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   "source": [
    "(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": {
    "ExecuteTime": {
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    "id": "28_D6j7jjQTy"
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   "outputs": [],
   "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": {
    "ExecuteTime": {
     "end_time": "2025-01-15T14:30:46.369670Z",
     "start_time": "2025-01-15T14:30:44.996905Z"
    },
    "id": "6eM1cTtMys1O"
   },
   "outputs": [],
   "source": [
    "mmm = MMM.load(\"model.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "Alternatively, load a model that has been saved to MLflow via `pymc_marketing.mlflow.log_inference_data` or has been autologged to MLflow via `pymc_marketing.mlflow.autolog(log_mmm=True)`, from the [PyMC-Marketing MLflow module](https://github.com/pymc-labs/pymc-marketing/blob/main/pymc_marketing/mlflow.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## If you have a hosted MLflow server, you will of course need to authenticate first.\n",
    "# RUN_ID = \"your_run_id\"\n",
    "# from pymc_marketing.mlflow import load_mmm\n",
    "# mmm = load_mmm(RUN_ID)\n",
    "\n",
    "# # Load the full model with the InferenceData\n",
    "# mmm = load_mmm(\n",
    "#     run_id=RUN_ID,         # The MLflow run ID from which to load the model\n",
    "#     full_model=True,       # Set to True to get the full MMM model with InferenceData\n",
    "#     keep_idata=True,       # Set to True if you want to keep the downloaded InferenceData saved locally\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X3NUoJ1bzBvK"
   },
   "source": [
    "## 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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   "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": 5,
   "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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AWL3KlYc1Yaw6j5WnbFmvbuWhNaYYxpoOYdnilCu/ltBdWN2hG6wJaQU1s/6wtgKRkBYAoNqKC3f95Cc/0b/8y7/o/vvv1/Hjx+tus3v3bu3fv18vfelLdcUVVyzzCgEAq8ZswS6qpQAAq4G1Ujrq1yyWpnMtIsxlg53+RK6erfK6t0iRfkbtAwAAAACAlaleIKveNK15Kg+tNCOIZd2QH9YytSEtZzpgVecDcP6xqkNYVXWHxVBW7XQtQloAgGZZMeGuxx57TO95z3v04IMPSpJsnaR0ybFjx3T8+HF96Utf0iWXXKI/+7M/05Of/OTlWioAYDUg2AUAWG0qw1ylQNdiw1w924phrq1SuIcwFwAAAAAAWF7WNhzSmp6mVZhlmpZTFcCau/LQnXP6lS1tV5yoZctTtKYnatnS86Xw1iz1iQAALJcVEe76yle+oltvvVWZTEbWWhljZCr+B1kKepma/2laa/Xggw/qN37jN/THf/zHeulLX7qs6wYArFClYFc6JhMl2AUAWKGaFubq8isWe7bK69kqhQhzAQAAAACAJqoMann5cg2iH9TKzx7gqncox6mZphWQTHg6ZFWuOnTnn6ZVt/Kw4n7N8+WqRQAAVpm2h7u++93v6l3vepcKhUI51FUKc3V3d2vPnj3q6emRJMXjcR0/flzxeFzSdNgrk8noPe95j/r6+nTNNde05xsBAKwMhayc84/IZOIy0RMSwS4AwEphrZSelBM/08Qw1zYp1E2YCwAAAAAANGZGUKsged6Cg1ozag+dYu1hsKL2sBzOKgW2AnVfwyhP03ICkhuoqDesM02rMqwFAMA60db/60WjUb397W8vB7ustero6NArXvEKveQlL9GTnvSkuvsdOnRIX/7yl/X5z39eU1NTMsaoUCjoHe94h7797W+rv79/eb8RAMDKUBXsOi5JsgS7AADtlIkXw1xnZGJnZPKpBR+CMBcAAAAAAKhrzurDpQa1wsWgllMnqOXOWlVoK6dkORVBLdcPa00Ht1w/vOUyTQsAgPm0Ndz16U9/WvF4vBzsevrTn66PfvSj2rZt25z7PfGJT9Tb3vY2vfKVr9RNN92kBx54QJKUSCT06U9/Wu94xzuWYfUAgBWFYBcAYCXIJmXiZ6cDXdnEgg9hQ12yPdtku0s1i4S5AAAAAABYF2rCWLJ5GW9meKv8uPX8gFcNW6oydFzJBOaZqNVoUCvoVyhWBrfcoL+vWzFpi9cwAABoOmNtnf/jL5NnPetZGh8flyQ96UlP0he+8AV1dHQs6BipVEqveMUr9Oijj8paqw0bNuiHP/xhK5aLGhMTE+1eAioYY8pT66LRqNr4VxtYfoWsnJFHZdIxgl3NYPxqZMkPTovLCYDFWg/Xk3xGJn5mOsyVnlzwIWyo25/M1V2czBXubsFCgVVsPVxLACwPricAmoFrCYBGWW/esFZnJCR5BaWS8eJjdYJaxikGqIpBLcetmbLVxKBWZeUhQS1gVeA9YmBlGhgYaOrx2ja566GHHtLY2Jgk/4Lzx3/8xwsOdklSR0eHbr31Vr3iFa+QJI2Pj+sXv/iFLr744qauFwCwQlUFu05IItgFAGihQlYmcU4mVgx0pSa00HdzbLBLtncbYS4AAAAAAFYLa4tTsirqDuvWH/q3y1O1ag9jTDGEFZAphagCEdlC5VQtt7yNShO4ZhzHKVYdBqerD91gueqwugqRoBYAAKtd28JdR44ckeQHu3bt2qXLLrts0ce67LLLtHv3bh0/frx8bMJdALAO1AS7jKw8gl0AgGby8jKJ88XpXGdlkiNacJgrEPFrFnu2yuvZLoV7eEEVAAAAAIB2snZGUKtqqpaXL0/cMqXg1qz1h9OhLOtG/PrDuiGt4mOlfY1kursl48iG0rLGD2PZiulapYBW1WNuoG7gCwAArF1tC3eVpnZJ0t69e5d8vCc84QnlcFep6hEAsIYVcn6wK0OwCwDQRNaTSY74Ya7YGT/MZQsLO4YTlFcKc/VukyIDhLkAAAAAAGgl682YoFVZf1gObJWnbc38Wb9yqpYctzgNKzxdZ1g5WcvxKxLr1x9WT82yFSEtf6qWK7khGTcoM7hRxnHlUaUGAADm0LZwVzabLd+ORCJLPl44HK57bADAGlTIyRl5xA92TRDsAgAsgbVSasKvWIydlpM4J3m5hR3DuPJ6tpanc9nODXyCFgAAAACApagMaxWDWVUViDMma9WrQHSqgljWDUnBwMypWpWhrRnHMMVgVmi6/rAqqFW6XwxwOfUDX3UZ41czAgAAzKNt4a7BwcHy7TNnziz5eGfPni3fHhgYWPLxAAArVOXELoJdAIDFyCblxE7LxE/LxE7L5NML2984sl2b5PVs98NcXZvqvgAMAAAAAACKZp2gla//WN0KRMefllWqQHQiUiggayrDWpU1iDM/eFWewlUMZFVP1SpO2aoMavHzPgAAWAHaFu7asmWLJMlaqwcffFATExOLDmVFo1EdPHhwxrEBAGtMOdg1KRM9SbALANCYfEYmcdYPdMXOyGQmF3gAI9u5QV7vNtme7bLdm/0XeAEAAAAAWK+qJmstNqzlVtcgBouTsKrqDwOarQLRSlVBLTkBWTcgOaFyYKs8WcsJ+gEuJm0DAIBVqG3vSFx++eUKBAIqFAoqFAr65Cc/qfe85z2LOtYnPvEJ5fN5SVIgENAVV1zRzKUCAFYCLy9n9DF/Ylf0pIxXINgFAKjPK8gkR/yaxfgZmeSIii/5Nsx2DMr2bJVXrFrk/zcAAAAAgDWttgbR5v26w5oaxPJjiwprTU/dmjWsVZqa5QZrgllLrEAEAABYxdoW7urq6tIznvEM/fCHP5Qk/f3f/70uuOAC3XDDDQs6zuc//3l97nOfkyn+4+3AgQPq7Oxs+noBAG3k5f2JXelJmeiJ6WBXINzulQEAVgJrpfSEnNgZP9CVOOu/0LyQQ4S6ZXu3l6sWFexo0WIBAAAAAFgG1pYDWeUpWqWgVs2ELeMV/HBX7SFqaxDdyMIna7nB8tSs6nBWULaq/pCwFgAAwGza2iXypje9ST/84Q9ljJHnefqzP/sz3Xfffbr55pu1e/fuOfc9ceKEPvaxj+kb3/iGJL/e0RijN73pTcuxdADAcqkKdp0k2AUA8GWTfs1ivFi1mE8tbH835Ae5erfJ6x2Swj2tWScAAAAAAM1gbTGUVagKbRmvUFWDOD1xqzDzEMapmpxl3bAU7CyGrkqPu7MGrWadrFUKa9VO1SKsBQAA0BRtDXddfvnl+vVf/3V96UtfkjFG1lp961vf0re+9S3t27dPl112mfbs2aOenh4ZYxSPx3Xs2DH97Gc/08GDByVNh7qMMXrJS16iyy+/vJ3fEgCgmbxCMdhVqmLMyevfTbALANajQlYmftYPdMVOy2QmF7a/ceV1b5bt3S7bs122c1AyTmvWCgAAAABAIxqpQixN3LKFGVWIVqoIUrmybkByIrLGldxAVUWiH7Sa+XNwbdWhLU/aogYRAABgpWhruEuS3ve+9+ns2bP6j//4j3K1orVWBw8e1IMPPjjrfpWhLmutrrrqKv3pn/7pci0bANBqXkHOaDHYNXmCYBcArDfWk0mOyMSG/brF5IiKL1s3yMh2Dhanc22X7d7svwgNAAAAAECr1E7Xqp2k5eVrAlv1qhDdqlCWDYb8x0p1iLWhrdr9jVM9XasU1iqGs2zFc3KChLUAAABWgba/uxEIBHTHHXfoL//yL3XnnXeWQ1sltuZTCKXnSqEuY4xe/epX681vfrMCgbZ/OwCAZvAKckYfk0lN+sGuQo4qRgBYDzJxObFhmdhpObHTkpdb0O421C3bu71ct6hApEULBQAAAACsG9abpQqxwelaxlSEsgKybkgKBqoDW04jVYgV07RqqxHd0HRgiynVAAAAa86KSEMFAgG9/e1v14tf/GLdeeed+sY3vqFsNlt321LYKxQK6QUveIFe9apX6aKLLlrO5QIAWsl6csYOyaSiMpMn/WBX307eoAeAtahctThcrFqMLWx/N1QOcnm926Vwb2vWCQAAAABYW6rqEPM1ga3aEFdh5u6OI5npukMbrKgwrAhy+c/Xma7luMWwVkBygjOrEKuma62It/IAAADQRsbWjsZaAbLZrB588EH913/9l8bHxzU5OSlrrfr7+zU4OKhLLrlE+/btUygUavdS17WJiYl2LwEVjDHq7++XJEWj0RlT74BVwXpyRg/JTI0Xg11ZP9gV7Gj3ytYXI3V3d0uSEonEwlrQAKBS7fXEszJTY8WqxWGZxHkt6CJjXHndm/2axZ7tsp2DfCIZWA/4twmAZuF6AqAZuJasXDXTtarCWfPUIVppuubQCVRXI1bVIBa3qflZdMZ0rZpwli0+Xg508bMsxPs6AJqDawmwMg0MDDT1eCsy7h8KhbR//37t37+/3UsBACwX68kZPSyTmpCZPCWTz/hVjAS7AGBVs5mkFD0p99xhOZOnpUJmYft3DMrr3S7bOyTbvZlPLAMAAADAelJTfSgvL2Mrw1qVga1G6xArJmqVw1p16hCNU11/WApnOTXhLaZrAQAAoMX41yYAoP2slTN2WCY1Xgx2peX1M7ELAFYlLy+TOCczOSwnPiybT0qSnFyuod1tICLbO1QMdG2Xgp2tXC0AAAAAYLnVC2zVnbg1W2ArILluMbAVkUIBWVMZ1qqYsFWjXJ04Y7pWdTXibPsDAAAA7UC4CwDQXqVg11Qx2JWbKlYx8mY+AKwK1krpqF+zGDstJ37Wf0G+JBice3/jyHZvmZ7O1TE449PSAAAAAIAVrqb6sN6ErYYCW07A/9CPE6ipRayYtlWjMqRlneqJWtQhAgAAYC1oebhrYmJCN9xwgzIZv34lEonoU5/6lHbu3Lmk4548eVK///u/Xz5uZ2enPv/5z5e77gEAq4C1MuNH/GBXbHg62BXqavfKAABzyWdkYsNyYqfL1++FsOE+2d7tfqCrZ5v/QjsAAAAAYGWxXk39YUVAq6FKxIrAlhuRDTUW2LJSRSAr6Ae93OmQVu19PiAEAACAta7l4a5PfepTevzxxyVJxhj9+Z//+ZKDXZK0c+dOvfa1r9W73/3u8rH/+q//WjfffPOSjw0AWAbWykwclZMc84MB2aS8vh0EuwBgJbKezNSYX7UYOyWTHFXx5fbGuCF5XduLdYtDUpgPZAAAAABAW5QDW5UViPkZk7f8wJZXvasxfjirGMyqrkQsBbbcupWG1YGtwPRErapKxACBLQAAAKCOloa7otGoPv/5z8sU/xH+ghe8QL/+67/etOO/7GUv07//+7/rm9/8pqy1+ru/+zu9/vWvZ3oXAKwCJnpMTmJEip+WySbk9e6QQly/AWDFyKX8msXYKTmTw1Ihs4CdjWzXJnl92xXa+kSpZ7MKyakF5cEAAAAAAA2yVpqtAnHGY4XqXaViGMsPZFk3JAUD/nQsx616Ts7Mt5RsvQpEJmwBAAAATdXScNfXv/51ZbNZ/0SBgG666aamn+PNb36zvvOd76hQKCidTusb3/iGXv7ylzf9PACA5jETpWDXGTmZuLze7UxxAYB2s55MclSmGOYyU2NaSBrLBrtk+/zJXLZnmxQIS0YyfPACAAAAABanZprW9JSt2glbhZm1iI5TrD0MyLoByQ0Xw1YVdYil2zWhK1sKdLmliVrBWQNcBLYAAACA1mtpuOvf/u3fJPmViS960Yu0a9eupp9j165deuELX6gvf/nLMsboX//1X9d1uCuZTOonP/mJzp07p2g0qsHBQQ0NDWn//v0KhULtXh4AyERPyEmcLwa7YvJ6tkvh3nYvCwDWp1xKTmxYZvKUnNiwVMg2vq9x5fVsKwa6tkvhPl7UBwAAAID5WDtPYKuiKrE2sGWMH8Zy/SlaNhiZDmLVTtgyTs2+zswaxKrboXJlYu2+AAAAANqrZeGuXC6ngwcPlu8///nPb9Wp9IIXvEBf/vKXZa3VAw88oEKhINd1599xkZLJpB566CEdPHhQBw8e1IMPPqjh4eHy80NDQ7rnnntadv56zp8/r7/8y7/Ut771LU1NTc14vq+vTy9+8Yt10003UVsJoG3M5Ck58bNS/KycVFRe7zYpQrALAJaN9WSSIzKTw3Jip4rTuRawe6RPXu8O2b4dst2b61ZyAAAAAMC6NO+UrdL9RmoRO+vUIhbv1+5bEcyaGdiqmLrltO49EwAAAACt1bJ3Yx577DHlcjlJUiQS0VVXXdWqU+mZz3ymIpGI0um0stmsHnvsMV100UVNP8+dd96pL33pSzp8+LA8z2v68RfrBz/4gd761rdqYmJi1m0mJyd111136Xvf+55uv/32lvz3AYC5mNiwnNhpKXFOTmpCXs82KdLf7mUBwNqXm/JrFmOn/OvwQqZzOcGK6Vw7qNAFAAAAsL5Y61ceenmpUAxo2bxsIebfTsSlQm6WKVtOVTjLBjtly/eD89cizlKDWBXacmbuCwAAAGDtaVm46+jRo5L8Ssbdu3e3tBIwHA5rz549euSRR8rnbkV46b777tNjjz3W9OMuxS9+8Qv94R/+oVKpVPmxzZs36znPeY42btyoM2fO6Lvf/a5isZgk6eTJk3r961+vf/7nf9bWrVvbtWwA64yJn5EzOSwlR+RMjcvr2SJ19Ld7WQCwNllPJnHeD3NNDsukxhe2e6RfXt8O2d4h2e4tfLobAAAAwNpjvYoJW7niVK2KyVuFUi1iYUZoyw9n9RQDViHZQEfNlK1icKt2ypYx01O03GJIywlN33ZD/v5ukFpEAAAAAFVaFu4qhYkkaePGja06Td1zRKPRlp+vpLOzUxdffLF+8Ytf1K1DbKV0Oq03velNVcGu17zmNbr55purwnSJRELvfe979fWvf12SNDIyoptuuklf+MIXlnW9ANYnEz8nJ3rSD3YlR/0ar47Bdi8LANaWXErO5Mli3eKw/+ZEo5ygvN5tsr075PUNSSGmcwEAAABYpWqqEU0xuDX9eK4Y4qqpRjRGMsVJWk5ANhgpBrbq/TIy3f7PTTaRkDWuH8yadcpWRWgLAAAAABahZeGueDxevj0wMNCq05T19/fXPXczhcNhPe1pT9O+fft0ySWXaN++fdq7d68cx9E111yz7OGu//t//6/OnDlTvv+yl71M73jHO2Zs193drY985COamJjQj370I0nSz372M33nO9/R8573vGVbL4D1xyRH5ESPS1PjfrCra5Ns54Z2LwsAVj9rZaZGZSZPyZk8JTM1urDdOwbk9e6Q7dsh27WJ6VwAAAAAVra6Aa18dV2il/cnclWYrkYMyroByQ0VKw/rhLYq95OKIa1QdUir+NUMbpTcoLzElKyoRQQAAADQWi0Ld5mKnvdWha0qJRKJuudupo9+9KMtOe5i5HI5/c3f/E35fk9PT91gV4njOPqTP/kTPf/5z5fn+T/g/u///b8JdwFoGTM1Jmf8qJSakJM4J9u5Qbar9ZMcAWDNKmRlYsN+mGvylEw+3fi+TlBe75Bs35C83iEp1NW6dQIAAABAI6z1aw9rqxBrfvmhrepqROs4xfrDgB/aCkb8qVnzViM69Sdr1Zm6pVneZzDGyER6infSM2sbAQAAAKDJWhbu6uqafsNoYmKiVaepe47Kc69V//mf/1n1Pb/whS9UX1/fnPvs3r1bv/RLv6Qf/OAHkqRf/OIXOnnypHbu3NnStQJYh1ITMmNHpHRUTvysbOegX8cIAGictVJ6Us7kST/QlTin4ufHG9u9Y1Be3w7Z3iH/Gmyc1q0VAAAAAEoqQ1uVU7VqA1t1Q1tuOZhl3ZAU6vJrD2unbdX8fFPezw36+5VCWm6oJrTVsrdEAAAAAKBlWvaTzLZt2yRJ1lodOXJEhUJBrtuaupd8Pq/Dhw+X72/durUl51lJ7rnnnqr7v/qrv9rQfs9//vPL4S5Juvvuu/WqV72qmUsDsN6lJuSMHpJJT8rEzsh2DMh2b2n3qgBgdfDyMvGz09O5sguYgFuezrVDXt+QFOxs3ToBAAAArD/zhrYq6hJnhLZKwSxX1g3Lhrpm1iI6gRnTsvwKxWJIqzRRq3S7fD9I1TwAAACANa1l4a4nPvGJ5dvJZFI//elPdeWVV7bkXA888ICSyWTdc69VP/nJT8q3XdfV0572tIb2u/zyy6vu33fffYS7ADRPelLO2GGZTFwmdlqK9BHsAoD5ZJNyJk/KTJ6SEz/jvxHSIBvpk9e3U7Zvh2zXZt7QAAAAALA4lSGtOvWI9SZtWakqmGXdiGyoTmCrXmirIphVnrTlBGUDoenAlhtkAjEAAAAAqIXhrp07d2rDhg0aHx+XJH3pS19qWbjri1/8Yvn24OCgdu3a1ZLzrBSe5+nYsWPl+7t37264ivLCCy9UJBJROp2WJB05cqQVSwSwHmXixYldcZnYsBTple3ZNuPFOwBY96wnkxzxw1yTp2RS443va1x5PVtl+3bK69shhXtat04AAAAAq19pklblZK1CbmZwa856xAVM2qo3Xat8uyK4xetFAAAAANCwlhbMP/e5z9U///M/S5K++tWv6jWveU3Tp2odOnRIX/nKV2SKPwxec801TT3+SjQ8PKxMJlO+v3379ob3NcZo69at5XDYyZMnlcvlFAwGm71MAOtJNiln9LHixK5TUqhbtmc7L9QBQEk+IxPzw1zO5LBUyMy/T5ENdharFnf6oVmXf7cBAAAA6571ilO2cjPrEcuViblZQlulSVshKdQla9zi5KwFTtoitAUAAAAAy6Kl4a7f+I3f0D//8z/LGKNCoaCbb75Zn/vc59TX19eU48diMb35zW+W53mS/ODS//gf/6Mpx17Jzp07V3V/69atC9p/y5Yt5XBXPp/X2NjYgo8BAGW5KTkjj8pkEjKTp6Rgp2wvwS4A65y1flXt5Ek50RMyyREVS0saYGS7NskrBrrUMcA1FQAAAFgvSqEtryB5uToViTmZQt7frnI3x5FMoBi+CkjBDtlSUMutDG05NfsF/HAWoS0AAAAAWLFaGu562tOepquuuko/+tGPZIzRkSNH9IY3vEG33367Nm/evKRjj4yM6MYbb9ShQ4fKU7ue8Yxn6NJLL23G0le0ZDJZdb+zs3NB+9dWONYer1GGH+hXlMrfD35vsGxyKTkjj0rZhJzJk7LBiGzfDslx5t8XK5aRqbptTaOBFGCd8zyZxFk5kydloidlMvHG93VD8vqG/OlcfTukQLh161xGXE8ANAPXEgDNwvUEbWXtdGCr+NWUp2z5j5nSc5WMUwxlBfzQViAs6wT9CVxV07ZqXosxASngh7PqhrZKlYmm/ms4vLo4O16HBdAsXE8ANAPXEmB9aGm4S5Le/e5362Uve5my2awk6ec//7le9KIX6ZZbbtGLXvQiua67oOMVCgV97Wtf02233aZYLCZjjKy1CofDes973tOKb2HFmZqaqrofDi/szb/a7WuP16j+/v5F7YfWa9Z0PGAuNpeWPXNYNmil2KjU0y8N7pZxFnZdx8rW1d01/0bAOmZzaWnihOzEcWnipFTITj85X+1156DMwG5pcJfUs0VmljdV1gquJwCagWsJgGbheoJmsl6hqiJRhVzF7Xx5Alf1NF/jh7KCAcnt9qdrlaoPy9O2gjK1r58bV3JDMoFiQCsQLge3Km/z+szy4HVYAM3C9QRAM3AtAdauloe7nvCEJ+g973mP3vve95aTopOTk7rlllv0F3/xF3rZy16mZz7zmbrkkkvU09NT9xiJREIPPvigfvzjH+tLX/qSRkdHZa0tH88Yo3e96116whOe0OpvZ0XIZDJV94PzvXFYIxQKzXk8AJiPzWdkzz4km4lL48elQIBgF4B1w6ai0vhx2fFjUvys/wn8Rjiu1LdDZnCXNLBLJlz/374AAAAAVgZrbQOhrdyMikQ/gBWsCF91VYS2gtPBrarBCkYKhGXKQa3QjMCW3JCM2/KX9AEAAAAAK8yy/CT48pe/XBMTE/roRz9aDmRZazU6Oqo77rhDd9xxh4wx2rBhg3p7e8shr0QioVgsVg5zlfaTVHWcP/qjP9Jv/uZvLse3siLUTt7K5XIL2r80RW224zUqGo0uaj+0hjGmnMaenJws/10Bmq6Qk3P+YSkTkzNxXNYY2f7d0lSq3StDkxiZ8qfYk4mkrLieYJ2znkzivEz0pF+5mJ5sfNdQl2zfTnn9O2V7tvpv5khSTlIu0Zr1riBcTwA0A9cSAM3C9QRVStO2iqEtU7pddb+gqmlbxvGDW07Qr0h0ApLTLRsITNcjukGptg7H8YNelfWIMyoSnTr7SVK++EvZ4i+0G6/DAmgWricAmoFrCbAyNbsJb9k+5vOGN7xBe/fu1S233FKuU5RUFdoaGRnRyMjIjOcqVT7X3d2t2267Tdddd90yfRcrQ2dnZ9X9hU7eqt2+9niN4n8MK