examples/causal_inference/bayesian_ab_testing_introduction.ipynb

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    "(bayesian_ab_testing_intro)=\n",
    "# Introduction to Bayesian A/B Testing\n",
    "\n",
    ":::{post} May 23, 2021\n",
    ":tags: case study, ab test\n",
    ":category: beginner, tutorial\n",
    ":author: Cuong Duong\n",
    ":::"
   ]
  },
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   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Dict, List, Union\n",
    "\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "\n",
    "from scipy.stats import bernoulli, expon"
   ]
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   "source": [
    "RANDOM_SEED = 4000\n",
    "rng = np.random.default_rng(RANDOM_SEED)\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "\n",
    "plotting_defaults = dict(\n",
    "    bins=50,\n",
    "    kind=\"hist\",\n",
    "    textsize=10,\n",
    ")"
   ]
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   "source": [
    "This notebook demonstrates how to implement a Bayesian analysis of an A/B test. We implement the models discussed in VWO's Bayesian A/B Testing Whitepaper {cite:p}`stucchio2015bayesian`, and discuss the effect of different prior choices for these models. This notebook does _not_ discuss other related topics like how to choose a prior, early stopping, and power analysis.\n",
    "\n",
    "#### What is A/B testing?\n",
    "\n",
    "From https://vwo.com/ab-testing/:\n",
    "\n",
    "> A/B testing (also known as split testing) is a process of showing two variants of the same web page to different segments of website visitors at the same time and comparing which variant drives more conversions.\n",
    "\n",
    "Specifically, A/B tests are often used in the software industry to determine whether a new feature or changes to an existing feature should be released to users, and the impact of the change on core product metrics (\"conversions\"). Furthermore:\n",
    "\n",
    "* We can test more than two variants at the same time. We'll be dealing with how to analyse these tests in this notebook as well.\n",
    "* Exactly what \"conversions\" means can vary between tests, but two classes of conversions we'll focus on are:\n",
    "  * Bernoulli conversions - a flag for whether the visitor did the target action or not (e.g. completed at least one purchase).\n",
    "  * Value conversions - a real value per visitor (e.g. the dollar revenue, which could also be 0).\n",
    " \n",
    "If you've studied [controlled experiments](https://www.khanacademy.org/science/high-school-biology/hs-biology-foundations/hs-biology-and-the-scientific-method/a/experiments-and-observations) in the context of biology, psychology, and other sciences before, A/B testing will sound a lot like a controlled experiment - and that's because it is! The concept of a control group and treatment groups, and the principles of experimental design, are the building blocks of A/B testing. The main difference is the context in which the experiment is run: A/B tests are typically run by online software companies, where the subjects are visitors to the website / app, the outcomes of interest are behaviours that can be tracked like signing up, purchasing a product, and returning to the website.\n",
    "\n",
    "A/B tests are typically analysed with traditional hypothesis tests (see [t-test](https://en.wikipedia.org/wiki/Student%27s_t-test)), but another method is to use Bayesian statistics. This allows us to incorporate prior distributions and produce a range of outcomes to the questions \"is there a winning variant?\" and \"by how much?\"."
   ]
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   "source": [
    "### Bernoulli Conversions"
   ]
  },
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    "Let's first deal with a simple two-variant A/B test, where the metric of interest is the proportion of users performing an action (e.g. purchase at least one item), a bernoulli conversion. Our variants are called A and B, where A refers to the existing landing page and B refers to the new page we want to test. The outcome that we want to perform statistical inference on is whether B is \"better\" than A, which is depends on the underlying \"true\" conversion rates for each variant. We can formulate this as follows:\n",
    "\n",
    "Let $\\theta_A, \\theta_B$ be the true conversion rates for variants A and B respectively. Then the outcome of whether a visitor converts in variant A is the random variable $\\mathrm{Bernoulli}(\\theta_A)$, and $\\mathrm{Bernoulli}(\\theta_B)$ for variant B. If we assume that visitors' behaviour on the landing page is independent of other visitors (a fair assumption), then the total conversions $y$ for a variant has the Binomial distribution:\n",
    "\n",
    "$$y \\sim \\sum^N\\mathrm{Bernoulli}(\\theta) = \\mathrm{Binomial}(N, \\theta)$$\n",
    "\n",
    "Under a Bayesian framework, we assume the true conversion rates $\\theta_A, \\theta_B$ cannot be known, and instead they each follow a Beta distribution. The underlying rates are assumed to be independent (we would split traffic between each variant randomly, so one variant would not affect the other): \n",
    "\n",
    "$$\\theta_A \\sim \\theta_B \\sim \\mathrm{Beta}(\\alpha, \\beta)$$\n",
    "\n",
    "The observed data for the duration of the A/B test (the likelihoood distribution) is: the number of visitors landing on the page `N`, and the number of visitors purchasing at least one item `y`:\n",
    "\n",
    "$$y_A \\sim \\mathrm{Binomial}(n = N_A, p = \\theta_A), y_B \\sim \\mathrm{Binomial}(n = N_B, p = \\theta_B)$$\n",
    "\n",
    "With this, we can sample from the joint posterior of $\\theta_A, \\theta_B$. \n",
    "\n",
    "You may have noticed that the Beta distribution is the conjugate prior for the Binomial, so we don't need MCMC sampling to estimate the posterior (the exact solution can be found in the VWO paper). We'll still demonstrate how sampling can be done with PyMC though, and doing this makes it easier to extend the model with different priors, dependency assumptions, etc.\n",
    "\n",
    "Finally, remember that our outcome of interest is whether B is better than A. A common measure in practice for whether B is better than is the _relative uplift in conversion rates_, i.e. the percentage difference of $\\theta_B$ over $\\theta_A$:\n",
    "\n",
    "$$\\mathrm{reluplift}_B = \\theta_B / \\theta_A - 1$$\n",
    "\n",
    "We'll implement this model setup in PyMC below."
