{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Nested Logit and Non-Proportional Patterns of Substitution" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/pymc_extras/model/marginal/graph_analysis.py:10: FutureWarning: `pytensor.graph.basic.io_toposort` was moved to `pytensor.graph.traversal.io_toposort`. Calling it from the old location will fail in a future release.\n", " from pytensor.graph.basic import io_toposort\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload\n", "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "from pymc_marketing.customer_choice.nested_logit import NestedLogit\n", "from pymc_marketing.paths import data_dir\n", "from pymc_marketing.prior import Prior" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "az.style.use(\"arviz-darkgrid\")\n", "plt.rcParams[\"figure.figsize\"] = [12, 7]\n", "plt.rcParams[\"figure.dpi\"] = 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've seen in the other example notebook for consumer choice how the Multinomial Logit model suffers from the IIA limitation that leads to implausible counterfactual inferences regarding market behaviour. If you haven't read that one, we advise you read it before continuing. We will now show how the nested logit model specification avoids this property by adding more explicit structure to the choice scenarios that are modelled. \n", "\n", "In this notebook we will re-use the same heating choice data set seen in the Multinomial Logit example and apply a few different specifications of a nested logit model. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idcasedepvaric_gcic_gric_ecic_eric_hpoc_gcoc_groc_ecoc_eroc_hpincomeagehedroomsregion
01gc866.00962.64859.90995.761135.50199.69151.72553.34505.60237.887256ncostl
12gc727.93758.89796.82894.69968.90168.66168.66520.24486.49199.195605scostl
23gc599.48783.05719.86900.111048.30165.58137.80439.06404.74171.474652ncostl
34er835.17793.06761.25831.041048.70180.88147.14483.00425.22222.952504scostl
45er755.59846.29858.86985.64883.05174.91138.90404.41389.52178.492256valley
...................................................
895896gc766.39877.71751.59869.78942.70142.61136.21474.48420.65203.006204mountn
896897gc1128.501167.801047.601292.601297.10207.40213.77705.36551.61243.767457scostl
897898gc787.101055.20842.791041.301064.80175.05141.63478.86448.61254.515607scostl
898899gc860.561081.30799.761123.201218.20211.04151.31495.20401.56246.485506scostl
899900gc893.941119.90967.881091.701387.50175.80180.11518.68458.53245.132654ncostl
\n", "

900 rows × 16 columns

\n", "
" ], "text/plain": [ " idcase depvar ic_gc ic_gr ic_ec ic_er ic_hp oc_gc \\\n", "0 1 gc 866.00 962.64 859.90 995.76 1135.50 199.69 \n", "1 2 gc 727.93 758.89 796.82 894.69 968.90 168.66 \n", "2 3 gc 599.48 783.05 719.86 900.11 1048.30 165.58 \n", "3 4 er 835.17 793.06 761.25 831.04 1048.70 180.88 \n", "4 5 er 755.59 846.29 858.86 985.64 883.05 174.91 \n", ".. ... ... ... ... ... ... ... ... \n", "895 896 gc 766.39 877.71 751.59 869.78 942.70 142.61 \n", "896 897 gc 1128.50 1167.80 1047.60 1292.60 1297.10 207.40 \n", "897 898 gc 787.10 1055.20 842.79 1041.30 1064.80 175.05 \n", "898 899 gc 860.56 1081.30 799.76 1123.20 1218.20 211.04 \n", "899 900 gc 893.94 1119.90 967.88 1091.70 1387.50 175.80 \n", "\n", " oc_gr oc_ec oc_er oc_hp income agehed rooms region \n", "0 151.72 553.34 505.60 237.88 7 25 6 ncostl \n", "1 168.66 520.24 486.49 199.19 5 60 5 scostl \n", "2 137.80 439.06 404.74 171.47 4 65 2 ncostl \n", "3 147.14 483.00 425.22 222.95 2 50 4 scostl \n", "4 138.90 404.41 389.52 178.49 2 25 6 valley \n", ".. ... ... ... ... ... ... ... ... \n", "895 136.21 474.48 420.65 203.00 6 20 4 mountn \n", "896 213.77 705.36 551.61 243.76 7 45 7 scostl \n", "897 141.63 478.86 448.61 254.51 5 60 7 scostl \n", "898 151.31 495.20 401.56 246.48 5 50 6 scostl \n", "899 180.11 518.68 458.53 245.13 2 65 4 ncostl \n", "\n", "[900 rows x 16 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_path = data_dir / \"choice_wide_heating.csv\"\n", "df = pd.read_csv(data_path)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Single Layer Nesting\n", "\n", "The important addition gained through the nested logit specification is the ability to specify \"nests\" of products in this way we can partition the market into \"natural\" groups of competing products ensuring that there is an inherent bias in the model towards a selective pattern of preference. As before we specify the models using formulas, but now we also add a nesting structure. \n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/4147728590.py:21: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n", " \"alphas_\": Prior(\"Normal\", mu=0, sigma=5, dims=\"alts\"),\n", "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/4147728590.py:22: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n", " \"betas\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alt_covariates\"),\n", "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/4147728590.py:23: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n", " \"betas_fixed_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"fixed_covariates\"),\n", "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/4147728590.py:24: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n", " \"lambdas_nests\": Prior(\"Beta\", alpha=2, beta=2, dims=\"nests\"),\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## No Fixed Covariates\n", "utility_formulas = [\n", " \"gc ~ ic_gc + oc_gc | income + rooms \",\n", " \"ec ~ ic_ec + oc_ec | income + rooms \",\n", " \"gr ~ ic_gr + oc_gr | income + rooms \",\n", " \"er ~ ic_er + oc_er | income + rooms \",\n", " \"hp ~ ic_hp + oc_hp | income + rooms \",\n", "]\n", "\n", "\n", "nesting_structure = {\"central\": [\"gc\", \"ec\"], \"room\": [\"hp\", \"gr\", \"er\"]}\n", "\n", "\n", "nstL_1 = NestedLogit(\n", " df,\n", " utility_formulas,\n", " \"depvar\",\n", " covariates=[\"ic\", \"oc\"],\n", " nesting_structure=nesting_structure,\n", " model_config={\n", " \"alphas_\": Prior(\"Normal\", mu=0, sigma=5, dims=\"alts\"),\n", " \"betas\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alt_covariates\"),\n", " \"betas_fixed_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"fixed_covariates\"),\n", " \"lambdas_nests\": Prior(\"Beta\", alpha=2, beta=2, dims=\"nests\"),\n", " },\n", ")\n", "nstL_1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will dwell a bit on the manner in which these nests are specified and why. The nested logit model partitions the choice set into nests of alternatives that share common unobserved attributes (i.e., are more similar to each other). It computes the overall probability of choosing an alternative as the product of (1) The probability of choosing a nest (marginal probability), and (2) the probability of choosing an alternative within that nest (conditional probability, given that nest). In our case we want to isolate the probability of choosing a central heatings system and a room based heating system. \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each of the alternatives `alt` is indexed to a nest. So that we can determine (§) the marginal probability of choosing `room` or `central` and (2) conditional probability of choosing `ec` given that you have chosen `central`. Our utilities are decomposed into contributions from fixed_covariates and alternative specific covariates: \n", "\n", "$$ U = Y + W + \\epsilon $$ \n", "\n", "and the probabilities are derived from these decomposed utilitiies in the following manner. \n", "\n", "$$ P(i) = P(\\text{choose nest B}) \\cdot P(\\text{choose i} | \\text{ i} \\in \\text{B}) $$\n", "\n", "where \n", "\n", "$$ P(\\text{choose nest B}) = \\dfrac{e^{W + \\lambda_{k}I_{k}}}{\\sum_{l=1}^{K} e^{W + \\lambda_{l}I_{l}}} $$\n", "\n", "and \n", "\n", "$$ P(\\text{choose i} | \\text{ i} \\in \\text{B}) = \\dfrac{e^{Y_{i} / \\lambda_{k}}}{\\sum_{j \\in B_{k}} e^{Y_{j} / \\lambda_{k}}} $$\n", "\n", "while the inclusive term $I_{k}$ is:\n", "\n", "$$ I_{k} = ln \\sum_{j \\in B_{k}} e^{Y_{j} / \\lambda_{k}} \\text{ and } \\lambda_{k} \\sim Beta(1, 1) $$\n", "\n", "such that $I_{k}$ is used to \"aggregate\" utilities within a nest a \"bubble up\" their contribution to decision through the product of the marginal and conditional probabilities. More extensive details of this mathematical formulation can be found in Kenneth Train's \"Discrete Choice Methods with Simulation\". \n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sampling: [alphas_, betas, betas_fixed_, lambdas_nests, likelihood]\n", "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/pymc/sampling/mcmc.py:328: UserWarning: `idata_kwargs` are currently ignored by the nutpie sampler\n", " warnings.warn(\n", "Sampling: [likelihood]\n" ] }, { "data": { "text/html": [ "
/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "
\n" ], "text/plain": [ "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n", "UserWarning: install \"ipywidgets\" for Jupyter support\n", " warnings.warn('install \"ipywidgets\" for Jupyter support')\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nstL_1.sample(\n",
    "    fit_kwargs={\n",
    "        \"target_accept\": 0.97,\n",
    "        \"tune\": 2000,\n",
    "        \"nuts_sampler\": \"nutpie\",\n",
    "        \"idata_kwargs\": {\"log_likelihood\": True},\n",
    "        \"progressbar\": False,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model structure is quite a bit more complicated now than the simpler multinomial logit as we need to calculate the marginal and conditional probabilities within each of the nests seperately and then \"bubble up\" the probabilies as a product over the branching nests. These probabilities are deterministic functions of the summed utilities and are then fed into our categorical likelihood to calibrate our parameters against the observed data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "clusteralts (5)\n",
       "\n",
       "alts (5)\n",
       "\n",
       "\n",
       "clusteralt_covariates (2)\n",
       "\n",
       "alt_covariates (2)\n",
       "\n",
       "\n",
       "clusternests (2)\n",
       "\n",
       "nests (2)\n",
       "\n",
       "\n",
       "clusterobs (900) x fixed_covariates (2)\n",
       "\n",
       "obs (900) x fixed_covariates (2)\n",
       "\n",
       "\n",
       "clusterfixed_covariates (2)\n",
       "\n",
       "fixed_covariates (2)\n",
       "\n",
       "\n",
       "clusteralts (5) x fixed_covariates (2)\n",
       "\n",
       "alts (5) x fixed_covariates (2)\n",
       "\n",
       "\n",
       "cluster900 x 5\n",
       "\n",
       "900 x 5\n",
       "\n",
       "\n",
       "clusterobs (900) x alts (5) x alt_covariates (2)\n",
       "\n",
       "obs (900) x alts (5) x alt_covariates (2)\n",
       "\n",
       "\n",
       "clusterobs (900)\n",
       "\n",
       "obs (900)\n",
       "\n",
       "\n",
       "clusterobs (900) x alts (5)\n",
       "\n",
       "obs (900) x alts (5)\n",
       "\n",
       "\n",
       "cluster900 x 2\n",
       "\n",
       "900 x 2\n",
       "\n",
       "\n",
       "cluster900 x 3\n",
       "\n",
       "900 x 3\n",
       "\n",
       "\n",
       "cluster900\n",
       "\n",
       "900\n",
       "\n",
       "\n",
       "\n",
       "alphas_\n",
       "\n",
       "alphas_\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "alphas\n",
