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    "(mmm_budget_allocation_example)=\n",
    "# Budget Allocation with PyMC-Marketing\n",
    "\n",
    "The purpose of this notebook is to explore the recently included function in the PyMC-Marketing library that focuses on budget allocation. This function's underpinnings are based on the methodologies inspired by Bolt work in his article, [\"Budgeting with Bayesian Models\"](https://bolt.eu/en/blog/budgeting-with-bayesian-models-pymc-marketing/).\n",
    "\n",
    "## Prerequisite Knowledge\n",
    "The notebook assumes the reader has knowledge of the essential functionalities of PyMC-Marketing. If one is unfamiliar, the [\"MMM Example Notebook\"](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html) serves as an excellent starting point, offering a comprehensive introduction to media mix models in this context.\n",
    "\n",
    "## Context\n",
    "The emphasis of this notebook is on enhancing marketing budgets. Contrarily to broader issues addressed in prior notebooks, our primary aim here is to unravel specialized knowledge on budget allocation tactics using the functionality.\n",
    "\n",
    "## Objectives\n",
    "To elucidate more efficient ways of resource allocation across diverse media channels.\n",
    "To deliver data-driven, actionable insights for budgeting decisions.\n",
    "\n",
    "## Introducing the budget allocator\n",
    "This notebook instigates an examination of the function within the PyMC-Marketing library, which addresses these challenges using Bayesian models. The function intends to provide:\n",
    "\n",
    "1. Quantitative measures of the effectiveness of different media channels.\n",
    "2. Probabilistic ROI estimates under a range of budget scenarios.\n",
    "\n",
    "## What to Anticipate\n",
    "Upon completing this notebook, readers should get a comprehensive understanding of the budget allocation function. They will then be equipped to incorporate this analytic tool into their marketing analytics routines for data-driven decision-making.\n",
    "\n",
    "## Installing PyMC-Marketing\n",
    "Before delving into the specifics of budget allocation, the initial step is to install the PyMC-Marketing library and ascertain its version. This step will confirm support for the budget allocation function. The following pip command can be run on your Jupyter Notebook:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vWMGdRlmyjcI"
   },
   "source": [
    "## Basic Setup\n",
    "Like previous notebooks revolving around PyMC-Marketing, this relies on a specific library set. Here are the requisite imports necessary for executing the provided code snippets subsequently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "28_D6j7jjQTy"
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   "source": [
    "import warnings\n",
    "\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from pymc_marketing.mmm.delayed_saturated_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 DelayedSaturatedMMM class using the load method below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "6eM1cTtMys1O"
   },
   "outputs": [],
   "source": [
    "mmm = MMM.load(\"model.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X3NUoJ1bzBvK"
   },
   "source": [
    "For more details on the `save()` and `load()` methods, consult the [pymc-marketing documentation](https://www.pymc-marketing.io/en/stable/guide/benefits/model_deployment.html) on Model Deployment.\n",
    "\n",
    "## Problem Statement\n",
    "Before jumping into the data, let’s first define the business problem we are trying to solve.In a progressively competitive scenario, marketers are tasked with distributing a predetermined marketing budget across various channels for optimizing Return on Investment (ROI). Consider a forthcoming quarter wherein a marketing team must decide the division of its operations between two advertising channels, represented as x1 and x2. These could effectively symbolize any medium, such as TV, digital advertising, print, etc.\n",
    "\n",
    "The task lies in making decisions that invoke data, comply with factual evidence, and align with business logic. For instance, how can one incorporate prior information like budget restrictions, platform trends, constraints, or even distinctive features of each channel into the decision-making process?\n",
    "\n",
    "## Introducing Budget Allocation Function\n",
    "The updated budget allocation function in PyMC-Marketing aims to tackle this issue by offering a Bayesian framework for optimal allocation. This enables marketers to:\n",
    "\n",
    "* Integrate the outcomes of Media Mix Modeling (MMM), quantifying each channel's effectiveness in metrics like ROI, incremental sales, etc.\n",
    "* Merge this empirical data with prior business knowledge and logic for making holistic and robust decisions.\n",
    "\n",
    "By utilizing this function, marketers can guarantee that the budget spread not only obeys the mathematical rigor furnished by the MMM outcomes but also incorporates business-specific factors, thereby achieving a balanced and optimized budget plan.\n",
    "\n",
    "## Getting started\n",
    "Media Mix Modeling (MMM) acts as a dependable method to estimate the contribution of each channel (e.g., x1, x2) to a target variable like sales or any variable. The function `plot_direct_contribution_curves()` allows for visualization of this direct channel impact. However, it is crucial to remember that this only unveils the \"observable space\" for values of X (spend) and Y (contribution)."
   ]
  },
  {
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    "outputId": "af09b2a1-1c30-429e-ab1f-a4af0c163434"
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   "outputs": [
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AAAAAAAAAACn0/wEEpqJyeDNinwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_curve_fig = mmm.plot_direct_contribution_curves();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iG_KsGWPzsrp"
   },
   "source": [
    "The observable space only encompasses our data points and does not illustrate what transpires beyond those points. As a result, it is not assured that the maximum contribution point for each channel lies within this observable range.\n",
    "\n",
    "By using the `xlim_max` parameter, we can predict the shape of the model fitting curve for the amount spent that was not previously observed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 828
    },
    "id": "47RuJ_epzm2U",