   ]
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   "source": [
    "@dataclass\n",
    "class BetaPrior:\n",
    "    alpha: float\n",
    "    beta: float"
   ]
  },
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   "source": [
    "@dataclass\n",
    "class BinomialData:\n",
    "    trials: int\n",
    "    successes: int"
   ]
  },
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   "source": [
    "class ConversionModelTwoVariant:\n",
    "    def __init__(self, priors: BetaPrior):\n",
    "        self.priors = priors\n",
    "\n",
    "    def create_model(self, data: List[BinomialData]) -> pm.Model:\n",
    "        trials = [d.trials for d in data]\n",
    "        successes = [d.successes for d in data]\n",
    "        with pm.Model() as model:\n",
    "            p = pm.Beta(\"p\", alpha=self.priors.alpha, beta=self.priors.beta, shape=2)\n",
    "            obs = pm.Binomial(\"y\", n=trials, p=p, shape=2, observed=successes)\n",
    "            reluplift = pm.Deterministic(\"reluplift_b\", p[1] / p[0] - 1)\n",
    "        return model"
   ]
  },
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   "source": [
    "Now that we've defined a class that can take a prior and our synthetic data as inputs, our first step is to choose an appropriate prior. There are a few things to consider when doing this in practice, but for the purpose of this notebook we'll focus on the following:\n",
    "\n",
    "* We assume that the same Beta prior is set for each variant.\n",
    "* An _uninformative_ or _weakly informative_ prior occurs when we set low values for `alpha` and `beta`. For example, `alpha = 1, beta = 1` leads to a uniform distribution as a prior. If we were considering one distribution in isolation, setting this prior is a statement that we don't know anything about the value of the parameter, nor our confidence around it. In the context of A/B testing however, we're interested in comparing the _relative uplift_ of one variant over another. With a weakly informative Beta prior, this relative uplift distribution is very wide, so we're implicitly saying that the variants could be very different to each other.\n",
    "* A _strong_ prior occurs when we set high values for `alpha` and `beta`. Contrary to the above, a strong prior would imply that the relative uplift distribution is thin, i.e. our prior belief is that the variants are not very different from each other.\n",
    "\n",
    "We illustrate these points with prior predictive checks."
   ]
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   "source": [
    "#### Prior predictive checks"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Note that we can pass in arbitrary values for the observed data in these prior predictive checks. PyMC will not use that data when sampling from the prior predictive distribution."
   ]
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   "source": [
    "weak_prior = ConversionModelTwoVariant(BetaPrior(alpha=100, beta=100))"
   ]
  },
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   "execution_count": 7,
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   "source": [
    "strong_prior = ConversionModelTwoVariant(BetaPrior(alpha=10000, beta=10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e1e0769",
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   "outputs": [],
   "source": [
    "with weak_prior.create_model(data=[BinomialData(1, 1), BinomialData(1, 1)]):\n",
    "    weak_prior_predictive = pm.sample_prior_predictive(samples=10000, return_inferencedata=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "id": "4df134b8",
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   "source": [
    "with strong_prior.create_model(data=[BinomialData(1, 1), BinomialData(1, 1)]):\n",
    "    strong_prior_predictive = pm.sample_prior_predictive(samples=10000, return_inferencedata=False)"
   ]
  },
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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 512
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n",
    "az.plot_posterior(weak_prior_predictive[\"reluplift_b\"], ax=axs[0], **plotting_defaults)\n",
    "axs[0].set_title(f\"B vs. A Rel Uplift Prior Predictive, {weak_prior.priors}\", fontsize=10)\n",
    "axs[0].axvline(x=0, color=\"red\")\n",
    "az.plot_posterior(strong_prior_predictive[\"reluplift_b\"], ax=axs[1], **plotting_defaults)\n",
    "axs[1].set_title(f\"B vs. A Rel Uplift Prior Predictive, {strong_prior.priors}\", fontsize=10)\n",
    "axs[1].axvline(x=0, color=\"red\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7757126",
   "metadata": {},
   "source": [
    "With the weak prior our 94% HDI for the relative uplift for B over A is roughly [-20%, +20%], whereas it is roughly [-2%, +2%] with the strong prior. This is effectively the \"starting point\" for the relative uplift distribution, and will affect how the observed conversions translate to the posterior distribution.\n",
    "\n",
    "How we choose these priors in practice depends on broader context of the company running the A/B tests. A strong prior can help guard against false discoveries, but may require more data to detect winning variants when they exist (and more data = more time required running the test). A weak prior gives more weight to the observed data, but could also lead to more false discoveries as a result of early stopping issues.\n",
    "\n",
    "Below we'll walk through the inference results from two different prior choices."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c03f75",
   "metadata": {},
   "source": [
    "#### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b999654",
   "metadata": {},
   "source": [
    "We generate two datasets: one where the \"true\" conversion rate of each variant is the same, and one where variant B has a higher true conversion rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7631f294",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:53:44.717335Z",
     "iopub.status.busy": "2022-06-01T18:53:44.716840Z",
     "iopub.status.idle": "2022-06-01T18:53:44.720982Z",
     "shell.execute_reply": "2022-06-01T18:53:44.720467Z"
    }
   },
   "outputs": [],
   "source": [
    "def generate_binomial_data(\n",
    "    variants: List[str], true_rates: List[str], samples_per_variant: int = 100000\n",
    ") -> pd.DataFrame:\n",
    "    data = {}\n",
    "    for variant, p in zip(variants, true_rates):\n",
    "        data[variant] = bernoulli.rvs(p, size=samples_per_variant)\n",
    "    agg = (\n",
    "        pd.DataFrame(data)\n",
    "        .aggregate([\"count\", \"sum\"])\n",
    "        .rename(index={\"count\": \"trials\", \"sum\": \"successes\"})\n",