       "\n",
       "alphas\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "alphas_->alphas\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "betas_fixed\n",
       "\n",
       "betas_fixed\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "alphas->betas_fixed\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "U\n",
       "\n",
       "U\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "alphas->U\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "betas\n",
       "\n",
       "betas\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "betas->U\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests\n",
       "\n",
       "lambdas_nests\n",
       "~\n",
       "Beta\n",
       "\n",
       "\n",
       "\n",
       "P_central\n",
       "\n",
       "P_central\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests->P_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "P_room\n",
       "\n",
       "P_room\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests->P_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "p_y_given_central\n",
       "\n",
       "p_y_given_central\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests->p_y_given_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "I_central\n",
       "\n",
       "I_central\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests->I_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "I_room\n",
       "\n",
       "I_room\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests->I_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "p_y_given_room\n",
       "\n",
       "p_y_given_room\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests->p_y_given_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "denom_top\n",
       "\n",
       "denom_top\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "lambdas_nests->denom_top\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "W\n",
       "\n",
       "W\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "w_nest\n",
       "\n",
       "w_nest\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "W->w_nest\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "betas_fixed_\n",
       "\n",
       "betas_fixed_\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "betas_fixed_->betas_fixed\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "betas_fixed->w_nest\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "betas_fixed->P_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "betas_fixed->P_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "betas_fixed->denom_top\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "X\n",
       "\n",
       "X\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "X->U\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "likelihood\n",
       "\n",
       "likelihood\n",
       "~\n",
       "Categorical\n",
       "\n",
       "\n",
       "\n",
       "y\n",
       "\n",
       "y\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "likelihood->y\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "prod_central_t\n",
       "\n",
       "prod_central_t\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "P_central->prod_central_t\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "prod_room_t\n",
       "\n",
       "prod_room_t\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "P_room->prod_room_t\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "U->p_y_given_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "U->I_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "U->I_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "U->p_y_given_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "p\n",
       "\n",
       "p\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "p->likelihood\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "p_y_given_central->prod_central_t\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "prod_central_t->p\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "I_central->P_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "I_central->denom_top\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "I_room->P_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "I_room->denom_top\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "p_y_given_room->prod_room_t\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "prod_room_t->p\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "denom_top->P_central\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "denom_top->P_room\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       ""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nstL_1.graphviz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But again we are able to derive parameter estimates for the drivers of consumer choice and consult the model implications is in a standard Bayesian model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  varsd = varvar / evar / 4\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  varsd = varvar / evar / 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
betas[ic]-0.0010.001-0.002-0.0000.0000.0002699.02760.01.00
betas[oc]-0.0060.001-0.008-0.0030.0000.0001041.01451.01.01
alphas[gc]2.4692.216-0.3356.4820.1340.133264.0668.01.02
alphas[ec]2.3802.213-0.6006.4170.1290.133301.0686.01.02
alphas[gr]0.1290.126-0.0730.4050.0030.0031602.01497.01.00
alphas[er]1.3880.3530.7602.0650.0110.0061015.01636.01.01
alphas[hp]0.0000.0000.0000.0000.000NaN4000.04000.0NaN
lambdas_nests[central]0.8070.1070.6060.9760.0040.002740.01236.01.01
lambdas_nests[room]0.6080.1100.3990.7900.0040.002749.01210.01.01
betas_fixed[gc, income]-2.1393.050-8.4671.3470.1400.138432.01051.01.01
betas_fixed[gc, rooms]-1.4032.643-6.6731.8910.0960.146782.01612.01.00
betas_fixed[ec, income]-2.0613.025-8.3911.3780.1350.139433.0982.01.01
betas_fixed[ec, rooms]-1.3472.600-6.6221.8070.0940.151805.01559.01.00
betas_fixed[gr, income]-0.1190.161-0.4570.0740.0040.0051367.02015.01.00
betas_fixed[gr, rooms]-0.0740.161-0.4260.1270.0040.0071440.01859.01.00
betas_fixed[er, income]-1.0911.137-3.5070.6530.0350.0301016.0927.01.00
betas_fixed[er, rooms]-0.6821.051-2.9091.0580.0320.0261052.01234.01.00
betas_fixed[hp, income]0.0000.0000.0000.0000.000NaN4000.04000.0NaN
betas_fixed[hp, rooms]0.0000.0000.0000.0000.000NaN4000.04000.0NaN
\n",
       "
"
      ],
      "text/plain": [
       "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "betas[ic]               -0.001  0.001  -0.002   -0.000      0.000    0.000   \n",
       "betas[oc]               -0.006  0.001  -0.008   -0.003      0.000    0.000   \n",
       "alphas[gc]               2.469  2.216  -0.335    6.482      0.134    0.133   \n",
       "alphas[ec]               2.380  2.213  -0.600    6.417      0.129    0.133   \n",
       "alphas[gr]               0.129  0.126  -0.073    0.405      0.003    0.003   \n",
       "alphas[er]               1.388  0.353   0.760    2.065      0.011    0.006   \n",
       "alphas[hp]               0.000  0.000   0.000    0.000      0.000      NaN   \n",
       "lambdas_nests[central]   0.807  0.107   0.606    0.976      0.004    0.002   \n",
       "lambdas_nests[room]      0.608  0.110   0.399    0.790      0.004    0.002   \n",
       "betas_fixed[gc, income] -2.139  3.050  -8.467    1.347      0.140    0.138   \n",
       "betas_fixed[gc, rooms]  -1.403  2.643  -6.673    1.891      0.096    0.146   \n",
       "betas_fixed[ec, income] -2.061  3.025  -8.391    1.378      0.135    0.139   \n",
       "betas_fixed[ec, rooms]  -1.347  2.600  -6.622    1.807      0.094    0.151   \n",
       "betas_fixed[gr, income] -0.119  0.161  -0.457    0.074      0.004    0.005   \n",
       "betas_fixed[gr, rooms]  -0.074  0.161  -0.426    0.127      0.004    0.007   \n",
       "betas_fixed[er, income] -1.091  1.137  -3.507    0.653      0.035    0.030   \n",
       "betas_fixed[er, rooms]  -0.682  1.051  -2.909    1.058      0.032    0.026   \n",
       "betas_fixed[hp, income]  0.000  0.000   0.000    0.000      0.000      NaN   \n",
       "betas_fixed[hp, rooms]   0.000  0.000   0.000    0.000      0.000      NaN   \n",
       "\n",
       "                         ess_bulk  ess_tail  r_hat  \n",
       "betas[ic]                  2699.0    2760.0   1.00  \n",
       "betas[oc]                  1041.0    1451.0   1.01  \n",
       "alphas[gc]                  264.0     668.0   1.02  \n",
       "alphas[ec]                  301.0     686.0   1.02  \n",
       "alphas[gr]                 1602.0    1497.0   1.00  \n",
       "alphas[er]                 1015.0    1636.0   1.01  \n",
       "alphas[hp]                 4000.0    4000.0    NaN  \n",
       "lambdas_nests[central]      740.0    1236.0   1.01  \n",
       "lambdas_nests[room]         749.0    1210.0   1.01  \n",
       "betas_fixed[gc, income]     432.0    1051.0   1.01  \n",
       "betas_fixed[gc, rooms]      782.0    1612.0   1.00  \n",
       "betas_fixed[ec, income]     433.0     982.0   1.01  \n",
       "betas_fixed[ec, rooms]      805.0    1559.0   1.00  \n",
       "betas_fixed[gr, income]    1367.0    2015.0   1.00  \n",
       "betas_fixed[gr, rooms]     1440.0    1859.0   1.00  \n",
       "betas_fixed[er, income]    1016.0     927.0   1.00  \n",
       "betas_fixed[er, rooms]     1052.0    1234.0   1.00  \n",
       "betas_fixed[hp, income]    4000.0    4000.0    NaN  \n",
       "betas_fixed[hp, rooms]     4000.0    4000.0    NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(nstL_1.idata, var_names=[\"betas\", \"alphas\", \"lambdas_nests\", \"betas_fixed\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see additionally lambda parameters for each of the nests. These terms measure how strongly correlated the unobserved utility components are for alternatives within the same nest. Closer to 0 indicates a high correlation, substitution happens mostly within the nest. Whereas a value closer to 1 implies lower within nest correlation suggesting IIA approximately holds within the nest. The options available for structuring a market can be quite extensive. As we might have \"nests within nests\" where the conditional probabilities flow through successive choices within segments of the market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two Layer Nesting\n",
    "\n",
    "In this PyMC marketing implementation we allow for the specification of a two-layer nesting representing succesive choices over a root nest and then nests within the child nests. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/302520228.py:19: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"alphas_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alts\"),\n",
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/302520228.py:20: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"betas\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alt_covariates\"),\n",
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/302520228.py:21: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"betas_fixed_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"fixed_covariates\"),\n",
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/302520228.py:22: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"lambdas_nests\": Prior(\"Beta\", alpha=2, beta=2, dims=\"nests\"),\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utility_formulas = [\n",