    "outputId": "5df16f8a-ef66-447b-8e7d-a093ca7c1504"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1auVykr1wb0eDXZJkcnNBmMx6Lb+qWlhrlY+Kh81bscFV1mhPrwJWztigdQ0AAAAA0PdILLXw3ve+VyMjI/qzP/sz/cVf/IXuu+8+vfWtb9Wtt96qqampXm8PAIDV5VOy86dlijmZ1HnJevInT0heotc7AwAAAAB02EZHIXrpi7Jf/C2Vn/YKuaF9MtVAV6W46T2Y5UXZ0tKmj191XUmViWPS+EwQ2IoP18YlKja4tQayjRjaJyfb0Ia2HSozN2/LOE2oZ6NMAQAAAKBdhLua/OiP/mjt8sjIiHK5nB5//HG9+93vliQdOXJE+/bt08hI+2OtjDH60z/9047vFQCAKLN0TebaGZlCViY9G7xjeWJGsvx3DwAAAAB7SqUkM/+oEv/4/8j4pQ0davyyYqfvVmX6BdLA2Ja3YlZpDOsEt//pcvuv79r6bYkPyZ+5Wd7sfdt62sr1r9jW8/Wd4pLs7NcVO/tV2blHZeRqNzkZ+QefpcqBp8mNzcgfPSSNTxP4AgAAANAzvNrb5N5775WJvOvLGCPnnJwLfri7dOmSLl261HCftTjn2r4vAACbZTKXZJPnpXxSJn1JGhiTG5+WjO311gAAAAAA7aqORyzm6uMRS8F4RFOsj0hUpRSMQtxgsKvKOF/26iPyj90SVGRteAErFxuSvLjM0vym9tCOXo1EbFa+/tZtDXdVxmfkH3zWtp2vbzgne/URxR75jOzcI6v+1Tdy8uYekTf3SP1QSf6BZ6j87NfLP/zsbdkuAAAAAFQR7moD4SwAwI7lnEzqvGzmspSbl83NyQ1NyY0e3r4RFQAAAACAtTkXjD4s5sKgVq4W2lJxqX5dm+MVOzEK0ZaW5PJJuaHJyJUxufiwFB9q+Nh8nbxE8DNnelbxhz+1pX2sxhkbjMfbAfxDN6gyPi2viy1lVc4bUOmWt/EzfYeZxScUv/euTf8ZGkne/Cl5/+/75Y8clHvFO2UOPLWzmwQAAACAVRDuaqHa0gUAwI7mnMzCt2Vz81L2iuzyotzIAbmRg73eGQAAAAD0D78SNmrlakEthWGtaHhLrtKxU3ZqFKIrF1R+xvfIxYek+LBk4xsKFZlSviP7aKUyc/POGYNnjEq33Cb7pTtl2gzgbYbzBlR86e1yU0/p2jl2tdKytLwgU8oHrW5D+9r6O2KvPKTEVz7UsT87m5uT/79/RfaVd0gTPR4bCgAAAKAvEO5q8uijj/Z6CwAArM+vyF57XGZ5Ucpcki1k5I8dlaLvuAYAAAAAbF61bauUkylGW7bC1q1iGOIqdy/g1JJfkVma68hS3txjKg1ObjpE1c2xiZXrX9G1tTfDTT1FxZfe3tGQUFRlfEalW962+4JdGw1cbfT+1VGKZ+6Rnf2GjPz6TbLyZ25W+fpb5R+6oWUw0Sw+0Z0/M78k/+47ZW99t/yp6zq7NgAAAAA0IdwFAMBuUynJzp+Syadl0rMypSX54zPSwFivdwYAAAAAu4NzUikfBrZywUjEhpGJQaCrk21bG1IbjzgslxiuX44PSYWMYk/+Q0dOY+RLywtSfGZzCwztk5NtCNx0QmV8Rv7BZ3V0zU7wD9+owsvvaHu8nz84ITd0QHbx2zJaOS3Cyahy9AXyj90kf+yonJcIwk87pbFsNWsGroz8g89S+dgL5U9eJ2OMXGxANjev2JP/sKGA1nqjFI18ebP3yZu9T5XxaZVuua0xHOec4vfe1b22Nb+k+L1/rMKr3scYTQAAAABdRbgLAIDdpFyQnXtMppiRSV6Q8UvyJ08E4zMAAAAAAPXGrWJOppitB7hKOfkqS8Wc4rnFYJzitjNysUEpGthKjKwIcclLrBoWMdfOdHZHpXyL2FGb4kPyZ26WN3tfx/bjvAGVbnnbjg3LuKmnqPiq98nOParY4/fIzt7fFFYKGEk2n5LyqSDEdfi5qhy+Uf7oYSmWkFlaUOzi/fIuPqDYpW9Ejl+/jaqX1g9cOXlzj8ibe6S99VYJaG10lKKXvij7pTtVfOnt8g/fKEmyVx9pK4S3FTZ9UXbu0eDPCgAAAAC6hHAXAAC7RWkpCHYVcjKpczKS/MmTUmyg1zsDAAAAgO1RG5UYNGxFG7dUzIbjE3OSX259fDwefOxGsMt4colqw1Y0tFUPbyk+LBm7pdN0ehTiVtcrX39rx8JdzhtQ8aW37/zRhMbIP3SDiodukErLspcfUuzB/ylvaV6tolhGTt6Vf5F35V9UGTkgIyObaz1ac902qh7aaOBqo6oBrdLzflDxB/9yw+cxlYISX/mQCi+/Q27qKYqd+WJX9tnMO3MP4S4AAAAAXUW4q8l999WfiHj+85+vRCKxqXWKxaK++c1v1j5/0YtetOW9AQD6WCETjGIsZGVSFyRr5U8cD97NDQAAAAB7RaUoFZcaG7eKucYwl1/a9m252GBDQMvFR2rtW8F1I2u2bXVUB0chOmOloX1bWsM/dIMq49NbbkiqjE2r9J07J8jULrvwuBL3/Un7DVO5+bbXbtVG1VJpWVpeCFrY4oP1P9Pm67Yw7tEsPtHVYFftPJWC4v/3Yy1Dcm0ff+9/V/Hl75advb+je1uNd+HrKu2GcZoAAAAAdi3CXU3e/OY3y4RPwtxzzz2anp7e1Dpzc3O1tYwxevjhhzu5TQBAP1lelL32eBDsSl+QvITcxHHJ8t84AAAAgF3E+bWQlorZ4GecavNWNcxVKW7zpoxcfEhKjMjFR4KWrcRIMB4xEYa44kOS9bZ5X2vo4CjEyszNWw+kGKPSLbfJfunOTQV/nKxKN/2oKk/9NztuBOF6tiPw1NxGVeOc7NVHFDtzj+zsN1YdDVm/bgvjHp1T/N67uh7sqtrq3wIvPSt78f6OBCDbYeSk5QUpPrMt5wMAAADQf3hVuAXnXC3g1Ym1AADYLJObk1l4QiaflslcDN4hPnFsy2M8AAAAAKDjKqV6UKuYrbVuBZeDkYn12Ml2MEHjVqIe2jIT+6WBUZXLJgxubX1MYi90ahRi5fpXdGA3kpt6ioovvX3DQSdnEyp+1/937VaqnWobA0+1NqpX/bZkjMziE4rfe9eqbWmtR0NuftyjvfrIlpvZtlvsya9s6/lMKb+tj24AAAAA+gvhrhY6FewCAGArTOaSbPK8tLwok7ksDY7LjR3dlS88AAAAANjlnJPK+XpQq7l1q5iTtqnVp7alanArPlILcFVHJLrE6MrGLSOZ0dHg2Gx2e3NmHdaJUYiV8Rn5B5/VuT0dvlGFl9+xZuio+fylW96268YwVm134MlLz8rOPSq58pbbwtoe9xiKnfnips/VK3bu0W09n4sPbuv5AAAAAPQXwl0tdLK5CwCAzTDJc7KZy1JuTjY3Lze8T27k0K4bUQEAAABgl/DLUnFpReuWws9NcUlyle3bjzcQhrXqYxJdYjQMcQ0HjVv9PKp+q6MQvQGVbnlbx3/GdFNPUfFV75Ode1Sxx++RnW0cjeeMVWXmZlWuf0UQLNvpP+OWlqXlhaCVKT4oDe2rjbHsReDJe+Qz8q6d6Uhb2KrjHpuVlmVn79/y+babkZOTCUYmdpmTDf5uAAAAAECX9PEzIN2Vz+drlwcHedcOAKBNzsksPiGbm5cyl2WXF+VGDsqNHOj1zgAAAADsZn450riVlQrZepCrkJUpL2/fXownlxitB7cGRsO2rbBxK9Hnwa02bXoUojeg4ktv715jljHyD92g4qEb1gxH7VjOyV59RLEz98jOfqMxnCYrf+ZmlU++VHb269u+Ne/qwy1HLm5W87jHlpYXGn4PdhP/4LPkzT3S9fNUjt288/9eAwAAANjVeJakS5588sna5dGw7h0AgDU5X/ba4zJLi1Lmomw+LX/siDQ01eudAQAAANjp/HLQtlXIrBydWMhsa3grGJcYCW8lRiOtWyNSbHDnNzbtEjt+FGJ8SIrPdLc7aSMBsnXuaxafWPP30siXN3ufvNn7uvGVrKsb/2qq4x79Qze0Pmcp3/L63aBy8qXbE+66/hVdPwcAAACA/ka4qwsqlYr+4i/+QpJkjNF1113X2w0BAHY+vyw7f1pmOSmTnpUpLcmfmJEGxnu9MwAAAAA7gV8JRyVmZArV8FamPjqxtLRNGzENQS03MFoLclWvp3Vre+25UYjtaKdd6/pba4Gldu4rV1HiqxtrQdsrvDP3rBrucvHdOZWi9vf+1N+2FXzcLH98Ovh3BQAAAABd1JfPtHz4wx9u635/+qd/qrGxsbbXLZVKunr1qv75n/9Zly5dql1/0003bXiPAIA+UikGwa58WiZ1XqZckD9xPHhRBAAAAEB/qIW3gtCWwpGJtSDXdoW3vETjyMTm1q34kGTs9uwF7dvtoxDX0vT1mEJW8W98tK12rcrIARkZ2dzcuvd16k4z1m7gXbhfpdJy678nQ/vkZHfdaMbKzM1SYlilW26T/dKd3Qnt2bhKt/ynvRGYBAAAALCj9W24y6zzA5dzTn/2Z3+2qfWdc7X1Pc/Ta1/72k2tAwDoA+WC7Nxjwbvvk+dl/LL8yRO7+4l3AAAAACs5J5WW6mMTC5ngcjXIVVqWuju8LuANyA2MBaGtaOvWwFjwBhMv0f09oLu2YxTiZmwkdLZmM1f7ISwvN9/29vo5nmPkS8sLUnxm5Y3xIfkzN/dsFOVmVUcluqmnqPjS25X4Sodb2Wxc9pV3yJ+4blseugEAAAD0t74Md3WbMUbOBT/R3X777br++ut7vCMAwI5UWpade1SmkJNJnpMxkj95UooN9HpnAAAAADajUpQK2droRFPIBCGu6ihFV+n+HghvoRdWC25tZHxi+GZZs/iE4vfetUYzF7rBlPKrZpTK19+6q8JdlfGZhlGJ/uEbVXj5HWv+vdoIf+SgYq94p8yBp0rZ7JbXAwAAAID19G24qxq+2up9WonH47rpppv01re+Vf/m3/ybTa0BANjjirmwsSsrkzwvWRuMYuSFFgAAAGDncr5UXArHJaaDIFchXW/iKue7vwcvUQ9vJUalgbEwvBUEufiZooP22njD1Wz261wvuHXomVL2qrylay0PbxifOD6t0i23yRSznW9YQltcfHDV2/xDN6gyPt2RYFTb+9HmgnzOG1DplretGJXopp6i4qveJzv3qGKP3yM7e3/T31kjf+K4TKUok7284txOkn/gmSo/+3XyDz9b8bHRTewOAAAAADanL8Ndq41bdM7px37sxyQF7Vsf+MAHdODAgbbWNMYokUhobGxMx48fVzwe79h+AQB7TD4tO39KppiTSZ2TbEJu8rhk+/K/ZQAAAGBnKRdaj02sjlN0/vprbIWXCFu3xsLw1mgkyEV4q+s20TS1K635dRr5B5+l8sl/JX/mhVJieMXh67dr+fKuPtL2drz0Rdkv/nZwrF/a4BfTHzYbdmprbWODUN9qjFHplttkv3TntgTvnDeg0vN+UPEH/3JD53PegIovvV1u6imt72CM/EM3qHjohrVDjaVlKX1JNndVkuSPHJLGj9Zv38X/9AEAAADsTn35KvItt9yy5u0mfGLmO77jOzQ9Pb0dWwIA9IvlRdlrj8vkMzLpWSk+KDd+TLJer3cGAAAA9Idq+1YhvWJsoimkg9GK3WS8ILg1MBaMTAwvu8RoMDaRMe0901ZgqalpatUQyQ62/tfp5M09Im/uEbmv/w/5B5+t8g2vqQXa7JWHutKuRahrdc4bkL//qRsKzG1EZebmddva3NRTVHzp7V1vVqsGtPzDN8rff33boxQr4zMq3fK29v9Nxoek+EzrUZTxIWn/U+Xvf+qG9g4AAAAA3dKX4a71bHYcIwAAazG5eZmFb8vk0zKZi1JiRG58RjK211sDAAAA9ha/IhVztQBXMEIxHbzJopiVXKWrp3fx4XBc4qjcwHgwMrEa6IoN7e7Gpz1qo4ElL31R9kt31kIoPdfmaMWNfp1Gkjf3sLy5h1UZn1b5Wa9R4v6PMjZxG1XDTpLXvXDX9a9o637+4RtVePkdbQeuNryPpoDWuqMUjVVl5mZVrn+F/IPP4rEVAAAAwJ5FuKvJo48+2ustAAD2IJO5Ips8Ky0vymQuS4MTcmNHeeIRAAAA2Cy/HI5ODANc+XR9lGIxK7XuY+kMG5cbGJVLhIGtavvWwKiUGGXk+i5jFp/YVBuRqRSU+MqHVHj5Hb1p8NrgCMnNfp1VXvqi7L1/zES6LaoMTsl4Mdnc3Pr3jYadnFNlfLrjoarK+EwQjGpTQ+DqkU/LXn1kS38n1g1otTtKEQAAAAD2MJ5pAgCgy0x6VjY1Ky1dk81elRuakhs9TLALAAAAWE+lGIxOzKebWrgyMqWlLp7YyCWGpcRYfWxiNcSVGJVig3w/v1c4p/i9d2068GQqBcXv/e8qvuq3t/XvxIZHSL7oNsXv2/zXWV8XW+FkVfz/vE+KD228jcoYlW65TfZLd3asOc15Ayrd8raN/92NBq6KS/IufkPek/8oO7d60Kv2dZ38LvkjB2XKmwhorTVKEQAAAAD2MMJdAAB0kUmek81clnJzsrl5uZEDciMHe70tAAAAYGdwTirngwBXtIWrkAnGKJbz3Tu3jYehrcaxiS4xJiVGJOt179zYMezVR7bchOSlZ2XnHg0asrbB5kZI/raMX+ryzrCeyrGbpcSwJG2qjcpNPUXFl96+pQa22lrhuMctt84lhlW57rtUue67Gr+WMOZl5Fp+XQS0AAAAAKB9hLsAAOgG52QWnwzGLGQuyy4vyo0ekhve3+udAQAAANuvUgzHJqZk8hmZQkombONSpdi983oDcoPjkRBXdYTiOO1bkCTFznyxI+t4Z+7ZlnDXpkdIEuzaESrXv2LllRtso/IP36jCy+9Ys7lt3X1Exz12UouvhRAXAAAAAGwd4a42FItFPfbYY1pYWFAmk1G5XN7wGq973es6vzEAwM7knMzCGdncNSlzSTafkj92VBqa7PXOAAAAgO7xy/XxiflU0MCVT3W9gcvFh2uBrWqIq9rCpdhA186LPaC0LDv79Y4s5V24X6XScvvj5TZjiyMk0VuV8ZlgzGIHuKmnqPiq960+2jH8GI2vrjruEQAAAACw4xHuWsNnPvMZ/eVf/qUeeOABVSqVLa1FuAsA+oTzZecfl1lekEnPyhRz8sdnpMHxXu8MAAAA2Dq/IhUztdataohL+bRMKdelkxq5xEjYuhVp4RockxJjkhfv0nmxF7nispSbl00vylz6pkyHeoWMfGl5QYrPdGS9VjoxQhK94bwBlW55W2cDVcasPdpRanvcIwAAAABgZyPc1cKVK1f0cz/3c3rggQckSc5t7kkeY4ycczK8CwoA+oNfkZ0/JbOcDIJdpSX548ekgdFe7wwAAABon/OlYm5l+1Y+LVPMqitDtoyVS4zVxyYOjtfHKCZGJet1/pzoH87JXn1EsTP3yL/4Dcn56kanmynluzqCrlMjJLG9nDeg4ktv7/wIxKjVRjtuYNwjAAAAAGDnItzVJJ1O661vfaueeOKJWjCrGtLaqM2GwgAAu1ClFAS78mmZ1HmZckH+xHEpMdLrnQEAAAArOSeV80GAqxbeSgVtXIVMEPDqOCOXGJUbHA8auAYnghauwYng+2Zju3BO9Duz+ITi9961LY1XLj7YvcVLy7Kz93dvfdQ4NY4z3IrK+IxKt7ytu8EuAAAAAMCeR7iryYc//GF9+9vfbgh1HThwQN/93d+t66+/XhMTE4rF+G0DAERUirJzjwWtBsnzMn5J/uQJxh0AAACg9/xKvXUrn6yPUsynpEqxK6d08ZFIgCts4BocD0Yo0sCFbWSvPKTEVz4kUyl0/VzO2PoovG5YXghGP6KrnDeg4r+6XbIxxR6/R3b2/obfd2esKjM3q/LUWyUFbWqr3uf6V8g/+KzOjmIEAAAAAPQlUkoRhUJB//N//s9aqMvzPL3zne/Uj/zIj8jzePIRANBCuSA796hMISuTPCfjfPmTJ6VYN4Z8AAAAAC00t3CFH00hJVPozhhFFxtc0b7lBsalwXHJ8nQTes8sPrFtwS5Jqszc3NU3+JhSvmtr72WVkYMykmxubv37NrVsFQ/dIJWWg2BdKR80sw3ta/hzLh5+9rr3AQAAAABgq3i2LeL+++9XoVCotXa9613v0o/+6I/2elsAgJ2qlA+CXcWszOI5yUj+1EnJS/R6ZwAAANiL/LJUyNRCXLVfhXR3Wri8RNi6NVFv36p+5Hte7GTOKX7vXdsW7JKkyvWv6Or6XR35uIM4G5ckGb/U/jFqHKO4ojlLkp17dO0mrtVatuJDUnxm7YhsO/cBAAAAAGALCHdFnDt3TpLknNPU1JR+5Ed+pMc7AgDsWKWlcBRj0NglY+Umj/MiFwAAALbGOam8XB+jmE9J+XCUYrELLVzGC5u3JleMUlRskHFi2JXs1UfkpS9u2/kq4zO1EFHXDO2Tk93ToxmdN6DiS2+XS4wqfu9dbf0ZVsZnVLrpR+UGxtZszvIP3dBWExcAAAAAADsR4a6IdDotSTLG6PnPf76stT3eEQBgRyrmwmBXRiZ1QbKe3OQJxs8AAACgfc6XCtlagMssL9bHKm6gsabt08WH62MUI7+UGJEMz39gb4md+eK2ncvZuEq3vK37Qcj4kPyZm+XN3tfd87SpuS1rq1aMRHzV+zbcttVW9JWWLQAAAADALsSr0BETExO1y2NjYz3cCQBgxypkZOdPBY1dqXPBqJqJ4wS7AAAA0Jpfrjdv5ZPBr+WkTCEjuUpnz2W8sHUrEt4aDAJdNMyib5SWZWfv377TveBHaoGkbitff2tHwl3Oxjc09nDF8d6Aije/RbFHP9tWu5Y/fEBu5JDs3KPtj0Q0hrYtAAAAAABCvBIdceTIkdrlaosXAAA1+ZTs/Ol6Y1dsUG7imGS9Xu8MAAAAvVYphg1cSSka5Cp0fpSiiw9L1fatSBsXLVyAghDQNo0udLKqHP/ObTmXFIwWrIxPb2nkZGV8RqUXvU3x+9obe9isOjrRP3yjiidesrF2rc2GtGjbAgAAAAD0OcJdES984Qs1NDSkfD6vb33rW73eDgBgJ1lelL32uEw+I5O+IMWHg2AXL54BAAD0D+ekcr7ewrWclKpjFUtLnT2X8cLQVhjeGpgIA13jtHABazCl/Ladq3Ls5u1tkDJGpVtuk/3SnTKVwoYPd95AbfRhbezhI5+WvfpIWyMWm0cnbrhdi5AWAAAAAACbQrgrYmRkRK961av06U9/WteuXdPXvvY1vfjFL+71tgAAvba0EAS7ChmZ9KyUGJUbnybYBQAAsFc5JxVzYftWPchl8kmpUuzsqWKD9Rauwcnax6CFq524BYAoFx/ctnNVrn/Ftp2ryk09RcWX3q7EVz60oYBXtXGrZTCruCTv4jfkPfmP4ejEevxqzdGJUQS3AAAAAADoGsJdTd7xjnfoy1/+slKplN7//vfrE5/4hAYHt+9JIQDAzmJy8zIL3w5e1EtflAbH5cameaENAABgL3BOKmZllhfDkYqLtUCX/HJnTxUfkRuabAxyDU1KMZ5zADpqaJ+cbNdHM1bGZ4KwUw/4h29U4eV3KH5ve6MVVzRuNUsMq3Ldd6ly3XdtfnQiAAAAAADoGsJdTQ4fPqz3v//9uv322/Xoo4/qP//n/6wPfOAD2rdvX6+3BgDYZiZ7VXbxSWl5USZzOXghbuwowS4AAIDdxjmplAvatxqCXCnJL3XwREZuIByjOBQ2cIVtXPLiHTwPgFXFh+TP3Cxv9r6unaI63rCXPxs2jFZ8/B7Z2fsbAm1tN241o4ELAAAAAIAdh3BXCy972cv00Y9+VG9/+9v1ta99Ta9+9av1H//jf9S//tf/Wk972tM0MTHR6y0CALrMZK7IJs8GIxmzV+SGpuRGDxPsAgAA2Mmck0pLK5u4lpOdDXEZL2zfmpAbmqyNU9TAuGS9zp0HwKaUr7+1a+GuFeMNeyk6WpHGLQAAAAAA9izCXU1uuOGGhs+dc0qlUrrrrrt01113bWpNY4wefvjhTmwPALANTOaSbPK8tHRNNntVbnh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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mmm.plot_direct_contribution_curves(show_fit=True, xlim_max=1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit of the model comes from the saturation function selected at the time of training. You can verify this by directly accessing the model method and plotting the class name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model was train using the LogisticSaturation function\n",
      "and the GeometricAdstock function\n"
     ]
    }
   ],
   "source": [
    "print(f\"Model was train using the {mmm.saturation.__class__.__name__} function\")\n",
    "print(f\"and the {mmm.adstock.__class__.__name__} function\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "--4LSceFihbn"
   },
   "source": [
    "Within PyMC-Marketing we have different saturation functions, you can observe all in the [transformer module](https://www.pymc-marketing.io/en/stable/api/generated/pymc_marketing.mmm.transformers.html).\n",
    "\n",
    "Let's understand a few saturation functions with a few examples!\n",
    "\n",
    "## Understanding Saturation Functions: Sigmoid and Michaelis-Menten\n",
    "What do we mean by saturation functions? We assume the effect of spend on sales is not linear and saturates at some point. Two prevalent functions deployed to comprehend and estimate the saturation effects in advertising channels are the sigmoid and the Michaelis-Menten functions.\n",
    "\n",
    "### Sigmoid Function\n",
    "The sigmoid function is formulated as:\n",
    "\n",
    "$$\n",
    "\\beta \\cdot \\frac{\\exp(-\\lambda x)}{1 + \\exp(-\\lambda x)}\n",
    "$$\n",
    "\n",
    "Key Elements:\n",
    "* β (beta): Denotes the Asymptotic Maximum or Ceiling Value. It is the point that the function approaches as the input x becomes immense.\n",
    "* λ (Lambda): Influences the steepness of the curve. A more substantial value of λ renders the curve steeper, while a lesser value makes it more even-paced.\n",
    "\n",
    "### Michaelis-Menten Function\n",
    "The Michaelis-Menten function is formulated as:\n",
    "\n",
    "$$\n",
    "\\frac{\\alpha \\times x}{\\lambda + x}\n",
    "$$\n",
    "\n",
    "Key Elements:\n",
    "* α (Alpha or Vmax): It represents the maximum contribution (y) a channel can make, also recognized as the plateau point.\n",
    "* λ (k): Denotes the elbow on the function in x, signifying the moment when the curve adjusts its direction.\n",
    "\n",
    "## Which Function to Use?\n",
    "The preference between the sigmoid and Michaelis-Menten functions ought to be steered by the data's goodness of fit. But it really comes down to your assumptions about where the peak might be and the speed at which it saturates the curve.\n",
    "\n",
    "> **Tip**: When you're choosing a saturation function, it's helpful to consider the data you already have. While saturation functions may have different parameterizations, they all have the same effect. If you already know about any relationship between spending and response, it's a good idea to pick a function with the same parameter values. This will help you understand and analyze its operation and outcomes better, which is essential within the Bayesian framework.\n",
    "\n",
    "Once these parameters are obtained, you can visualize it using the `arviz.summary` function (each parameter has the prefix `saturation` or `adstock` respectively) and, if desired, you can recreate the curves for each channel independently based on them. More crucially, these parameter values are indispensable when using the `budget_allocator` function, which leverages this information to optimize your marketing budget across distinct channels. This section is fundamental to budget optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>saturation_beta[x1]</th>\n",
       "      <td>0.361</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2589.0</td>\n",
       "      <td>2545.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_beta[x2]</th>\n",
       "      <td>0.295</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1288.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x1]</th>\n",
       "      <td>4.081</td>\n",
       "      <td>0.355</td>\n",
       "      <td>3.406</td>\n",
       "      <td>4.750</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2843.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x2]</th>\n",
       "      <td>2.984</td>\n",
       "      <td>1.208</td>\n",
       "      <td>0.705</td>\n",
       "      <td>5.253</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.023</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x1]</th>\n",
       "      <td>0.398</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2911.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x2]</th>\n",
       "      <td>0.197</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "saturation_beta[x1]  0.361  0.018   0.327    0.394      0.000    0.000   \n",