    "    )\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ff1e081",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:53:44.723895Z",
     "iopub.status.busy": "2022-06-01T18:53:44.723644Z",
     "iopub.status.idle": "2022-06-01T18:53:44.740069Z",
     "shell.execute_reply": "2022-06-01T18:53:44.739208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>trials</th>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>successes</th>\n",
       "      <td>22979</td>\n",
       "      <td>22970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A       B\n",
       "trials     100000  100000\n",
       "successes   22979   22970"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example generated data\n",
    "generate_binomial_data([\"A\", \"B\"], [0.23, 0.23])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f9d751",
   "metadata": {},
   "source": [
    "We'll also write a function to wrap the data generation, sampling, and posterior plots so that we can easily compare the results of both models (strong and weak prior) under both scenarios (same true rate vs. different true rate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0030ee2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:53:44.744134Z",
     "iopub.status.busy": "2022-06-01T18:53:44.743691Z",
     "iopub.status.idle": "2022-06-01T18:53:44.750940Z",
     "shell.execute_reply": "2022-06-01T18:53:44.750225Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_scenario_twovariant(\n",
    "    variants: List[str],\n",
    "    true_rates: List[float],\n",
    "    samples_per_variant: int,\n",
    "    weak_prior: BetaPrior,\n",
    "    strong_prior: BetaPrior,\n",
    ") -> None:\n",
    "    generated = generate_binomial_data(variants, true_rates, samples_per_variant)\n",
    "    data = [BinomialData(**generated[v].to_dict()) for v in variants]\n",
    "    with ConversionModelTwoVariant(priors=weak_prior).create_model(data):\n",
    "        trace_weak = pm.sample(draws=5000)\n",
    "    with ConversionModelTwoVariant(priors=strong_prior).create_model(data):\n",
    "        trace_strong = pm.sample(draws=5000)\n",
    "\n",
    "    true_rel_uplift = true_rates[1] / true_rates[0] - 1\n",
    "\n",
    "    fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n",
    "    az.plot_posterior(trace_weak.posterior[\"reluplift_b\"], ax=axs[0], **plotting_defaults)\n",
    "    axs[0].set_title(f\"True Rel Uplift = {true_rel_uplift:.1%}, {weak_prior}\", fontsize=10)\n",
    "    axs[0].axvline(x=0, color=\"red\")\n",
    "    az.plot_posterior(trace_strong.posterior[\"reluplift_b\"], ax=axs[1], **plotting_defaults)\n",
    "    axs[1].set_title(f\"True Rel Uplift = {true_rel_uplift:.1%}, {strong_prior}\", fontsize=10)\n",
    "    axs[1].axvline(x=0, color=\"red\")\n",
    "    fig.suptitle(\"B vs. A Rel Uplift\")\n",
    "    return trace_weak, trace_strong"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4385419f",
   "metadata": {},
   "source": [
    "#### Scenario 1 - same underlying conversion rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f72ff74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:53:44.754473Z",
     "iopub.status.busy": "2022-06-01T18:53:44.754190Z",
     "iopub.status.idle": "2022-06-01T18:54:38.153873Z",
     "shell.execute_reply": "2022-06-01T18:54:38.153222Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p]\n"
     ]
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      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 21 seconds.\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p]\n"
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      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 20 seconds.\n"
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      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 512
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace_weak, trace_strong = run_scenario_twovariant(\n",
    "    variants=[\"A\", \"B\"],\n",
    "    true_rates=[0.23, 0.23],\n",
    "    samples_per_variant=100000,\n",
    "    weak_prior=BetaPrior(alpha=100, beta=100),\n",
    "    strong_prior=BetaPrior(alpha=10000, beta=10000),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eefc18cc",
   "metadata": {},
   "source": [
    "* In both cases, the true uplift of 0% lies within the 94% HDI.\n",
    "* We can then use this relative uplift distribution to make a decision about whether to apply the new landing page / features in Variant B as the default. For example, we can decide that if the 94% HDI is above 0, we would roll out Variant B. In this case, 0 is in the HDI, so the decision would be to _not_ roll out Variant B."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9935152f",
   "metadata": {},
   "source": [
    "#### Scenario 2 - different underlying rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e52fc97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:54:38.158443Z",
     "iopub.status.busy": "2022-06-01T18:54:38.158135Z",
     "iopub.status.idle": "2022-06-01T18:55:29.277627Z",
     "shell.execute_reply": "2022-06-01T18:55:29.276609Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='24000' class='' max='24000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [24000/24000 00:09<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 21 seconds.\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='24000' class='' max='24000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [24000/24000 00:09<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 20 seconds.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Inference data with groups:\n",
       " \t> posterior\n",
       " \t> log_likelihood\n",
       " \t> sample_stats\n",
       " \t> observed_data,\n",
       " Inference data with groups:\n",
       " \t> posterior\n",
       " \t> log_likelihood\n",
       " \t> sample_stats\n",
       " \t> observed_data)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 512
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_scenario_twovariant(\n",
    "    variants=[\"A\", \"B\"],\n",
    "    true_rates=[0.21, 0.23],\n",
    "    samples_per_variant=100000,\n",
    "    weak_prior=BetaPrior(alpha=100, beta=100),\n",
    "    strong_prior=BetaPrior(alpha=10000, beta=10000),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62643de",
   "metadata": {},
   "source": [
    "* In both cases, the posterior relative uplift distribution suggests that B has a higher conversion rate than A, as the 94% HDI is well above 0. The decision in this case would be to roll out Variant B to all users, and this outcome \"true discovery\".\n",
    "* That said, in practice are usually also interested in _how much better_ Variant B is. For the model with the strong prior, the prior is effectively pulling the relative uplift distribution closer to 0, so our central estimate of the relative uplift is **conservative (i.e. understated)**. We would need much more data for our inference to get closer to the true relative uplift of 9.5%.\n",
