.92035322, 7.25440894, ..., 6.87728937, 8.06935242,\n",
       "        7.56770198],\n",
       "       [8.03306373, 7.39680746, 7.25755206, ..., 7.00757091, 7.84290967,\n",
       "        7.26012284],\n",
       "       [8.33456819, 7.20027874, 7.31973645, ..., 7.75039679, 7.82626829,\n",
       "        7.80742732],\n",
       "       ...,\n",
       "       [8.94765105, 7.70763812, 7.61407127, ..., 7.78762832, 8.99827288,\n",
       "        8.39836798],\n",
       "       [8.85830913, 7.8746613 , 7.93131064, ..., 8.10139339, 8.35978334,\n",
       "        8.96823834],\n",
       "       [8.95828822, 8.38036743, 8.30057404, ..., 8.58287919, 9.11905947,\n",
       "        8.29209666]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86dac8f1-8be6-465c-9da1-a326387c0ecd' class='xr-section-summary-in' type='checkbox'  ><label for='section-86dac8f1-8be6-465c-9da1-a326387c0ecd' 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>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e55275e5-bbda-4277-ab1d-d29c21372bf9' class='xr-index-data-in' type='checkbox'/><label for='index-e55275e5-bbda-4277-ab1d-d29c21372bf9' 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(DatetimeIndex([&#x27;2021-09-06&#x27;, &#x27;2021-09-13&#x27;, &#x27;2021-09-20&#x27;, &#x27;2021-09-27&#x27;,\n",
       "               &#x27;2021-10-04&#x27;, &#x27;2021-10-11&#x27;, &#x27;2021-10-18&#x27;, &#x27;2021-10-25&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample<br>chain<br>draw</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-9793e24e-84fa-491f-8985-908da58bba67' class='xr-index-data-in' type='checkbox'/><label for='index-9793e24e-84fa-491f-8985-908da58bba67' 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(MultiIndex([(0,   0),\n",
       "            (0,   1),\n",
       "            (0,   2),\n",
       "            (0,   3),\n",
       "            (0,   4),\n",
       "            (0,   5),\n",
       "            (0,   6),\n",
       "            (0,   7),\n",
       "            (0,   8),\n",
       "            (0,   9),\n",
       "            ...\n",
       "            (3, 990),\n",
       "            (3, 991),\n",
       "            (3, 992),\n",
       "            (3, 993),\n",
       "            (3, 994),\n",
       "            (3, 995),\n",
       "            (3, 996),\n",
       "            (3, 997),\n",
       "            (3, 998),\n",
       "            (3, 999)],\n",
       "           name=&#x27;sample&#x27;, length=4000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db325935-7107-44f8-afbe-12cce03a7152' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db325935-7107-44f8-afbe-12cce03a7152' class='xr-section-summary' >Attributes: <span>(4)</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>2024-08-17T12:10:33.755948+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 352kB\n",
       "Dimensions:  (date: 8, sample: 4000)\n",
       "Coordinates:\n",
       "  * date     (date) datetime64[ns] 64B 2021-09-06 2021-09-13 ... 2021-10-25\n",
       "  * sample   (sample) object 32kB MultiIndex\n",
       "  * chain    (sample) int64 32kB 0 0 0 0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3 3 3 3\n",
       "  * draw     (sample) int64 32kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    y        (date, sample) float64 256kB 7.936 6.92 7.254 ... 8.583 9.119 8.292\n",
       "Attributes:\n",
       "    created_at:                 2024-08-17T12:10:33.755948+00:00\n",
       "    arviz_version:              0.19.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_date = mmm.X[\"date_week\"].max()\n",
    "\n",
    "# New dates starting from last in dataset\n",
    "n_new = 8\n",
    "new_dates = pd.date_range(start=last_date, periods=1 + n_new, freq=\"W-MON\")[1:]\n",
    "\n",
    "initial_budget_scenario = pd.DataFrame(\n",
    "    {\n",
    "        \"date_week\": new_dates,\n",
    "    }\n",
    ")\n",
    "\n",
    "# Same channel spends as last day\n",
    "initial_budget_scenario[\"x1\"] = initial_budget_dict[\"x1\"]\n",
    "initial_budget_scenario[\"x2\"] = initial_budget_dict[\"x2\"]\n",
    "\n",
    "# Other features\n",
    "initial_budget_scenario[\"event_1\"] = 0\n",
    "initial_budget_scenario[\"event_2\"] = 0\n",
    "\n",
    "initial_budget_scenario[\"t\"] = 0\n",
    "\n",
    "response_initial_budget = mmm.sample_posterior_predictive(\n",
    "    X_pred=initial_budget_scenario, extend_idata=False\n",
    ")\n",
    "\n",
    "response_initial_budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_response_original_scale_optimize = (\n",
    "    response[\"y\"] * mmm.get_target_transformer()[\"scaler\"].scale_\n",
    ")\n",
    "\n",
    "# Plotting optimized response\n",
    "y_response_original_scale_optimize.mean(dim=\"sample\").plot(\n",
    "    label=f\"Optimized Response | Daily spent {sum(mmm.optimal_allocation_dict.values()):.0f}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=y_response_original_scale_optimize.date.values,\n",
    "    y1=y_response_original_scale_optimize.quantile(0.025, dim=\"sample\"),\n",
    "    y2=y_response_original_scale_optimize.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Plotting initial budget response\n",
    "response_initial_budget[\"y\"].mean(dim=\"sample\").plot(\n",
    "    label=\"Initial Budget Response | Daily spent 5.00M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=response_initial_budget.y.date.values,\n",
