examples/case_studies/bayesian_ab_testing.ipynb

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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",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f9d767",
   "metadata": {},
   "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](https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf), 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?\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9592100b",
   "metadata": {},
   "source": [
    "### Bernoulli Conversions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448ef06b",
   "metadata": {},
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0c80bf2",
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   "source": [
    "@dataclass\n",
    "class BinomialData:\n",
    "    trials: int\n",
    "    successes: int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7ba4f2",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc675d9b",
   "metadata": {},
   "source": [
    "#### Prior predictive checks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1f6ca4",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "weak_prior = ConversionModelTwoVariant(BetaPrior(alpha=100, beta=100))"
   ]
  },
  {
   "cell_type": "code",
   "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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    "execution": {
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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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4df134b8",
   "metadata": {
    "execution": {
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   "outputs": [],
   "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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\n",
      "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>23133</td>\n",
       "      <td>23191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A       B\n",
       "trials     100000  100000\n",
       "successes   23133   23191"
      ]
     },
     "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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     "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"
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       "      100.00% [24000/24000 00:06<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 18 seconds.\n"
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\n",
      "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 00:07<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",
      "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 00:06<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 19 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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      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 18 seconds.\n"
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SkiRJkiRJUmoMICVJkiRJkiSlxgBSkiRJkiRJUmoMICVJkiRJkiSlxgBSkiRJkiRJUmr+H4dnFpIVa2izAAAAAElFTkSuQmCC\n",
      "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": {
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       "    .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 00:06<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": {
      "image/png": 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\n",
      "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, but the average revenue per visitor for a website is much higher at \\\\$100, this 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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 565
      },
      "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",
      "/Users/severinhatt/miniconda3/envs/pymc-hack/lib/python3.10/site-packages/pymc/aesaraf.py:996: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
      "  aesara_function = aesara.function(\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",
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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",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\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 00:04<00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
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     },
     "metadata": {},
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    {
     "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",
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     "metadata": {},
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    {
     "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 00:04<00:00 Sampling chain 1, 0 divergences]\n",
       "    </div>\n",
       "    "
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 5_000 draw iterations (2_000 + 10_000 draws total) took 8 seconds.\n",
      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 432x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 590
      },
      "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",
      "/Users/severinhatt/miniconda3/envs/pymc-hack/lib/python3.10/site-packages/pymc/aesaraf.py:996: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
      "  aesara_function = aesara.function(\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [theta, lam]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "    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 00:04<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",
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       "</style>\n"
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     },
     "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 00:04<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 9 seconds.\n",
      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 590
      },
      "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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\n",
      "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",
      "/Users/severinhatt/miniconda3/envs/pymc-hack/lib/python3.10/site-packages/pymc/aesaraf.py:996: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
      "  aesara_function = aesara.function(\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [theta, lam]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
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       "    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": [
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "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 00:04<00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
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     "metadata": {},
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    {
     "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",
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       "</style>\n"
      ],
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     },
     "metadata": {},
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    {
     "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 00:04<00:00 Sampling chain 1, 0 divergences]\n",
       "    </div>\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 8 seconds.\n",
      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 512,
       "width": 590
      },
      "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](http://doingbayesiandataanalysis.blogspot.com/) 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!\n",
    "\n",
    "---\n",
    "\n",
    "Author: [Cuong Duong](https://github.com/tcuongd) (2021-05-23)\n",
    "\n",
    "### References\n",
    "\n",
    "* [Stucchio, C. (2015) _Bayesian A/B Testing at VWO_](https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf)"
   ]
  },
  {
   "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: Wed Jun 01 2022\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.4\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "aesara: 2.5.1\n",
      "xarray: 2022.3.0\n",
      "\n",
      "matplotlib: 3.5.2\n",
      "arviz     : 0.12.1\n",
      "pandas    : 1.4.2\n",
      "numpy     : 1.22.4\n",
      "pymc      : 4.0.0b5\n",
      "\n",
      "Watermark: 2.3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p aesara,xarray"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
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  "toc": {
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   "title_cell": "Table of Contents",
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   "toc_position": {
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    "left": "10px",
    "top": "150px",
    "width": "288px"
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   "toc_section_display": true,
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