    "\n",
    "The above examples demonstrate how to calculate perform A/B testing analysis for a two-variant test with the simple Beta-Binomial model, and the benefits and disadvantages of choosing a weak vs. strong prior. In the next section we provide a guide for handling a multi-variant (\"A/B/n\") test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c871fb6e",
   "metadata": {},
   "source": [
    "### Generalising to multi-variant tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724802d2",
   "metadata": {},
   "source": [
    "We'll continue using Bernoulli conversions and the Beta-Binomial model in this section for simplicity. The focus is on how to analyse tests with 3 or more variants - e.g. instead of just having one different landing page to test, we have multiple ideas we want to test at once. How can we tell if there's a winner amongst all of them?\n",
    "\n",
    "There are two main approaches we can take here:\n",
    "\n",
    "1. Take A as the 'control'. Compare the other variants (B, C, etc.) against A, one at a time.\n",
    "2. For each variant, compare against the `max()` of the other variants.\n",
    "\n",
    "Approach 1 is intuitive to most people, and is easily explained. But what if there are two variants that both beat the control, and we want to know which one is better? We can't make that inference with the individual uplift distributions. Approach 2 does handle this case - it effectively tries to find whether there is a clear winner or clear loser(s) amongst all the variants.\n",
    "\n",
    "We'll implement the model setup for both approaches below, cleaning up our code from before so that it generalises to the `n` variant case. Note that we can also re-use this model for the 2-variant case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2023b905",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:55:29.282520Z",
     "iopub.status.busy": "2022-06-01T18:55:29.282144Z",
     "iopub.status.idle": "2022-06-01T18:55:29.289131Z",
     "shell.execute_reply": "2022-06-01T18:55:29.288312Z"
    }
   },
   "outputs": [],
   "source": [
    "class ConversionModel:\n",
    "    def __init__(self, priors: BetaPrior):\n",
    "        self.priors = priors\n",
    "\n",
    "    def create_model(self, data: List[BinomialData], comparison_method) -> pm.Model:\n",
    "        num_variants = len(data)\n",
    "        trials = [d.trials for d in data]\n",
    "        successes = [d.successes for d in data]\n",
    "        with pm.Model() as model:\n",
    "            p = pm.Beta(\"p\", alpha=self.priors.alpha, beta=self.priors.beta, shape=num_variants)\n",
    "            y = pm.Binomial(\"y\", n=trials, p=p, observed=successes, shape=num_variants)\n",
    "            reluplift = []\n",
    "            for i in range(num_variants):\n",
    "                if comparison_method == \"compare_to_control\":\n",
    "                    comparison = p[0]\n",
    "                elif comparison_method == \"best_of_rest\":\n",
    "                    others = [p[j] for j in range(num_variants) if j != i]\n",
    "                    if len(others) > 1:\n",
    "                        comparison = pm.math.maximum(*others)\n",
    "                    else:\n",
    "                        comparison = others[0]\n",
    "                else:\n",
    "                    raise ValueError(f\"comparison method {comparison_method} not recognised.\")\n",
    "                reluplift.append(pm.Deterministic(f\"reluplift_{i}\", p[i] / comparison - 1))\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58ba7529",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:55:29.292649Z",
     "iopub.status.busy": "2022-06-01T18:55:29.292215Z",
     "iopub.status.idle": "2022-06-01T18:55:29.300151Z",
     "shell.execute_reply": "2022-06-01T18:55:29.299352Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_scenario_bernoulli(\n",
    "    variants: List[str],\n",
    "    true_rates: List[float],\n",
    "    samples_per_variant: int,\n",
    "    priors: BetaPrior,\n",
    "    comparison_method: str,\n",
    ") -> az.InferenceData:\n",
    "    generated = generate_binomial_data(variants, true_rates, samples_per_variant)\n",
    "    data = [BinomialData(**generated[v].to_dict()) for v in variants]\n",
    "    with ConversionModel(priors).create_model(data=data, comparison_method=comparison_method):\n",
    "        trace = pm.sample(draws=5000)\n",
    "\n",
    "    n_plots = len(variants)\n",
    "    fig, axs = plt.subplots(nrows=n_plots, ncols=1, figsize=(3 * n_plots, 7), sharex=True)\n",
    "    for i, variant in enumerate(variants):\n",
    "        if i == 0 and comparison_method == \"compare_to_control\":\n",
    "            axs[i].set_yticks([])\n",
    "        else:\n",
    "            az.plot_posterior(trace.posterior[f\"reluplift_{i}\"], ax=axs[i], **plotting_defaults)\n",
    "        axs[i].set_title(f\"Rel Uplift {variant}, True Rate = {true_rates[i]:.2%}\", fontsize=10)\n",
    "        axs[i].axvline(x=0, color=\"red\")\n",
    "    fig.suptitle(f\"Method {comparison_method}, {priors}\")\n",
    "\n",
    "    return trace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c36ecc",
   "metadata": {},
   "source": [
    "We generate data where variants B and C are well above A, but quite close to each other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55ea29a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:55:29.303810Z",
     "iopub.status.busy": "2022-06-01T18:55:29.303500Z",
     "iopub.status.idle": "2022-06-01T18:55:53.640679Z",
     "shell.execute_reply": "2022-06-01T18:55:53.639997Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p]\n"
     ]
    },
    {
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       "      100.00% [24000/24000 00:09<00:00 Sampling 4 chains, 0 divergences]\n",
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      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 22 seconds.\n"
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",
      "text/plain": [
       "<Figure size 648x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 656
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = run_scenario_bernoulli(\n",
    "    variants=[\"A\", \"B\", \"C\"],\n",
    "    true_rates=[0.21, 0.23, 0.228],\n",
    "    samples_per_variant=100000,\n",
    "    priors=BetaPrior(alpha=5000, beta=5000),\n",
    "    comparison_method=\"compare_to_control\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1c2d20",
   "metadata": {},
   "source": [