5e1lt8ftIaXl3P+ESkTk5k4LivJ9u3yX7zkj9yaYc30b6aV5fcW61MhKzM5LGfSD3RV1S3Ow3ZulNe3U7Z/p2zHYPUbNOvs7xPXEwDNwLUEQLNwPVknStO2yr9yMoXp2/Ly/v2aaVvWccvTtawbkQ1VBLZKoS2n+uV0a5xyWKsyuGWL4a3y9K5GK9h5PW/V4XVYAM3C9QRAM3AtAdauZZ3hfO211+qLX/yiPvzhD+s73/mOPM8rh7XqqfectVaO4+i6667TW9/6Vu3atauVS16Rurq6qu4nk8kF7V+7fe3xAKAuryBn5FGZTEwmelJGkte/y3+REgDWgkxMTvSkzORJOfGzavjdPuPK693uT+jq2yGF+LcVAAAA0BLWm65F9PL+dK3y/ZxMaRpXxRta1hjJBCQ34E/KCkRknaBfm14KbDmBqgCWlSombAXrBLeKzzlUJAIAAAAAWm/Zf/rcuXOnPv7xj+v48eP6zGc+ox/84Ac6efJkQ/vu2LFDz372s/V7v/d72rNnT2sXuoJt2bKl6v65c+cWtP/Zs2fLtwOBgDZs2NCUdQFYw6wnZ+yQTDomM3lSxsvL69/tv5AJAKuV9WSSIxV1i9HGdw12yvbt8Cd09W7nTR0AAABgqcqViKWg1nRFYjnI5RWqdvGnbQUWMW0rJAVCfsirVJEYCBXrE0P1qxUBAAAAAGiTtr0LtXv3bt16662S/HDS/fffr/PnzysajSoajUqS+vr6NDAwoM2bN+vyyy/X1q1b27XcFWVoaEiRSETpdFqSNDw83PC+1tqqcNeuXbsUDDJ1B8AcrJUzelhmasIPdhVy/sSuQLjdKwOAhSvkZGKn5UyekJk8JZNPN7yr7RiU179Ttm+XbOcG3uwBAAAAGlFVk5iTCnmZYj2iCqUJXLnqaVtSMZwVkJyg/+GKyrCWE/Sfq6k7tFXTtiqCW5U1iXwwAwAAAACwyqyIn2S3bNmiX/u1X2v3MlYNx3G0Z88ePfLII5KkEydOKJlMNlSveOTIEWUymfL9Cy+8sGXrBLAGWCszfkQmNS4TG5bJZ4rBrki7VwYAjcul/Mlc0RNyYqclW5h/H8mvW+zZJtu/U17fTuoWAQAAgFrWmzldq5GaxGJAy7oBKdhRE9wq1SSa6n3KQa1ZQltuiA9gAAAAAADWpBUR7sLC7d+/vxzuKhQKOnjwoK666qp59/vZz35Wdf/KK69syfoArAHWykwck5Mc84NduSk/3BDsaPfKAGB+6Uk50RNyoidkkiMqfvZ/XjYQke3bOV236DLhFAAAAOuU9aqDWoV607dqaxIdf6rWjJrEoOS49WsSHbduWKsyxMW/ywEAAAAA6xnhrlXq2muv1ec+97ny/W9+85sNhbu++c1vzjgOANRjoifkJEek+GmZbFJe7w6m1gBYuayVSY7ITBYDXenJxneN9Mvr3yXbt1O2axOf9gcAAMDaV5qoNaMmsWIC14zgVrEm0QRkgxFZp7u6JtEJ+AGuyn2qahJDNdO3wsX9q/cBAAAAAADVCHetUgcOHNDg4KDGx8clSf/2b/+mt7zlLert7Z11n+PHj+s//uM/yvcvvvhi7dy5s+VrBbD6mMlTchLnpPgZOZm4vJ7tUri73csCgGpeXiZ+Vk70uEz0pEw+1eCORl7PVtn+XfL6dnF9AwAAwNriFSdsFUrViPmZE7isV97cSn4wyy1WJQY7/fBVqSrRlEJdTvU+bkgKhKpCW36IK0xNIgAAAAAATUS4a4U4depU1RStoaEh3XPPPbNuHwwG9brXvU4f+tCHJEnxeFwf+MAH9P73v7/u9p7n6dZbb5XnTb9w88Y3vrFJqwewlpj4GTmx01LivJxUVF7PNikye3AUAJZVPuMHUKMn5MSG/UkDjXCC8nqHihO6dkiBcGvXCQAAALRCVXArW78q0U5XklcGpMK5VgAAhv5JREFUt6wbkgJhWaciuOUUaxMrQljWmDo1iSHZQEVFohMkuAUAAAAAwDIh3LWK3XDDDbrrrrt05swZSdIXv/hF9fX16eabb1YoFCpvl0gk9N73vlc/+tGPyo9ddtllet7znrfsawawspnEeTnRk1JyRM7UmLzuLVJHf7uXBWC9yyTklOoW42dVfItqXjbQIdu/U17/btmerf6bVgAAAMBKVQ5u5aYnbM0b3AoWg1tBKRBpILjl1J24NV2fWAxvAQAAAACAFYN3uBZgeHhY1113Xd3nCoVC1XZPfepT6273mc98RgcOHGjKeiKRiD75yU/qhhtuUCrl1xD97d/+rf71X/9VV199tTZs2KCzZ8/qnnvuUSwWK++3adMmfexjH2vKGgCsHWZqTM7EMWlqXE5yVLZrk9Q52O5lAViPrJVJjctEi4Gu1Hjju0b65fXvlO3b5V/HmCYAAACAlWBRE7fqBLfKdYl+hWIl67h1Jm4FixO7ivf5wAMAAAAAAKsOP80vgLW2KsQ1l9m2s7axSRONuvjii3X77bfrrW99q6LRqCTp/Pnz+qd/+qe62+/YsUO33367tm7d2tR1AFjlUhMyY0ekdFRO4pxs56Bs18Z2rwrAemI9mfhZP8w1eUImm2xwRyPbtUle/y55/bukSF9LlwkAAADM4BXKIS15OZmK6VvLF9wKS467vN83AAAAAABYFoS71oBnP/vZ+trXvqaPfOQj+ta3vlWe4lWpr69PL37xi3XTTTepu7u7DasEsGKlY3LGDsukYzKxM7Id/bLdW9q9KgDrgZeXiZ2WU5zQpUKmsf2MK693u2z/Lnl9O6VgR2vXCQAAgPXLejODWoVceeqWKeT9bUqbS+VaxLpViW5AMjVVibMFtwLh6apEglsAAAAAAKxbxjZ7lBTaKplM6ic/+YnOnDmjyclJDQ4OamhoSFdccYVCoVBTzzUxMdHU42FpjDHq7++XJEWj0aZPicMalU3KOf9wMdh1Sgr3yPZsp8ZsvTMqB4ETiUTx3QmgSQpZmclTciaOy4kN+2+SNcINy+vfKa9vl2zvdv+NMax8XE8ANAPXEgDNUns98bxiVWJuOqhVEd4yXt6fylU0HdzyJ2tZJzBddViauuXUBLeMIxVDWjMnboUJbgGrEK/DAmgWricAmoFrCbAyDQwMNPV4TO5aY7q6unT11Ve3exkAVoPclJyRR2WyCZnYsBTqItgFoDVyKb9uMXpCTvx01WSDudhQj7z+XbL9u2S7N0vGafFCAQAAsGZY6we1vPz0xC0vJ5sLSYWcnMSkVChU71IObgVkg53F+xV1iU5wZnCrWIvoB7fCNVWJxeAXAAAAAADAEvDqAgCsR7m0H+zKxGUmT0rBiGzvEMEuAM2TSciJHpcTPS6TOK9Gx6zYzg3y+nfJ698lRQa4LgEAAKC+8sQt/6uZMXEr7we8iqzjyLghKRySgh2yHa4f3nKC/i83UPVhAmvMzKrEQNivWqyYwAUAAAAAANBqhLsAYL3JZ+WMPCKTSfjBLjck27eTiTgAlsZaKT05HeiaGmtwRyPbvVle/255/bulcHdLlwkAAIBVwCv44axiYMtUVCeWp3BVBreMKVYj+kEsG+qanrTllgJcrqyRTHe3JCMbysqWpmyVQlylmsRSgAsAAAAAAGAFINwFAOtJISdntFjFGD3hV00Q7AKwWNbKTI3KRI/LmTghk5lsbD/jyOvZLjuwW17fTinY0dp1AgAAYOUo1SVWTtkqB7eKQS5vui7RSsXgVtCfmhWIyDoVVYmlr5WnKE/XCsq64emKxEBEZsMmyQ3Jm5yUtY1NlwUAAAAAAGgnwl0AsF54BTmjj8lkYn6wyxg/2OXwvwIAC2A9mfhZOdETMtETMrlkY/s5QXl9Q/L6d8v27WASAgAAwFo1X11iIVe1uXXc8oQtG+z0qxLdivCWE6yq6raOWzFpq2LqllucuhUIzfoBJmOMTCDc0m8fAAAAAACg2XhHHwDWA+v5wa50TCZ6SsZaeQO7/RfLAWA+Xl4mdlpO9ISc6AmpkGlsPzcsr3+XvP5dsr3bCZMCAACsdtabNbglLydTaLAu0Q0WqxIDkuNWb18Oa4WkYk2iLYW23BD/pgQAAAAAAOsOr4YAwFpnrZzRwzKpqMzkSRkvJ69/N1NzAMytkJWZPCUnelzO5LBfk9MAG+yU7d8tb2C3bPcWal8BAABWC2slWyhXJJqKENf0/UL1Lk5Acv3pWjbUvYC6xOnw1nSIKzhjShcAAAAAAAAIdwHA2matzPjjMqlxmdiwTD4jr3+X/8I5ANTKZ2QmT8qZOCYndtp/c68BNtwnb2CXbP9u2c6NvCEHAACwEs06dSsnFfL+/aqpW045pGXdiGyou2Lill+jWBnkt8apO2mrkbpEAAAAAAAAzI5wFwCsYSZ6XE5yVCZ2WiY3Ja9vpxTsaPeyAKwk+bSc6AmZUqBLdt5dJMl2bpDXv9ufBBjpI9AFAADQbhVTtsphrVmmblmpGNwKFINYkTmnblnJf6w0aasY2LKlqsRAmLpEAAAAAACAFuFVFwBYo8zksJzEeSl+ViabkNc7JIW62r0sACtBLiUnetwPdMXPqrFAl5Ht3jwd6Ap3t3qVAAAAKLFenYrEmvu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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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>saturation_beta[x1]</th>\n",
       "      <td>0.362</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>2403.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_beta[x2]</th>\n",
       "      <td>0.269</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x1]</th>\n",
       "      <td>3.948</td>\n",
       "      <td>0.381</td>\n",
       "      <td>3.228</td>\n",
       "      <td>4.653</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>2316.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x2]</th>\n",
       "      <td>3.137</td>\n",
       "      <td>1.155</td>\n",
       "      <td>1.092</td>\n",
       "      <td>5.325</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1341.0</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x1]</th>\n",
       "      <td>0.402</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2441.0</td>\n",
       "      <td>2597.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x2]</th>\n",
       "      <td>0.187</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>1.00</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    0.403      0.000    0.000   \n",
       "saturation_beta[x2]  0.269  0.081   0.192    0.396      0.003    0.002   \n",
       "saturation_lam[x1]   3.948  0.381   3.228    4.653      0.008    0.005   \n",
       "saturation_lam[x2]   3.137  1.155   1.092    5.325      0.030    0.021   \n",
       "adstock_alpha[x1]    0.402  0.031   0.345    0.461      0.001    0.000   \n",