    "    \"gc ~ ic_gc + oc_gc \",\n",
    "    \"ec ~ ic_ec + oc_ec \",\n",
    "    \"gr ~ ic_gr + oc_gr \",\n",
    "    \"er ~ ic_er + oc_er \",\n",
    "    \"hp ~ ic_hp + oc_hp \",\n",
    "]\n",
    "\n",
    "\n",
    "nesting_structure = {\"central\": [\"gc\", \"ec\"], \"room\": {\"hp\": [\"hp\"], \"r\": [\"gr\", \"er\"]}}\n",
    "\n",
    "nstL_2 = NestedLogit(\n",
    "    df,\n",
    "    utility_formulas,\n",
    "    \"depvar\",\n",
    "    covariates=[\"ic\", \"oc\"],\n",
    "    nesting_structure=nesting_structure,\n",
    "    model_config={\n",
    "        \"alphas_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alts\"),\n",
    "        \"betas\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alt_covariates\"),\n",
    "        \"betas_fixed_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"fixed_covariates\"),\n",
    "        \"lambdas_nests\": Prior(\"Beta\", alpha=2, beta=2, dims=\"nests\"),\n",
    "    },\n",
    ")\n",
    "nstL_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alphas_, betas, lambdas_nests, likelihood]\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/pymc/sampling/mcmc.py:328: UserWarning: `idata_kwargs` are currently ignored by the nutpie sampler\n",
      "  warnings.warn(\n",
      "Sampling: [likelihood]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "
\n"
      ],
      "text/plain": [
       "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nstL_2.sample(\n",
    "    fit_kwargs={\n",
    "        \"target_accept\": 0.97,\n",
    "        \"tune\": 2000,\n",
    "        \"nuts_sampler\": \"nutpie\",\n",
    "        \"idata_kwargs\": {\"log_likelihood\": True},\n",
    "        \"progressbar\": False,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/density_utils.py:488: UserWarning: Your data appears to have a single value or no finite values\n",
      "  warnings.warn(\"Your data appears to have a single value or no finite values\")\n",
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/28403481.py:2: UserWarning: The figure layout has changed to tight\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(nstL_2.idata, var_names=[\"alphas\", \"betas\"])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  varsd = varvar / evar / 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
betas[ic]-0.0020.001-0.004-0.0010.0000.0003245.02923.01.0
betas[oc]-0.0050.001-0.008-0.0030.0000.0002170.02065.01.0
alphas[gc]-0.1460.723-1.5181.2050.0160.0132065.02207.01.0
alphas[ec]0.1030.732-1.2891.4550.0160.0121986.02094.01.0
alphas[gr]-0.6430.760-2.0430.8060.0160.0142158.02322.01.0
alphas[er]0.6100.753-0.8331.9280.0170.0122036.02293.01.0
alphas[hp]0.0000.0000.0000.0000.000NaN4000.04000.0NaN
\n",
       "
"
      ],
      "text/plain": [
       "             mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "betas[ic]  -0.002  0.001  -0.004   -0.001      0.000    0.000    3245.0   \n",
       "betas[oc]  -0.005  0.001  -0.008   -0.003      0.000    0.000    2170.0   \n",
       "alphas[gc] -0.146  0.723  -1.518    1.205      0.016    0.013    2065.0   \n",
       "alphas[ec]  0.103  0.732  -1.289    1.455      0.016    0.012    1986.0   \n",
       "alphas[gr] -0.643  0.760  -2.043    0.806      0.016    0.014    2158.0   \n",
       "alphas[er]  0.610  0.753  -0.833    1.928      0.017    0.012    2036.0   \n",
       "alphas[hp]  0.000  0.000   0.000    0.000      0.000      NaN    4000.0   \n",
       "\n",
       "            ess_tail  r_hat  \n",
       "betas[ic]     2923.0    1.0  \n",
       "betas[oc]     2065.0    1.0  \n",
       "alphas[gc]    2207.0    1.0  \n",
       "alphas[ec]    2094.0    1.0  \n",
       "alphas[gr]    2322.0    1.0  \n",
       "alphas[er]    2293.0    1.0  \n",
       "alphas[hp]    4000.0    NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(nstL_2.idata, var_names=[\"betas\", \"alphas\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again the parameter estimates seem to be recovered sensibly on the beta coefficients, but the question remains as to whether this additional nesting structure will help support plausible counterfactual reasoning. Note how the model struggles to identify the the intercept terms and places weight on "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
    "axs = axs.flatten()\n",
    "az.plot_ppc(nstL_1.idata, ax=axs[0])\n",
    "axs[0].set_title(\"Single Level Nesting Model\")\n",
    "az.plot_forest(\n",
    "    [nstL_1.idata, nstL_2.idata],\n",
    "    var_names=[\"betas\"],\n",
    "    combined=True,\n",
    "    ax=axs[1],\n",
    "    model_names=[\"Nested Single Level\", \"Nested 2 Level\"],\n",
    ")\n",
    "axs[2].set_title(\"Two Level Nesting Model\")\n",
    "az.plot_ppc(nstL_2.idata, ax=axs[2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both models seem to recover posterior predictive distributions well, but vary slightly in the estimate parameters. Let's check the counterfactual inferences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Interventions in Structured Markets\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [likelihood]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "
\n"
      ],
      "text/plain": [
       "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [likelihood]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "
\n"
      ],
      "text/plain": [
       "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_policy_df = df.copy()\n",
    "new_policy_df[[\"ic_ec\", \"ic_er\"]] = new_policy_df[[\"ic_ec\", \"ic_er\"]] * 1.5\n",
    "\n",
    "idata_new_policy_1 = nstL_1.apply_intervention(new_choice_df=new_policy_df)\n",