    "* The relative uplift posteriors for both B and C show that they are clearly better than A (94% HDI well above 0), by roughly 7-8% relative.\n",
    "* However, we can't infer whether there is a winner between B and C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3cdb6808",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:55:53.644223Z",
     "iopub.status.busy": "2022-06-01T18:55:53.643940Z",
     "iopub.status.idle": "2022-06-01T18:56:20.091615Z",
     "shell.execute_reply": "2022-06-01T18:56:20.090705Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [p]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='24000' class='' max='24000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [24000/24000 00:10<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 22 seconds.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 656
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = run_scenario_bernoulli(\n",
    "    variants=[\"A\", \"B\", \"C\"],\n",
    "    true_rates=[0.21, 0.23, 0.228],\n",
    "    samples_per_variant=100000,\n",
    "    priors=BetaPrior(alpha=5000, beta=5000),\n",
    "    comparison_method=\"best_of_rest\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c883952",
   "metadata": {},
   "source": [
    "* The uplift plot for A tells us that it's a clear loser compared to variants B and C (94% HDI for A's relative uplift is well below 0).\n",
    "* Note that the relative uplift calculations for B and C are effectively ignoring variant A. This is because, say, when we are calculating `reluplift` for B, the maximum of the other variants will likely be variant C. Similarly when we are calculating `reluplift` for C, it is likely being compared to B.\n",
    "* The uplift plots for B and C tell us that we can't yet call a clear winner between the two variants, as the 94% HDI still overlaps with 0. We'd need a larger sample size to detect the 23% vs 22.8% conversion rate difference.\n",
    "* One disadvantage of this approach is that we can't directly say what the uplift of these variants is over variant A (the control). This number is often important in practice, as it allows us to estimate the overall impact if the A/B test changes were rolled out to all visitors. We _can_ get this number approximately though, by reframing the question to be \"how much worse is A compared to the other two variants\" (which is shown in Variant A's relative uplift distribution)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90791ebd",
   "metadata": {},
   "source": [
    "### Value Conversions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635ee63e",
   "metadata": {},
   "source": [
    "Now what if we wanted to compare A/B test variants in terms of how much revenue they generate, and/or estimate how much additional revenue a winning variant brings? We can't use a Beta-Binomial model for this, as the possible values for each visitor are now in the range `[0, Inf)`. The model proposed in the VWO paper is as follows:\n",
    "\n",
    "The revenue generated by an individual visitor is `revenue = probability of paying at all * mean amount spent when paying`:\n",
    "\n",
    "$$\\mathrm{Revenue}_i = \\mathrm{Bernoulli}(\\theta)_i * \\mathrm{Exponential}(\\lambda)_i I(\\mathrm{Bernoulli}(\\theta)_i = 1)$$\n",
    "\n",
    "We assume that the probability of paying at all is independent to the mean amount spent when paying. This is a typical assumption in practice, unless we have reason to believe that the two parameters have dependencies. With this, we can create separate models for the total number of visitors paying, and the total amount spent amongst the purchasing visitors (assuming independence between the behaviour of each visitor):\n",
    "\n",
    "$$c \\sim \\sum^N\\mathrm{Bernoulli}(\\theta) = \\mathrm{Binomial}(N, \\theta)$$\n",
    "\n",
    "$$r \\sim \\sum^K\\mathrm{Exponential}(\\lambda) = \\mathrm{Gamma}(K, \\lambda)$$\n",
    "\n",
    "where $N$ is the total number of visitors, $K$ is the total number of visitors with at least one purchase.\n",
    "\n",
    "We can re-use our Beta-Binomial model from before to model the Bernoulli conversions. For the mean purchase amount, we use a Gamma prior (which is also a conjugate prior to the Gamma likelihood). So in a two-variant test, the setup is:\n",
    "\n",
    "$$\\theta_A \\sim \\theta_B \\sim \\mathrm{Beta}(\\alpha_1, \\beta_1)$$\n",
    "$$\\lambda_A \\sim \\lambda_B \\sim \\mathrm{Gamma}(\\alpha_2, \\beta_2)$$\n",
    "$$c_A \\sim \\mathrm{Binomial}(N_A, \\theta_A), c_B \\sim \\mathrm{Binomial}(N_B, \\theta_B)$$\n",
    "$$r_A \\sim \\mathrm{Gamma}(c_A, \\lambda_A), r_B \\sim \\mathrm{Gamma}(c_B, \\lambda_B)$$\n",
    "$$\\mu_A = \\theta_A * \\dfrac{1}{\\lambda_A}, \\mu_B = \\theta_B * \\dfrac{1}{\\lambda_B}$$\n",
    "$$\\mathrm{reluplift}_B = \\mu_B / \\mu_A - 1$$\n",
    "\n",
    "$\\mu$ here represents the average revenue per visitor, including those who don't make a purchase. This is the best way to capture the overall revenue effect - some variants may increase the average sales value, but reduce the proportion of visitors that pay at all (e.g. if we promoted more expensive items on the landing page).\n",
    "\n",
    "Below we put the model setup into code and perform prior predictive checks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "46f94b80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:20.095845Z",
     "iopub.status.busy": "2022-06-01T18:56:20.095471Z",
     "iopub.status.idle": "2022-06-01T18:56:20.099711Z",
     "shell.execute_reply": "2022-06-01T18:56:20.098858Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class GammaPrior:\n",
    "    alpha: float\n",
    "    beta: float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49ec3cc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:20.103411Z",
     "iopub.status.busy": "2022-06-01T18:56:20.103033Z",
     "iopub.status.idle": "2022-06-01T18:56:20.107674Z",
     "shell.execute_reply": "2022-06-01T18:56:20.106917Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class RevenueData:\n",
    "    visitors: int\n",
    "    purchased: int\n",
    "    total_revenue: float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf970faf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:20.111156Z",
     "iopub.status.busy": "2022-06-01T18:56:20.110839Z",
     "iopub.status.idle": "2022-06-01T18:56:20.120492Z",
     "shell.execute_reply": "2022-06-01T18:56:20.119891Z"
    }
   },
   "outputs": [],
   "source": [
    "class RevenueModel:\n",
    "    def __init__(self, conversion_rate_prior: BetaPrior, mean_purchase_prior: GammaPrior):\n",
    "        self.conversion_rate_prior = conversion_rate_prior\n",
    "        self.mean_purchase_prior = mean_purchase_prior\n",
    "\n",