       "saturation_beta[x2]  0.295  0.162   0.190    0.450      0.007    0.005   \n",
       "saturation_lam[x1]   4.081  0.355   3.406    4.750      0.007    0.005   \n",
       "saturation_lam[x2]   2.984  1.208   0.705    5.253      0.033    0.023   \n",
       "adstock_alpha[x1]    0.398  0.030   0.343    0.454      0.001    0.000   \n",
       "adstock_alpha[x2]    0.197  0.041   0.113    0.271      0.001    0.001   \n",
       "\n",
       "                     ess_bulk  ess_tail  r_hat  \n",
       "saturation_beta[x1]    2589.0    2545.0    1.0  \n",
       "saturation_beta[x2]    1288.0     723.0    1.0  \n",
       "saturation_lam[x1]     2843.0    2601.0    1.0  \n",
       "saturation_lam[x2]     1250.0     792.0    1.0  \n",
       "adstock_alpha[x1]      3042.0    2911.0    1.0  \n",
       "adstock_alpha[x2]      1791.0    1913.0    1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    data=mmm.fit_result,\n",
    "    var_names=[\n",
    "        \"saturation_beta\",\n",
    "        \"saturation_lam\",\n",
    "        \"adstock_alpha\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GEGsiGlf142o"
   },
   "source": [
    "## Example Use-Cases\n",
    "The `budget_allocator` function within PyMC-Marketing boasts a myriad of applications that can solve various business predicaments. Here, we present five critical use cases that exemplify its utility in real-world marketing scenarios.\n",
    "\n",
    "### What are we optimizing?\n",
    "Before jumping into the examples, we need to understand the basis of our optimizer.\n",
    "\n",
    "We aim to optimize the allocation of budgets across multiple channels to maximize the overall contribution to key performance indicators (KPIs), such as sales or conversions. Each channel has its own sigmoid or michaelis-menten curve, representing the relationship between the amount spent and the resultant performance.\n",
    "\n",
    "These curves vary in characteristics: some channels saturate quickly, meaning that additional spending yields diminishing returns, while others may offer more linear growth in contribution with increased spending.\n",
    "\n",
    "To solve this optimization problem, we employ the Sequential Least Squares Quadratic Programming ([SLSQP](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-slsqp.html#optimize-minimize-slsqp)) algorithm, a gradient-based optimization technique. SLSQP is well-suited for this application as it allows for the imposition of both equality and inequality constraints, ensuring that the budget allocation adheres to business rules or limitations.\n",
    "\n",
    "The algorithm works by iteratively approximating the objective function and constraints using quadratic functions and solving the resulting sub-problems to find a local minimum. This enables us to effectively navigate the multidimensional space of budget allocations to find the most efficient distribution of resources.\n",
    "\n",
    "The optimizer aims to maximize the total contribution from all channels while adhering to the following constraints:\n",
    "\n",
    "1. Budget Limitations: The total spending across all channels should not exceed the overall marketing budget.\n",
    "2. Channel-specific Constraints: Some channels may have minimum or maximum spending limits.\n",
    "\n",
    "By leveraging the SLSQP algorithm, we can optimize the multi-channel budget allocation in a rigorous, mathematically sound manner, ensuring that we get the highest possible return on investment.\n",
    "\n",
    "### Maximizing Contribution\n",
    "Assume you're managing the marketing for a retail company with a substantial budget to allocate for advertising across multiple channels. Suppose you're already apportioning funds to channels x1 and x2. Still, you're contemplating ways to optimize the forthcoming quarter's outlay to maximize the overall contribution.\n",
    "\n",
    "Without, you might have considered scattering your money linearly without an MMM model - equal investments in each channel. However, you wish to explore better alternatives now that you possess an MMM model. Given that you lack prior knowledge, you impose the same restrictions on both channels. They must each expend a minimum of 1 million euros and no more than 5 million, equating to your total budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "iAtdq9uU10xr"
   },
   "outputs": [],
   "source": [
    "total_budget = 5  # Imagine is 5K or 5M\n",
    "# Define your channels\n",
    "channels = [\"x1\", \"x2\"]\n",
    "# The initial split per channel\n",
    "budget_per_channel = total_budget / len(channels)\n",
    "# Initial budget per channel as dictionary.\n",
    "initial_budget_dict = {channel: budget_per_channel for channel in channels}\n",
    "# bounds for each channel\n",
    "min_budget, max_budget = 1, 5\n",
    "budget_bounds = {channel: [min_budget, max_budget] for channel in channels}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nd5hRZu92MRi"
   },
   "source": [
    "We can use our function and see the results with this information saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "g6BD5BhY2Dcm",
    "outputId": "f546dc05-ef6d-4af2-d210-28c271d928eb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response = mmm.allocate_budget_to_maximize_response(\n",
    "    budget=total_budget,\n",
    "    num_days=8,\n",
    "    time_granularity=\"weekly\",\n",
    "    budget_bounds=budget_bounds,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x1': 3.066965516511223, 'x2': 1.949799865924329}\n"
     ]
    }
   ],
   "source": [
    "print(mmm.optimal_allocation_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mmm.plot_budget_allocation(samples=response, figsize=(12, 8))\n",
    "fig.suptitle(\"Response vs spent per channel\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = mmm.plot_allocated_contribution_by_channel(samples=response)\n",
    "fig.suptitle(\"Estimated Contribution per channel over time\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 611,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.suptitle(\"Optimal total marketing contribution\")\n",
    "response[\"y\"].mean(dim=\"sample\").plot()\n",
    "plt.fill_between(\n",
    "    x=response.y.date.values,\n",
    "    y1=response.y.quantile(0.025, dim=\"sample\"),\n",
    "    y2=response.y.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ");"
   ]
  },
  {
   "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": 13,
   "metadata": {
    "id": "mnlhEUUw2LqS"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 352kB\n",
       "Dimensions:  (date: 8, sample: 4000)\n",
       "Coordinates:\n",
       "  * date     (date) datetime64[ns] 64B 2021-09-06 2021-09-13 ... 2021-10-25\n",
       "  * sample   (sample) object 32kB MultiIndex\n",
       "  * chain    (sample) int64 32kB 0 0 0 0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3 3 3 3\n",
       "  * draw     (sample) int64 32kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    y        (date, sample) float64 256kB 7.016 8.482 7.54 ... 8.639 8.33 8.36\n",
       "Attributes:\n",
