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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": null,
   "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": 84,
   "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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Dl+zZs0dPP/20JNs9eMyYMYqPj9eAAQMUGBiosrIy7dq1Sz/88IP9nDFjxmj16tX27cWLF+uBBx5wKdhWMRB24YUXqnXr1tUeP3fuXL3++usO+3r06KGbbrpJcXFx9u8cydaxa/ny5Xr77bftoc7CwkI9+OCDWrJkibp06VLlNXbv3q3Zs2c77Dv55JN16623aujQoerWrZtDqGv//v3KyMjQ+vXrtXLlSqe6HQIAAAAA0BQYpmma3i4CAAAAAABPmzhxosMvt8866yy999579V5369atGj16tMO+f//737rjjjucqmP69Ok1jsaaPXu25syZU2l/ZGSk5s6daw8Q1eaee+7RypUr7dtdunTRyy+/rOjo6BrPW7JkiSZNmmQPIBmGoU8//VT9+/ev9pzPPvtMCQkJDvtOP/10vfLKKzrppJNqrXX//v365ptvdPvtt1f5+K5du3T++efbt7t169agI9huvvlmh7BLucsvv1zPPfdclZ1sKjp8+LBGjRrlEFAaNGiQZsyYoc6dO1d7XmlpqZ599ll98MEH9n3t27fXDz/8YA/lnGjTpk269tpr7dtt2rTRp59+qvDw8FprlGxhoN27d2vIkCHVHhMVFVXl/htvvLHGUZ7bt2/XLbfc4tDhq3379lq6dKnCwsKqvd7kyZMrhaDuvvtuPfjgg9V2tNq7d69uvfVWZWVl2fcFBATos88+U9++fau9VsWf13Lnnnuu/vOf/1TZVU2yhbzuvfdeh0BO+/btlZSUVG2HtCeffNIhKHrhhRfqpZdeqtTJqSqmaWrVqlXq06dPtfeE4uJiXX/99frll1/s+0499VS9/PLLOuWUU2pc/80339SMGTPs2y1atND333/v1M+zqzx9/3D1HlzR+vXrNXbsWPt2bd8lFY8vFxoaqtdff10DBw6s9ZpFRUUaOnSo8vPz7fvefffdGn8uT1RcXKy4uDiHDlzz5s3TOeecU+XxaWlpuvHGGx2CpzfeeKMmTpxY4/0tNzdX999/vzZu3GjfN2LECL3xxhtVHl/xcxUdHa133nmn2p+rilJSUtS2bdtaP78AAAAAADR2jGUEAAAAAKAequp8cuDAAY9es02bNi4Fu/73v/85BLtCQ0P1zjvv1BrskqRRo0Y5dOoyTVNvv/12tccXFRU5dNWSpD59+uj99993OgjSuXPnaoMZviomJkYvvviiU8EuSXrnnXccgl2nn3663nzzzRqDXZKt088TTzyh8847z77v4MGD+uKLL6o8/sQgjyRdd911Tge7JKlnz55OB0hONGLECD3xxBM1jlI75ZRT9MYbbzgcc/DgQX366afVnrN//34tXLjQYd/o0aP18MMP1ziq8KSTTtI777zjMG7w+PHjlToTOaNfv36aNWtWjQGUoKAgTZs2zeHzcPDgwRo7oW3dutVh++GHH3Yq2CXZQpcndourypIlSxw+D926ddO7777rVDDmrrvu0s0332zfLioq0oIFC5yqzRXN6f4xY8YMp4Jdki1Md/nllzvsc2U048qVKx2CXSeddFKNP9cvvPCCQ7Druuuu0+TJk2u9v7Vr106vvfaaunXrZt+XmJio9PT0Ko+v+JkfP36808EuSRo4cCDBLgAAAABAs0C4CwAAAACAeqgq3FVYWOjRa953331OB7skVQpjjR8/Xr169XL6/Jtvvlk9e/a0by9btky5ublVHvvFF184hNsCAgI0Y8YMl35h3xhNnjy5xnDRiQoLC/XRRx/Ztw3D0NSpU51+jQzD0MSJEx1CURU7WZU7sdOPJPv4QE8KCAhQQkKCUyPjTj/9dF1//fUO+z7++GOVlZVVefxHH33kMGKyTZs2mjhxolN1de3atdJYxOXLl1ca71gbZ0Iuki1kNGLECId9J45qrMiT75Vpmpo3b57DvkmTJqlDhw5OrzFhwgSFhobatz///HOVlpa6rUap+dw/zjvvvGpHqVanYnex//73vw7jNWtSVSCyuvtVamqqfvrpJ/t2586dnf4Zk2xjcsePH++w77PPPqvyWG/cnwAAAAAAaIwIdwEAAAAAUA9VhQ5ODJ+4W2BgoOLj450+Pjc3V2vWrLFvt2zZUtdcc41L1wwICNDFF19s3y4rK3P45f+Jvv/+e4ftCy+8sMl3Vjn99NNrHO1XUXJyskMXnTPPPNOl8yXp5JNPVr9+/ezbv//+uw4dOlTpuBPDOJL0888/u3Sduhg6dKh69Ojh9PF///vfHbZ3796t33//vcpjV61a5bA9cuTIGkc4VnT11Vc7/MyWlpY6jE6sTZ8+fTRgwACnj4+NjXXYzszMrPZYT75Xv/zyi3bs2GHf7tq1q8NoU2e0bt1acXFx9u38/PxqOzLVVXO5f5w4KtVZ/fv3V2RkpH3barXq22+/rfW8nJwch8+4YRg1jqH8+uuvHbavuuoql8N1F110kUPXuarG2EreuT8BAAAAANAYEe4CAAAAAKAeCgoKKu1zdjRfXURFRbkUZklJSZFpmvbt2NhYWSwWl69bMXyUmppa6Zji4mKlpaU57LvyyitdvlZjM3jwYJeO37Bhg8P20KFD63TdE98T0zS1efPmSsf079/fYfuLL77QBx98UG1nLHdwNTTUu3dvnXzyyQ77qnouhYWFlcJEF154oUvXslgsDgElqerPcnUGDRrk0vUqhtwqdio6UcX36rHHHtNvv/3m0vWqU/Ezd8455zjVWa0iZ+4DddVc7h+GYbj8OSpXMZRVsSNXVZYsWeIwYnHQoEE1hi8rBrHqcn9q1aqVw8/077//XuV3ZcXP/PTp07V+/XqXrwcAAAAAQFMXUPshAAAAAACgOkeOHKm0Lzg42GPXO7FzizM2bdrksF1cXOwwEtBZ27dvd9jOycmpdMyOHTt07Ngxh33R0dEuX6uxiYqKcun4iu/Jrl276vSe7Nq1y2E7Ozu70jFnnHGG+vXrZ++IU1paqqefflpz587V5ZdfrmHDhmnAgAFuDSS62oWs/JydO3fat6sKNW3bts0hpGIYhk4//XSXr9WvXz+HDlGudJ/q1q2bS9eqGKSsaYzetddeqw8//NAexszKylJ8fLwGDx6siy66SEOGDHFpnOqJKn7mDh06VKfPXMXXqqr7QF01l/tHt27d1KpVqzqde+WVV2rGjBn27pAbN25UVlaWwsPDqz1n8eLFDttXXXVVtccWFhZW6pq3ceNGZWRkuFxrcXGx/e9lZWU6ePBgpZ+HUaNGafbs2bJarZKkgwcPauzYserfv78uueQSnXPOOYqMjHR65C0AAAAAAE0V4S4AAAAAAOrh8OHDlfZ17NjRY9dzpWuXJB04cMBhOyUlRSkpKfWuo6rnnZub67AdGhqqdu3a1ftavq5NmzYuHV/xPfn888/1+eef17uOqt4TSXr++ef197//3eHx3bt3680339Sbb76poKAg9e3bV7GxsRo8eLAGDx5cr4Bi165d633OiWMry1V8fm3atKk01s0Z3bt3r3Hdmrh6vYqhlJo6pp122ml66KGH9J///Me+zzRNrVu3TuvWrZMktWvXTtHR0Ro0aJD+9re/OR2kq/iZW758uZYvX+7s06iWK69dbZrL/cPV+8WJ2rdvr2HDhumHH36w71u0aJEeeuihKo/fvHmzQ1jLYrHooosuqnb9gwcPOnR6lKQXX3yxzvWeKC8vTz179nTY16FDBz399NN69NFHHX42Nm/ebO/e17p1a/Xv318DBw7U4MGDFR0dTdgLAAAAANDs8P+EAQAAAACoh6o6DNUl3OKskJAQl453Z/jiROWdVk5UMZDTunVrj1zb17j6nlQVXHKHil2PyvXu3VufffaZzjrrrCofLx+HN2/ePN199906++yz9a9//culjlYnqktXooqhqao+txVHGtZlvKhUuT5XfkbqMsrQFXfffbf+7//+Tx06dKjy8dzcXK1YsULPP/+8Ro8erQsvvFCvv/56jR3BJM/dB6r7zNVFc7l/1PVzW67iaMbFixdXGxqsOLbx0ksvrfF+5anPiVT9Z+XKK6/Um2++WSn4VS4/P1+rV6/WSy+9pBtuuEHDhw/Xiy++qIMHD3qsVgAAAAAAfA3hLgAAAAAA6qHiuDNJ6tOnjxcqqVr5+C74Dk+9JxU77pzo5JNP1nvvvacPPvhA1157rU466aRqjy0sLNSXX36pUaNGadq0aQ3yGaqp9uq4K2jl6cCWq6688kotX75c06ZN09ChQ2sM4/zxxx+aOXOmLrzwQiUlJVV73Ikj8typLu8b6mfEiBEO4b99+/Zp7dq1lY4rLi7W0qVLHfbVNJJR8uz3RU2flbi4OC1dulQvvfSSLrzwwhqDfdnZ2Xrrrbd0wQUXVAqvAQAAAADQVDGWEQAAAACAeli/fr3Ddvv27avtQOINFUeA/eMf/9B9993nkWtVHBlZsdMSbMLCwpSTk2Pf/uCDDzRw4MAGufbAgQPt19q1a5dSUlK0ceNGpaSkaPv27Q7Hmqap+fPnKy8vTy+88ILT1zh69KjL4/Qqdp6qanRdxcBHbd2qnL2WL3aICg4O1lVXXaWrrrpKJSUl2rp1q1JSUvTTTz/pp59+qtTlKjc3V/fdd5/eeOMNDR06tNJ6FX82X3jhBY0aNcqDz8B1jeX+UdNozYYQEBCgK6+8UvPmzbPvW7RoUaX3ffny5Q6duMLDwxUbG1vj2hV/7gIDA7V58+YGGYMYGBioSy+9VJdeeqnKysqUnp5u/7xv2LDB4Z4p2UKojz32mMrKynT11Vd7vD4AAAAAALyJzl0AAAAAANTRmjVrlJWV5bBv+PDhPtUJqGLI5s8//2ywax05ckS5ubkeu15j1bZtW4ftXbt2eaWO7t27Kz4+Xk8//bSWLl2qFStW6KGHHqpU35IlS/S///3P6XX37Nnjci0Vz6kY9Klq3+HDh3XkyBGXr1Xx9a4qSOZLAgMDNWDAAN1xxx169dVXtXbtWr3//vu64oorHO41x48f15NPPqnjx49XWqPie+rJ+0B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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": 86,
   "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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      "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": 87,
   "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": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>0.363</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2198.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_beta[x2]</th>\n",
       "      <td>0.270</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x1]</th>\n",
       "      <td>3.942</td>\n",
       "      <td>0.385</td>\n",
       "      <td>3.227</td>\n",
       "      <td>4.675</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2235.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturation_lam[x2]</th>\n",
       "      <td>3.162</td>\n",
       "      <td>1.210</td>\n",
       "      <td>0.899</td>\n",
       "      <td>5.309</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.035</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x1]</th>\n",
       "      <td>0.402</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2451.0</td>\n",
       "      <td>2767.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adstock_alpha[x2]</th>\n",