       "adstock_alpha[x2]    0.187  0.040   0.109    0.262      0.001    0.001   \n",
       "\n",
       "                     ess_bulk  ess_tail  r_hat  \n",
       "saturation_beta[x1]    2108.0    2403.0   1.00  \n",
       "saturation_beta[x2]    1301.0    1057.0   1.01  \n",
       "saturation_lam[x1]     2455.0    2316.0   1.00  \n",
       "saturation_lam[x2]     1341.0    1108.0   1.01  \n",
       "adstock_alpha[x1]      2441.0    2597.0   1.00  \n",
       "adstock_alpha[x2]      1798.0    1972.0   1.00  "
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "id": "iAtdq9uU10xr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total budget for the 8 Weeks: 40M\n"
     ]
    }
   ],
   "source": [
    "from pymc_marketing.mmm.budget_optimizer import optimizer_xarray_builder\n",
    "\n",
    "time_unit_budget = 5  # Imagine is 5K or 5M\n",
    "campaign_period = 8  # Imagine 8 weeks for this case\n",
    "print(\n",
    "    f\"Total budget for the {campaign_period} Weeks: {time_unit_budget * campaign_period}M\"\n",
    ")\n",
    "# Define your channels\n",
    "channels = [\"x1\", \"x2\"]\n",
    "# The initial split per channel\n",
    "budget_per_channel = time_unit_budget / len(channels)\n",
    "# Initial budget per channel as dictionary.\n",
    "initial_budget = optimizer_xarray_builder(\n",
    "    np.array([budget_per_channel, budget_per_channel]),\n",
    "    channel=[\"x1\", \"x2\"],\n",
    ")  # Using this function we can create the iniall allocation strategy for each channel\n",
    "\n",
    "# bounds for each channel\n",
    "min_budget, max_budget = 1, 5\n",
    "budget_bounds = optimizer_xarray_builder(\n",
    "    np.array([[min_budget, max_budget], [min_budget, max_budget]]),\n",
    "    channel=[\"x1\", \"x2\"],\n",
    "    bound=[\"lower\", \"upper\"],\n",
    ")  # Using this function we can create a budget bounds for each channel as well"
   ]
  },
  {
   "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": 9,
   "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": [
    "model_granularity = \"weekly\"\n",
    "allocation_strategy, optimization_result = mmm.optimize_budget(\n",
    "    budget=time_unit_budget,\n",
    "    num_periods=campaign_period,\n",
    "    budget_bounds=None,\n",
    ")\n",
    "\n",
    "response = mmm.sample_response_distribution(\n",
    "    # allocation_strategy can be a dict or an xarray.DataArray\n",
    "    # (e.g. optimizer_xarray_builder(value=[1, 2], channel=[\"x1\", \"x2\"]))\n",
    "    allocation_strategy=allocation_strategy,\n",
    "    time_granularity=model_granularity,\n",
    "    num_periods=campaign_period,\n",
    "    noise_level=0.01,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Budget Allocation Summary ===\n",
      "\n",
      "Channel x1:     2.80M\n",
      "Channel x2:     2.20M\n",
      "\n",
      "------------------------------\n",
      "Total Budget:     5.00M\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n=== Budget Allocation Summary ===\\n\")\n",
    "for channel, budget in response.allocation.to_dataframe().iterrows():\n",
    "    print(f\"Channel {channel:>2}: {budget['allocation']:>8.2f}M\")\n",
    "print(\"\\n\" + \"-\" * 30)\n",
    "print(f\"Total Budget: {sum(response.allocation.to_numpy()):>8.2f}M\")\n",
    "print(\"-\" * 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
    "ax.set_title(\"Response vs spent per channel\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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(\n",
    "    \"Estimated Contribution per channel over time\", fontsize=18, fontweight=\"bold\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "response[\"y\"].mean(dim=\"sample\").plot(ax=ax)\n",
    "ax.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",
    ")\n",
    "ax.set_title(\"Optimal total marketing contribution\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RbzCfU3x2RYs"
   },
   "source": [
    "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_budget.sel(channel=\"x1\").item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "mnlhEUUw2LqS"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
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       "model_id": "370ea54e9b66431995c405c211caef3b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  --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",
       "html[data-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 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\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:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\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: 352kB\n",
       "Dimensions:  (sample: 4000, date: 8)\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 6.751 7.723 7.387 ... 7.947 8.044\n",
       "Attributes:\n",
       "    created_at:                 2025-01-18T22:24:44.478425+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0</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-342cec3c-a375-464c-847f-5b978f032cb8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-342cec3c-a375-464c-847f-5b978f032cb8' 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'>sample</span>: 4000</li><li><span class='xr-has-index'>date</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f9cdc919-8ad7-440f-89ef-9c7d239ac7fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9cdc919-8ad7-440f-89ef-9c7d239ac7fa' 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-3ad910dd-b282-4622-a2ba-acb9c994a2cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ad910dd-b282-4622-a2ba-acb9c994a2cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa212204-52d8-4439-8505-020d94a572f0' class='xr-var-data-in' type='checkbox'><label for='data-fa212204-52d8-4439-8505-020d94a572f0' 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-b6551cc5-3425-4357-88d2-c511ff685951' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6551cc5-3425-4357-88d2-c511ff685951' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0beed2e-19db-4589-b41e-b11f3a973428' class='xr-var-data-in' type='checkbox'><label for='data-e0beed2e-19db-4589-b41e-b11f3a973428' 