    "    y1=response_initial_budget.y.quantile(0.025, dim=\"sample\"),\n",
    "    y2=response_initial_budget.y.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Adding labels, legend, and title\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Response\")\n",
    "plt.legend()\n",
    "plt.title(\"Comparison of Optimized and Initial Budget Responses\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aqIaFFHy28ni"
   },
   "source": [
    "This information will allow you to compare the optimization results against what could have been your initial configuration and budget distribution. While our budget distribution changes, the contribution estimate remains almost the same for this budget. Could we be spending more than needed? Furthermore, how can we identify the optimal spent amount if this situation arises?\n",
    "\n",
    "One approach to explore this could be to create various scenarios and observe how our estimated contribution fluctuates according to each scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dbsTmbLZ2-x8"
   },
   "source": [
    "### Creating Budget Scenarios\n",
    "Envision the subsequent situation: You're managing marketing operations for a rapidly growing retail company. The management team has allocated a considerable advertising budget and anticipates substantial results in the next quarter. However, given the uncertainty in economic trends, you are tasked with designing a budget allocation strategy capable of accommodating various scenarios.\n",
    "\n",
    "Before the advent of a robust MMM model, your approach might have been simplistic yet naive: distribute the funds equally across the two primary channels, x1 and x2. This would result in a linear, evenly split distribution of 2.5 million euros each, given a total budget of 5 million euros.\n",
    "\n",
    "Nevertheless, with the MMM model now at your disposal, you realize a more sophisticated approach is feasible. You're eager to investigate how this budget could be optimized across multiple scenarios:\n",
    "\n",
    "1. Status Quo Scenario: What if the market stays stable? What is the best allocation?\n",
    "2. Growth Scenario: What if the market suddenly becomes more favorable? How should the extra budget be allocated?\n",
    "3. Recession Scenario: What if there is a market downturn and the budget gets cut by 40%?\n",
    "\n",
    "Given that you are treading into uncertain waters, you set certain constraints. Each channel must have a minimum spending of 1 million euros, ensuring base-level visibility. The maximum cap is ±5 million euros (depending on your scenario), respecting the total budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "qbE1GDaB2sbL"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n",
      "Sampling: [y]\n",
      "Sampling: [y]\n",
      "Sampling: [y]\n"
     ]
    }
   ],
   "source": [
    "# Initialize two variables to save the results and base conditions for each scenario.\n",
    "scenarios_result = []\n",
    "scenarios = np.array([0.6, 0.8, 1.2, 1.8])\n",
    "for scenario in scenarios:\n",
    "    tmp_response = mmm.allocate_budget_to_maximize_response(\n",
    "        budget=total_budget * scenario,\n",
    "        num_periods=8,\n",
    "        time_granularity=\"weekly\",\n",
    "        budget_bounds=budget_bounds,\n",
    "    )\n",
    "    scenarios_result.append(tmp_response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8pErqyBw3USg"
   },
   "source": [
    "We now have two lists containing the expected results with and without model optimization. This will return the same plot as before but based on each of the scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "id": "74q2NZd03SMr",
    "outputId": "485ced2a-34c4-4005-fba2-af180e42d974"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 989,
       "width": 1989
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_main, ax_main = plt.subplots(nrows=2, ncols=2, figsize=(20, 10))\n",
    "for i, scenario in enumerate(scenarios_result):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    _, ax = mmm.plot_budget_allocation(samples=scenario, ax=ax_main[row, col])\n",
    "    ax.set_title(f\"Budget Allocation for Scenario {scenarios[i] * total_budget:.0f}M\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N7G3lVcI3idn"
   },
   "source": [
    "The graph indicates that boosting the budget beyond an spend level greater than 3 Million induces extremely marginal changes in the potential outcome. Therefore, one can use the budget detailed in scenario three as a cap for our budget.\n",
    "\n",
    "However, is this the best method to invest our resources? So far, we have considered general constraints for each channel. However, since our curve saturates and beyond a specific point, it does not significantly elevate its contribution. Wouldn't it be crucial to incorporate these limitations?\n",
    "\n",
    "### Adding Business or Channel Constraints\n",
    "There may be instances where, despite the recommendation to invest more in a particular channel than another, it may not be feasible.\n",
    "\n",
    "Consider this: Given your current budget, you have already maxed out the number of people you can reach within a specific platform. Therefore, further spending will only increase the frequency without adding new reach. In this scenario, does it make sense to raise the budget? This is a classic example of budget limitations based on platform restrictions.\n",