       "    created_at:                 2024-06-08T20:59:37.211938+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.0</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-ac1d76b1-08a7-47c4-9970-cf0067d78cf8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac1d76b1-08a7-47c4-9970-cf0067d78cf8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 8</li><li><span class='xr-has-index'>sample</span>: 4000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e981e852-e879-4537-8c6b-826a7ee3dc9f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e981e852-e879-4537-8c6b-826a7ee3dc9f' 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-709e6ad4-e178-4e60-a801-16870233aafa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-709e6ad4-e178-4e60-a801-16870233aafa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c268b8a-d798-42f6-afd9-e00c41454b5f' class='xr-var-data-in' type='checkbox'><label for='data-2c268b8a-d798-42f6-afd9-e00c41454b5f' 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-f7076bca-469e-4b9a-8ac4-d924c1bc3397' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7076bca-469e-4b9a-8ac4-d924c1bc3397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4f83064-b01d-4ee5-a39d-ee6e88bee376' class='xr-var-data-in' type='checkbox'><label for='data-f4f83064-b01d-4ee5-a39d-ee6e88bee376' 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-5627f1de-9b4c-4989-8a4b-c023d9765bf1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5627f1de-9b4c-4989-8a4b-c023d9765bf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23be0393-08e1-4c4e-87fe-e7cd15aa387b' class='xr-var-data-in' type='checkbox'><label for='data-23be0393-08e1-4c4e-87fe-e7cd15aa387b' 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-9762b537-c470-4806-b5b7-e7fb429dd736' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9762b537-c470-4806-b5b7-e7fb429dd736' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f06e1d06-6a21-40d0-a813-6f57f7c6a122' class='xr-var-data-in' type='checkbox'><label for='data-f06e1d06-6a21-40d0-a813-6f57f7c6a122' 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-ceef12ec-0a19-4f5f-b1b7-861d1ebc797b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ceef12ec-0a19-4f5f-b1b7-861d1ebc797b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(date, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.016 8.482 7.54 ... 8.33 8.36</div><input id='attrs-5f6a61b7-8ef0-4612-ac16-0be1ae79088c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f6a61b7-8ef0-4612-ac16-0be1ae79088c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ee101b9-553b-4e88-b45a-6380f1a72d77' class='xr-var-data-in' type='checkbox'><label for='data-7ee101b9-553b-4e88-b45a-6380f1a72d77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.01598984, 8.48177897, 7.53993408, ..., 7.23436785, 7.00556897,\n",
       "        7.33915959],\n",
       "       [7.17613443, 8.47107811, 7.48825186, ..., 7.83844794, 7.77076094,\n",
       "        7.27731727],\n",
       "       [7.41636682, 8.37254031, 7.31028155, ..., 7.24905327, 7.75200392,\n",
       "        7.47996657],\n",
       "       ...,\n",
       "       [7.92695282, 9.02384086, 8.18409272, ..., 8.01590648, 8.38370361,\n",
       "        7.94649223],\n",
       "       [8.100642  , 9.05908718, 8.15872426, ..., 8.12908178, 8.76577129,\n",
       "        8.32753234],\n",
       "       [7.53281025, 9.83279976, 8.34732199, ..., 8.63873289, 8.32952984,\n",
       "        8.36022397]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-211d1480-891d-4e76-99f3-9033c743996b' class='xr-section-summary-in' type='checkbox'  ><label for='section-211d1480-891d-4e76-99f3-9033c743996b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-93ac66b1-1d45-44d2-8dd4-a2cd663479ed' class='xr-index-data-in' type='checkbox'/><label for='index-93ac66b1-1d45-44d2-8dd4-a2cd663479ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2021-09-06&#x27;, &#x27;2021-09-13&#x27;, &#x27;2021-09-20&#x27;, &#x27;2021-09-27&#x27;,\n",
       "               &#x27;2021-10-04&#x27;, &#x27;2021-10-11&#x27;, &#x27;2021-10-18&#x27;, &#x27;2021-10-25&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample<br>chain<br>draw</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-faf8e28c-a446-45d3-837b-2d9a57e3ab79' class='xr-index-data-in' type='checkbox'/><label for='index-faf8e28c-a446-45d3-837b-2d9a57e3ab79' 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-c595a5ef-1974-4193-9ead-7017b830e59b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c595a5ef-1974-4193-9ead-7017b830e59b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-06-08T20:59:37.211938+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.15.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 352kB\n",
       "Dimensions:  (date: 8, sample: 4000)\n",
       "Coordinates:\n",
       "  * date     (date) datetime64[ns] 64B 2021-09-06 2021-09-13 ... 2021-10-25\n",
       "  * sample   (sample) object 32kB MultiIndex\n",
       "  * chain    (sample) int64 32kB 0 0 0 0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3 3 3 3\n",
       "  * draw     (sample) int64 32kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    y        (date, sample) float64 256kB 7.016 8.482 7.54 ... 8.639 8.33 8.36\n",
       "Attributes:\n",
       "    created_at:                 2024-06-08T20:59:37.211938+00:00\n",
       "    arviz_version:              0.18.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.15.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_date = mmm.X[\"date_week\"].max()\n",
    "\n",
    "# New dates starting from last in dataset\n",
    "n_new = 8\n",
    "new_dates = pd.date_range(start=last_date, periods=1 + n_new, freq=\"W-MON\")[1:]\n",
    "\n",
    "initial_budget_scenario = pd.DataFrame(\n",
    "    {\n",
    "        \"date_week\": new_dates,\n",
    "    }\n",
    ")\n",
    "\n",
    "# Same channel spends as last day\n",
    "initial_budget_scenario[\"x1\"] = initial_budget_dict[\"x1\"]\n",
    "initial_budget_scenario[\"x2\"] = initial_budget_dict[\"x2\"]\n",
    "\n",
    "# Other features\n",
    "initial_budget_scenario[\"event_1\"] = 0\n",
    "initial_budget_scenario[\"event_2\"] = 0\n",
    "\n",
    "initial_budget_scenario[\"t\"] = 0\n",
    "\n",
    "response_initial_budget = mmm.sample_posterior_predictive(\n",
    "    X_pred=initial_budget_scenario, extend_idata=False\n",
    ")\n",
    "\n",
