diff --git a/examples/case_studies/bayesian_ab_testing.ipynb b/examples/case_studies/bayesian_ab_testing.ipynb deleted file mode 100644 index 093349c85..000000000 --- a/examples/case_studies/bayesian_ab_testing.ipynb +++ /dev/null @@ -1,2196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b48cad3d", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:36.476575Z", - "iopub.status.busy": "2022-06-01T18:53:36.476139Z", - "iopub.status.idle": "2022-06-01T18:53:39.372124Z", - "shell.execute_reply": "2022-06-01T18:53:39.370978Z" - } - }, - "outputs": [], - "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" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "30cc5507", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:39.376825Z", - "iopub.status.busy": "2022-06-01T18:53:39.376264Z", - "iopub.status.idle": "2022-06-01T18:53:39.388197Z", - "shell.execute_reply": "2022-06-01T18:53:39.387403Z" - } - }, - "outputs": [], - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a5376bb4", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:39.391621Z", - "iopub.status.busy": "2022-06-01T18:53:39.391347Z", - "iopub.status.idle": "2022-06-01T18:53:39.395688Z", - "shell.execute_reply": "2022-06-01T18:53:39.394570Z" - } - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class BetaPrior:\n", - " alpha: float\n", - " beta: float" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a0c80bf2", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:39.399745Z", - "iopub.status.busy": "2022-06-01T18:53:39.399180Z", - "iopub.status.idle": "2022-06-01T18:53:39.403589Z", - "shell.execute_reply": "2022-06-01T18:53:39.402642Z" - } - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class BinomialData:\n", - " trials: int\n", - " successes: int" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b625c349", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:39.406921Z", - "iopub.status.busy": "2022-06-01T18:53:39.406669Z", - "iopub.status.idle": "2022-06-01T18:53:39.412545Z", - "shell.execute_reply": "2022-06-01T18:53:39.412035Z" - } - }, - "outputs": [], - "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, - "id": "4a103373", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:39.415596Z", - "iopub.status.busy": "2022-06-01T18:53:39.415337Z", - "iopub.status.idle": "2022-06-01T18:53:39.418295Z", - "shell.execute_reply": "2022-06-01T18:53:39.417874Z" - } - }, - "outputs": [], - "source": [ - "weak_prior = ConversionModelTwoVariant(BetaPrior(alpha=100, beta=100))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1d8702af", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:39.420933Z", - "iopub.status.busy": "2022-06-01T18:53:39.420683Z", - "iopub.status.idle": "2022-06-01T18:53:39.423573Z", - "shell.execute_reply": "2022-06-01T18:53:39.423001Z" - } - }, - "outputs": [], - "source": [ - "strong_prior = ConversionModelTwoVariant(BetaPrior(alpha=10000, beta=10000))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9e1e0769", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:39.427592Z", - "iopub.status.busy": "2022-06-01T18:53:39.427300Z", - "iopub.status.idle": "2022-06-01T18:53:43.647086Z", - "shell.execute_reply": "2022-06-01T18:53:43.645945Z" - } - }, - "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": { - "iopub.execute_input": "2022-06-01T18:53:43.652280Z", - "iopub.status.busy": "2022-06-01T18:53:43.651840Z", - "iopub.status.idle": "2022-06-01T18:53:44.243767Z", - "shell.execute_reply": "2022-06-01T18:53:44.242862Z" - } - }, - "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)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a3d30bb9", - "metadata": { - "execution": { - "iopub.execute_input": "2022-06-01T18:53:44.248078Z", - "iopub.status.busy": "2022-06-01T18:53:44.247715Z", - "iopub.status.idle": "2022-06-01T18:53:44.713458Z", - "shell.execute_reply": "2022-06-01T18:53:44.712846Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "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": [ - "
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" - ], - "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" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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" - ] - }, - "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", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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\n", - " \n", - " 100.00% [24000/24000 00:06<00:00 Sampling 4 chains, 0 divergences]\n", - "