       "      <td>0.187</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>2127.0</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]  0.363  0.020   0.324    0.400      0.000    0.000   \n",
       "saturation_beta[x2]  0.270  0.093   0.191    0.401      0.003    0.012   \n",
       "saturation_lam[x1]   3.942  0.385   3.227    4.675      0.008    0.006   \n",
       "saturation_lam[x2]   3.162  1.210   0.899    5.309      0.035    0.035   \n",
       "adstock_alpha[x1]    0.402  0.030   0.342    0.458      0.001    0.000   \n",
       "adstock_alpha[x2]    0.187  0.041   0.108    0.264      0.001    0.001   \n",
       "\n",
       "                     ess_bulk  ess_tail  r_hat  \n",
       "saturation_beta[x1]    2006.0    2198.0    1.0  \n",
       "saturation_beta[x2]    1080.0     712.0    1.0  \n",
       "saturation_lam[x1]     2235.0    1970.0    1.0  \n",
       "saturation_lam[x2]     1095.0     753.0    1.0  \n",
       "adstock_alpha[x1]      2451.0    2767.0    1.0  \n",
       "adstock_alpha[x2]      1626.0    2127.0    1.0  "
      ]
     },
     "execution_count": 88,
     "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": 89,
   "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": 90,
   "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": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Budget Allocation Summary ===\n",
      "\n",
      "Channel x1:     2.78M\n",
      "Channel x2:     2.22M\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": 92,
   "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": 93,
   "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": 94,
   "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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 2.5$"
      ],
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_budget.sel(channel=\"x1\").item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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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    },
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       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\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",
       "  border: 2px solid transparent !important;\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) !important;\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",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\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",
       "  border-color: var(--xr-background-color-row-even);\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",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\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-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\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",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\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 7.247 8.171 7.661 ... 10.63 9.111\n",
       "Attributes:\n",