    "response_initial_budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B3sV+GwBA19lvg/5HuAtgPZ5//vmq8ejRozt87ciRI6vS7C+88EK753384x+vKvn+X//1X+tsT7F06dL48pe/HKtWrYqIiGKxGKecckqH1wUAsKncdNNNVeMDDzyw2+ZeunRp3H777ZVxmqZxwAEHdNv8AL3RlClTqsZtK9x0xV577VUVbpg6dWrl9Whb3/3ud+Pss8+OlStXVo4NGDAg3vKWt8Txxx8fn/3sZ2Py5Mnx9re/PUaOHNnuHEmSRJZlkWXZGqGK5uPtfQhgQO9hvw0AoOvst0H/oy0jwHrMmTOnatyZ9HuSJDFq1KiYOXNmu3M1GzNmTHzzm9+MM844IxobG+O1116LD3zgA3HSSSfFIYccEltvvXVkWRZz5syJv/71r3HFFVfEjBkzIqK8sf2tb30rdtxxxy7+hABruu222+LWW2+N5557LubNmxd1dXVRX18fO++8c+y3337xrne9K7bccsueXiZQ4+6///64//77K+OhQ4fGW97ylm6b/8c//nEsXbq0Mt5///1jxIgR3TY/QG/05JNPVo3bVrjpiiFDhsQ222wT06dPj4iIZcuWxfTp09d4HXrzzTfHD37wg8o4TdM4+eST41Of+lQMHTp0jXlLpVI89NBD8ctf/rIqqDFmzJh44oknIiLikksuiUsvvbTyvebjQO9mvw3oj+y3Ad3Bfhv0T8JdAOvR+glMRFS9468jBg8eXPl61apVsWLFiqirq1vjvCOOOCLq6+vjnHPOiZdeeikWLFgQF110UVx00UVrnXvcuHFx1llnxX777depNQGsz1133VU1XrZsWcyfPz9efPHF+OMf/xgXXHBBfPCDH4wzzjij6j4H0GzZsmVx1llnVR37xCc+0W33jGeeeSb+93//t+rYqaee2i1zA/RmbdsgvuENb+iWeUeNGlUJd0VEzJs3r+r7ixcvjm984xuVcZIkcdFFF8W73vWutc6ZJEnsu+++se+++0apVOqWdQK9g/02oD+y3wZsKPtt0H9pywiwHm03m9rbKFqXtucvWbJkree++c1vjltvvTVOPPHEyLJsnfO+613viiuuuMJGE9AjVq5cGT//+c/jgx/8YNUf+QCafeMb34gXX3yxMt5hhx3i5JNP7pa5ly5dGl/4wheqWn4dddRRnhcBRMSCBQuqxkOGDOmWeTfbbLN1Ps4vfvGLWLhwYWU8efLkdQa72tJSEfoX+20Aa7LfBqyP/Tbov1TuAliPFStWVI1bt4roiGKxuM75Wrvrrrviv/7rv+L5559f77y/+93v4vbbb4+PfvSjcfrpp3d6EwygPePGjYtDDjkk9tlnn9hpp52ivr4+SqVSvP766/Hoo4/GjTfeGH/+858r5z///PPxyU9+Mn71q1/F8OHDe3DlQC255ppr4oYbbqiMi8Vi/Pd//3e3PF8plUpx5plnxjPPPFM5ts0228TXvva1DZ4boC9oG3DobDWctRk4cOA6H+eWW26pfF1XVxef+tSnuuVxgb7JfhvQn9hvA7qD/Tbo34S7ANaj7ZOihoaGTl3fOuHe3nzNLrvssvif//mfynjEiBExefLkeNvb3hZjx46NPM/j1Vdfjfvuuy+uvvrqeOaZZ6KhoSGuueaaePTRR+PHP/6xUs1Al9XX18cvf/nLmDRpUrvfHzt2bIwdOzaOOOKIuPvuu+OLX/xipVrD9OnT45vf/GZceOGFm3LJQI36/e9/H9/+9rerjp177rmx5557dsv83/rWt+K2226rjIcOHRqXXnppDB06tFvmB+jt2r4ubFsdp6uWLVu21sdZuHBhPPXUU5Xxm970phgxYkS3PC7QN9lvA/oD+21Ad7HfBmjLCLAebd/lvK53Aran7fntbQjdeuutVRtNu+22W9x0003x6U9/OsaNGxeDBw+Ourq62HrrreODH/xgXH/99fH+97+/cv4jjzwSZ599dqfWBdDakCFD1rrR1NZBBx0Ul112WeR5y/sEbrnllnj66ac31vKAXuLee++NL37xi9HY2Fg5dvrpp8dRRx3VLfNfdtllce2111bGdXV1cdlll8W4ceO6ZX6AvmDYsGFV48WLF3fLvIsWLVrr4zzzzDNV9/6JEyd2y2MCfZf9NqA/sN8GdAf7bUCEcBfAerXdbGrbemJ9Wp+f53m770w877zzKuPmJ01bbLHFWucsFovxzW9+s+qJ1c033xzTpk3r1NoAumrfffeND3zgA5VxqVSK3/3udz24IqCnTZ06NT7zmc9UVVE46aSTuq0t189+9rOqP87leR7f/e53Y//99++W+QH6ivr6+qrxnDlzumXetvO0bhE0f/78qu9tueWW3fKYQN9lvw1gTfbbgLbstwHNhLsA1mPUqFFV485sjJdKparz284VEfGXv/wlXnvttcr4Pe95T4wePXq9cxcKhTjxxBOrjv3f//1fh9cGsKE++MEPVo3vvffeHloJ0NOefvrp+OQnP1nV+uuYY46JL33pS90y///93//Ff/7nf1bGSZLEt771rTj44IO7ZX6AvmTXXXetGj/22GMbPOfixYtj+vTplfHAgQNj2223rYzbhjLahjYA2rLfBtA++21AM/ttQGvCXQDrseOOO1aNZ82a1eFrX3/99WhoaKiMd9hhhzXOeeSRR6rGnUnDtz23OzbtATpqzz33jEKhUBm//PLLPbgaoKfMmDEjTjzxxKqqLYcffnice+653TL/HXfcEV/96lejVCpVjv3Hf/xHVcscAFrsvffeVeNHH310g+ecNm1a1X14woQJVS2DhgwZUnV+6z8+ALTHfhtA++y3ARH224A1CXcBrMeWW24ZQ4cOrYyffPLJDl/7xBNPVI3b22yaO3du1Xhd5eHbGjlyZNV43rx5Hb4WYEOlaVrV9sc9CPqfOXPmxOTJk6uqIhx00EFxwQUXRJpu+MvNe++9N0477bRYtWpV5dhpp50WH/vYxzZ4boC+asKECVXtyaZMmdKp0ER7brrppqrxfvvtVzXeWK0ggb7LfhtA++y3AfbbgPYIdwF0wD777FP5+p///GfMmDGjQ9dNmTKlatx2AzwiqjbdIyKWL1/e4XW1PXfgwIEdvhagO7S+D7W9nwF929y5c2Py5MlVgYH9998/Lr744qp3GXfVo48+Gp/5zGdi5cqVlWMnn3xy/Ou//usGzw3Qlw0cODCOOOKIyrhUKsWvf/3rLs+3YMGCuO222yrjLMvi6KOPrjpn5513rvojQ3dUCwP6PvttAO2z3wb9l/02YG2EuwA6oG1/6d///vcduq71BnhdXV28+c1vXuOcESNGVI2fe+65Dq/r2WefrRpvvvnmHb4WYEPNmzcvFi1aVBm7B0H/sXjx4jj55JPj+eefrxybMGFCXH755TFgwIANnv/pp5+OT37yk1VtvT7ykY/EF7/4xQ2eG6A/OOGEE6rGV111Vbz00ktdmuvCCy+MJUuWVMaHHHJIjBkzpuqcoUOHxm677VYZ/+1vf1ujak5XtW7/GBGxevXqbpkX6Hn22wDWZL8N+i/7bcC6CHcBdMA73vGOqkT8ddddFw0NDeu85t57740XXnihMj7ooINi0KBBa5w3YcKEqvHvfve7Dq/r5ptvrhpPnDixw9cCbKg///nPVeNdd921h1YCbErLly+PU045JR5//PHKsV122SWuvPLKGDJkyAbPP2PGjDjxxBNjwYIFlWNHHnlknHPOORs8N0B/seuuu8a73vWuynjFihXxpS99qVOVayIi7rrrrrjuuusq42KxGJ/97GfbPfe9731v1eNdccUVnVx1+wYPHlw1bv3fB6B3s98GsCb7bdA/2W8D1ke4C6ADRo4cGccee2xlPGPGjHVuVK9YsSK++c1vVsZJkqy1pOl+++0Xm222WWX8xBNPxM9+9rP1runhhx9eo7XG29/+9vVeB9AdGhoa4oc//GHVsbe85S09tBpgU1m1alX827/9WzzwwAOVY9ttt11cddVVMWzYsA2ef86cOfGJT3wiXnvttcqxQw89NM4///xIkmSD5wfoT77+9a/HqFGjKuMpU6bEqaeeWlWFa13uueee+PznPx+NjY2VY//+7/8e48aNa/f8Y489Nurr6yvjq6++usNVeCLK7SPb07ZK2LRp0zo8J1Db7LcBVLPfBv2T/TagI4S7ADro05/+dNU7hi+55JK46qqrqja6I8r9sE8++eSqEu7vfve7Y/fdd2933rq6ujjppJOqjn3zm9+M73//++2+q7pUKsVvf/vbOPnkk6vezfj2t7899txzzy79bED/NmfOnJg/f36Hz29sbIyzzz47nnnmmcqxLbbYIo444oiNsDqgVpRKpTjzzDPjrrvuqhwbM2ZMXH311TFy5MgNnn/BggVx8sknx8yZMyvH3vrWt8aFF14YWZZt8PwA/U19fX1ceOGFUVdXVzl2zz33xHve85647bbb1lodZ/bs2XHOOefEJz/5yVixYkXl+MEHHxyTJ09e6+MNHjy46l3fpVIpvvCFL8RFF10UixcvbveaUqkUDz/8cHzpS1+Kn//85+2eM2HChEjTli3M73znO/Hggw/GypUr17oWoPew3wb0VfbbgI6w3wZ0VFJa29viAFjDXXfdFf/6r/9atcG03XbbxYEHHhj19fUxffr0uPPOO6s2iXbaaaf41a9+tc6yqStXroyTTjop7r///qrj9fX18aY3vSnGjh0bWZbFq6++Gn//+99j1qxZVee94Q1viF/96ldV78oG+pd3vOMdlXvDmDFj4k9/+lOHr7399tvjS1/6UnzoQx+K9773vWvdHI+I+Mc//hHnnXde3HfffVXHzz///Dj66KO7tnigV5g1a1a84x3vqDqWJEnVH9w7YsyYMfHHP/5xjeM33HBDnHnmmVXH0jTt9DsI3//+98d5553XqWsA+rK//vWv8fnPf36NgFV9fX0ccMABsdVWW8WgQYPi9ddfj2effTYeeeSRNapoHXbYYXHBBRdEsVhc7+NddNFF8b//+79VxwYMGBD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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_response_original_scale_optimize = (\n",
    "    response[\"y\"] * mmm.get_target_transformer()[\"scaler\"].scale_\n",
    ")\n",
    "\n",
    "# Plotting optimized response\n",
    "y_response_original_scale_optimize.mean(dim=\"sample\").plot(\n",
    "    label=f\"Optimized Response | Daily spent {sum(mmm.optimal_allocation_dict.values()):.0f}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=y_response_original_scale_optimize.date.values,\n",
    "    y1=y_response_original_scale_optimize.quantile(0.025, dim=\"sample\"),\n",
    "    y2=y_response_original_scale_optimize.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Plotting initial budget response\n",
    "response_initial_budget[\"y\"].mean(dim=\"sample\").plot(\n",
    "    label=\"Initial Budget Response | Daily spent 5.00M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=response_initial_budget.y.date.values,\n",
    "    y1=response_initial_budget.y.quantile(0.025, dim=\"sample\"),\n",
    "    y2=response_initial_budget.y.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Adding labels, legend, and title\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Response\")\n",
    "plt.legend()\n",
    "plt.title(\"Comparison of Optimized and Initial Budget Responses\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aqIaFFHy28ni"
   },
   "source": [
    "This information will allow you to compare the optimization results against what could have been your initial configuration and budget distribution. While our budget distribution changes, the contribution estimate remains almost the same for this budget. Could we be spending more than needed? Furthermore, how can we identify the optimal spent amount if this situation arises?\n",
    "\n",
    "One approach to explore this could be to create various scenarios and observe how our estimated contribution fluctuates according to each scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dbsTmbLZ2-x8"
   },
   "source": [
    "### Creating Budget Scenarios\n",
    "Envision the subsequent situation: You're managing marketing operations for a rapidly growing retail company. The management team has allocated a considerable advertising budget and anticipates substantial results in the next quarter. However, given the uncertainty in economic trends, you are tasked with designing a budget allocation strategy capable of accommodating various scenarios.\n",
    "\n",
    "Before the advent of a robust MMM model, your approach might have been simplistic yet naive: distribute the funds equally across the two primary channels, x1 and x2. This would result in a linear, evenly split distribution of 2.5 million euros each, given a total budget of 5 million euros.\n",
    "\n",
    "Nevertheless, with the MMM model now at your disposal, you realize a more sophisticated approach is feasible. You're eager to investigate how this budget could be optimized across multiple scenarios:\n",
    "\n",
    "1. Status Quo Scenario: What if the market stays stable? What is the best allocation?\n",