    "idata_new_policy_2 = nstL_2.apply_intervention(new_choice_df=new_policy_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that both nesting structures recover non-proportional patterns of product substitution. We have elided the IIA feature of the multinomial logit and can continue to assess whether or not the behaviour implications of these utility theory makes sense. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
policy_sharenew_policy_sharerelative_change
product
gc0.6343190.6591940.039215
ec0.0718340.043684-0.391880
gr0.1494110.1824900.221392
er0.0906180.048654-0.463084
hp0.0538160.0659780.225980
\n",
       "
"
      ],
      "text/plain": [
       "         policy_share  new_policy_share  relative_change\n",
       "product                                                 \n",
       "gc           0.634319          0.659194         0.039215\n",
       "ec           0.071834          0.043684        -0.391880\n",
       "gr           0.149411          0.182490         0.221392\n",
       "er           0.090618          0.048654        -0.463084\n",
       "hp           0.053816          0.065978         0.225980"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_df_1 = nstL_1.calculate_share_change(nstL_1.idata, nstL_1.intervention_idata)\n",
    "change_df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
policy_sharenew_policy_sharerelative_change
product
gc0.6354060.6767660.065091
ec0.0726200.023849-0.671587
gr0.1484220.2097510.413212
er0.0877110.032070-0.634372
hp0.0558410.0575640.030863
\n",
       "
"
      ],
      "text/plain": [
       "         policy_share  new_policy_share  relative_change\n",
       "product                                                 \n",
       "gc           0.635406          0.676766         0.065091\n",
       "ec           0.072620          0.023849        -0.671587\n",
       "gr           0.148422          0.209751         0.413212\n",
       "er           0.087711          0.032070        -0.634372\n",
       "hp           0.055841          0.057564         0.030863"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_df_2 = nstL_2.calculate_share_change(nstL_2.idata, nstL_2.intervention_idata)\n",
    "change_df_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising the Substitution Patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = nstL_1.plot_change(change_df=change_df_1, figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = nstL_2.plot_change(change_df=change_df_2, figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can additionally test counterfactuals about the removal of products from the market. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alphas_, betas, betas_fixed_, lambdas_nests, likelihood]\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/pymc/sampling/mcmc.py:328: UserWarning: `idata_kwargs` are currently ignored by the nutpie sampler\n",
      "  warnings.warn(\n",
      "Sampling: [likelihood]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "
\n"
      ],
      "text/plain": [
       "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
policy_sharenew_policy_sharerelative_change
product
gc0.6343190.7936850.251239
ec0.0718340.0899010.251504
gr0.1494110.000000-1.000000
er0.0906180.1164140.284656
hp0.0538160.000000-1.000000
\n",
       "
"
      ],
      "text/plain": [
       "         policy_share  new_policy_share  relative_change\n",
       "product                                                 \n",
       "gc           0.634319          0.793685         0.251239\n",
       "ec           0.071834          0.089901         0.251504\n",
       "gr           0.149411          0.000000        -1.000000\n",
       "er           0.090618          0.116414         0.284656\n",
       "hp           0.053816          0.000000        -1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_kwargs = {\n",
    "    \"target_accept\": 0.97,\n",
    "    \"tune\": 2000,\n",
    "    \"nuts_sampler\": \"nutpie\",\n",
    "    \"idata_kwargs\": {\"log_likelihood\": True},\n",
    "    \"progressbar\": False,\n",
    "}\n",
    "\n",
    "new_policy_df = nstL_1.choice_df.copy()\n",
    "new_policy_df = new_policy_df[\n",
    "    (new_policy_df[\"depvar\"] != \"hp\") & (new_policy_df[\"depvar\"] != \"gr\")\n",
    "]\n",
    "\n",
    "new_utility_formulas = [\n",
    "    \"gc ~ ic_gc + oc_gc | income\",\n",
    "    \"ec ~ ic_ec + oc_ec | income \",\n",
    "    \"er ~ ic_er + oc_er | income  \",\n",
    "]\n",
    "nstL_1.nesting_structure = {\"central\": [\"gc\", \"ec\"], \"room\": [\"er\"]}\n",
    "nstL_1.alternatives = [\"gc\", \"ec\", \"er\"]\n",
    "idata_new_policy_3 = nstL_1.apply_intervention(\n",
    "    new_choice_df=new_policy_df,\n",
    "    new_utility_equations=new_utility_formulas,\n",
    "    fit_kwargs=fit_kwargs,\n",
    ")\n",
    "\n",
    "change_df_3 = nstL_1.calculate_share_change(nstL_1.idata, nstL_1.intervention_idata)\n",
    "change_df_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Different Market\n",
    "\n",
    "Let's now briefly look at a different market that highlights a limitation of the nested logit model. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
personIddisp_sunshinedisp_keeblerdisp_nabiscodisp_privatefeat_sunshinefeat_keeblerfeat_nabiscofeat_privateprice_sunshineprice_keeblerprice_nabiscoprice_privatechoicelastChoicepersonChoiceIdchoiceIdkeebler_last_chosennabisco_last_chosensunshine_last_chosen
01000000000.991.090.990.71nabisconabisco11010
11100000000.491.091.090.78sunshinenabisco22010
21000000001.031.090.890.78nabiscosunshine33001
31000000001.091.091.190.64nabisconabisco44010
41000000000.891.091.190.84nabisconabisco55010
...............................................................