    "    def create_model(self, data: List[RevenueData], comparison_method: str) -> pm.Model:\n",
    "        num_variants = len(data)\n",
    "        visitors = [d.visitors for d in data]\n",
    "        purchased = [d.purchased for d in data]\n",
    "        total_revenue = [d.total_revenue for d in data]\n",
    "\n",
    "        with pm.Model() as model:\n",
    "            theta = pm.Beta(\n",
    "                \"theta\",\n",
    "                alpha=self.conversion_rate_prior.alpha,\n",
    "                beta=self.conversion_rate_prior.beta,\n",
    "                shape=num_variants,\n",
    "            )\n",
    "            lam = pm.Gamma(\n",
    "                \"lam\",\n",
    "                alpha=self.mean_purchase_prior.alpha,\n",
    "                beta=self.mean_purchase_prior.beta,\n",
    "                shape=num_variants,\n",
    "            )\n",
    "            converted = pm.Binomial(\n",
    "                \"converted\", n=visitors, p=theta, observed=purchased, shape=num_variants\n",
    "            )\n",
    "            revenue = pm.Gamma(\n",
    "                \"revenue\", alpha=purchased, beta=lam, observed=total_revenue, shape=num_variants\n",
    "            )\n",
    "            revenue_per_visitor = pm.Deterministic(\"revenue_per_visitor\", theta * (1 / lam))\n",
    "            theta_reluplift = []\n",
    "            reciprocal_lam_reluplift = []\n",
    "            reluplift = []\n",
    "            for i in range(num_variants):\n",
    "                if comparison_method == \"compare_to_control\":\n",
    "                    comparison_theta = theta[0]\n",
    "                    comparison_lam = 1 / lam[0]\n",
    "                    comparison_rpv = revenue_per_visitor[0]\n",
    "                elif comparison_method == \"best_of_rest\":\n",
    "                    others_theta = [theta[j] for j in range(num_variants) if j != i]\n",
    "                    others_lam = [1 / lam[j] for j in range(num_variants) if j != i]\n",
    "                    others_rpv = [revenue_per_visitor[j] for j in range(num_variants) if j != i]\n",
    "                    if len(others_rpv) > 1:\n",
    "                        comparison_theta = pm.math.maximum(*others_theta)\n",
    "                        comparison_lam = pm.math.maximum(*others_lam)\n",
    "                        comparison_rpv = pm.math.maximum(*others_rpv)\n",
    "                    else:\n",
    "                        comparison_theta = others_theta[0]\n",
    "                        comparison_lam = others_lam[0]\n",
    "                        comparison_rpv = others_rpv[0]\n",
    "                else:\n",
    "                    raise ValueError(f\"comparison method {comparison_method} not recognised.\")\n",
    "                theta_reluplift.append(\n",
    "                    pm.Deterministic(f\"theta_reluplift_{i}\", theta[i] / comparison_theta - 1)\n",
    "                )\n",
    "                reciprocal_lam_reluplift.append(\n",
    "                    pm.Deterministic(\n",
    "                        f\"reciprocal_lam_reluplift_{i}\", (1 / lam[i]) / comparison_lam - 1\n",
    "                    )\n",
    "                )\n",
    "                reluplift.append(\n",
    "                    pm.Deterministic(f\"reluplift_{i}\", revenue_per_visitor[i] / comparison_rpv - 1)\n",
    "                )\n",
    "        return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c439d5",
   "metadata": {},
   "source": [
    "For the Beta prior, we can set a similar prior to before - centered around 0.5, with the magnitude of `alpha` and `beta` determining how \"thin\" the distribution is.\n",
    "\n",
    "We need to be a bit more careful about the Gamma prior. The mean of the Gamma prior is $\\dfrac{\\alpha_G}{\\beta_G}$, and needs to be set to a reasonable value given existing mean purchase values. For example, if `alpha` and `beta` were set such that the mean was 1 dollar, but the average revenue per visitor for a website is much higher at 100 dollars, his could affect our inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7483a55e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:20.123438Z",
     "iopub.status.busy": "2022-06-01T18:56:20.123173Z",
     "iopub.status.idle": "2022-06-01T18:56:20.125998Z",
     "shell.execute_reply": "2022-06-01T18:56:20.125540Z"
    }
   },
   "outputs": [],
   "source": [
    "c_prior = BetaPrior(alpha=5000, beta=5000)\n",
    "mp_prior = GammaPrior(alpha=9000, beta=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07f83462",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:20.128773Z",
     "iopub.status.busy": "2022-06-01T18:56:20.128453Z",
     "iopub.status.idle": "2022-06-01T18:56:20.131567Z",
     "shell.execute_reply": "2022-06-01T18:56:20.131063Z"
    }
   },
   "outputs": [],
   "source": [
    "data = [\n",
    "    RevenueData(visitors=1, purchased=1, total_revenue=1),\n",
    "    RevenueData(visitors=1, purchased=1, total_revenue=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f2b0d0c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:20.134405Z",
     "iopub.status.busy": "2022-06-01T18:56:20.134184Z",
     "iopub.status.idle": "2022-06-01T18:56:22.729728Z",
     "shell.execute_reply": "2022-06-01T18:56:22.728285Z"
    }
   },
   "outputs": [],
   "source": [
    "with RevenueModel(c_prior, mp_prior).create_model(data, \"best_of_rest\"):\n",
    "    revenue_prior_predictive = pm.sample_prior_predictive(samples=10000, return_inferencedata=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "006b62e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:22.733478Z",
     "iopub.status.busy": "2022-06-01T18:56:22.733169Z",
     "iopub.status.idle": "2022-06-01T18:56:22.940968Z",
     "shell.execute_reply": "2022-06-01T18:56:22.940270Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 566
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "az.plot_posterior(revenue_prior_predictive[\"reluplift_1\"], ax=ax, **plotting_defaults)\n",
    "ax.set_title(f\"Revenue Rel Uplift Prior Predictive, {c_prior}, {mp_prior}\", fontsize=10)\n",
    "ax.axvline(x=0, color=\"red\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d143b491",
   "metadata": {},
   "source": [
    "Similar to the model for Bernoulli conversions, the width of the prior predictive uplift distribution will depend on the strength of our priors. See the Bernoulli conversions section for a discussion of the benefits and disadvantages of using a weak vs. strong prior.\n",
    "\n",
    "Next we generate synthetic data for the model. We'll generate the following scenarios:\n",
    "\n",
    "* Same propensity to purchase and same mean purchase value.\n",