    "2. Growth Scenario: What if the market suddenly becomes more favorable? How should the extra budget be allocated?\n",
    "3. Recession Scenario: What if there is a market downturn and the budget gets cut by 40%?\n",
    "\n",
    "Given that you are treading into uncertain waters, you set certain constraints. Each channel must have a minimum spending of 1 million euros, ensuring base-level visibility. The maximum cap is ±5 million euros (depending on your scenario), respecting the total budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "qbE1GDaB2sbL"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize two variables to save the results and base conditions for each scenario.\n",
    "scenarios_result = []\n",
    "scenarios = np.array([0.6, 0.8, 1.2, 1.8])\n",
    "for scenario in scenarios:\n",
    "    tmp_response = mmm.allocate_budget_to_maximize_response(\n",
    "        budget=total_budget * scenario,\n",
    "        num_days=8,\n",
    "        time_granularity=\"weekly\",\n",
    "        budget_bounds=budget_bounds,\n",
    "    )\n",
    "    scenarios_result.append(tmp_response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8pErqyBw3USg"
   },
   "source": [
    "We now have two lists containing the expected results with and without model optimization. This will return the same plot as before but based on each of the scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "id": "74q2NZd03SMr",
    "outputId": "485ced2a-34c4-4005-fba2-af180e42d974"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 989,
       "width": 1989
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_main, ax_main = plt.subplots(nrows=2, ncols=2, figsize=(20, 10))\n",
    "for i, scenario in enumerate(scenarios_result):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    _, ax = mmm.plot_budget_allocation(samples=scenario, ax=ax_main[row, col])\n",
    "    ax.set_title(f\"Budget Allocation for Scenario {scenarios[i] * total_budget:.0f}M\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N7G3lVcI3idn"
   },
   "source": [
    "The graph indicates that boosting the budget beyond an spend level greater than 3 Million induces extremely marginal changes in the potential outcome. Therefore, one can use the budget detailed in scenario three as a cap for our budget.\n",
    "\n",
    "However, is this the best method to invest our resources? So far, we have considered general constraints for each channel. However, since our curve saturates and beyond a specific point, it does not significantly elevate its contribution. Wouldn't it be crucial to incorporate these limitations?\n",
    "\n",
    "### Adding Business or Channel Constraints\n",
    "There may be instances where, despite the recommendation to invest more in a particular channel than another, it may not be feasible.\n",
    "\n",
    "Consider this: Given your current budget, you have already maxed out the number of people you can reach within a specific platform. Therefore, further spending will only increase the frequency without adding new reach. In this scenario, does it make sense to raise the budget? This is a classic example of budget limitations based on platform restrictions.\n",
    "\n",
    "In such cases, a dictionary can be created with limits for each channel, and the optimization function can be reapplied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "constrained_response = mmm.allocate_budget_to_maximize_response(\n",
    "    budget=total_budget,\n",
    "    num_days=8,\n",
    "    time_granularity=\"weekly\",\n",
    "    budget_bounds={\"x1\": [0, 4], \"x2\": [0, 3]},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mmm.plot_budget_allocation(samples=constrained_response, figsize=(12, 8))\n",
    "fig.suptitle(\"Response vs spent per channel\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xiIprHPQ3wPd"
   },
   "source": [
    "Utilizing bounds is crucial, as all saturation curves in PyMC-Marketing are asymptotic. Therefore, without a constraint on your factors, the optimizer will consistently seek to use 100% of the funds because it will always observe an increase within `y` (target).\n",
    "\n",
    "If you have yet to notice, this is why, in the previous example, the optimizer always utilized the whole budget in all scenarios. However, should you provide these limits, the optimizer can verify whether it is necessary to use your entire budget.\n",
    "\n",
    "Let us examine the subsequent plot closely!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [y]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit_constrained_response = mmm.allocate_budget_to_maximize_response(\n",
    "    budget=2,\n",
    "    num_days=8,\n",
    "    time_granularity=\"weekly\",\n",
    "    budget_bounds={\"x1\": [0, 1.2], \"x2\": [0, 1.5]},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mmm.plot_budget_allocation(\n",
    "    samples=limit_constrained_response, figsize=(12, 8)\n",
    ")\n",
    "fig.suptitle(\"Response vs spent per channel\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PPPFEXH755TF79uyIiBgzZkz87//+b5x33nmNelwAAADapkcffTT++7//u+DYpZdeGh/5yEc2eF3d4FdjKkXXPbdDhw5FX1vXihUr4owzzogZM2bkjw0ePDiuuOKKTZ6zVDRX+8p1Q3ANPU4ul4sJEybkx7vuumvsuuuuTfLYLU24CwAAAABKSPfu3aNHjx4FbRbXhJzquuiii2LAgAEbnbNjx45x3HHHxUc/+tH4yle+Eu+9915ERPzhD3+IM844I7p06VJwfnl5eX7erbbaquBrvXr1Kuox15g1a1b85Cc/yY+z2Wz86le/io9//OMNnp8kSRxyyCExbNiwOOGEE/Ib3b/5zW/iS1/6UgwcOLDoxwYAAKDtGTNmTHz/+9+Pmpqa/LHzzjsvjjnmmI1eW7eKVGPCXXWrfm1qRaqqqqo4++yzY/LkyfljAwYMiFtuuaXe3+etUd2WkkuXLm2SeZcsWbLBx5k3b14sXLgwPx46dGiTPG4pSLX0AgAAAACAQnUDVQsWLGjwvMaErCIi+vXrFxdeeGF+vHTp0njyyScbv8BG+M1vflPQquLCCy9cb7BrXT179oyrrroqP66uri5oVwkAAED7M2XKlDjzzDMLQlmnnnpqfPOb3yzq+rqBrIZa+61PU7QbrKmpiR/84Afx73//O3+sd+/ecfvtt0efPn0aPV9zmDVrVuyxxx5F/Zs1a1a967t3714wnjt3bpOsq+48dR+n7t5Jqfw8m4JwFwAAAACUmLrvPl03HLW5PvnJT0ZZWVl+/PzzzzfZ3HUtX748/vznP+fHAwcOjC9/+ctFXz9kyJAYMWJEftzcQTQAAABK1/Tp0+Mb3/hGQZu/4447Ln7wgx8UPUffvn0LxnPmzCn62jVVsNfYZpttir52jZ/85CfxyCOP5MfdunWLW2+9NbbbbrtGz9VccrlcVFdXF/Uvl8vVu37w4MEF4xdffLFJ1jVt2rSC8e67714wborwXanSlhEAAAAASsy6rSUialsVNvb65cuXx7Jly6Kqqqre17t37x7vv/9+RES88cYbm77QjZg4cWJBMO2www6LVKpx7zfdf//9Y8KECRFR++7h2bNnR79+/Zp0nQAAAJS2d955J0455ZSCtnuHH354XHrppY2aZ+eddy4Yz549u+hr6wbBdtppp0Y99i9+8Yv44x//mB936tQpbr755nphqNZun332KRhPmTIlcrlco/c21vXOO+8UVOYaMGBAvcpcdVtarhsCbO2EuwAAAACgxCxevLhgXF5evsHzly9fHk899VQ88cQT8fLLL8eMGTNi1apVm/RYTWnixIkF4z59+sTMmTMbNce6VcYiIt59913hLgAAgHZk7ty5MXr06PyblCIiDjrooLjqqqsa/QaiuuGuutWgNuSll14qGDcm3HXTTTfFb37zm/w4m83GjTfeGEOHDi16ji1lwIAB8eqrr27W9f369csH5957772YMGFC7Lvvvps854MPPlgwbmiu5moHWQqEuwAAAACgxNQNXPXo0WO95957771xzTXXxPz58zfpsZYsWbJJ1xWj7ruar7jiirjiiis2a85FixZt1vUAAAC0HvPnz4/Ro0fHrFmz8sf222+/uO666+q9GagYgwYNiq222ir/d/fkyZOLvvb555/Pf55Op+tVqFqf3//+9/HLX/4yP85kMvHLX/4yRo0aVfRjtzZf+MIX4vrrr8+P77nnnk0Od1VVVcX9999fcOy4446rd16fPn1i6623zlf4euGFFzbp8UpR4yKMAAAAAECzmj9/fr2g1voqVV1xxRXx4x//eJODXRERuVxuk6/dmHXbZTSVZcuWNfmcAAAAlJ6lS5fGaaedFm+++Wb+2JAhQ+Kmm26KDh06bNKcmUwmPv7xj+fH7733XlEhoLlz5xacN2zYsA2+EWuNv/zlL/Gzn/0sP06lUvHzn/88Dj744EauvHU54YQTIpvN5scPP/xwverexbr99tsLqoB/5CMfieHDh9c7L0mSggDZ9OnTY/r06Zv0mHVlMoW1s2pqappk3mIJdwEAAABACWloU3nw4MH1jv3tb3+LO++8s+DYoEGD4jvf+U785je/iUcffTQmTJgQU6dOjVdffbXgX//+/Ztt/euqqqpq8jmbM4wGAABAaVi5cmWcccYZBa0Qd91117j11lujS5cumzX3EUccUTC+++67N3rNPffcE9XV1fnx4YcfvtFrnnjiifjhD39Y8HfspZdeGp/5zGcasdrWqWfPnvG1r30tP87lcvGjH/2o0W9Oe/HFF+PGG28sOHbOOees9/zPfvazBeN1q4dtjs6dOxeMt3RVcW0ZAQAAAKCE/Oc//ykYd+vWLQYNGlTvvLoblN/5znfiv/7rvyJJko0+xtKlSzdvkUXq3r17wfiee+6JYcOGbZHHBgAAoHVatWpVfOc734nx48fnj+2www5x++23R7du3TZ7/oMPPjh23XXXfFWnv/zlL/HFL35xvW0D33rrrfjNb36TH/fu3bvBtoDrGjt2bJx77rmxatWq/LELL7xwo9e1Jd/5znfiP//5T7z88ssRUftzPPXUU+PWW2+Nnj17bvT6l156KU499dRYsWJF/thJJ50UBx544HqvOeSQQ2KnnXbKV3v7+9//HrfddluccsopRa05l8s1uK9S901yU6dOjb322quoOZuCyl0AAAAAUCKWLl0a999/f8Gxj3/845FOpwuOvf322/HGG2/kx/vuu2+ceeaZRQW7Vq5cGYsXL26aBW9Er169CsZvv/32FnlcAAAAWqdcLhcXXHBBPPXUU/lj/fv3jzvuuKPe35ibKkmS+O53v1vwmGeeeWaMGTOm3rnTpk2L0aNHR0VFRf7YWWedtcG2kC+++GKceeaZBdd8+9vfjtGjRzfJ+luLbDYbV199dcEbv6ZNmxZHHnlk3HvvvbFy5coGr5s/f3788pe/jOOPPz4WLlyYPz5kyJD4/ve/v8HHTKVScdlllxW0Ufz5z38eF110Ubz//vvrve6VV16Jn/3sZ3Httdc2+PVddtklunbtmh/fdNNN8fTTT6/3e2hqKncBAAAAQIm47bbb6lXVOv744+udN2PGjILxJz7xiaIf4/nnn29Ua8NiAmPrM2zYsLjjjjvy42eeeSaOOeaYTZ4PAACAtm327Nnx17/+td6xgw8+uFHz9O/fP/7xj3+s9+uf/OQn4xvf+EbccsstERGxePHiGD16dOyzzz4xZMiQSKVS8eqrr8azzz5b8Df05z73ufjyl7+8wcf+/e9/H8uWLSs49qtf/Sp+9atfNep7uOKKK+Lzn//8Bs/59Kc/HbNmzap3vKampmB86KGHNnj9mWeeGWeddVaj1tUYO++8c9x+++1x+umnx7x58yIiYuHChfHjH/84rrjiihg5cmT0798/unbtGh9++GHMmDEjJkyYUFDxLCJixIgRceONN0Z5eflGH3P48OHx4x//OH7605/m/9v96U9/ivvvvz+GDRsWu+22W3Tr1i1WrFgRs2fPjqlTp8bMmTMjIgpaSa4rk8nEscceG3feeWdERMybNy+++c1vRkREhw4dIpVaW1vrlltuiREjRjTyJ7Vhwl0AAAAAUAKeffbZuOmmmwqO7bfffg22hahbeWvdd49uTN3KYBuTzWYLxpWVlUVfO2rUqEin01FdXR0REU8++WR8+OGHRbVfAAAAoP1p6M1IuVwu/3dlsYo5/7vf/W6sXLky7rrrrvyxSZMmxaRJkxo8/8gjj4zLLrtso/M29D00dv0R9QNaDamuri5q7vWd05g3f22qPfbYI+6777645JJL4oknnsgfX758eTz55JMbvDaTycQpp5wSZ599dr39iQ054YQTomfPnnHBBRfkg3ZVVVUxbty4GDdu3CZ9H+ecc068+OKLMXHixILjdat3bcp/643RlhEAAAAAWthDDz0U3/rWtwo2bjt27Bg/+clPGjx/q622Khi/+eabRT3O1KlT45FHHmnU2uoGxzbUxqCubt26xWc/+9n8ePny5fHzn/+8UY8PAAAAzSGVSsWPf/zjuPnmm2Po0KHrPW/XXXeNX/ziF3HttdcWVTmK+nr37h033nhj/P73v48jjjhig20tIyJ69OgRJ510Ujz66KNx3nnnNSrYtcahhx4ajz/+eJxyyimx9dZbb/DcbbfdNkaPHr3eyl0REZ06dYq77rorrr322jjiiCNihx12iM6dOxdU7WouSW5LxPAAAAAAoI361Kc+VdAC4be//W3sv//+RV37/PPPx+233x6PPfZYwfF0Oh1XXXVVHHnkkQ1eN3fu3Pj4xz+eH3fv3j0eeuih6N2793of6913342TTz65XruG/v37b/CdsuPGjYuTTjopP/7MZz4TV1999Qa/r7qPe+SRRxZU/DrttNPiu9/9bqTT6aLnef3112PBggUNVjIDAACAzTVjxox46aWXYt68eVFdXR19+/aNQYMGxW677dbSS2tzKisr46WXXoo333wz5s+fH5WVldGxY8fo06dPDBo0KHbddddIkqTJHi+Xy8W0adPitddeiwULFsTKlSujU6dOse2228agQYNixx13bLLHag7CXQAAAACwGeqGu6655prYe++9C86prq6OJUuWxJIlS2LWrFkxZcqUmDhxYrz++uv15uvUqVNceeWVcfjhh2/wcb/yla8UtALYaaed4sorr6z3buPKysr4y1/+Etdcc03Mnz8/ysvLI51Ox/LlyyNi4+Gu5cuXx0c/+tH8+UmSxIknnhiHHHJIbLPNNlFWVlZw/oABA+rN8cADD8T5559fcGzo0KFx+umnx8c//vHIZDINPvbMmTPj6aefjsceeyyee+65OOuss+Lss89e/w8FAAAAoI0R7gIAAACAzVA33LU5DjjggLjoootip5122ui5kyZNipNOOilWrVpVcHzHHXeMXXfdNcrKyuKDDz6IKVOm5INZERGXXXZZ3HTTTfk1byzcFRHx85//PG677baivodXX32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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_response_original_scale_optimize = (\n",
    "    limit_constrained_response[\"y\"] * mmm.get_target_transformer()[\"scaler\"].scale_\n",
    ")\n",
    "\n",
    "# Plotting optimized response\n",
    "y_response_original_scale_optimize.mean(dim=\"sample\").plot(\n",
    "    label=f\"Optimized Response | Daily spent {sum(mmm.optimal_allocation_dict.values()):.2f}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=y_response_original_scale_optimize.date.values,\n",
    "    y1=y_response_original_scale_optimize.quantile(0.025, dim=\"sample\"),\n",
    "    y2=y_response_original_scale_optimize.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Plotting initial budget response\n",
    "response_initial_budget[\"y\"].mean(dim=\"sample\").plot(\n",
    "    label=f\"Initial Budget Response | Daily spent {5.00}M\"\n",
    ")\n",
    "plt.fill_between(\n",
    "    x=response_initial_budget.y.date.values,\n",
    "    y1=response_initial_budget.y.quantile(0.025, dim=\"sample\"),\n",
    "    y2=response_initial_budget.y.quantile(0.95, dim=\"sample\"),\n",
    "    alpha=0.1,\n",
    ")\n",
    "\n",
    "# Adding labels, legend, and title\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Response\")\n",
    "plt.legend()\n",
    "plt.title(\"Comparison of Optimized and Initial Budget Responses\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h6yOpHXE4OLE"
   },
   "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.\n",
    "\n",
    "## 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 periods.\n",
    "\n",
    "Nevertheless, it is pivotal to acknowledge that these models do possess their own set of limitations. The primary ones are the assumptions of time invariance and generalized behavior, signifying an incomplete comprehension of market dynamics. The no-impact variance assumptions are not specific to the curves, as the model does not account for these effects. Hence, curves should always be carefully considered.\n",
    "\n",
    "\n",
    "## Other methods to explore\n",
    "Even if the method is promising, use other optimization options which includes the full posterior could be a powerful and interesting solution as it's described on the following blog [\"Using bayesian decision making to optimize supply chains\"](https://twiecki.io/blog/2019/01/14/supply_chain/)\n",
    "\n",
    "The current methodology is similar to the ones used on other libraries as [Robyn from Meta](https://facebookexperimental.github.io/Robyn/docs/features/#budget-allocation) and Google [Lightweight from Google](https://github.com/google/lightweight_mmm). You can explore the solutions and compare if needed.\n",
    "\n",
    "## Conclusion\n",
    "MMM models and methodologies used here are designed to bridge the gap between theoretical rigor and actionable marketing insights. They represent a significant stride towards a more data-driven, analytical approach to marketing budget allocation, which could change how organizations invest in customer acquisition and retention.\n",
    "\n",
    "Although it is a promising tool, it is essential to highlight that this methodology and software release is still experimental. Like any emerging technology, it comes with inherent limitations and assumptions that users should be aware of. The models can offer actionable insights, but they should be cautiously used and in tandem with various forms of analysis. Context is crucial, and while models aim to encapsulate general trends, they might not account for all nuances.\n",
    "\n",
    "Consequently, your engagements, feedback, and thoughts are not merely welcomed but actively solicited to make this tool as practical and universally applicable as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sat Jun 08 2024\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.14\n",
      "IPython version      : 8.24.0\n",
      "\n",
      "pytensor: 2.20.0\n",
      "\n",
      "matplotlib: 3.8.4\n",
      "arviz     : 0.18.0\n",
      "pandas    : 2.2.2\n",
      "numpy     : 1.26.4\n",
      "\n",
      "Watermark: 2.4.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  }
 ],
 "metadata": {
  "colab": {
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  "kernelspec": {
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