       "    created_at:                 2025-06-16T17:27:32.627065+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.23.0</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-d834f00f-5a23-4d5a-a54b-225f65bf06e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d834f00f-5a23-4d5a-a54b-225f65bf06e4' 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-22eae974-b85f-461c-b193-c23beb24bdd5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22eae974-b85f-461c-b193-c23beb24bdd5' 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-f9c2a184-5d02-42bf-9bc9-e3a554590103' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9c2a184-5d02-42bf-9bc9-e3a554590103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-016904ac-7ae9-4774-a32a-287cc1112616' class='xr-var-data-in' type='checkbox'><label for='data-016904ac-7ae9-4774-a32a-287cc1112616' 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-228425a3-d668-4d5b-8df5-c07541c08873' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-228425a3-d668-4d5b-8df5-c07541c08873' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-265cefe2-1b8e-4407-880f-cfef249c3bfc' class='xr-var-data-in' type='checkbox'><label for='data-265cefe2-1b8e-4407-880f-cfef249c3bfc' 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-855f4c75-e8ef-475a-be35-bfbe92ed5655' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-855f4c75-e8ef-475a-be35-bfbe92ed5655' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08fe050c-f5ee-4a4c-8e04-f48212a9a322' class='xr-var-data-in' type='checkbox'><label for='data-08fe050c-f5ee-4a4c-8e04-f48212a9a322' 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-e4952aa3-12c8-4c1b-a013-d754e600ceae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4952aa3-12c8-4c1b-a013-d754e600ceae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f61a874-14f4-402e-bc25-fcba4cc91fcb' class='xr-var-data-in' type='checkbox'><label for='data-5f61a874-14f4-402e-bc25-fcba4cc91fcb' 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-3f35e95f-fc77-4117-b89e-0126c4bb7f89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f35e95f-fc77-4117-b89e-0126c4bb7f89' 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'>7.247 8.171 7.661 ... 10.63 9.111</div><input id='attrs-66018420-8c0e-46ae-85d8-826e3d03d4fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66018420-8c0e-46ae-85d8-826e3d03d4fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ad841e-ff8f-4155-b1dd-ee5b08b2ec85' class='xr-var-data-in' type='checkbox'><label for='data-29ad841e-ff8f-4155-b1dd-ee5b08b2ec85' 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([[ 7.24694731,  8.17077489,  7.66068268, ...,  9.0907981 ,\n",
       "         8.91243481,  7.82049566],\n",
       "       [ 7.25567085,  8.33231888,  7.69936687, ...,  9.3023059 ,\n",
       "         9.63623042,  7.76726168],\n",
       "       [ 7.3209977 ,  8.74678042,  8.44054748, ...,  8.87320069,\n",
       "         9.52392751,  8.05051326],\n",
       "       ...,\n",
       "       [ 8.23762182,  9.15946845,  7.96896777, ...,  9.51293391,\n",
       "        10.03146007,  8.44210133],\n",
       "       [ 8.34148829,  9.55843846,  8.77842999, ...,  9.84012948,\n",
       "        10.38373255,  8.90157153],\n",
       "       [ 8.31328795,  9.31921048,  8.23122352, ..., 10.32436764,\n",
       "        10.62863505,  9.11090499]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50a632ef-abe8-4d12-93b4-958fb7bdb69e' class='xr-section-summary-in' type='checkbox'  ><label for='section-50a632ef-abe8-4d12-93b4-958fb7bdb69e' 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-1585c0f7-0f94-4771-8b51-3f8ea0f3b57b' class='xr-index-data-in' type='checkbox'/><label for='index-1585c0f7-0f94-4771-8b51-3f8ea0f3b57b' 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-274f2e78-d7c4-448b-b8be-e6beed704baa' class='xr-index-data-in' type='checkbox'/><label for='index-274f2e78-d7c4-448b-b8be-e6beed704baa' 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-d800715a-cb53-4caf-904c-5cf98e1d2227' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d800715a-cb53-4caf-904c-5cf98e1d2227' 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>2025-06-16T17:27:32.627065+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.23.0</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 7.247 8.171 7.661 ... 10.63 9.111\n",