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-efd0db7b-da73-416a-8653-bfc3405a90f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efd0db7b-da73-416a-8653-bfc3405a90f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cac597c-b378-453d-b917-1ff9f4ce2538' class='xr-var-data-in' type='checkbox'><label for='data-7cac597c-b378-453d-b917-1ff9f4ce2538' 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-2710cd7b-8dab-4df5-9ea5-c4d4ff55d898' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2710cd7b-8dab-4df5-9ea5-c4d4ff55d898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3589d03-df5b-4e89-9faf-5162fcb58b12' class='xr-var-data-in' type='checkbox'><label for='data-c3589d03-df5b-4e89-9faf-5162fcb58b12' 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-55ff4d77-9a7e-4dc9-a2b8-9de9edf2965c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55ff4d77-9a7e-4dc9-a2b8-9de9edf2965c' 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'>6.751 7.723 7.387 ... 7.947 8.044</div><input id='attrs-ca6ef113-afe1-4c1c-ba11-b3497460d55d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca6ef113-afe1-4c1c-ba11-b3497460d55d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-991e810e-7958-45c2-8d1e-2223cc675cde' class='xr-var-data-in' type='checkbox'><label for='data-991e810e-7958-45c2-8d1e-2223cc675cde' 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([[6.75050777, 7.72260106, 7.38729532, ..., 6.53566444, 7.10966499,\n",
       "        6.82363244],\n",
       "       [6.58264749, 8.16491421, 7.17052605, ..., 7.44196054, 7.42607899,\n",
       "        8.29718032],\n",
       "       [7.0812689 , 7.87492195, 7.89185933, ..., 7.87200079, 7.877538  ,\n",
       "        7.47066869],\n",
       "       ...,\n",
       "       [8.0079324 , 8.77202918, 8.09727556, ..., 7.69150142, 8.16580651,\n",
       "        8.15275051],\n",
       "       [7.97736232, 8.75759872, 8.16142253, ..., 7.99060881, 8.31167391,\n",
       "        7.80121112],\n",
       "       [8.61931227, 8.47138451, 8.41561802, ..., 8.08413183, 7.94727701,\n",
       "        8.04369628]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8863d5f-227f-4731-bdef-687a5321c823' class='xr-section-summary-in' type='checkbox'  ><label for='section-e8863d5f-227f-4731-bdef-687a5321c823' 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><input type='checkbox' disabled/><label></label><input id='index-654a2487-e452-4f00-8ee9-39fcdba12502' class='xr-index-data-in' type='checkbox'/><label for='index-654a2487-e452-4f00-8ee9-39fcdba12502' 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><input type='checkbox' disabled/><label></label><input id='index-b5815a6b-f4e1-4fd3-8b5d-346043ff198f' class='xr-index-data-in' type='checkbox'/><label for='index-b5815a6b-f4e1-4fd3-8b5d-346043ff198f' 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-66c0d2cc-bfb3-4302-b16c-814b28b5b358' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66c0d2cc-bfb3-4302-b16c-814b28b5b358' 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>2025-01-18T22:24:44.478425+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 352kB\n",
       "Dimensions:  (sample: 4000, date: 8)\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 6.751 7.723 7.387 ... 7.947 8.044\n",
       "Attributes:\n",
       "    created_at:                 2025-01-18T22:24:44.478425+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0"
      ]
     },
     "execution_count": 15,
     "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.sel(channel=\"x1\").item()\n",
    "initial_budget_scenario[\"x2\"] = initial_budget.sel(channel=\"x2\").item()\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",
    "    initial_budget_scenario, extend_idata=False\n",
    ")\n",
    "\n",
    "response_initial_budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 {allocation_strategy.sum():.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(\n",
    "    \"Comparison of Optimized and Initial Budget Responses\",\n",
    "    fontsize=18,\n",
    "    fontweight=\"bold\",\n",
    ");"
   ]
  },
  {
   "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": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "id": "74q2NZd03SMr",
    "outputId": "485ced2a-34c4-4005-fba2-af180e42d974"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization for budget: 3.00M\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization for budget: 4.00M\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization for budget: 6.00M\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization for budget: 9.00M\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Cjuy0YyVlmaxcWebKn3YNXaEAAAAAADCKyfMBAAAAAGgVTWme/7+frs77/2V5Vq3uv1aWyY47VvK0faob3a2mUilyzNEdfee/uFrYDgAAAAAAg02eDwAAAABAK2lr9IKPPFLP//vn3ke0rt2R5rWv7sxJbxybmTOrmTevlqOPXbTReY56YUe+/JVVKcvkxhu7NzoeAAAAAAA2V1mvJwvuTrn80WTlwiRFMnZKivFTk2l7p6hUm13ikJHnAwAAAACwMSMtR2948/yF/7kiq1b1Hlerycc+OiEvfXFn3/sbe8TrWk/fvy1tbUlPT7JocZm5c2uZOXN4/XABAAAAABh5ylpPytu+m/rN/5Ny7k1J98r1D2wfm2LmM1I55DUpDjguRbXhkfuQkucDAAAAALA+IzlHrzRysVqtzI+u6EpR9Ibqp5w8dkDQvjna2orMmtUfrv/5/tpglQkAAAAAwChV/+PV6fnU4ald9vaUf/5N0r0iSbn+V/eKlH/+TWqXvT09nzo89T9e1cTKB5c8HwAAAACA9RnpOXpDm+d/f2tPli0rU5ZJW1vy5pPGbNV8O+7QX/5DD9e3tjwAAAAAAEax2tXnp3bp3yZLHkxvsJ8kxZrXE617vUyWPJjapa9P7apPNaLUISfPBwAAAADgiVohR2/o3vcPzOndTaYoeh/TOmHC1vXujx/f/4Nevrx8ipEAAAAAALBh9Ru/mvrPPrzmbJ0wv6gm2++VYuqeyZhJvZdXLUn56J+SR+5Jylr/+LKe+s8/lmL81FSedWKDP8HgkucDAAAAALCuVsnRG9o8v3BhfyC+445bv+l9ZZ0penq2ejoAAAAAAEahcvGDqf3onAwI+yfulMpf/EMqB70qxdqw/4n3rVqS+q2Xpf7LC9bsslMkKVP78ftT7H1UiskzGvQJBp88HwAAAACAtVopR9/6xHszrLshf7229fMtXtwf3k+auL7t/gEAAAAA4KnVfvGJpGdV33nxtGPS9vZfpnr4yRsM/JOkGDMp1cNOStvbf5li35en9xG1RdKzuumPnd1a8nwAAAAAANZqpRy9oc3z22zbv9yCR+pbPd8f/9if2E+ZImwHAAAAAGDzlPVaytt/kLXt4sVuz071tV9K0Tlhk+coOsan+povpNjtOekN/suUf7g8ZX3rc/BmkecDAAAAAJC0Xo7e0Ob5ndY82rUskzvv6kl3d7mROzbs/vtrmb+g/we2995tW10fAAAAAACjSzn35mT1kvSG9Un1Lz+corL50XlRqaT6lx/uv7B6acq5vxukKhtPng8AAAAAQNJ6OXpDm+cPPqgtYzqTokhWr05+dEXXFs/11f9e2Xe83XZFZu1WHYwSAQAAAAAYTR7/85qDIpm2T4pp+2zxVMW0fZJpT+u/8NifNzx4mJPnAwAAAACQpOVy9IY2z3d0FDn88PaUZe9uNedfsCJLlmz+dvs33dydb/3v6hRFb3D/4qM6hqBaAAAAAABaXbn8sb7jYrvdt3q+decoVzy+1fM1izwfAAAAAICk9XL0hjbPJ8lbTx2bpDckn7+gntNOX5LHHtv0wP