3151136000000001.091.190.990.55privateprivate93152000
3152136000100000.781.351.040.65privateprivate103153000
3153136000000001.091.171.290.59privateprivate113154000
3154136000000001.091.221.290.59privateprivate123155000
3155136000000001.291.041.230.59privateprivate133156000
\n",
       "
3156 rows × 20 columns
\n",
       "
"
      ],
      "text/plain": [
       "      personId  disp_sunshine  disp_keebler  disp_nabisco  disp_private  \\\n",
       "0            1              0             0             0             0   \n",
       "1            1              1             0             0             0   \n",
       "2            1              0             0             0             0   \n",
       "3            1              0             0             0             0   \n",
       "4            1              0             0             0             0   \n",
       "...        ...            ...           ...           ...           ...   \n",
       "3151       136              0             0             0             0   \n",
       "3152       136              0             0             0             1   \n",
       "3153       136              0             0             0             0   \n",
       "3154       136              0             0             0             0   \n",
       "3155       136              0             0             0             0   \n",
       "\n",
       "      feat_sunshine  feat_keebler  feat_nabisco  feat_private  price_sunshine  \\\n",
       "0                 0             0             0             0            0.99   \n",
       "1                 0             0             0             0            0.49   \n",
       "2                 0             0             0             0            1.03   \n",
       "3                 0             0             0             0            1.09   \n",
       "4                 0             0             0             0            0.89   \n",
       "...             ...           ...           ...           ...             ...   \n",
       "3151              0             0             0             0            1.09   \n",
       "3152              0             0             0             0            0.78   \n",
       "3153              0             0             0             0            1.09   \n",
       "3154              0             0             0             0            1.09   \n",
       "3155              0             0             0             0            1.29   \n",
       "\n",
       "      price_keebler  price_nabisco  price_private    choice lastChoice  \\\n",
       "0              1.09           0.99           0.71   nabisco    nabisco   \n",
       "1              1.09           1.09           0.78  sunshine    nabisco   \n",
       "2              1.09           0.89           0.78   nabisco   sunshine   \n",
       "3              1.09           1.19           0.64   nabisco    nabisco   \n",
       "4              1.09           1.19           0.84   nabisco    nabisco   \n",
       "...             ...            ...            ...       ...        ...   \n",
       "3151           1.19           0.99           0.55   private    private   \n",
       "3152           1.35           1.04           0.65   private    private   \n",
       "3153           1.17           1.29           0.59   private    private   \n",
       "3154           1.22           1.29           0.59   private    private   \n",
       "3155           1.04           1.23           0.59   private    private   \n",
       "\n",
       "      personChoiceId  choiceId  keebler_last_chosen  nabisco_last_chosen  \\\n",
       "0                  1         1                    0                    1   \n",
       "1                  2         2                    0                    1   \n",
       "2                  3         3                    0                    0   \n",
       "3                  4         4                    0                    1   \n",
       "4                  5         5                    0                    1   \n",
       "...              ...       ...                  ...                  ...   \n",
       "3151               9      3152                    0                    0   \n",
       "3152              10      3153                    0                    0   \n",
       "3153              11      3154                    0                    0   \n",
       "3154              12      3155                    0                    0   \n",
       "3155              13      3156                    0                    0   \n",
       "\n",
       "      sunshine_last_chosen  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        1  \n",
       "3                        0  \n",
       "4                        0  \n",
       "...                    ...  \n",
       "3151                     0  \n",
       "3152                     0  \n",
       "3153                     0  \n",
       "3154                     0  \n",
       "3155                     0  \n",
       "\n",
       "[3156 rows x 20 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = data_dir / \"choice_crackers.csv\"\n",
    "df_new = pd.read_csv(data_path)\n",
    "last_chosen = pd.get_dummies(df_new[\"lastChoice\"]).drop(\"private\", axis=1).astype(int)\n",
    "last_chosen.columns = [col + \"_last_chosen\" for col in last_chosen.columns]\n",
    "df_new[last_chosen.columns] = last_chosen\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/3385932704.py:22: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"alphas_\": Prior(\"Normal\", mu=0, sigma=5, dims=\"alts\"),\n",
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/3385932704.py:23: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"betas\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alt_covariates\"),\n",
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/3385932704.py:24: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"betas_fixed_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"fixed_covariates\"),\n",
      "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_49846/3385932704.py:25: DeprecationWarning: The Prior class has moved to pymc_extras.prior module and will be removed in a future release. Import it from `from pymc_extras.prior import Prior`. \n",