    "* Lower propensity to purchase and higher mean purchase value, but overall same revenue per visitor.\n",
    "* Higher propensity to purchase and higher mean purchase value, and overall higher revenue per visitor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e109784",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:22.945634Z",
     "iopub.status.busy": "2022-06-01T18:56:22.945281Z",
     "iopub.status.idle": "2022-06-01T18:56:22.951156Z",
     "shell.execute_reply": "2022-06-01T18:56:22.950422Z"
    }
   },
   "outputs": [],
   "source": [
    "def generate_revenue_data(\n",
    "    variants: List[str],\n",
    "    true_conversion_rates: List[float],\n",
    "    true_mean_purchase: List[float],\n",
    "    samples_per_variant: int,\n",
    ") -> pd.DataFrame:\n",
    "    converted = {}\n",
    "    mean_purchase = {}\n",
    "    for variant, p, mp in zip(variants, true_conversion_rates, true_mean_purchase):\n",
    "        converted[variant] = bernoulli.rvs(p, size=samples_per_variant)\n",
    "        mean_purchase[variant] = expon.rvs(scale=mp, size=samples_per_variant)\n",
    "    converted = pd.DataFrame(converted)\n",
    "    mean_purchase = pd.DataFrame(mean_purchase)\n",
    "    revenue = converted * mean_purchase\n",
    "    agg = pd.concat(\n",
    "        [\n",
    "            converted.aggregate([\"count\", \"sum\"]).rename(\n",
    "                index={\"count\": \"visitors\", \"sum\": \"purchased\"}\n",
    "            ),\n",
    "            revenue.aggregate([\"sum\"]).rename(index={\"sum\": \"total_revenue\"}),\n",
    "        ]\n",
    "    )\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5e2d75f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:22.955570Z",
     "iopub.status.busy": "2022-06-01T18:56:22.955206Z",
     "iopub.status.idle": "2022-06-01T18:56:22.962507Z",
     "shell.execute_reply": "2022-06-01T18:56:22.961792Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_scenario_value(\n",
    "    variants: List[str],\n",
    "    true_conversion_rates: List[float],\n",
    "    true_mean_purchase: List[float],\n",
    "    samples_per_variant: int,\n",
    "    conversion_rate_prior: BetaPrior,\n",
    "    mean_purchase_prior: GammaPrior,\n",
    "    comparison_method: str,\n",
    ") -> az.InferenceData:\n",
    "    generated = generate_revenue_data(\n",
    "        variants, true_conversion_rates, true_mean_purchase, samples_per_variant\n",
    "    )\n",
    "    data = [RevenueData(**generated[v].to_dict()) for v in variants]\n",
    "    with RevenueModel(conversion_rate_prior, mean_purchase_prior).create_model(\n",
    "        data, comparison_method\n",
    "    ):\n",
    "        trace = pm.sample(draws=5000, chains=2, cores=1)\n",
    "\n",
    "    n_plots = len(variants)\n",
    "    fig, axs = plt.subplots(nrows=n_plots, ncols=1, figsize=(3 * n_plots, 7), sharex=True)\n",
    "    for i, variant in enumerate(variants):\n",
    "        if i == 0 and comparison_method == \"compare_to_control\":\n",
    "            axs[i].set_yticks([])\n",
    "        else:\n",
    "            az.plot_posterior(trace.posterior[f\"reluplift_{i}\"], ax=axs[i], **plotting_defaults)\n",
    "        true_rpv = true_conversion_rates[i] * true_mean_purchase[i]\n",
    "        axs[i].set_title(f\"Rel Uplift {variant}, True RPV = {true_rpv:.2f}\", fontsize=10)\n",
    "        axs[i].axvline(x=0, color=\"red\")\n",
    "    fig.suptitle(f\"Method {comparison_method}, {conversion_rate_prior}, {mean_purchase_prior}\")\n",
    "\n",
    "    return trace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "975d4114",
   "metadata": {},
   "source": [
    "#### Scenario 1 - same underlying purchase rate and mean purchase value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e4d49ea2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:22.966097Z",
     "iopub.status.busy": "2022-06-01T18:56:22.965775Z",
     "iopub.status.idle": "2022-06-01T18:56:40.115813Z",
     "shell.execute_reply": "2022-06-01T18:56:40.115117Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [theta, lam]\n"
     ]
    },
    {
     "data": {
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
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       "      100.00% [6000/6000 00:14<00:00 Sampling chain 0, 0 divergences]\n",
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       "    /* Turns off some styling */\n",
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       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6000/6000 00:13<00:00 Sampling chain 1, 0 divergences]\n",
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 5_000 draw iterations (2_000 + 10_000 draws total) took 28 seconds.\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 432x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 591
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = run_scenario_value(\n",
    "    variants=[\"A\", \"B\"],\n",
    "    true_conversion_rates=[0.1, 0.1],\n",
    "    true_mean_purchase=[10, 10],\n",
    "    samples_per_variant=100000,\n",
    "    conversion_rate_prior=BetaPrior(alpha=5000, beta=5000),\n",
    "    mean_purchase_prior=GammaPrior(alpha=9000, beta=900),\n",
    "    comparison_method=\"best_of_rest\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01ccc4a",
   "metadata": {},
   "source": [
    "* The 94% HDI contains 0 as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7be20a",
   "metadata": {},
   "source": [
    "#### Scenario 2 - lower purchase rate, higher mean purchase, same overall revenue per visitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4b661564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:40.119186Z",
     "iopub.status.busy": "2022-06-01T18:56:40.118906Z",
     "iopub.status.idle": "2022-06-01T18:56:57.769578Z",
     "shell.execute_reply": "2022-06-01T18:56:57.768797Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [theta, lam]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6000/6000 00:13<00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6000/6000 00:14<00:00 Sampling chain 1, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 5_000 draw iterations (2_000 + 10_000 draws total) took 28 seconds.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 591
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scenario_value_2 = run_scenario_value(\n",
    "    variants=[\"A\", \"B\"],\n",
    "    true_conversion_rates=[0.1, 0.08],\n",
    "    true_mean_purchase=[10, 12.5],\n",
    "    samples_per_variant=100000,\n",
    "    conversion_rate_prior=BetaPrior(alpha=5000, beta=5000),\n",
    "    mean_purchase_prior=GammaPrior(alpha=9000, beta=900),\n",
    "    comparison_method=\"best_of_rest\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd5b4aa",
   "metadata": {},