2667vz9/+4NPV6b2BfqSQnv2nsUJULAAAAAEArK2v9x5VB2BF93TnWnXsEkucDAAAAANBqOXpboxc88MD2vOyYjvz4iq4URXL7HbX85XGL8sY3jMkxR3emfT0V1WplbrixJ9/+31X5yZVdKcve60WRnPj6MZkxwyNeAQAAAIDWsnz58txxxx259dZbc+utt+a2227LvHnz+t6fMWNGfv7znw95HSeeeGKuv/76Lbr34osvzhFHHDHIFQ2uYtx2/ScLZ2/9hAsfWP/cI5A8HwAAAAAYrmTojdNqOXrDm+eT5IP/MiH33784d95VS1EkS5eW+dznV+Zzn1+Z9vaBY//quEWZO6+Wnp7e87LsDdnLMjn4oLb80z+Oa/wHAAAAAAAYIhdffHEuu+yy/PGPf0y9vum7fLOFpsxcc1CmfOgPKRfPSzF5xhZNVS5+MOWDt65n7pFLng8AAAAADCcy9CZosRy90vAVk4wZU+SiCyfl8MPa+8LzpDdA7+oaeP7n+2vp7s6A3WnKMjniOe258DMTU60WzfgIAAAAAABD4oYbbsg999wzbEP/arW6ya+iGP75bbHLs5K2MUl6w+faFedt8Vy1n3wwyZowu31Mil0OG5Qam0meDwAAAAAMJzL0xmu1HL0pO88nyTbbVPLF/5yYiy9Zla9csjKPL+z9Qaz9e/DEvw9l2fuaOLHIKSeNzSknjRG0AwAAAACjwrhx47L//vvn9ttvz4oVK5pWx2GHHZZLL720aesPhaKtM8XeL0p5xw+TJOXtl6f2kxmpHv3+zZqn9n//lvIP303Sm1sXex2Voq1jkKttDnk+AAAAADCcydCHVqvl6E1rnk+SougNzl//ujH50Y9X55rfduemm3vyyCP1rPsLIZMmFTn4oLY894j2vOIvOzNxYlM2zAcAAAAAGHKdnZ058MADc8ABB+TpT396DjjggOyxxx6pVCo56qijmhr8t6rqUe9Oz50/XrNlepn6bz6fcvb1qbzoPans8fynvLd+369T/9lHU875XXoD/zIpqqm+8F2NKL1h5PkAAAAAwHAgQ2+OVsrRm9o8v1ZnZ5HjjxuT448bkyQpyzKLl5Tp7k6mTC7S3m5HGgAAAABgdPjUpz7V7BJGnWLaPqk8/x9S/+V/ZG1wX879XWqXvCa1CdNSzHxGiqm7J52TerdZX7Uk5WP39Qb9yxasmaVcc2+RyvPOSLHD05r2eYaSPB8AAAAAaCYZenO0Uo4+LJrnn6goikyZLGAHAAAAAKAxqi8+O+WSh1Le8s2sfWRsUibL5qe864qU671r7dUia78sKA4+IdWX/L+hL3iYkOcDAAAAAIwOrZKje14qAAAAAAAkaXvlf6Ry7IeSts6suwNOvzIZEP/3h/1p60zl2H9L2yvPb1zBAAAAAADQQK2Qozd85/l/fv+yvuN3nTkuUyZvWf/+okX1fPyTK5L07u7/wXMnDEp9AAAAAACMXtXDT05l/79M/bqLU7/lW8niuesZtU7wP3lmKgefkMrhJ6eYsH3D6mwEeT4AAAAAAE800nP0hjfPf/f7q1Os+QWDM942NlMmb9k8K1aUA+YStgMAAAAAMBiKCdun+qKzUn3RWb2PoJ33+2T5oylXLkrKMsW4bZLxU1PMOCjFpJ2aXe6QkecDAAAAALA+IzlHb3jzfJKUZfpC8uE0FwAAAAAAG/bggw/m7LPPzm233ZZHHnkk3d3d2WabbbLDDjvk0EMPzQte8II885nPbHaZg6qYtNOwC/YbSZ4PAAAAALBpRmOGnoy8HL0pzfMAAAAAAIw8c+fOzdy5Ax+/umLFisybNy833XRTLrroohxyyCF53/velwMOOKBJVQIAAAAAQOPJ0EeGEds8X6/3H1cqzasDAAAYXc56b33jg4CmOv/wZlcAMLrdfPPNed3rXpcPfOADOeGEE5pdDsOAPB8AAAAAoJcMvflGbPP8kqVl3/G4sZ7zCgAAAAAwVKZMmZKjjjoqz3/+87PPPvtkhx12yJgxY7JkyZLce++9ueqqq/LNb34zy5YtS5J0d3fn/e9/f6ZMmZKXvOQlTa6eZpPnAwAAAACtTIY+sozY5vlbb+tJkhRFsu22wnYAAAAAgKHwD//wDznooIPS2dn5pPe23XbbHH744Tn88MPz5je/Of/0T/+U66+/PklSr9dz9tln59BDD80222zT6LKfpFw0Nz3nPz/pWd17YczktL3tihTb7Lp18z4+Oz2fPzpZtaT3QvvYtL3j2hQTp21lxa1Dng8AAAAAtKpWydCT0ZOjN/UBqcUWZOS1Wplrf9uVi76wsu/annuO2N8BAAAAAAAY1g477LD1hv5PNHXq1Fx00UXZb7/9+q4tW7YsX/jCF4ayvE1W+/nHkp5VScqkqKT6N5/Z6sA/SYptd031bz67JvAuk+6VqV/1ia2ed7iR5wMAAAAAPFmrZOjJ6MnRhySlPvrlCzdp3BvetCRt1U2ft6urzMJFZWq1gdef99z2zagOAAAAAIChMHbs2HzgAx/Ia17zmr5rP/rRj3LWWWc1saqkXPJQyt9/O0lvB3jl0DeksveLBm3+yt4vSnnoG1K/4b+SlKn/7uupvPBdKSZsP2hrDBV5PgAAAABAYwzXDD0ZXTn6kDTPz3uwnqJIynLDY8oymT+/vsVrrN3lZtq0Sl7+so3/xgYAAAAAAEPv4IMPzp577pk//vGPSZKHHnoo999/f3bbbbem1VS/9TtJuSaPbh+bygveOehrVF5wZuo3f7P3cbb1ntRv+16qzzl10NcZbPJ8AAAAAIDGGY4ZejK6cvTKUE5eFE9+bez9jb3WKstkhx0qOf9TEzN+3BY8LxYAAAAAgCFx8MEHDzh/8MEHm1PIGuUfvrfmqEjlGX+bYuK0QV+jmDgtlWe8LklvF3p523cGfY2hJM8HAAAAAGiM4ZahJ6MrRx+Sned32qmSDcXfDz7U+1sJRZFM276S6iY+5rUoko6OIhMnFtl9VjWHPas9R7+0I52dgnYAAAAAgOFku+22G3C+cOHCJlWSlN2rUj50e995se8xQ7ZWsd/Lk+svTlKmfPC2lD1dKdo6hmy9wSDPBwAAAABorOGUoSejL0cfkub5K3+8zQbfe/rBj/XtOHPpJZMyfadNTNsBAAAAABgRVq5cOeC8s7OzSZUk5UN/SMramkImpNjtOUO2VrHrs5OOCUnXsqTek/KhP6TY+RlDtt5gkOcDAAAAADTWcMrQk9GXo1cautoaZdmMVQEAAAAAaIQ5c+YMON92222bVEmShbPXHBQptt0tRWXoGsCLaluK7Wb1X3j8/iFbq1Hk+QAAAAAAg2tYZejJqMvRh2Tn+afy13/V/9sR48Z5RCsAAAAAQCtZuXJlbrjhhr7ztra27LXXXk2rp1y5qP9kwrShX3CdNcpVizY8bgSQ5wMAAAAADK7hlqEnoy9Hb3jz/L99cEKjlwQAAAAAoEEuueSSLFu2rO/8oIMOysSJE5tX0Or+WjJ2ytCvN2Zy//GqpUO/3hCS5wMAAAAADK5hl6Enoy5HrzR8RQAAAAAAmmLu3LnZZ599+l5HHXXUU46/++67N2v+X//61/nMZz4z4NpJJ520uWUOrnUfL7ty4dCvt+4uOUP4aFsAAAAAAAbXqMzQk1GXo2ueBwAAAABgvd7whjfkLW95S6688sqsXr16g+OWLFmS888/P29961vT3d3dd/2www7LS1/60kaUumFjJvUfL39s6Ndbd43OJu8WBAAAAADAkGmJDD0ZdTl6W8NXBAAAAABgg+bNm5eXvOQl632vVqsNGLfffvutd9xXvvKVHHbYYVtdS1mWufrqq3P11Vdn7Nix2WeffbLnnntmypQpGTNmTJYuXZo//vGPuemmm7Jy5coB986aNSsXXHDBVtewtYrJM9YclSkX3J2yZ3WKts4hWavsXpVyQf9OQ8WUmUOyDgAAAADAaCVDH3yjLUcfFs3ztVqZu+6u5b77almypJ6ly8rU65s3xxmnjxua4gAAAAAAGqgsywEB/1PZ0LiyLAezpCTJypUrc8stt+SWW27Z6Nijjz465513XqZMmTLodWyuYsd1vhzpWZ3yz9ek2OuFQ7JWOfu3Sc+q/rV32HdI1mkmeT4AAAAA0Ewy9ME32nL0pjbP//GPPfnKf63KFf+3Ok/xtIJNImwHAAAAABhcb33rW/OrX/0qt912W1asWPGUYzs6OvL85z8/J554Yp7znOc0qMKNKybtlEyekSx+MElSv/6SVIYo9K9f95X+k8nTU0yePiTrNIM8HwAAAABgoFbI0JPRl6M3rXn+K/+1Mp8+f0VqtWRDv8BRFP3H6xtTFL3X1x0HAAAAADCSzZw5M3fffffGBzZg7tNOOy2nnXZa6vV6Zs+endmzZ+fhhx/O0qVL093dnXHjxmXy5MmZNWtW9ttvv3R0dAxJ3Vursu/LUv/tF5Mk5d3/l/rs61LZ9fBBXaM++/qUd/8kSbFmzZcP6vzNJM8HAAAAAIYLGfrQGE05elOa5y++ZGU+8ane37B4YlD+VAH7E98bgqcmAAAAAADwBJVKJbNmzcqsWbOaXcoWqRx6Yuq//dKaszK1b56e4rTLU0yZOSjzl4vnpfbN09Mb+JdJilQOff2gzN1s8nwAAAAAgKc20jP0ZHTl6A1vnr/n3p58+vwVfcF5WSZH/kV7XvLijrS1FTn7/y1L0husX/zFSVm2rMwjj9Rz8+978vNfdGX58jJFkWy7bZGz3jU+O0yrNPojAAAAAAAwghTT9k6x/1+mvP3yJEWy9OH0fOU1aXvtF1LsuN9WzV3OvzM93zgtWfrQ2tVS7Hdsimn7bHXdzSbPBwAAAAAYHUZTjt7w5vkvfXllarXe40ol+eC543PcK8YkSR58sDZg7LMObe87fvUJybJl9XzmwpX52n+vysKFZT75qRX54kWTsvusasPqBwAAAABg5Kke8y/p+dMvk1VLkhTJ4/el5z9flspfvD2Vw09OMW7bzZqvXLko9d9+KfVfXpDUutK3W86YSam+7F+G4BM0njwfAAAAAGD0GC05ekOb57u7y/z05119u9Sc8DedfUH7ppgwoZKz3z0+e+1RzQfOW54Fj9Rzxj8syf9+c0rGjys2PgEAAAAAAKNSMXl6qq88P7Wvn5KU9SRFUutK/RefSP1Xn0mx78tS2f15KWYcnGy/V4pq+4D7y1p38sgfU867OfX7fpPyzh8lPauz9vGySZkU1VSP/48Uk2c0/gMOMnk+AAAAAMDoMlpy9IY2z99xZ09Wr+49rlSSU940dovmedUrx+T3t/Xksu+szrx59Xz54pX5h78bN4iVAgAAAADQaipPe2ly/KdT+967Bu5y07Mq5W3fTe227/YPbutMxkzuPV69JOle9YTZyjV/rpmj2pHqKz6Wyr5HD/XHaAh5PgAAAADA6DMacvRKIxf78/29j3EtimTnmZXMmPHUj2et18sNvvd3p4/t2/Hm+z9YPWg1AgAAAADQuioH/02qp343mTwj/bvdrAnu1331rEqWze99da988vvr3jdpeqpv/k4qh7y6CZ9oaMjzAQAAAABGp1bP0RvaPL9kSX94PmvWk4P24gnVrH6KDH2HHarZZ+9qyjJ5+OF6br+jZ7DKBAAAAACghVVmHJy2M36ayhGnJx3jM3D3m015pfeejvGpHHF671wzn9HYDzHE5PkAAAAAAKNXK+fobY1cbNWq/rB94oTiSe+PGzfw2tKlZcaOffK4tWbOrOauu3t3v3nggVr236+hHwcAAAAAgBGqGDs51WM+kMoL/in1G7+a8t5fpJxzY+9OOU+lbUyKnZ+ZYq8XpvLMN6QYO7kxBTeYPB8AAAAAYHRr1Ry9oen0uHWC89VdT35//BPC9vkL6pk2bcOb43d29B8/+lh9q+sDAAAAAGB0KcZMSvV5ZyTPOyNlrTvlQ7clSx5OVi5KueLx3jFjt0nGbZNM3DHF9ANSVNubXPXQk+cDAAAAAJC0Xo7e0Ob5bbftD86XLi2f9H5bW5HtpxZ55NHe9+66uycHPH3DJT48vz9gf6pHwgIAAAAAwMYU1fYUw+Sxsc0mzwcAAAAA4IlaIUff8DYwQ2DWbtW+4/tn19Y7Zs89+8P131zTvcG5Fi2u57Y/9KRYs7nNlCkbfhwsAAAAAACw6eT5AAAAAAC0ooY2z++xRzXt7UlZJg8/XM+SJU9+NOvhh/Vu01+WyVVXd+Wuu3vWO9en/mNFurp6xyXJ0/Zp6Cb6AAAAAADQsuT5AAAAAAC0ooY2z3d0FDnwgP5Q/Jprn7wTzcuO7kilkhRF0tOTvOX0Jfne5auzaFE9PT1l7r23J+9579Jc9p3VfbvUTNu+kv32rT5pLgAAAAAAYPPJ8wEAAAAAaEUN397l+c/ryO9u6t195qc/78oxR3cOeH/GjGr++q86853v9Ybpjy8s88/vX/akedbuUFMUySknjUml4jGvAAAAAAAwWOT5AAAAAAC0mobuPJ8kx7y0I0lvWP6zn3flkUef/KjXM985LrNmVVOWvWF6WQ58JenbpeYvnt+e1//t2EaVDwAAAAAAo4I8HwAAAACAVtPwnednzqzmN7/cJuWajH38+CfvMDNlciVf/sKkvP9fluVXv37yo2DLMqlWk795VWfOfvf4oS4ZAAAAAABGHXk+AAAAAACtpuHN80kyedLGN7zffmolF35mUm77Q09+flVXHphdy5KlZSZPKrLPPm156Ys7suuu1QZUCwAAAAAAo5M8HwAAAACAVtKU5vnNccDT23LA04d9mQAAAAAAMKrJ8wEAAAAAGO42vmUMAAAAAAAAAAAAAACMcJrnAQAAAAAAAAAAAABoeZrnAQAAAAAAAAAAAABoeW2NXrAsyzz0cL3vfOrUSjrai82ao6urzKOP9c8xfafqoNUHAAAAAADI8wEAAAAAaD0Nb57/vyu78q73LEuSjB9f5KdXTNmisP34v1mcFSvKJMkFn56YFxzZMei1AgAAAADAaCXPBwAAAACg1VQaveD3vr86ZW9Gnr9+RWcmTNj8EiZMqOS4V3SmLJOyTL7zvdWDXCUAAAAAAIxu8nwAAAAAAFpNQ5vnu7rLXH9Dd9/5y4/Z8t1lXv6y/nt/e113arVyq2oDAAAAAAB6yfMBAAAAAGhFDW2ev+eeWlat2VRm3LgiBx3YvsVzHXhAW8aP73087IoVZe65pzYYJQIAAAAAwKgnzwcAAAAAoBU1tHn+vvt6A/GiSPbZu7pVcxVFkb336p/jvvuF7QAAAAAAMBjk+QAAAAAAtKKGNs8/vrDed7zddlu/9NSp/XM8/nj9KUYCAAAAAACbSp4PAAAAAEAramvkYl1dZd9xx5Y/4XW9c6xcWW54IAAAAAAAo1L3+6c3Z+GiSPu585qz9iCQ5wMAAAAAjA6jLUdvaPP8pEn9O8ssXLT14fiixf1zjBtbbPV8AAAAAAC0miY1ao/w/nB5PgAAAADAaDG6cvStf9bqZpgyuTcQL8vk3nt7tnq+e++t9c+9TUM/CgAAAAAAI0Yjm7VbozFcng8AAAAAMJqMnhy9oTvP77VXte/40cfK3HFnT/bbd8tKuOPOnix4pN53vvus6lOMBgAAAABgNCp2fXZStEZDeyPJ8wEAAAAARofRlqM3tHl+j93bst12RR5/vHef/c9euCKfPX/SFs31uc+v6DuePLnY4tAeAAAAAIDW1fbm7zS7hBFJng8AAAAAMDqMthy94c9GfemLO1OWvY96/eWvunPxJSs3e46v/NfKXHV1d4qi9xcdXvKijiGoFAAAAAAARi95PgAAAAAArabhzfNvPmVM2tt7Q/KyTD756RX51w8ty/IV5UbvXbGizIc+sjyf+NSKvvvb2pLT3jy2AZUDAAAAAMDoIc8HAAAAAKDVNPzZqDvuUM1bTxubz3xuZV9g/j/fWp0f/KgrL39ZRw47tD177lHNpEmVFEWyeHE9f7qvlutv7M6PftSVZcvLlGty+aJI3nLq2EyfXm30xwAAAAAAgJYmzwcAAAAAoNU0vHk+SU5/y7jcdXctP/1ZV1/gvmxZmW99e3W+9e3VG7xv3ZC9LJOjX9KRt711XIOqBgAAAACA0UWeDwAAAABAK6k0a+FP/PuEvP51Y1KWveF5UfReL8v1v5KBY05645j8+0cmNKd4AAAAAABGlXLl4pSL56VcNLfZpTScPB8AAAAAgI0ZKTl6U3aeT5Jqtch73zM+f/H89lz4nytzy+97Bry/brC+7p+HPrMtb3vruBx+WHsDqwUAAAAAYDSp3/Hj1O/+Scr7r0sWzUnKeu8bRZH2c+c9aXy58IGUi3uvF+3jUsw4qJHlDil5PgAAAAAATzRSc/SmNc+v9dwjOvLcIzpyz709+e113bnl9z155JF6Fi3uTde3mVJk+6mVHHJwW5797PbsuUfTSwYAAAAAoEXV7/1Faj/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      "text/plain": [
       "<Figure size 1500x1000 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 1511
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scenarios = np.array([0.6, 0.8, 1.2, 1.8])\n",
    "\n",
    "fig, axes = plt.subplots(\n",
    "    nrows=2, ncols=2, figsize=(15, 10), sharex=True, sharey=True, layout=\"constrained\"\n",
    ")\n",
    "for i, scenario in enumerate(scenarios):\n",
    "    tmp_budget = time_unit_budget * scenario\n",
    "    print(f\"Optimization for budget: {tmp_budget:.2f}M\")\n",
    "    tmp_allocation_strategy, tmp_optimization_result = mmm.optimize_budget(\n",
    "        budget=tmp_budget,\n",
    "        num_periods=campaign_period,\n",
    "        budget_bounds=budget_bounds,\n",
    "    )\n",
    "\n",
    "    tmp_response = mmm.sample_response_distribution(\n",
    "        allocation_strategy=tmp_allocation_strategy,\n",
    "        time_granularity=model_granularity,\n",
    "        num_periods=campaign_period,\n",
    "        noise_level=0.01,\n",
    "    )\n",
    "\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    _, ax = mmm.plot_budget_allocation(samples=tmp_response, ax=axes[row, col])\n",
    "    ax.set_title(f\"Budget Allocation for Scenario {tmp_budget:.0f}M\")\n",
    "\n",
    "fig.suptitle(\n",
    "    \"Budget Allocation for Different scenarios\", fontsize=18, fontweight=\"bold\"\n",
    ");"
   ]
  },
  {
   "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": [
    "new_budget_bounds = optimizer_xarray_builder(\n",
    "    np.array([[0, 4], [0, 3]]),\n",
    "    channel=[\"x1\", \"x2\"],\n",
    "    bound=[\"lower\", \"upper\"],\n",
    ")\n",
    "\n",
    "constrained_allocation_strategy, constrained_optimization_result = mmm.optimize_budget(\n",
    "    budget=time_unit_budget,\n",
    "    num_periods=campaign_period,\n",
    "    budget_bounds=new_budget_bounds,\n",
    ")\n",
    "\n",
    "constrained_response = mmm.sample_response_distribution(\n",
    "    allocation_strategy=constrained_allocation_strategy,\n",
    "    time_granularity=model_granularity,\n",
    "    num_periods=campaign_period,\n",
    "    noise_level=0.01,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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": [
    "fig, ax = mmm.plot_budget_allocation(samples=constrained_response, figsize=(12, 8))\n",
    "ax.set_title(\"Response vs spent per channel\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "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": [
    "constrained_bounds = optimizer_xarray_builder(\n",
    "    np.array([[0, 1.2], [0, 1.5]]),\n",
    "    channel=[\"x1\", \"x2\"],\n",
    "    bound=[\"lower\", \"upper\"],\n",
    ")\n",
    "\n",
    "limit_constrained_allocation_strategy, limit_constrained_optimization_result = (\n",
    "    mmm.optimize_budget(\n",
    "        budget=2,\n",
    "        num_periods=campaign_period,\n",
    "        budget_bounds=constrained_bounds,\n",
    "    )\n",
    ")\n",
    "\n",
    "limit_constrained_response = mmm.sample_response_distribution(\n",
    "    allocation_strategy=limit_constrained_allocation_strategy,\n",
    "    time_granularity=model_granularity,\n",
    "    num_periods=campaign_period,\n",
    "    noise_level=0.01,\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",
    "ax.set_title(\"Response vs spent per channel\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "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 {limit_constrained_allocation_strategy.sum():.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(\n",
    "    \"Comparison of Optimized and Initial Budget Responses\",\n",
    "    fontsize=18,\n",
    "    fontweight=\"bold\",\n",
    ");"
   ]
  },
  {
   "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": [
    "(\n",
    "    limit_constrained_allocation_strategy_with_noise,\n",