    "\n",
    "In such cases, a dictionary can be created with limits for each channel, and the optimization function can be reapplied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "lhqA7s903ZoT",
    "outputId": "08b9dd7c-fb47-4638-889e-7a9686d49234"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    }
   ],
   "source": [
    "constrained_response = mmm.allocate_budget_to_maximize_response(\n",
    "    budget=total_budget,\n",
    "    num_periods=8,\n",
    "    time_granularity=\"weekly\",\n",
    "    budget_bounds={\"x1\": [0, 4], \"x2\": [0, 3]},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mmm.plot_budget_allocation(samples=constrained_response, figsize=(12, 8))\n",
    "fig.suptitle(\"Response vs spent per channel\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xiIprHPQ3wPd"
   },
   "source": [
    "Utilizing bounds is crucial, as all saturation curves in PyMC-Marketing are asymptotic. Therefore, without a constraint on your factors, the optimizer will consistently seek to use 100% of the funds because it will always observe an increase within `y` (target).\n",
    "\n",
    "If you have yet to notice, this is why, in the previous example, the optimizer always utilized the whole budget in all scenarios. However, should you provide these limits, the optimizer can verify whether it is necessary to use your entire budget.\n",
    "\n",
    "Let us examine the subsequent plot closely!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 828
    },
    "id": "wwMqqJqA3umN",
    "outputId": "3130dd81-19dc-4a94-b36c-b1a871247cd3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(\n",
    "    show_fit=True, xlim_max=3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vhod0OqSUcqt"
   },
   "source": [
    "Although in principle we assign a limit of 1.5 spending for each channel, we can observe that at least for x1 after spending more than 1.2, we enter the plateau effect of the curve where we get almost zero return in `y` for each unit extra of `x`.\n",
    "\n",
    "Additionally, based on the information above, a **9M** budget or a **3M** budget did not represent a big change in contribution. So let's adjust the budget as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    }
   ],
   "source": [
    "limit_constrained_response = mmm.allocate_budget_to_maximize_response(\n",
    "    budget=2,\n",
    "    num_periods=8,\n",
    "    time_granularity=\"weekly\",\n",
    "    budget_bounds={\"x1\": [0, 1.2], \"x2\": [0, 1.5]},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mmm.plot_budget_allocation(\n",
    "    samples=limit_constrained_response, figsize=(12, 8)\n",
    ")\n",
    "fig.suptitle(\"Response vs spent per channel\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_response_original_scale_optimize = (\n",
    "    limit_constrained_response[\"y\"] * mmm.get_target_transformer()[\"scaler\"].scale_\n",
    ")\n",
    "\n",
    "# Plotting optimized response\n",
    "y_response_original_scale_optimize.mean(dim=\"sample\").plot(\n",
    "    label=f\"Optimized Response | Daily spent {sum(mmm.optimal_allocation_dict.values()):.2f}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=y_response_original_scale_optimize.date.values,\n",
    "    y1=y_response_original_scale_optimize.quantile(0.025, dim=\"sample\"),\n",
    "    y2=y_response_original_scale_optimize.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Plotting initial budget response\n",
    "response_initial_budget[\"y\"].mean(dim=\"sample\").plot(\n",
    "    label=f\"Initial Budget Response | Daily spent {5.00}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=response_initial_budget.y.date.values,\n",
    "    y1=response_initial_budget.y.quantile(0.025, dim=\"sample\"),\n",
    "    y2=response_initial_budget.y.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Adding labels, legend, and title\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Response\")\n",
    "plt.legend()\n",
    "plt.title(\"Comparison of Optimized and Initial Budget Responses\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new result is much clearer. By using less than half of the budget with an optimal distribution, we could achieve the same outcomes as our initial setup, which distributed the budget evenly across all channels. On the other hand, the optimal allocation distributes the budget in levels known by the model, reducing the uncertainty around the estimated impact. This is because the initial allocation was at unprecedented levels of spend in the model, resulting in greater uncertainty.\n",
    "\n",
    "By default, if you need more prior knowledge about the limits, we recommend using alpha (the plateau) as the limit for each channel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note that the estimate provided assumes consistent spending each week. However, in the field of marketing, even with a fixed spending level, the actual spending can fluctuate based on factors such as the number of people bidding on your ad or viewing ads on a given day.\n",
    "\n",
    "To account for this unpredictable variation, we have included a parameter called `noise_level` that allows you to introduce white noise into the projection. This can provide a sense of what the outcome might look like if the recommended budget could potentially fluctuate by a certain extent. The default value for `noise_level` is 1%, but you can adjust it as needed. In the example below, we have used a value of 10%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    }
   ],
   "source": [
    "limit_constrained_response_with_noise = mmm.allocate_budget_to_maximize_response(\n",
    "    budget=2,\n",
    "    num_periods=8,\n",
    "    time_granularity=\"weekly\",\n",
    "    budget_bounds={\"x1\": [0, 1.2], \"x2\": [0, 1.5]},\n",
    "    noise_level=0.1,  # New parameter\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mmm.plot_budget_allocation(\n",
    "    samples=limit_constrained_response_with_noise, figsize=(12, 8)\n",
    ")\n",
    "fig.suptitle(\"Response vs spent per channel\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_response_original_scale_optimize = (\n",
    "    limit_constrained_response_with_noise[\"y\"]\n",
    "    * mmm.get_target_transformer()[\"scaler\"].scale_\n",
    ")\n",
    "\n",
    "# Plotting optimized response\n",
    "y_response_original_scale_optimize.mean(dim=\"sample\").plot(\n",
    "    label=f\"Optimized Response | Daily spent {sum(mmm.optimal_allocation_dict.values()):.2f}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=y_response_original_scale_optimize.date.values,\n",
    "    y1=y_response_original_scale_optimize.quantile(0.025, dim=\"sample\"),\n",
    "    y2=y_response_original_scale_optimize.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Plotting initial budget response\n",
    "response_initial_budget[\"y\"].mean(dim=\"sample\").plot(\n",
    "    label=f\"Initial Budget Response | Daily spent {5.00}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=response_initial_budget.y.date.values,\n",
    "    y1=response_initial_budget.y.quantile(0.025, dim=\"sample\"),\n",
    "    y2=response_initial_budget.y.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Adding labels, legend, and title\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Response\")\n",
    "plt.legend()\n",
    "plt.title(\"Comparison of Optimized and Initial Budget Responses\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h6yOpHXE4OLE"
   },
   "source": [
    "## Benefits and Limitations\n",
    "In marketing analytics, curve-fitted Media Mix Models (MMMs) provide enriching insights by simplifying intricate systems, facilitating optimization, aiding scenario planning, and delivering quantifiable metrics for strategy evaluation. Each of these advantages presents compelling reasons to incorporate such models. The optimization consider the delay effect overtime, meaning the optimization is generated across num_periods.\n",
    "\n",
    "Nevertheless, it is pivotal to acknowledge that these models do possess their own set of limitations. The primary ones are the assumptions of time invariance and generalized behavior, signifying an incomplete comprehension of market dynamics. The no-impact variance assumptions are not specific to the curves, as the model does not account for these effects. Hence, curves should always be carefully considered.\n",
    "\n",
    "\n",
    "## Other methods to explore\n",
    "Even if the method is promising, use other optimization options which includes the full posterior could be a powerful and interesting solution as it's described on the following blog [\"Using bayesian decision making to optimize supply chains\"](https://twiecki.io/blog/2019/01/14/supply_chain/)\n",
    "\n",
    "The current methodology is similar to the ones used on other libraries as [Robyn from Meta](https://facebookexperimental.github.io/Robyn/docs/features/#budget-allocation) and Google [Lightweight from Google](https://github.com/google/lightweight_mmm). You can explore the solutions and compare if needed.\n",
    "\n",
    "## Conclusion\n",
    "MMM models and methodologies used here are designed to bridge the gap between theoretical rigor and actionable marketing insights. They represent a significant stride towards a more data-driven, analytical approach to marketing budget allocation, which could change how organizations invest in customer acquisition and retention.\n",
    "\n",
    "Although it is a promising tool, it is essential to highlight that this methodology and software release is still experimental. Like any emerging technology, it comes with inherent limitations and assumptions that users should be aware of. The models can offer actionable insights, but they should be cautiously used and in tandem with various forms of analysis. Context is crucial, and while models aim to encapsulate general trends, they might not account for all nuances.\n",
    "\n",
    "Consequently, your engagements, feedback, and thoughts are not merely welcomed but actively solicited to make this tool as practical and universally applicable as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sat Aug 17 2024\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.14\n",
      "IPython version      : 8.26.0\n",
      "\n",
      "pytensor: 2.22.1\n",
      "\n",
      "numpy     : 1.26.4\n",
      "matplotlib: 3.9.1\n",
      "pandas    : 2.2.2\n",
      "arviz     : 0.19.0\n",
      "\n",
      "Watermark: 2.4.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  }
 ],
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  "colab": {
   "provenance": []
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