       "Attributes:\n",
       "    created_at:                 2025-06-16T17:27:32.627065+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.23.0"
      ]
     },
     "execution_count": 96,
     "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": 97,
   "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": 98,
   "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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      "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": 99,
   "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": 100,
   "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=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": 101,
   "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": 102,
   "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": 103,
   "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": 104,
   "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": 105,
   "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": 106,
   "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": 107,
   "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": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal allocation: <xarray.DataArray (channel: 2)> Size: 16B\n",
      "array([1.05121752, 0.92452914])\n",
      "Coordinates:\n",
      "  * channel  (channel) <U2 16B 'x1' 'x2'\n",
      "Solver result:  message: Optimization terminated successfully\n",
      " success: True\n",
      "  status: 0\n",
      "     fun: 1.9757466541155402\n",
      "       x: [ 1.051e+00  9.245e-01]\n",
      "     nit: 19\n",
      "     jac: [ 1.000e+00  1.000e+00]\n",
      "    nfev: 31\n",
      "    njev: 19\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_contribution\")\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_contribution\",\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": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean response for [1.05121752 0.92452914] = 4.99999999992943\n"
     ]
    }
   ],
   "source": [
    "resp_dist_sym = optimizer.extract_response_distribution(\"total_contribution\")\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": 110,
   "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": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n",
      "Last updated: Mon Jun 16 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.18\n",
      "IPython version      : 8.37.0\n",
      "\n",
      "pytensor: 2.31.3\n",
      "\n",
      "ipykernel     : 6.29.5\n",
      "debugpy       : 1.8.14\n",
      "matplotlib    : 3.10.3\n",
      "pymc_marketing: 0.14.0\n",
      "numpy         : 1.26.4\n",
      "pandas        : 2.3.0\n",
      "pytensor      : 2.31.3\n",
      "arviz         : 0.21.0\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  }
 ],
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