   "source": [
    "* The 94% HDI for the average revenue per visitor (RPV) contains 0 as expected.\n",
    "* In these cases, it's also useful to plot the relative uplift distributions for `theta` (the purchase-anything rate) and `1 / lam` (the mean purchase value) to understand how the A/B test has affected visitor behaviour. We show this below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "68a7a343",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:57.772929Z",
     "iopub.status.busy": "2022-06-01T18:56:57.772669Z",
     "iopub.status.idle": "2022-06-01T18:56:58.125044Z",
     "shell.execute_reply": "2022-06-01T18:56:58.124254Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 993.6x331.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 339,
       "width": 1001
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = az.plot_posterior(\n",
    "    scenario_value_2,\n",
    "    var_names=[\"theta_reluplift_1\", \"reciprocal_lam_reluplift_1\"],\n",
    "    **plotting_defaults,\n",
    ")\n",
    "axs[0].set_title(f\"Conversion Rate Uplift B, True Uplift = {(0.04 / 0.05 - 1):.2%}\", fontsize=10)\n",
    "axs[0].axvline(x=0, color=\"red\")\n",
    "axs[1].set_title(\n",
    "    f\"Revenue per Converting Visitor Uplift B, True Uplift = {(25 / 20 - 1):.2%}\", fontsize=10\n",
    ")\n",
    "axs[1].axvline(x=0, color=\"red\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8786b390",
   "metadata": {},
   "source": [
    "* Variant B's conversion rate uplift has a HDI well below 0, while the revenue per converting visitor has a HDI well above 0. So the model is able to capture the reduction in purchasing visitors as well as the increase in mean purchase amount."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1873dc0a",
   "metadata": {},
   "source": [
    "#### Scenario 3 - Higher propensity to purchase and mean purchase value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "db019cc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:56:58.128834Z",
     "iopub.status.busy": "2022-06-01T18:56:58.128315Z",
     "iopub.status.idle": "2022-06-01T18:57:14.655619Z",
     "shell.execute_reply": "2022-06-01T18:57:14.654686Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [theta, lam]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6000/6000 00:15<00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6000/6000 00:13<00:00 Sampling chain 1, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 5_000 draw iterations (2_000 + 10_000 draws total) took 30 seconds.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 591
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = run_scenario_value(\n",
    "    variants=[\"A\", \"B\"],\n",
    "    true_conversion_rates=[0.1, 0.11],\n",
    "    true_mean_purchase=[10, 10.5],\n",
    "    samples_per_variant=100000,\n",
    "    conversion_rate_prior=BetaPrior(alpha=5000, beta=5000),\n",
    "    mean_purchase_prior=GammaPrior(alpha=9000, beta=900),\n",
    "    comparison_method=\"best_of_rest\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec9cb93",
   "metadata": {},
   "source": [
    "* The 94% HDI is above 0 for variant B as expected.\n",
    "\n",
    "Note that one concern with using value conversions in practice (that doesn't show up when we're just simulating synthetic data) is the existence of outliers. For example, a visitor in one variant could spend thousands of dollars, and the observed revenue data no longer follows a 'nice' distribution like Gamma. It's common to impute these outliers prior to running a statistical analysis (we have to be careful with removing them altogether, as this could bias the inference), or fall back to bernoulli conversions for decision making."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dff0b91",
   "metadata": {},
   "source": [
    "### Further Reading\n",
    "\n",
    "There are many other considerations to implementing a Bayesian framework to analyse A/B tests in practice. Some include:\n",
    "\n",
    "* How do we choose our prior distributions? \n",
    "* In practice, people look at A/B test results every day, not only once at the end of the test. How do we balance finding true differences faster vs. minizing false discoveries (the 'early stopping' problem)?\n",
    "* How do we plan the length and size of A/B tests using power analysis, if we're using Bayesian models to analyse the results?\n",
    "* Outside of the conversion rates (bernoulli random variables for each visitor), many value distributions in online software cannot be fit with nice densities like Normal, Gamma, etc. How do we model these?\n",
    "\n",
    "Various textbooks and online resources dive into these areas in more detail. Doing Bayesian Data Analysis {cite:p}`kruschke2014doing` by John Kruschke is a great resource, and has been translated to PyMC [here](https://github.com/JWarmenhoven/DBDA-python).\n",
    "\n",
    "We also plan to create more PyMC tutorials on these topics, so stay tuned!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12411a2c-6e4b-465b-9f2b-20334273ec42",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "* Authored by [Cuong Duong](https://github.com/tcuongd) in May, 2021 ([pymc-examples#164](https://github.com/pymc-devs/pymc-examples/pull/164))\n",
    "* Re-executed by [percevalve](https://github.com/percevalve) in May, 2022 ([pymc-examples#351](https://github.com/pymc-devs/pymc-examples/pull/351))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf62130-fa7b-4509-a914-38c41e516cca",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    ":::{bibliography}\n",
    ":filter: docname in docnames\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ea3916",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a1a4b30a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T18:57:14.659862Z",
     "iopub.status.busy": "2022-06-01T18:57:14.659573Z",
     "iopub.status.idle": "2022-06-01T18:57:14.683102Z",
     "shell.execute_reply": "2022-06-01T18:57:14.682294Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Thu Sep 22 2022\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.10\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "pytensor: 2.7.3\n",
      "xarray: 2022.3.0\n",
      "\n",
      "matplotlib: 3.5.2\n",
      "sys       : 3.8.10 (default, Mar 25 2022, 22:18:25) \n",
      "[Clang 12.0.5 (clang-1205.0.22.11)]\n",
      "numpy     : 1.22.1\n",
      "pymc      : 4.0.1\n",
      "arviz     : 0.12.1\n",
      "pandas    : 1.4.3\n",
      "\n",
      "Watermark: 2.3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor,xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed3eeaf-7907-4d47-89b7-8ed85838c42d",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
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