      "  \"lambdas_nests\": Prior(\"Beta\", alpha=1, beta=1, dims=\"nests\"),\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utility_formulas = [\n",
    "    \"sunshine ~ disp_sunshine + feat_sunshine + price_sunshine \",\n",
    "    \"keebler ~ disp_keebler + feat_keebler + price_keebler  \",\n",
    "    \"nabisco ~ disp_nabisco + feat_nabisco + price_nabisco  \",\n",
    "    \"private ~ disp_private + feat_private + price_private  \",\n",
    "]\n",
    "\n",
    "\n",
    "nesting_structure = {\n",
    "    \"private\": [\"private\"],\n",
    "    \"brand\": [\"keebler\", \"sunshine\", \"nabisco\"],\n",
    "}\n",
    "\n",
    "\n",
    "nstL_3 = NestedLogit(\n",
    "    choice_df=df_new,\n",
    "    utility_equations=utility_formulas,\n",
    "    depvar=\"choice\",\n",
    "    covariates=[\"disp\", \"feat\", \"price\"],\n",
    "    nesting_structure=nesting_structure,\n",
    "    model_config={\n",
    "        \"alphas_\": Prior(\"Normal\", mu=0, sigma=5, dims=\"alts\"),\n",
    "        \"betas\": Prior(\"Normal\", mu=0, sigma=1, dims=\"alt_covariates\"),\n",
    "        \"betas_fixed_\": Prior(\"Normal\", mu=0, sigma=1, dims=\"fixed_covariates\"),\n",
    "        \"lambdas_nests\": Prior(\"Beta\", alpha=1, beta=1, dims=\"nests\"),\n",
    "    },\n",
    ")\n",
    "nstL_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alphas_, betas, lambdas_nests, likelihood]\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "There were 5 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "Sampling: [likelihood]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "
\n"
      ],
      "text/plain": [
       "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/rich/live.py:256: \n",
       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nstL_3.sample(\n",
    "    fit_kwargs={\n",
    "        \"target_accept\": 0.97,\n",
    "        \"tune\": 2000,\n",
    "        \"nuts_sampler\": \"numpyro\",\n",
    "        \"progressbar\": False,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  varsd = varvar / evar / 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
alphas[sunshine]-0.5302.884-5.5945.3270.0770.0531387.01786.01.0
alphas[keebler]-0.1452.876-4.9555.9570.0770.0521389.01777.01.0
alphas[nabisco]0.8312.870-4.5966.2520.0770.0511382.01844.01.0
alphas[private]0.0000.0000.0000.0000.000NaN4000.04000.0NaN
betas[disp]0.0130.048-0.0760.1060.0010.0012349.02125.01.0
betas[feat]0.1120.087-0.0280.2920.0020.0022134.01868.01.0
betas[price]-2.2970.649-3.478-1.0790.0220.012829.01423.01.0
\n",
       "
"
      ],
      "text/plain": [
       "                   mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alphas[sunshine] -0.530  2.884  -5.594    5.327      0.077    0.053    1387.0   \n",
       "alphas[keebler]  -0.145  2.876  -4.955    5.957      0.077    0.052    1389.0   \n",
       "alphas[nabisco]   0.831  2.870  -4.596    6.252      0.077    0.051    1382.0   \n",
       "alphas[private]   0.000  0.000   0.000    0.000      0.000      NaN    4000.0   \n",
       "betas[disp]       0.013  0.048  -0.076    0.106      0.001    0.001    2349.0   \n",
       "betas[feat]       0.112  0.087  -0.028    0.292      0.002    0.002    2134.0   \n",
       "betas[price]     -2.297  0.649  -3.478   -1.079      0.022    0.012     829.0   \n",
       "\n",
       "                  ess_tail  r_hat  \n",
       "alphas[sunshine]    1786.0    1.0  \n",
       "alphas[keebler]     1777.0    1.0  \n",
       "alphas[nabisco]     1844.0    1.0  \n",
       "alphas[private]     4000.0    NaN  \n",
       "betas[disp]         2125.0    1.0  \n",
       "betas[feat]         1868.0    1.0  \n",
       "betas[price]        1423.0    1.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    nstL_3.idata,\n",
    "    var_names=[\"alphas\", \"betas\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nathanielforde/mambaforge/envs/pymc-marketing-dev/lib/python3.12/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest(\n",
    "    nstL_3.idata,\n",
    "    var_names=[\"alphas\", \"betas\"],\n",
    "    combined=True,\n",
    "    r_hat=True,\n",
    ")\n",
    "ax[0].axvline(0, color=\"k\", linestyle=\"--\")\n",
    "ax[0].set_title(\"Parameter Estimates from the Nested Logit Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model suggests plausibly that price increases have a strongly negative effect of the purchase probability of crackers. However, we should note that we're ignoring information when we use the nested logit in this context. The data set records multiple purchasing decisions for each individual and by failing to model this fact we lose insight into individual heterogeneity in their responses to price shifts. To gain more insight into this aspect of the purchasing decisions of individuals we might augment our model with hierarchical components or switch to alternative Mixed logit model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing a Market Structure\n",
    "\n",
    "Causal inference is hard and predicting the counterfactual actions of agents in a competitive market is very hard. There is no guarantee that a nested logit model will accurately represent the choice of any particular agent, but you can be hopeful that it highlights expected patterns of choice when the nesting structure reflects the natural segmentation of a market. Nests should group alternatives that share unobserved similarities, ideally driven by a transparent theory of the market structure. Well-specified nests should show stronger substitution within nests than across nests, and you can inspect the substitution patterns as above. Ideally you can always try and hold out some test data to evaluate the implications of your fitted model. Discrete choice models are causal inference models and their structural specification should support generalisable inference across future and counterfactual situations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sat Oct 18 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.12.12\n",
      "IPython version      : 9.6.0\n",
      "\n",
      "pytensor: 2.35.0\n",
      "\n",
      "arviz         : 0.22.0\n",
      "pymc_marketing: 0.16.0\n",
      "pandas        : 2.3.3\n",
      "matplotlib    : 3.10.7\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pymc-marketing-dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}