    "    limit_constrained_optimization_result_with_noise,\n",
    ") = mmm.optimize_budget(\n",
    "    budget=2,\n",
    "    num_periods=campaign_period,\n",
    "    budget_bounds=constrained_bounds,\n",
    ")\n",
    "\n",
    "limit_constrained_response_with_noise = mmm.sample_response_distribution(\n",
    "    allocation_strategy=limit_constrained_allocation_strategy_with_noise,\n",
    "    time_granularity=model_granularity,\n",
    "    num_periods=campaign_period,\n",
    "    noise_level=0.1,\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",
    "ax.set_title(\"Response vs spent per channel\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "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 {limit_constrained_allocation_strategy_with_noise.sum():.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(\n",
    "    \"Comparison of Optimized and Initial Budget Responses\",\n",
    "    fontsize=18,\n",
    "    fontweight=\"bold\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "We can explore other methods to optimize our resource differently. What happens if we don't have an specific budget? What happens if we want to know how much money we need to spend to reach a certain sales?\n",
    "\n",
    "## Creating custom constraints (adjusting towards a target response)\n",
    "Another way to approach optimization is to adjust towards a target response. This can be useful if you want to ensure that the response is above a certain level. Instead of optimize a given budget, we can optimize find the right budget to reach a target response.\n",
    "\n",
    "The following example shows how to create a custom constraint to minimize the budget to reach a target response. In short words, we are asking to the optimizer, what't the minimum budget to reach a certain response?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal allocation: <xarray.DataArray (channel: 2)> Size: 16B\n",
      "array([1.05215684, 0.92872675])\n",
      "Coordinates:\n",
      "  * channel  (channel) <U2 16B 'x1' 'x2'\n",
      "Solver result:  message: Optimization terminated successfully\n",
      " success: True\n",
      "  status: 0\n",
      "     fun: 1.980883586505455\n",
      "       x: [ 1.052e+00  9.287e-01]\n",
      "     nit: 20\n",
      "     jac: [ 1.000e+00  1.000e+00]\n",
      "    nfev: 33\n",
      "    njev: 20\n"
     ]
    }
   ],
   "source": [
    "import pytensor\n",
    "import pytensor.tensor as pt\n",
    "\n",
    "from pymc_marketing.mmm.budget_optimizer import BudgetOptimizer\n",
    "from pymc_marketing.mmm.constraints import Constraint\n",
    "from pymc_marketing.mmm.utility import _check_samples_dimensionality\n",
    "\n",
    "target_response = 5\n",
    "\n",
    "\n",
    "def mean_response_eq_constraint_fun(budgets_sym, total_budget_sym, optimizer):\n",
    "    \"\"\"Enforces mean_response(budgets_sym) = target_response, i.e. returns (mean_resp - target_response).\"\"\"\n",
    "    resp_dist = optimizer.extract_response_distribution(\"total_contributions\")\n",
    "    mean_resp = pt.mean(_check_samples_dimensionality(resp_dist))\n",
    "    return mean_resp - target_response\n",
    "\n",
    "\n",
    "def minimize_budget_utility(samples, budgets):\n",
    "    return -pt.sum(budgets)\n",
    "\n",
    "\n",
    "optimizer = BudgetOptimizer(\n",
    "    num_periods=campaign_period,\n",
    "    model=mmm,\n",
    "    response_variable=\"total_contributions\",\n",
    "    utility_function=minimize_budget_utility,\n",
    "    default_constraints=False,\n",
    "    custom_constraints=[\n",
    "        Constraint(\n",
    "            key=\"target_response_constraint\",\n",
    "            constraint_fun=mean_response_eq_constraint_fun,\n",
    "            constraint_type=\"eq\",\n",
    "        )\n",
    "    ],\n",
    ")\n",
    "\n",
    "allocation, res = optimizer.allocate_budget(\n",
    "    total_budget=10,\n",
    "    budget_bounds=None,\n",
    ")\n",
    "\n",
    "print(\"Optimal allocation:\", allocation)\n",
    "print(\"Solver result:\", res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great the optimization, reveals that with a budget of 1.9M we can reach a response of 5M response. We can also check the response distribution to see how the response would look like. As in other occasions, we can plot and get the full posterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean response for [1.05215684 0.92872675] = 4.999999999999525\n"
     ]
    }
   ],
   "source": [
    "resp_dist_sym = optimizer.extract_response_distribution(\"total_contributions\")\n",
    "resp_mean_sym = pt.mean(_check_samples_dimensionality(resp_dist_sym))\n",
    "test_fn = pytensor.function([optimizer._budgets_flat], resp_mean_sym)\n",
    "\n",
    "# Try different budgets\n",
    "test_val_1 = test_fn(res.x)\n",
    "print(f\"Mean response for {res.x} = {test_val_1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "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": [
    "target_constrained_response = mmm.sample_response_distribution(\n",
    "    allocation_strategy=allocation,\n",
    "    time_granularity=model_granularity,\n",
    "    num_periods=campaign_period,\n",
    "    noise_level=0.01,\n",
    ")\n",
    "\n",
    "fig, ax = mmm.plot_budget_allocation(\n",
    "    samples=limit_constrained_response, figsize=(12, 8)\n",
    ")\n",
    "ax.set_title(\"Response vs spent per channel\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h6yOpHXE4OLE"
   },
   "source": [
    "## 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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sun Jan 19 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.16\n",
      "IPython version      : 8.31.0\n",
      "\n",
      "pytensor: 2.26.4\n",
      "\n",
      "pytensor      : 2.26.4\n",
      "matplotlib    : 3.10.0\n",
      "arviz         : 0.20.0\n",
      "pymc_marketing: 0.10.0\n",
      "pandas        : 2.2.3\n",
      "numpy         : 1.26.4\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
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  "kernelspec": {
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