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" - ] - }, - "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" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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" - ] - }, - "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", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " 100.00% [24000/24000 00:06<00:00 Sampling 4 chains, 0 divergences]\n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "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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" - ] - }, - "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": { - "image/png": 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\n", 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" - ] - }, - "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 . 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", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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I9x7NbrYzHbkvtEXhoeWr5UZmAaKYfS2tLFHL8117ruG56MR7zpmk623B+zS9Dhc/YOrQ8mobFParmaUVHooU9rnBpIfVxWpaRosxTs3y161oflXLNeAUU4eNj5ICDqUrUKjddN2ynggBvyBF3CaSdo7+MbVp7U+qfjgL+Gb25KWw7NGkkzDAm7F5PwU3lFnO70g3qzvFGPuSdvaDSE+YNiBFbJsIIXyO9AQF4PfAmjG12R+SzQ/gzBDC4WWWdz7pRDqPdJD1y9ZrK1K1uj8u43dpjctJBZCNsnwNIt1oLiVdGL9VZp0OyvI+j3RBWyumdvG9SQduJHX2XXrROJt0Ah1PeitDj5japvYmBRd+TKp1Q4zxzRjjUNJFBOCGMtvozXas9zXAR0jb87NA3xjjAFJB6T+kg/m2EMIahQSF5ZKCoNC0X4BDaL8/kPp32DLLSx/SzVAhWPc30gX9MlKNrN6kQMIWpKdV65IKhcUdtn2BdFM+FzgC6BMb+4xYn1Twa+iTo8rjIldZIOlAUkEGUl9ZU8tM2tHH/Vukp9NbF827J6nw/U/SMXx9qK5/qfWK/p9RZnzFfaElWc2mv5AKbw8Co7KaSYXfZAGpAPSbFmbzA1IhYF/S/jKAtM4FO2efLT3xmUN66r03sEZMfZX0Ju1zF5AuSJdkNU7aagjpKd41wLBsf16DxqcyJ4cQWnoZxVWkwHfht+1HCkIsIK3nCWXSdOS+UK0vhhAmhRAWhhCmhRAeDSF8K6ROI8vZlHSTAOmpYDPZtbZQUN6sMDxbr1Etpc0UnrRvVjK88L01aYcUn3dzdB6pkLlrTP0S9CVd494nBaOadbpbzXk3xnhDdp14LJvNaSXnmNFFi1hKuqH9PKkc1CvbJweTggBzgC+F9NaYThNCeDyEMCuEMC+EMCGEcG0WeKukPdu3kHZyhfN8cdri6aEd+3SJwlv3RoUy/aa0wx7Z5xIq3yC2Vh77ykmk/ov2J+3D/UjHwGzSuaxQc+U6YHh27ViTVJOvDrig0g0hqabp10gv4xiY5e1DpN9+NdL581pS+enD2fgBNDYN+UpWg6HUc9k8N87WeTDpGDyQVDNiWxprkbVVS9fhtUj9qfyBxutMP1LfPS1qZ7m/YFllgNZchzvyunVQ9ncGMCDbVzYm9UHUlVSzc0SFtENIzUb/SPptB5H248Jvc2oo39VKR+4LbVGocdRS58iF46R0PWpyvsvhGp6XTrnnjDGell2HC0aXXIeLH7R3ZHm1LQr7VUuxluLzb8O+tRyV0QrX0l1bmE+rdETP9deQTpxHknbCwg93eNH4irInJqeSLhZ7Z0EsIFVNJO0kM4AbSBeAP5eZTWstAD5aOEnEGBeTenz/MakweyhFB0u2Hj/Kvv4lxnhKUd6mkk6qa5AKED8OIVxfCK5lBd1Ck5uTYlEzphjjiyGEj1Mhut5G/wM+FRvban4A/DKrAXE28O0Qwm+y4WSF6sLN6hEx9bVTyNci0hsS9iUFbY4NIZwTG5saFmoMfS/G+HBRugWkGhkv5bA+yxRC2JX0GlOAL8QY7y7KyzMhhH1IAb21SPvWDzojX6ROsvfNotCFpySFN6D8hHRi/m3JiRLg5eyE/BQpGHkwjYWiwm9+TYzx2kKC7Enrf4ELO2A92iSEUBzJ70qqfVVH2jd/TKrhV5qmw4/7GGPZKq3Av7Pf+1nSCX83UnCnLQpBjXpSobJUS/tCS75DupF4HfhkbGz/vYD0m0Aq6B0bQqj06t2eWdqG47Fkuu2yzxepIMb4AGVqpsYY/0tqPz6AdH47hrZ3UNmHVKg9OvtdiKkpyDey8+lRwLkhhD/H8q9/fYu0foVz3gLgipDav59MOo//oSTfVe8LobGpYjX2zH7LcjYmPVWeQzo37JT9fTWEcGBs3rlzcQ2elppRFMYVTz+AxmatbU1b/L01aQvTv9/CtNXoSTqmxkNDwfz2EMIsUk2EfUIIu8QYHylKU+15t1Wy43O/MsNnAL8PIcwklYFOoqT5amhs0lONZTUB34HGJ8AbZH9fDCH8BvhameOqPdt3mWljjPOy8/kgmu5b7dmni+c/IdsPBpDOb/9oYV5tUaidML5wLq5We/aVIgOBg2KMxeeo20MIvyRdZ88E7o8xNvTBEWOcEkL4Iqn56TDSOeahCvM+J8b4m6K0/wkhnJBNfzDpAd9GWZ4L6/STkN66tBfpYVCTcmCMsdkDgOyc/bcQwsukm82jQwjfbMtvnNWGKDyYeKlM2l7An2OMhT5ViDHOJ5VLWppv1eX+EhXLACF1tVGoAd7SdbgjyzADSWX54pv817OHhc+TalSdRWN/j8X6kJqAFv82M0i/zR6kdfs0JTe/7dkXQvPuE1otxlgakJtEurkvG5TJ7pM2yb6W9tFZq/Nde6/heenMe85WaU95NTQ2Ma3GuTHGc4q+T8o++4cQ1o3lK1UU73PF+9byUkYrlDu3b2E+rZJ3kzpIVSTnA4eExmYeu5Mii8/EZb8B6cgsX7cV33SWuIUULNo8VO7ZvTUuqRCRvi373DA0baryYdJNAaQb53IKO+/6NN7IQbrwdiFt4GZBt5j6jMijhtOvCgd+iV+TtssAUpvNgj2yvE4sPvBL8jaBVK2zG41P+CDVOIGOO5G11qHZ5zPFwaaCGOO7NLZtbbEKec5+H8u0Pw/prRSFp5a/Lh0PqdNRGm92irfX8vKbt2Stor81aHyCM4D0ZLR3mTSdedw3kx0z92Zfd25p2oIQQo8QwmYhhMtIhSlIBdJyzdLK7gvLmH9d0XzPr1D4vowUcKmj8TgodVdxsKmMwm/ZnoBAof13q367Mv6vQjCp8FaYjUlBgHJ+XeGcd1v2We4pe0Wt2Bem0fJr5lv6W1hmfs+S+mRbj/SUdzXScXIiKQC7HnBXSH1tFSu+NrW0bxX2m345pS1O35q05dLn4cZyAdYY4/001kZqOCbaed7NS+E42SE0f9XwQqrfr8o1AZ9PCrTuRqoNMYh0Y7hNUT5Oo0xNbtq3fVuTtjh9nvtlsULZLs9rxWrZ5/RKE4QQLgohTC7z9402LqulfaXg8ZJgU8G/iv5v1tQru/l7Ivta6fy4kPLHyaOkfQvgj4VgU4kxy5h3Wdm1/2XSfvrh1qQJIfQLqQ+Ze2is4VSpGfYv25KfzIepvtxfrKUywJo01q6p6jpcTRmmxFxS7Y/S+c6nsbbxp1uoPVWpSeHt2Wfe+0K158p3y8yrUFvxCyG9sa7UsaRaXABds2tJQa3Od3meK9ujM+8589JSeXUO1e9XpS+eup/GMl+zfq1D6mvq9KJBxc1Hl5cyWumDpKrlXsMpxjgjhPA3UsHu06TgSqE5XYu1mzI7ZZ+HZlHOSgptZtclPampRrmaCNC0HfggGvsdKrSvnBKzNwmUijHGEMJbpCrSW9N4US+kfbjC0w9o+xOJch6okK9ZIYTnSP3+bE3jzVjh9x5eUjOl1MDss/hkfCcp6vnzrIbKTcATbb2xzkHht23p6fB9pIL1JiGEvoVoewd7vMLwbWnsSO7J0LSbpmKFi1rxb34X6cR1UAjhDlJzogcrBE5rovTpURYc2pbUuelJwE4hhN1i6ky1oFOO+xDCKFKtl91IT/n70RgQK2jpLXNnhxAqPf14grR+5VTaF1qyEY3HXdl9O6YOch8gNUUsbf/d2mUXqtJWvImChg5Lv0pqmheyvJXeCFXzhr5FVOi3IsY4LoRQeBK/NelJa6llnccr9cVX1b4Q82luWzy/Zn2dZDdxF4cQniLtV8NIzS++UzRZe5r7Faet5klxIX3VT5lz8EAL4x4knVOKj4n2nHdbLaQmSkeRykBbkYIUPUom60XaLxtuLmN6c8xQchJTnxFfLRlWTwpwHhhCuDHL43dCCH8oCRy0Z/vmkTYP00l93nVEc86WDCI9bCnVrEBf7b5S5D8V8lDcuWulhw2FG++y50fSDeHs0oHZNed9Uh+oVc07q3V+LCk4M4zyD6Faupbc38Lx+8cY4yVlhs+jqLuBNmhPub9YS9fh4n10Wdfh9pZhKnmmhXJx4d5kEOmYKn0oOK2FB4XLug5XtS/Eps2r2uv8LA/9SX1inkoqkxT62P0VqZxSKH8W38PV6nzX3mt4Xh4oN7CD7jlbrdryaozxPBq7fmmXGON7IXUYfypwUlZr9Y+k8+MWpAD4hjTuW+X2K6htGa1wPmr3dbQjmtRBCix9BjgihPBXUuBpEa1rBlOIovWjdRHZPsuepKJmF1NIEf2ii1lxh7OFCHelDrgK/ke68BT3HVD4v6Xqbcuab2u0NI/CuOJ8FX7vHpQvJJUq/r1/TnpaeiDpRvskYHEI4WngVuDSCk+/8taa7VKoNl1HOnA6I+BUqQPm4khxm37zGOODIYQfkJoFHpD9EUJ4ldRs4OIY47jqstsxsuqwf8tunl8lPa06jaZPCzv8uA+pH4ZraFpomEmqNVVYdl9aeIMeab8pPMVYkqUfS9rf/xJTs9xyKu0LLSk+Tluzb1fqq2RZy+6ZfZarfQNACGEzUtC2eH+dTSrE15POH4Np+ber5P2sZkklb5H2j0rrV/Y8TuNT+GbXuZz2hQ4X05tE/kJ6aHMATQNOxU/TelP5dygcK8XTzykzvrVpi7+3Jm259Hmo9loHbb/WtUpIHUH/k8ZCNaRjZAqNhcnCsvuSfzPDtvg2qZzWl9TXxc1F49qzfVuTtnh8pf2yrft0qcLxX+4GtlqFmmSVgjTEGD8HfK7wPYTwCGWepOe0r1R66NLQmXkLzVEK03SvML6lBzpLljFNxXmH9FamU4oGLSL9roVO+VfL0rV07p1O4/VqEem3eZbU1UClB7dTW3jY25L2lPuLtXQd7ln0f0vX4Y68brXmfApp/UqDS5WOU2g8DjtqX2i3mN6kfSjpoflmNK0hCCmAewmpf7L5JTV6anW+a+81PC+dec/ZKh1cXm2rb5GCSoXy23dKxl9I6oc10LTfueWljFY4fnuEELpUeQ4FOi7gdDfpAN2LFIkfANwRY2xN4arQzO+0ck9+lxM9lz1JVTq6g9py8y/83re29cl9dtI9KKS3vx1MeuKyDSmivSOpg+d9YvO+RzpKR22XalV6g03hN5+eNZ9pkxjjj0J6m9thpOqmO5Lan48CTgshHBdjbE1twk4VY3w3hHA3qTD+WZoGnDr0uA+p49hLSYWXG0hPFl6MTV+7+iNSgaKl4/C8kjbarVX124wy7dm3l7XsaaSaFYNamOZK0sX7WdIF89GY+tYCIISwN6mQ1hHnsFznmeO+0FmeJAWcNioZXvzwYjiNHYuWKjzFK745nEUKnval5afh5dIWlv3hVqYtl76jtXStq+q820rfJwUQ3ifVSLs7Fr1KOGsaVQhK13Tfiqmfoymkm4Fy+9aHqW77vl1mfBNZk5RBLaQtpG/LPl2qEBTKs/ZvoUuIjUMIfSo0c26tFWZfyUtWe/kU0jXpR6ROx98obk4dQniY9Oavltb5kFi5L7xKankNXtbyi5vEDqJM4KHG163c978c94VcxBjvyQIVp5LeyLUWabuMIb3QptAPWukD3Vqd79p7De8Mud5ztkEty6tNxBgXZP2rHUpqibA5qabVq6Tj+W80dpVSvG8tL2W0wnV0RnuCTdBBAacY4+LsqeypNPbB8adWJn+XFOkr23lbjRWeUCyrZ/t1SqYv/r+ljZ9HXwPDSW/5amn+xfkqVH2u+veOMT5BVoU46/PqAFLtp/VIfcyMrpw6F1NI+8z6LUxT2Cb11PapMhRVNw8hDI2VX5dZUdbG+WfAz7KC6a6kfgR2A/4QQmhSeF2O/Df7LL3B6ejjfl/S079XSB3LlztxtuZpS2cqPk7Xp2kziWLlzjdt8T4p4FSpyvt6pOruS4ADY/lXrLbnt1sjhNCjhVpO5c5b7dGufSGEcAtNayW0xSFZ06m2qFQ1+tVsWB2pENOssBpC6EI6rqDoTTkxxvoQwlhSM7Nybw8qKByPr5QMf4X0+uvWpJ3SyodNbdWaa2m5a13V591WKPQRdUqM8S9lxre0X+1E6qeuGo9VWXivtG+1Z/sW9pWhIYTVKzT33qzM9NCOfbqMwvksz33vgeyzK/BxUs3WalW9r6zACut8WYyx0ssllrf1bk+5v7WK99HBNL45uVhHl2Fae2+S13W4XfvCMppjtahSc7wY4/8o81a1bHmFt3qWNo2syfkuh2t4Xjr9nrMl7S2vZn3ttbW/vYLzsiZ5TWRB1L9S5uUPIYTtaKyF+0RxmuWkjJbbdbQjOg0vKNSw6E6q/vq3FqYtVjiYDwghVKrqW0nhBNxRUctns8++2U7STAhhE1K12uLpi//fpYVO96p989Ey5xFC6E9jW/TifD3eOEnZ15a2SYzxg6zw9KVs0DahacfrHbGNCuuzewu/7V7Z52ud1H9TS56h8clluyP8McYl2dO+/UlVkfvS9LX3HX1ctEXh2FhUMryjj/tCYfDFcgW1bL/Zq3R4jb1BYxXbPctNkBVG9si+PltumlYoFHI2rDC+oSBd4eINqUpwtbqTauk1E0LYmMaCcLXrV6q9+8JqNO0Yvy1/pf2ztEbhWjOxeGDWv0rhlbWVOrjensa+EMaUjCv0C1Y2bUhvTiq8CrdS2s1DCJVuVD5WIW1eWrpeFsYV7zPtPe+25TzzXIXxLR0nhSYG1fy1ucZWCKG4f6OJJaPbs30fofH8Xml9C2nfobHWUB77NNDwxrJCM4483v5b8CiNb9o6M1TuzLs12rOvrKhaXOeQ3ua8cblxNdSecn+rZEGKwg3dsq7DHVWGGZ0dN+UUzqczgAntWEax9u4L1Z4r2xyUCyGsTeP56PqS0bU837XnGp6Xzr7nLDwcqXQdbm95tR/V71fVdMx+TPb5QIyxtMud5aGMtkH22e7raIcFnGKM/wbOIXW2dnqFXuzLuZpUsBtO+benNAghlD6RL1RLG1g6bU6eBwpvxSlth1lwTvY5kfR65YJbSOu1NnB4aaJsXU7MIY9fD6nn+1KnkzqfnEV6m0fBGBprnZzfUgGq9PeusJyCedlnHU1vsgrbaFALaduq8FahzYGDSkeGENai8be9McflViW7yBT6y/helr+yQgjdsr4eCt9b+s0X0lhtu7j6d0cfF60SQhhE44mztFDW0cd94ZXgW1QISp4AjGhpuZ0teypSqPFwWoXC4PGkc0o9bXyFe5FCh93bVhhf+O3WCiGsWToyhLAlja+jrtZZFbZLYV8YR3UdvpbTrn0hxrhHjLGuyr8HiufVQoC8MH4rGvuDKfdq90Lh94uh/JsbC0/q/h1jLH16+ufsc1QIYf8yaU8gHVPzaF6TYwypxl0X4IwK+S4U6q4rM+88HBZCKK0pSQhhNxr7zGl4otie826mNdeuwr61ZemIbH7frZQwxvhAO/arPcosb1kPGH6afc4j9XdRrOrtG2OcSXqhCMAZWVC8OG1fGq/H18fmb6dszz5dsDWpFtJs8jtvFM7JZ2ZftwN+V7p+bVD1vrICq7jOmZ+yfDwYK/Y81Zf726JQ83VZ1+GOKsP0IfWt2UQIoSeN54Cbyhyv1WrXvtCOc2Wb9q/snuiPpBZBj8WSPsJqfL5rzzU8L512z5lZ1nW4XeXVGOM57di3zqk033JCCDuSyvBQ/i2Py0MZrdBC6eEWpmmVjqzhRIzx3BjjN2Ib+pOJMY6l8dWc54YQLiwuVIb0CtR9Qgh/onn1tHGkSPPAEMKnyVl2ovhe9vWgEMLvQvaq6hDC6iF1gPf5bPz3ip9CxBgnAVdkXy8KIRxZqMmRHQB3kw7O9loPuDWEsEE27z4hhDOAwpu1fh6L+h2Iqf33KaQb1n2Ae0II2xcuaFnBe5sQws9o3lHgSyGEn4YQRhdOOCGEuuwpUOG1tE/HGIvfulF4OrhLSG+2a7cY48Ok3w/gihDCoYWTWAhhG9LJbjCpKudv8lhmDs4ktQ8fBjwWQjg4u7ADqWZHCOF00tOQ4gLINSGEK0MIH8+eIBSm34AUtOlFOvkUnxxadVyEEPYIIdRnf3u0dwWL5tsthLAtqZZj4QLQ5LXFnXDc/4u0j28B/DYLfhFCGBBC+Cap477l5k1/RX5Kasc9HPhHCOltBiGEniGEE4BCf1eXxzKviG+lR7LPj1S4+I8ldYhaB9wQUq0jQgjdQwiHkF7F3J4OKeeSnsxeXigghBAGhRB+TnpzDMA5ORZ0l6d94fAQwl9DCAeG9FYVsrwMzLbv/aSA/XuUf3PKxcAk0tt1/h5SHxSEEPqHEH5BY02eZjdKMcbnaAzAXxVC+GSWtmsI4UhSs2iA82NJ89zsAdI52devhRC+Xjh/ZYWoW0nli0djjH8vXXYI4ZzCuaalH2cZFgJ3hdQUjRBClxDCATQGXu+NMZa+/bDa8y40XrsOCSFUCm4XXkv+6xBCQ43bEMJoUgGwM9+Y9kAI4awQwhZF18O6EMJHQgi30hjI/HmMsbgPmXZvX1J5YxEpKHNVCGGNLO16pCD6eqTaEj8vk7bqfbpIoZD8WIyxWd85IYSrsv1vYgvzKCtb3x9mX78CPBFC+GzJ8ds1hLB5COGHlH+lOyxf+0pnKazzl0MIxxaVG9cLIVxNKj+3+Ja2ztaecn8bFa7Dlbqg6Ojr1kzgRyGE00Lqc4isDHY7sCmp8+CftWP+pZarfSGE8JOsjNk/+16XHYt3kboJmUVjP06lanK+a881PJvugew8+ECF9WqNzrznhMbr8JE1Kq+2SQhhzxDC10IIGxVdhweHEE4hvTSiG3BJjPGe0rS1LKNl09WR+mWG5T3g1A7fIkWUIb357PUQwqwQwnQao6WHU/KKw5iaShUigjeFEGaEECZmf4fmkbEY4w009kt1MvBeCGEa6Yag8LaFn8UYy0UMv0bqALYPKTgwO4QwA3iRVDvnKzlk8ThSNbkJ2e81k1TLrCvpwvGLMut0R5ZuIenG7wlgbkivvp1PqvL5bZpHlNck1UB4Kpt+KultGU8CHyJVET6+JM0DwOukJgAxhPBe0TZah+odSXoSNZgUkJgTQpiV5f1DpAvXwbF8++pOF2OcCHyC1LHbRqSL0pwQwvshhPmkIMr5pCrFxTdlvYCjSQG2mSGE6SGED0jVnA8j1XD6cixqj9sZx0WxEMLk4j9SAOxpUueP9cCPYoy3lUnaYcd99mTogmyak4Hp2XE7jXRMjAEuav/a5yvG+Dqp4DWf1HTu1ez3mE16a0pPUt5Pb8diniFd2PvS2DyvOA9LSf3xLc3Gj8uOrTmkGiML2rn8KaRtfwwwOdsuU2nsS+HCGGNpNfaqLWf7QldSZ5K3A1Oz/X0q6Xx1Cel89l9g3xhjs74zYozzSLU6p5Jqdbwc0qt3ZwDfJB1vZ5UrzGROAP4NrE4KaH5ACnBeTepX4O80FhxLl/1HUqeXXUjBsNkhhNmkJ/Ubkvapz7blx2ijb5B+n0ez5c4B7iA1pRpPet18aZ4nUt15F1I/lAtJ57H3QwhvZeeYR4qm+R7purcu6Vo3N4Qwh3SN3JLGG9POsD4pYP0fYF52Pf+AVLv0U9k0v6cxeNJEe7ZvTC8KOYHUhPEIUjlpBunG6mNZPj7dQfs0wH7Z5w0tTFO1GOPZpPWbSQoQ3EA6fueE1BH7POAlUsfgfUhPkC8pmc3ytK90lqtI5ctuwOWkdZ5O2i+OJJ1rXqxZ7ipoZ7m/tf5K2rd3C81rV3bGdet20vnzArKyJams/nFSufKYrDySl6tYvvaFL5LKmLOyc9U80rG4D6kp3MdijGWbFdX4fFf1NTwnnXnPCalfYEhlzjkhhEnZdfi8bN4dXV5tq/WBX5OOpfnZbzSV9LC4P2l9TmohfS3LaDuTtsEk0n19uyyXAaeY+qQ5iVSwu5a0sj1IP+5/SZG5o2gsNBU7kVQ1LZJuxtbP/qppW1kpf98jvUb4dlKBoR9pB7oD+GiMsWyToJh6yd+D9Er717LB80mFle1o3hldNXm7mdTfyz9IF4nFpCrlp5A6rC372vYY45WkjukuIEWQF5Oq6k0lPWX/Bo1tOQsOIv3Wj5IK8P1IJ5AXSU9CNo8xNrlgZNHtvUmF97dINwyFbVR1J/bZiXxH0tteniE9behBuoG4IMtLu3/fPMUYnya9We7bpBPAbNLBPY+0Dj8HRpdU4T2TdCN+N+lk0YN0Yn+d9GaGrWOM5Trob81xUajOO5f2dTBY2q55cZbXPwE7xxh/UC5RRx/3McYzSH2LPUe66HQjBSlPJ92glD02ai3G+DfSDcilpCr7fUjb6BHS+nw8tqNfsuwJbqH25ecqTHMrqWBwL2k/7U7aPucBHyE9UapajPEC4EDgQdJ1aT6pEHJ4jPHk9sy7wvKWl33hftJN6d009o0xgHRduS/LzxYxxor9gmSF3S1IBZg3SPv/VNI1YJ8YY8Wn0jHGWaQO0M8kXSfqSb/HE8CXSZ1uVvwtYoxfIgW67ycV6LqR2vr/BPhwbN4nQUHhXPNMhfGtMZ5UC+kKUiG3K+n4+BWwbazwOvgqz7tkNxv7kAX7SR3tr09jnxHEGN8gXcuvJd2MdiXdOFyXzbOlIEnevkk6Z7xAuikdQCqER9JvtkOM8ZSWag62Y/sSY7yadE2+kVS7uDepM+QrsrSlzfiK01a9T4dUS3KPLL+VmtAX9r+nK81nWWKMl5HKRGeQ9on/kbZ3P1IQ/R7Ssb1RjPHw0t9qOdtXOkVML4b4KKl8+AZpf1xMuq4cEGP8UQ2z16Jqy/1tmP9EUtCoN2W6hsim6cjrVj2pI+8zSLVEepAefPwd2CmW79i+asvhvvAj0racSDrfFK4H3wNGxRhbvOGu1fmundfwPM6DnXnPWUh3AikYuJgUsF+fohqhHV1ebaNHSC1rniOVG/pky/8LsFeM8YRYphZuQQ3LaNB4P3BFS+WE1qqrr8+rlYKkFVkI4SLSCexXMcZq39KgFVBInQpOJF2ch8fW97nXnmXuQboITooxbtDRy9PyI4TwKqmweUCsUJ1bqkbWVOG3pGYKXy4zvhvpRrovqcC93NWo0aopq5H9V+AfMcZyfbZ0xDLPIdWQuDrGeHRnLFO1F0IYSqq5NQ/YMMb47jKSaBWSXSf/R6pZtVGMsdybM9tkuazhJKkmdiddfH5Z64yoc2VPOS4mNXU9ZhmTS1ULqbPuADxrsEl5yvrIOI30BPinFSbbhlQ75VaDTVrO3EKqxfDJQl8+UgfZPfv8o8EmlXEEqYXK5XkEm8CAkyQghDCE1MzkYi8+q6wfkarcfjt7uiF1hN2yz7J9B0nt8AXSm7ouiulFLeUU9r/ltvmWVk1Z/zNnkTo8bqlTfKm9diN1XeADZjUR0psWzyRVQMitnOZNhaRCH1jL2+uI1YlijO9lb77YitQvzcTa5kgroxjjX/Fco45RD5xLemNXWTHGX+JNlpZTMca7srd89Q0hdGupfxapWjHGrwJfrXU+tFwaTnoR08vL6OOpTQw4abkRQvgGqaO4VosxDu2gvBxG6uitLUbnVfVQqoWss8Vba50PSWqrGOO1tc6D1F4xxvNrnQdJq6YY4/+Ac/KerwEnLU/6kdqMLg960/a8dO2IjEgroxjjA1jTRZKkmogxnkMH3FxKUjHfUidJkiRJkqRc2Wm4JEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXHWrdQYkSVrZhRAeAHaPMdbVOi+lQgjnAGcDe8YYHygaXg88GGPco2T6ocDPgb2BYaSHV4NjjDM6J8eSJElaERhwkiSt0rLASrGlwEzgReAq4OoYY+k0nZKnlgJUIYSJwPrAhjHGiZ2TMyD9Jh8D/gyMB+qB+XkF1UIIrwEjgcdjjDu1L6sN82zr9jsmxnhVHsvOW+F3Lhn8AWlb3Ar8KsY4p0y6iaT9paAemAW8AlwHXBxjXBxC+ClwFvCLGOO3l5GXS4Hjga/FGC+oZn3yEELYDjgY+DDwEWAt4K0Y4zpVzq83cCbwOdJvNgt4ADg7xji2Qpp1gB8CnwBWB94BbgPOjTFOryYfkiSt6Aw4SZKUnJt9dgc2Jt3A7g5sC5xcq0zV0KbA3OIBIYQewD7Av2KMXywZ1+4FhhD2JAWb6oEdQwhbxBhfaveMG7dtsdOBgcBvgBkl457PYZkd7WpgIlAHDAc+BZwDHBhC2DHGuLBCusL6dgU2BA4BdiTVWDsEuJQUbDk6hPC9GOOicjMJIfQFDgMWAH/KY4Xa4QvAacAiYCwp4FSVEEJP4F5gZ+AZ0u+1LvAZYL8Qwl4xxidL0owAHgPWBG4HXgW2y/L0iRDCzjHGqdXmSZKkFZUBJ0mSgBjjOcXfQwg7Aw8BJ4UQfhVjnFCTjNVIjPHVMoOHkprQvd1Bi/1S9vkL4NvZ91PbO9PSbQsQQjiaFHC6oJNriOXlqpImkGeSauVtDXyeFJAqp8n6hhA2B54GDg4h7B5jfDCE8C9SYPEA4JYK8/kc0B+4fjkIplxFWt+XY4wLq6jRVuwMUrDpJuCwGONSgBDCDaQaS1eEELYsDM/8gRRsOjXG+LvCwBDCr4GvAT8BTmxHniRJWiEZcJIkqYwY46MhhFeBzYBtgCYBpxDC9sA3gV2A1YB3gTtJTWg6KiDTohDCHsD9pBo9/wR+BIwmBYkeA74bY3ymlfNq0odTSZOso0IIR2X/Xw0cVZKuoFkfUC0sb3VSrbJxwPeAo4HDQwjfijHOb8088lDUZK0nqabPF4ENgD/HGI+u1OdVlnYD0n5ydYzx6JJxfUg1Xg6jsRbXf4Dfxhj/3N58xxinhhBuA04ibfNKAafSdC9n67wvqVbOg8AlpIDTCVQOOJ2QfV5Sfa7zEWN8Po/5hBDqaAwMfas4qBRjvD2E8DCwK2n/uD9LsxGpielE4MKSWZ5NCpoeEUL4eozxgzzyKUnSisK31EmSVFmhP6ImzYpCCMcAj5Ju0u8HLiA1vzkeeCaEsF4n5rGc7Ul9ziwg3QTfRWoy9XAIYdcq53kBqXkRwAukoNa5ZP3UAJOycecW/V3VhvkfRQryXBVjXEzqV2gwqSlTLdxMCt48Rlr3/1Q7oxDCIOAR4KfAEuAKUkBoCHB9COHH7cxrQdn9tQ3pCsHC24H3gI+V25dDCFuQ9rHXYowPVpPR5dQIYD3SepWr0XhX9rlX0bDC//eU1HoixjibdJ7oA+yQc14lSVruWcNJkqQyQgi7AQFYCDxVNHwT4GJSjYbdY4xvFY3bi9T/y29ItXVq5RPAKTHG3xcGhBAOorFJUCi9OV6WGOMFWQ2e04DnS5qp3ZbVrlq/XPO1Vjqe1GH7Ndn3K0nNm75EbfoIWh/YIsb4fg7zuoDUmfW3Y4y/KAwMIfQibZPvhBBuak9NnRDCEFI/TpCCW61NtzmNnZA/CRBjXBRCuAr4FnAsqW+oYoXaTZe2YTlHk2qKtdbEGnTcXuiI7LUK48dln5u0Mc3HsjRj2pU7SZJWMAacJEkCsqZS0LTT8DrgGzHGd4om/Uo2zWnFwSaAGON9IYQ7gANCCP2zGg61MJ7Ur0yDrEnQg6Tgwq6kplPLhSy4tymplsj/AGKML4UQngV2CSFsWuntYB3o+3kEm7KmgocDzxQHmwBijPNDCN8GPk7q+Pr5Nsz66CzIV+g0/GDS29FuJL2trpLTQwgzaNppeG/g1hjjw0XTXUpqMnpMCOGHRX0Z9czWZyGtbLZXyC/N367XkgdpWw25PAzMPmdWGF8YPqidaSRJWiUYcJIkKTm75Hs9cFyM8cqS4Ttmn7uHEEaXmc+apJv5TYB/55vFVnu4Qg2mB0g3/R9hOQo40VhjpvS3vorUCfYJpNpOnempZU/SKqNJ+0N9UVCzWPfsc9M2zveoMsOuijEes4x0p2Wf9cAcUkfj1wIXFU8UYxyf9e20JykgVmhO9mlSn2U3xBintDazre3LazlX2vSwo9JIkrRSMOAkSRIQY6yDhte97whcDlwUQpgUY7yvaNLVs89vLmOW/dqRnXqgLoTQpYWmb4V+GMuNf7dCmsnZ58AK4ztdCGEwcCgwg9S8rNj1wHnAkSGEs2KMCzoxa5OXPUmrFPaX0dlfJW3dX/aMMT4QQuhOCladT6r19HqMsaU+oTZsw1v5LiEFnI6nMeB0fPbZ6uZ0K5BCbaRKx8eAkumqTSNJ0irBgJMkSUWyN0n9K4RwAPAscHXW59HcbJKGG8wY46wOysZMUhOc1YFmtUiyt2mtln2dUSb9WhXmO7Ro/suLI4Fe2d+8EEK5aVYn1ay5vrMyFWOsVCOlEOArV4YaVGZY4bc+P8aYey2tGOMi4MVsf30FODeE8I8Y43M5zP4W4H1SE9G1SEGxPYDXgftaSNfMCtKHU8w+N6kwfmT2WdxfUzVpJElaJRhwkiSpjBjjiyGES0mvSf8a8JNs1BPANqR+kP7RQYt/gdT0bUfgjjLjPwT0Jd2Ulwt67VKhdtQe2WcewYhSSwBCCF1jjEvakK7QnO7PwNwy4weSakCdQCcGnFowPftct8y4bcsMe4oUpKr27YCtEmOcm/UH9RfgF8A+OcxzYQjhauDrpCZ8g0lNxC5tISBXydEs/304vQ78F9gkhLBhmTfV7Zt9Fgfb7s8+P1Z6zIUQ+gM7A/NI5w1JklYpXZY9iSRJq6wfA/OBb2RNvwB+T3rt/PnZG+uaCCH0CCG0N7hwVfb5wxDCoJL59yQFFIqnKzUSOKkk3UGkG/7xwMPlErXT1OxzvdYmCCHsBGwOjI0xfiHGeHzpH3AYMAnYo/j3DiEcHUKoz96m1pkKfTsdE0JoeHAXQlgX+EHpxDHG94DrgG1DCN8vTlOUdkQIYcMc8nYjqU+mj4YQ9sxhftDYdO4EUtBoEVUEgmKMe8QY69rwt0dO+S8r+81HZU0SC3msp7Evq1+EELoUTX8QKWj4CkX9n8UYXwfuIdXe+mrJYs4lBYavyWpOSpK0SrGGkyRJFcQY3wohXEzqaPlbwFkxxldDCMcCVwAvhxDuJjWX6U4KtuxKagY3qh2LvprUUfPngNeyN99NJjUt+2S2nAeBn1VIfzfwqxDCvqTaUhuT3kY2n9QReqV+odpjDPAZ4JYQwp2kWh2TYox/aiHNl7LPyypNEGNcGkK4EjiHFPQo9J1VCAYsbk+m2yrG+GQI4SFgN+CpEMJ9pCaMBwD/pHzNp5NJQcAfAkeEEB4h9bM1nNT/0mjg80BpjZq25q0+hHA26S11PwF2as/8snnGovUFuDnGWKmPsJoIIYwCziwZPLgkGPmNkrcOjgHWJ72pb2LR8F8D+5Nq1T0ZQhhDOt4+Q6qBd2yZ4+ck4DHgtyGEvYGxwPak/q9eA75b9cpJkrQCs4aTJEkt+z/SjeapWT82xBivJTWru47UvO1k0qviNwZuoqR2UVtlNS2+ABxBqrHyKeDbpADUm9ny9mmhE+0nSc3nembT7ktqBrRbjPGh9uStBZeRfquBpODcj4DjKk0cQhhIuolfCFyzjHlfQWqWdlQIoUc2bMvs8y/tyHO1DiKt7zrAKaS3/n2LtI2ayZo97p5N+z6pP6ozSAGJ2aQmm/fmkbEY422ktyPumPXrlIdLKvy/vBhKavJX+APoUzKsVZ2yZ8fUR0nBwUGkbbMPqUP70THGJ8ukeZ3UnPIqUqDp68AI4LfAjjHGqaVpJElaFdTV1/uWVkmSVgYhhD1IfcqcG2M8p7a56VghhGeBxTHG7WqdF0mSJDVnkzpJkrRCyWpHbUWqKSRJkqTlkAEnSZK0QokxzgS61jofkiRJqsw+nCRJkiRJkpQr+3CSJEmSJElSrqzhJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmSctWt1hmQik2fPr2+1nlojf79+wMwe/bsGudEncntvupy26+aBq23HnVz5lDfrx8z/vvfWmdHnchjftXkdl81rWzbffDgwXW1zoNUYA0nSZKkMurmzGnyKUmSpNYz4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFx1q3UGJEmSlhf3jqlv+P+zFYa3ZJ+963LOkSRJ0orJGk6SJEmSJEnKlTWcJEmSctLamlDlWDtKkiStTKzhJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlqlutMyBJkpSne8fU1zoLkiRJqzxrOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScpVt1pnQJIkLb/uHVNfddp99q7LMSeSJElakVjDSZIkSZIkSbmyhpMkSdJywNpkkiRpZWINJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVfdap0BSZK0crp3TH3VaffZuy7HnEiSJKmzWcNJkiRJkiRJubKGkyRJWu60p3aUJEmSas8aTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVfdap0BSZKklcWcOdO4f8zlxPgoc+ZMo1+/1QijdmavvY+nb9/BbZ5ffX09zzx9G888fTtTpkyiW7furL32Zuy6+xFstNE2FdNNmzaNyy67jEcffZRp06ax2mqrscsuu3D88cczeHDzfMyYMYOHH36YV155hbFjxzJ+/HgWL17McccdxwknnFBxOa+++ipPP/00Y8eO5ZVXXmHy5MkA3HLLLQwfPrzN6ytJklYeBpwkSZJyMGPGZC656ARmz36fNYasz6ab7cbkyeN56slbePXVR/jyly9lwMA12zTPm2/6IS88fzc9e/Zh5CY7sGDBXN544xlef/0pPnXwd9h6m/2bpZk8eTInnHACU6ZMYf3112e33XZj/Pjx3HzzzTz88MNcdtllrLlm03y88MIL/OQnP2nzOl9xxRU89NBDbU4nSZJWfgacJEmScnDbLT9l9uz3Gb3dwRxw4Depq6ujvr6ev93xS55+6lZuu/X/OPLo81s9vxee/ycvPH83gwcP5/gvXcSAAUMAmDjhOa668lT+dscv2WjEtgwaNLRJup/85CdMmTKFgw8+mG9961sN+fjFL37Brbfeyk9/+lMuuOCCJmlWW201DjnkEDbddFNGjRrFXXfdxfXXX7/MPG6xxRaMGDGiId3RRx/NtGnTWr2OkiRp5WXASZIkqZ3efjvy+utP06fPQPb95GnU1dUBUFdXx76fPI2XX7qPceOeYPLk8QwdunGr5vnww9cC8LFPfLUh2ASwwYYfYZttD+SpJ2/h8cduYN9PntYwLsbI008/zcCBAzn99NOb5OP000/nvvvu44knnmDcuHGMHDmyId2WW27Jlltu2fD9nnvuaVUejzzyyFZNJ0mSVj0GnCRJUoMddtiBoUOHcvPNN3Pddddx441/Z8aMyfTrtxof/sgn2WPPY+jatRszpr/DfWMuY9y4J5k/fzZDhmzA3h/9EmHUzmXnO3v2VB55+Dpei48yY8ZkunbtztBhI9l++0+z5Yc+2mz6SRNf4KWX7mPihOeYNes9FiyYS/8Ba7DxiO3YbY+jGDx4WLM0l192EhMnPMcZ37iFKe9N4KEHr+Gdd8ZRVwfrrrslH93ny6y9zqa5/2YAr459BIBRo3ahe/eeTcZ1796TUaN24dln/8HYVx5qVcBpxozJvDt5PN269WDUqF2bjd9yy4/y1JO3MHbsw00CTg8//DAAu+66Kz17Ns1Hz5492XXXXfn73//Oww8/3CTgJEmSlDffUidJkpr5wQ9+wJVXXslqq6/Dxhtvz/z5H/DA/Vfwtzt+ydSpb3LRH4/j9TeeYZ11N2fttTflnXde4/rrvs0bb/y72bze+t9YLvzdETz26J9ZvGQRG4/cgXXW2Yx33n6NG2/4Pnff9btmae6++3c8/dStdO3ajfXX34pNwk507dKNZ565nT9eeDRTpkyqmPenn7qVa//0DRYsnMvITbZnwMC1GD/+SS6/7CSmTJmY58/UYPLkcQAMX3tU2fHDhodsuvGtmt8776T5rbXWCLp1615xftOnvcWCBR80DB83LqUbNap8PkIITaaTJEnqKNZwkiRJTUyePJmePXty44038vyLawAwc8a7/OHCo3j233/nv5NeZPPN9+ST+3+Nrl1TUeKpJ2/hb3f8kgfuu6LJ29MWLPiA668/kw8+mM5++5/Bdtt/mi5d0vOumTPf49o/fYNHH7mekSO3Z8TG2zWk23PPY1ln3c3p02dgw7DCG9vuuP0X3PmP8znq6AvK5v/xx27gi4f/gjBql4Z0hX6UHn7oWg759PeaTP+rXx7MjBmTm83n+5ts0vjluzs2GbfnXsex197HN3yfmaUfMKB8p+CFzsJnTH+n7PhSjfMbUnZ8z5596NWrH/Pnz8nynmpNFd4SV9opeEFh+DvvtC4fkiRJ1TLgJEmSmjnjjDMYMmQIUA/AwEFrsdWHP87jj93IosUL2He/0xuCTQDbjj6IMf+6hP/+90WWLFncMO7Zf/+DWTPfY9ttD2KHHT/TZBkDB67Jpz51Fhf98ViefPKWJgGnTcJOzfJUV1fH6O0O5vnn7uL18U+zYMEH9OzZt9l0O+50WEOwqZBu74+ewNNP3cqEMjWwNt9iT+Z+MLPZ8A0e+HPD/xP3+HyTcUOHNW2OtmDBXAB69OjVbD4APXv0TtMtnFt2fKlCraXuWbpyevTozfz5cxqWDTB3bvq/V6/y+ejdu3eT6SRJkjqKASdJktREt27d2GabbZoNX221dQDYaMOtmzXz6tKlK4MGDePtt19l7gcz6D8g1YwaN+4JADbbYo+yyxq+9ih69OjD/958udm4OXOmEV99hPfem8iC+XNYunQJALPnTKO+filTp/6P4VnTsmIjN9mh2bC+fQfTu/cAZs+e2mzcJ/Y9tWzePnvDBQ3/33jo98tOU1CfBebIOuluPr5t6htmV35+aZrmcy0Mq5SuXBpJkqSOYMBJkiQ1sfrqq9OtW/MiQo+efYDG5mHNx6faM4uXLGoYVmhCds1VX2txmYsXL2jy/aknb+GuO3/D4sULK6Yp7ruo2MCBa5Ud3rNnH+bNm9ViPqrVs0f6bRYumFd2/KKF85tMt8z59SzMr3JNpEWLFjSZFqBPn/T/vHnl87FgwYIm00mSJHUUA06SJKmJlmrVpPGtf+dIoebPqE13o3evfq1K89b/xvL3v51Hjx692W//r7PRiG3o3391undPzcRuvOEH/OfFeytWG2pL/gDuvuu3ZZvUPbtWY+Bq4k0/ajJu1Ga7sdlmuzd8HzRoKO+88xqzZr1XdhmF4YMGD21VngYNGpqlm1J2/MKF85g/fzYAAwc2znPYsGG89tprvPde+XwUhg8d2rp8SJIkVcuAkyRJ6jADB67F+1MmsdPOh7Hhhlu3Ks3LL99PfX09H93nRLYdfWCz8dOm/i/XPL780v1lOw1/buDAoi93Nhk3aPCwJgGnocM2YezYh3j77Vh2GYXhQ4du3Ko8Dcv6iHrvvTdYvHhRsyaMhfkNHjycXr36cu+YFH3r0nUk8CD33f8qAwc3j8jdd/+r2XQbN6TZZ++WA4ySJEnVMOAkSZI6zMiRO/D6+Kd45eUHWx1wmjc3NXsr1zTuvfcm8M47r+Wax69/89aywz/7mdUb/r/xr837fio2atQu3H/fZcRXH2Xx4oV069ajYdyiRQt4dewjabpNd2tVngYNHsZaa43g3XdfJ8ZH2XzzPZqMf+k/Y7L57dqh+ZAkSapW2+qcS5IktcG2ow9kwMA1efKJm3j0kT+zZMniJuPr6+uZOOE54quPNgxbY8j6ADzz9O0sXtzYH9ScOdO45aYfNXQevjwZvnZgo4224YMPpnPXnb9p6Jy7vr4+NdmbO4ONN96+oeZSwSsvP8Bvzj+MKy8/udk8d9n1iwD88+7fM3vW+w3DJ058nmeevp2uXbuz406H5ZIPSZKkvFnDSZIkdZiePfty+BHnce01X+fuu37LIw9fx1pDR6Q3xs2awtSpbzJnzjR23OmzhFE7A7D1Nvvx2GN/4bXXHuP8Xx/KuutuzqJFC5g44XkGDFiDTTfdjbFjH6rxmjV38CHf5ZKLv8RTT97ChDeeZa2hG/Pu5PFMmTKR/v3X4FMHn9UszfwFH/D++/8t2zn6Vh/+BOPGPcGLL9zDby74HCM2Hs3CBfN4441nqK9fyoEHncngwcNyyQfAcccd1/B/oa+nO+64gyeeeKJh+OWXX94kzaOPPsoVV1zR8H3WrFQ77cwzz6R799QMcOedd+bYY4+t+LtJkqSVkwEnSZLUoYYNG8lXT/kTTzx+E6+OfYg3//sSS5cupl+/1VhzzQ3ZZdfD2WLLvRum7917ACd+5Qr+de9FvPH6M8RXH6Vf/9XZZtsD2HOv47jrHxfUbmVaMGjwME46+WruG3MZMT7K2FcepG+/wYze7mD22vt4+vVbrU3zq6ur49DPnMP662/FM0/fzrjXHqdr1+5suOHW7Lr7EYwYMTrXfLz88svNhk2ZMoUpU8p3XA4wffr0sulee62x2eP666+/rFWVJEkrobpCVWtpeTB9+vQVYofs378/ALNnz65xTtSZ3O6rrlV52xc6ll4VtaUPpxWZnYY3tyof86syt/uqaWXb7oMHD/akruWGfThJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknLVrdYZkCRJHefeMfW1zoIkSZJWQdZwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlKtutc6AJEmSaufeMfVVp91n77occyJJklYm1nCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJylW3WmdAkiRJK6Z7x9S3K/0+e9fllBNJkrS8sYaTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuepW6wxIkqSW3TumvtZZkCRJktrEGk6SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrrrVOgOSJK0K7h1TX+ssSJIkSZ3GGk6SJEmSJEnKlTWcJEmSVBPtqfm3z951OeZEkiTlzRpOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFx1q3UGJEmSpLa6d0x91Wn32bsux5xIkqRyrOEkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKzsNlySpldrTSbEkSZK0KrGGkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknLVrdYZkCRJkjrTvWPqWxzfu9dCAObNbz7dPnvXdUieJEla2VjDSZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlqlutMyBJklZtU99/kwcfvJrXX3+aD+ZMp3fvAWw0Ylv22PMYhgxZv1XzmDJlEn/4/ZEsXryQddbdnC+feFmzaWbNfI9//OMCxo97krq6OkZusgOf/OTp9B+wRrNpFy6cxx4bbsiaixdzw5tvtnmdbrnpRzz33J3suddx7LX38RWn+9UvD2bGjMkce9yFbLjR1g3D7xtzGfffd3mTabt370nPnv1YbfW1WXvtUWyx5UdZb70tK877+9/dEYAzvnELgwcPa/M6SJIktYcBJ0mSVDMTJjzLtdd8k4UL57La6usQRu3MtGlv8eIL/2TsKw9w1NG/Yf0NtmpxHkuXLuW2W3/KkiWLWpzmT9d8g8mTx7HhRtsA8NJ/xjBt6v848aQrqaurazL9/fddznvduvGHt9+mrtwMO8lqq63N+uun9V+ydDFz585k8jvj+O+kF3n8sRvZcMOtOeTQ7zNo0NAa5lKSJKk5A06SpFXKvWPq25ymd6+FHZATLVw4nxv/8gMWLpzLLrt+kX0+dhJduqTW/s8++w9uvfnH3PCX73H6GX+lR49eFefz5BM38d9JLzJ6u4N5+qlby04zduxDTJ48jj33Op699j4OgH/dezEPPnAVY8c+xGab7d4w7XvvTeDxx27g8zNmsNmCBTmucdutv/5WHHLo95sNf+ONf3Pn389nwoRnuezSr3DiVy6nX7/VapBDSZKk8uzDSZIk1cQrrzzAnDlTWX31ddnnY19pCDYBbL31fozadDdmz36f5579R8V5TJ/2Nv+69yI2CTux5ZYfrTjdO29HALYdfWDDsO22O7jJuIK/33EevXsP4PSpU6tar86w0Ubb8KUTL2XNNTdi5ozJ3PmPC2qdJUmSpCYMOEmSpJp4+61XAdhgw4/QpUvXZuNHjNgWgLGvPFhxHrfd9n9AHQce+K0WlzVv7iwAevce0DCsd5+BAMydO7Nh2PPP382ECc/y8X1Pof/Spa1bkRrp0aM3++53GgAvv3Qfs2ZNqXGOJEmSGhlwkiRJNbFo4XygaRCoWO8+afg777xWdvwzT9/BG68/wz4fO5GBg9ZqcVkDB6c+jqZMmdgwrPB/oUPt+fPn8M+7fs8GG3yYD3/4E61ej1raeOPt6NNnEEuXLmHChGdrnR1JkqQG9uEkSZJqok/fQQDMmPFO2fHTp6Xhc+fOZMGCufTs2adh3KyZ7/HPu3/HOutuznbbf3qZy9pkkx351z0Xcfedv+XTnzkbgLvv/C11dV0YuUl6m9u/7r2YuXNnsP+Bv23PanW6YcNG8vrrT/PeuxNqnZVVQjX9wBXss3ctu6CXJKlzGXCSJEk1seFGW/PQg1fzWnyM2bOn0r//6g3jlixZzHPPNfbdtLAk4HTH7b9g0aL5HPSpM5v0/VTJ0KEbs/0Oh/LE4zdy3i8Oahi+8y6fZ621RvD225Gnn7qVHXc6jLXW2qhh/IK6OrrXVx9guP++y7n/vsurTt8ahcDdvHmzOnQ5kiRJbWHASZIk1cSIEaNZZ93N+d+bL3P1VadzwAHfYOiwkUyf/jb33H0h06e9TV1dF+rrl1JXFFR64YV/EuOj7L7H0QwdunGrl7ff/l9joxHb8vr4pwAYOXIHwqidqa+v5293/JJ+/Vdnz73SG+zGjn2YT2ywARN69KDn0qWM+uu57H/AN+jVq2+b1nHo0JEMGzay4viXX76fhQvntWmepeqzgFgd1p6RJEnLDwNOkiSpJurq6vj853/Kddd+m7fffpXLLj2xYVzXrt345H6nc+c/zqeuro5evfoB8MEH07nz7xew+hrrsfseR7d5mZtuuiubbrprk2HPPH07/3vzZQ77/E/o2bMPkyeP5y/Xn8W2ixfz9fff57/du/Prl+5jyZJFHPa5H7dteZvtxl57H19x/IQJz7Y74DQv6/S80OeVJEnS8sCAkyRJqpkBA9fky1+5nHGvPcbEic8zf94cBg5aiy23/CiLlyyivr6e1Vdfl27degAwadKLzJ07gx49e3PN1V9rMq/58+YAMOW9CVx+2UkAHH7EeU2a4pWaO3cm997zRzbeeHu22GIvAB55+Dq6devJhW9HBmRvqnvyM9/kwQeuYp99TmS11dfJ/XeoVn19fUOn6msWNQWUJEmqNQNOkiSpprp06UIYtQth1C5Nhj/26F8A2GjEts3SzJj+DjOml+9sfMGCuUyc8BwAS5cuaXHZ/7z79yxaNJ/9D/h6w7D33n2DIUPWZ8DS5xuGrbvuFgC8+96E5Srg9Pr4p5g7dyZdunRlww0+UuvsSJIkNTDgJEmSljuLFs3niSduoq6ujtGjP9UwfLPNdudHP3m8bJoJbzzLFZd/lXXW3Zwvn3jZMpfx3//+h+ee/Qe773E0q6+xbsPwurouLFw0v8m0CxelZm9d6pbdQXlnWbhwHnfdmd6ot+WH9qH/gDVqnCNJkqRGy0+pSZIkrXLeffd1FpUEd+bOnclf/vxdpk97i9HbHcyw4ZvkvtylS5fwtzt+yaBBw9ht9yObjFtrrY14f8pEnu/VK00LPP/sXdTV1S03zdYmTHiWSy7+Eu+99waDBg9j30+eWussSZIkNWENJ0mSVDOPPnw9r7zyAMOGB/r3X4N582YxaeILLFo0n8232JNP7ve1Zc+kCk8+cROT3xnH4Uf8ku7dezUZt8tuh/PCC//kmHXWYce5c3mrWzdee+0xPvyRfRk8eFiH5KeSSZNe4JabfgTAkqWLmTd3Fu+8M445c6YCsNFG23DIp79P376DOzVfkiRJy2LASZIk1cyozXZjzpxpTJ48njf/+xI9e/ZhvfU/xLbbHsgWW+7dIcucPXsqY/51KaNG7dqs3yiANdfckCOO+jX//uOXeahPH/ovXcr223+aj33i5A7JT0umTXuLadPeAqB795707NmP1VYbzpYf2pstt9yHddfbotPzJEmS1Bp19fX1tc6D1GD69OkrxA7Zv39/AGbPnl3jnKgzud1XDveOaftppnfWtGre/PnLmFIrk89+ZvWG/2/869Qa5kSdraOO+X32rst1fsqX1/lV08q23QcPHuyJRssN+3CSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknLVrdYZkCSpLe4dU1/rLEiSJElaBms4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJy1a3WGZAkrXruHVNf6yxIkiRJ6kDWcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5apbrTMgSZIkrQruHVPfrvT77F2XU04kSep41nCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUq261zoAkSZKkZbt3TH3VaffZuy7HnEiStGzWcJIkSZIkSVKurOEkSZIkreSsHSVJ6mzWcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCv7cJIkVaU9/YFIkiRJWrlZw0mSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlCsDTpIkSZIkScqVASdJkiRJkiTlyoCTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKVbdaZ0CSVDv3jqmvdRYkSZIkrYSs4SRJkiRJkqRcWcNJkiRJUkXtqQ27z951OeZEkrQiMeAkSZ3grbfe4tJLL+Xpp59m9uzZrLnmmuy1114cc8wx9O7du83zW7x4Mddffz133XUXb7/9Nr1792arrbbi2GOPJYTQbPqpU6fy+OOP89hjj/H666/z7rvv0qVLFwYPXp/NttiTHXf8DN279yq7rHnzZvPIw9fx6tiHmT79Lerr6+k/YAgjNtqW3XY/ksGrDW9z/iVJK445c6Zx/5jLifFR5syZRr9+qxFG7cxeex9P376D2zy/+vp6brvtNm677TYmTZpEjx492HTTTTnyyCPZZpttyix/Do8//jiPPvoo48aN45133mHp0qWsvfba7LrrrnzhC19gwIABZZe1cOFCbrjhBu655x7efPNNunbtyogRI/j0pz/Nxz/+8TbnXZLUenX19fbfoeXH9OnTV4gdsn///gDMnj27xjlRZ6p2u8cY+cpXvsLcuXMJIbDOOuvw8ssvM3nyZDbeeGMuvvhi+vbt2+r5LV68mNNPP51nnnmGwYMHs/XWW/P+++/zwgsv0L17d8477zy23377JmnOPvts/vnPf9K1a1c22WQT1l57bWbOnMkLL7zEwoXzWHOtjTj2uAvp23dQk3Rz5kzjkou/xPRpb9G372DWWWczunTtxttvR2bOmEzPnn04+tjfsc46m7XpN1nR9O6VgnHz5s+vcU7UmT77mdUb/r/xr1NrmBN1No/5RjNmTOaSi05g9uz3WWPI+gwdujGTJ4/n/SmTGDBwTb785UsZMHDNiunL1XA699xzueuuu+jTpw/bbbcdc+fO5d///jdLly7lu9/9Lvvvv3+T6S+66CKuuuoq6urq2Gijjdhggw2YO3cuL7/8MrNmzWLNNdfkD3/4A+uss06TdPPnz+fkk0/mpZdeom/fvmy55ZYsWbKEl156iXnz5nHooYfyjW98o2F6y3erppVtuw8ePNhqhVpuWMNJkjrQkiVL+MEPfsDcuXM56aSTOPLIIwFYtGgRZ555Jo8++ii///3v+fa3v93qef7pT3/imWeeYbPNNuN3v/tdQ7Dqnnvu4Qc/+AHnnHMON998M3369GlIM3DgQE488UQOPPBAVltttYbhN9/yLn+65htMnjyOO/9xAZ/57DlNlvXAfVcwfdpbbLzx9nz+i/9Hjx69s/VazN/vOI9nnrmdO/9+Pl868dJqfyJJ0nLstlt+yuzZ7zN6u4M54MBvUldXR319PX+745c8/dSt3Hbr/3Hk0ee3en533303d911F8OHD+fiiy9myJAhADz33HOccsop/OIXv2Dbbbdl6NChDWl69+7N4YcfziGHHMLw4Y21amfNmsX3vvc9nnrqKX74wx9yySWXNFnWhRdeyEsvvcTGG2/M+eef37Cst956i9NOO42bbrqJ0aNHs/vuu7fnJ5IkVWCn4ZLUgR5++GEmTZrEiBEjOOKIIxqGd+/enbPOOouuXbvyt7/9jZkzZ7ZqfosXL+Yvf/kLAN/85jeb1Iz62Mc+xk477cT06dP529/+1iTdGWecwdFHH90k2AQwYOCaHHBgerr7ysv3s3jxoibjJ0x8DoDddj+yIdgE0LVrN/ba+3gA3nprLNaWlaSVz9tvR15//Wn69BnIvp88jbq6VHGirq6OfT95Gn36DGTcuCeYPHl8q+d57bXXAnDyySc3BIAAPvKRj3DQQQexcOHChutcwVFHHcXJJ5/cJNgEMGDAAL7//e8D8OKLLzJ58uSGcQsXLmy4Fn79619vsqy1116bU045BYCrr7661XmXJLWNASdJ6kAPP/wwAHvttVdDQb1gjTXW4MMf/jCLFy/msccea9X8XnzxRWbOnMnw4cPZdNNNm43/6Ec/2mS5rTF02CYALF68kHlzmwa+unXtXjlhtj69evVvtm6SpBXfq2MfAWDUqF3o3r1nk3Hdu/dk1KhdABj7ykOtmt/kyZMZP348PXv2ZNddd202vppr2JAhQxg8OPUjNWXKlIbhkyZNYv78+XTv3p2tttqqWbptt90WgFdeeYX33nuv1cuTJLWeASdJ6kDjxo0DYNSoUWXHFzr4LkzX2fMDmDbtfwB07dqd3n2adro6YuPtAHjowWtYuLCxL5MlSxZz379SM7pttj2g1cuSJK04Jk9O15Lha5e/5gwbHrLpWlfD6bXXXgNgo402onv35g80Ctewt956iw8++KBV85w1axazZs0CYPXVG/tdmzdvHgD9+vWjS5fmtzx9+vRpyEMhX5KkfNmHkyR1oEL1/jXXLN+hamH4O++8k+v8Zs6cydy5c5v041TJo49cD8DITXakW7ceTcbttvtRvPnmS4wf/yS/Pu8Q1ll3c7p27cZbb73K3A9msNPOn2fvj36pVXmXJK1YZs5I15wBA8pfcwqdhc+Yns81rE+fPvTr1485c+YwefJkRowYscx53nDDDSxZsoSRI0c2aXI3aNCglLcZM5g3b16zN8JOmTKFRYtSM/LWXoMlSW1jDSdJ6kBz584FoFf2xqNShQJwYbq85tfaeb70nzE8/9xddOvWg30+dmKz8b169eXIo85nm20O4IMPphNffYRXXn6AmTMmM2TIBqy3/ofo2tVnF5K0MlqwIF1HevQof83pmfXtt2BhPtcwaNt18cUXX+Saa66hrq6OU089tcm49dZbjyFDhqQOzkv6NQS4/fbbm+VLkpQv7xIkqRNU6uOorZ1tF6Yvnt+9YxrnsWRJ4/8PPVxP/wGV5//W/8Zy6y0/AWC//c9gzTU3bDbNjBmTufaabzBz5nsc9Kkz2STsRPfuvfjfmy9x5z8u4C/Xn8XeH/0ye+x5dJvWQ5K0/Ksnu4ZUuoa1dX5lrmHVevvttznrrLNYtGgRRx11FKNHj242zVFHHcV5553HhRdeSJcuXdhrr71YsmQJ99xzD1dffTVdu3ZlyZIl9kMoSR3EgJMktcMPf/jDZsO22morDjroICA1D5g1a1ZDXxKlFixY0DBdaxSmqzS/RYsa+1nq0bPyPN97bwLXXH0GCxfOY6+9j2fb0QeVne6Wm3/Mu+++zuc+/1M232LPhuEjN9mRI9dYn9/99ovcf99lbPmhvVl99XVbtQ6SpBVDzx7pOrJwQYVrTta3X2G6ZVnWNQxg/vz5TaYt5/333+fUU09l6tSpHHDAAZx4YvMaugCHHnookydP5vrrr+e8887jvPPOaxi3ww470Lt3b+6//34GDBhQNr0kqX0MOElSO9x5551lhxcCTkOHDmXWrFm89957jBw5stl0hTfjDB06tFXLGzZsWJN0pWbOTMN79x5AzwoBp6lT3+SqK05l7twZ7LLrF9lzr+MqzmvCG/+ma9fubLrZbs3GD15tOOusuzkT3vg3E9541oCTJK1kBg0ayjvvvMasWeWvOYXhgwbncw2bN28es2fPBipfF6dPn84pp5zC//73P/bZZx/OOuusFmsonXzyyey///48+OCDvPPOO/Tu3Zttt92WnXbaiWOPPRZInZhLkvJnwEmS2uGJJ55ocfzIkSN57bXXiDGy8847NxsfY2yYrjUK0xXSlXrn7TR86LDy85s+/R2uvPwUZs9+n+23/zQf/8TJFZc1c+a7QOq7o0uXrmWn6dWrHwBz581qVf4lSSuOocM2YezYh3j77fLXnMLwoUM3btX8CtewN954g0WLFjV7U13h2jZ8+HD69u3bLP2sWbM49dRTmTBhArvvvjtnn3122TfQldpggw3YYIMNmgybPn06r732Gv3796/45ldJUvvYabgkdaBddtkFgPvuu6/ZuKlTp/L888/TtWtXdtppp1bN70Mf+hADBgzgrbfe4tVXX202/qX/jAFg0013bTZu1sz3uPKKU5g581222eYA9jvg6y0uq3//NQCYN282U6e+2Wz8kiWLG242Bg8a1qr8S5JWHKNGpWtYfPVRFi9e2GTcokULeHXsI2m6TZvXgi1n2LBhjBgxggULFvDII480G/+vf/0LgN12az6/Dz74gNNPP51x48axww478OMf/5hu3ap/dv7nP/+ZJUuWsN9++9GjR49lJ5AktZkBJ0nqQLvuuivrrbce48eP509/+lPD8MWLF/Ozn/2MxYsXc8ABBzS8vrngr3/9K4cddhjnnntuk+HdunXjc5/7HAC//OUv+eCDDxrG/efFfxHjo/TpM4itt9m/SboPPpjOlVeeyvRpb/GhrT7GgZ86c5mdpA4ePIxhwzYB4PZbf8bcuTMbxi1Zspi77vwNM2dMpmfPvmw8crvW/yiSpBXC8LUDG220DR98MJ277vxNQ6ff9fX13H3Xb5k7dwYbb7w9w0pq1b7y8gP85vzDuPLy5rVoDz/8cAB+//vf8/777zcMf/7557n99tvp3r07n/3sZ5ukmT9/Pl//+td55ZVX2GabbfjZz37WrHZUOdOmTePtt99uMqy+vp6bb76Z6667jiFDhnDcceWblUuS2s8mdZLUgbp168YPf/hDTjrpJC688ELuu+8+1l57bV566SUmT57MiBEjOPnk5gXyGTNmMGnSJFZbbbVm44444gieeeYZnn32WQ499FCGr/0R5syZxqSJz9O1azcO/cwP6NmzaVOE22/9Ge9PmZQ1javjtuztdKU+vu8p9O07qOH7pw7+DldecQoTJjzL+b/6DOusuxndu/Xk7bcjM2e+S5cuXTnoU2fSu7cdrkrSyujgQ77LJRd/iaeevIUJbzzLWkM35t3J45kyZSL9+6/Bpw4+q1ma+Qs+4P33/9usVhTAJz7xCR5//HHuueceDjvsMEaPHs28efN45plnWLp0KWeeeSbDhw9vkuaiiy7i+eefB2DgwIH84he/KJvXI488sknTuQkTJnDyySczcuRIhg8fTpcuXXjllVeYPHkyQ4YM4be//S39+/ev/seRJLXIgJMkdbBRo0Zx9dVXc9lll/H0008zfvx41lxzTQ4//HCOPfbYVr+hrqB79+785je/4brrruPuu+8mvvoIPXr0ZtSmu7HnnscyfO3QLM28+akT1qVLl/DiC/+sOO899z6+ScBp+NqBk0/5E488cj3jxz/JxAnPUV+/lH79VmerD3+CnXb+HMOHN1+eJGnlMGjwME46+WruG3MZMT7K2FcepG+/wYze7mD22vt4+vVr/mCkJXV1dZx77rlstdVW3H777Tz++ON0796drbfemiOPPJLRo0c3SzNrVmM/geWaqBfst99+TQJO66yzDvvttx8vvvgiTz75JPX19ay99toce+yxfOELX6Bfv35tyrskqW3qClVjpeXB9OnTV4gdsvA0rPAmFa0altftfu+YFeKwWaH17tULgHnZ67q1avjsZ1Zv+P/Gv06tYU7U2Tzm87PP3i03316eLK/XeXWslW27Dx48eMU56LTSsw8nSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXPmWOkmSJEnLnfa+FGNF6rBcklZG1nCSJEmSJElSrqzhJEmSJKlDtLeWkiRpxWXASZJqzMK4JEmSpJWNTeokSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKPpwkKQf2wyRJkiRJjQw4SZIkSVrptPVhUO9eCwGYN7+effau64gsSdIqxSZ1kiRJkiRJypUBJ0mSJEmSJOXKJnWSJEmSVKQ9fTPaHE+SEgNOkqRO8c47r9c6C1Xr1bMnAPMXLKhxTlQrK/L+q7ZbVY75YcNG1DoLkqSVmAEnSVKnOP3U7WqdBalNTiv63/1XK6Mb/jq11lmQJK3E7MNJkiRJkiRJuTLgJEmSJEmSpFzZpE6SMsvqILR3r4UAzJtffUeikiRJkrQqMOAkaaXSnrfKqGNd8Nunap2Fqq0qHQirRFG/TSvy/qu285iXJKn9DDhJkjrFivw2pN69egEwb/78GudEtbIi779qO495tUd7Hn7ts3ddjjmRpNoy4CRJkiRJy4H21tRuT8DKQJmkvBlwkpS7WhaWJEmSJEm1Z8BJUlm17AvJfpgkSZLazjKUpOWJASepg1nbR5IkSSuzWjXHyyPAVu1biC2jS8tWV19vFFxqq3PPPbce4Oyzz/ZKswpxu6+63ParJrf7qsttv2pyu6+a3O5Sx+lS6wxIkiRJkiRp5WLASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVe+pU6SJEmSJEm5soaTJEmSJEmScmXASZIkSZIkSbky4CRJkiRJkqRcGXCSJEmSJElSrgw4SZIkSZIkKVcGnCRJkiRJkpQrA06SJEmSJEnKlQEnSZIkSZIk5apbrTMgLU9CCDsB3wN2AHoB44ErgN/FGJd09LxCCP2ArwGHAiOAeuC/wKPAV2OMi6pYLS1Drbd7UdqewL+BzYG3YozrtHFV1Aa12u4hhJ2Bg4A9gQ2AAcDbwBjgZzHG8dWvlQBCCOsAPwQ+AawOvAPcBpwbY5zekfPJc79S29Vi24cQRgKHAB8HRgJrAdOBJ4ALYoz3t2ultEy1POZL0l8OHJt9Hen5vGPV+FxfBxwJHAN8COgNTAaeBr4XY3ytqpWSVjLWcJIyIYSDgIeA3YBbgQuBHsD5wF86el4hhA2A50gXvJnAH4GLgbGkAFTPNq6SWqHW273ET4H127JMVafG2/1m4OvAfOA64HekgNNxwPMhhB3bvkYqCCGMIAVujwGeIm2HN4DTgMdDCKt31Hzy3K/UdjXc9j8CfkYKNN0J/Ir0oGg/4L4QwqntWzO1pJbHfEn6A0jBpjnVrYnaosbn+l7AHcBVwFDgeuAC0vl/W2CT6tdMWrlYw0kCQggDgEuBJcAeMcZnsuHfB+4DDg0hfC7GuMwbhmrmFULoTro5WR84KMZ4R8k8uwJL27+mKlbr7V6Sfg9S7baTSMFGdZDlYLufD/wpxvh2yby+A/wEuATYsp2ruSr7A7AmcGqM8XeFgSGEX5OOsZ8AJ+Y9nzz3K1WtJtseuBv4eYzxueKZhBB2B+4FfhlC+GuM8Z2q1krLUqvtTtE0Q0jH/w2kAMTuVa2J2qKW2/1XwP7A/5FqMzUpo2fleklYw0kqOBQYAvylcJMAEGOcT2oaAfCVDpzXEcCHgd+UBpuytEtijPWtXL5ar9bbHWi4Ub0KGBNjvKgtK6Cq1HS7xxh/XhpsyvwcmAds0dons2oqhLAR8DFgIql2UbGzgQ+AI0IIfTtgPnnuV2qjWm77GONVpcGmbPiDwAOkWm47tX5t1Fo1PuaLXZJ9frW1eVf1arndsxpRJ5Kazn23NNgEYBcYUiMDTlKyV/Z5d5lxDwFzgZ2yPnY6Yl5fyD6vCiFsEEL4SgjhrBDCF73x7FC13u4FvwUGk5pUqeMtL9u9VD2wOPvfvn6qU9ge95TeBMQYZ5OaOfUh9a+U93zy3BfUdrXc9i0p3HgubnEqVavm2z2EcDTwKeDEGOPUNuZf1anldv886R76amBACOHwrMz+pRDCxlWtjbQSM+AkJSH7bNbBX4xxMTCB1AR1ow6a12hSfy77AuNI1Xt/ClwLTAohHIs6Qq23OyGEg4GjgDNijP9tdc7VHjXf7hV8BugPPBFjnNGK6dVcxe2RGZd9Lqt/jWrmk+e+oLar5bYvP6MQ1gf2JgUbH1rW9KpKTbd7to1/A1wbY7xtGctQfmq53UdnnwOB14E/kcrsFwOvhRAuzLrCkIQBJ6lgYPY5s8L4wvBBec8re9o9AOgO/JLUv8v6pLdkHEuq9XBZCGGvZnNSe9VsuwOEENYiFVDuijFe3oplKB813e7lhBA2JHUevpjUobiqk9e2rWY+ee5Xartabvtmsmv7daQXfpzTljdmqU1qtt1DCIVaLnMAO4bvXLU83tfMPn8IPEPqc7E/Kbj8Oqkvzu8vY7nSKsNOw7XSCCFMpG1v+Louxnh4K6etyz7z6EepdF5diz5vjjF+q2jaK0MI/UhNrr5N6nhWRVbg7Q6pg9HuwAk5zH+VsoJv9yZCCGsCd5H6//lqjPGxHJar8vLattXMJ8/9Sm3Xads+q93wJ2BnUifS57VzmapeR273r5E6B9/PgOJypyO3e6Hc/g5wcIxxXvb9vhDCocCzwBkhhJ/GGBe2c/nSCs+Ak1Ymr5OapbVWcae9hScYA8tNSKqBVDxdS9o0rxjj3BDCQlKnoreWmf5WUsBpu1Yse1W0Qm73EMKRwAHAUTHGt1oxfzW1Qm73Ulmw6T5Stf7TYox/aMUyVVle27aa+eS5X6ntarntG2TBpmtJTWRvBA73pR8dqibbPYQwkvT2sitjjHe2Ip/KVy2P90Jw8e6iYBMAMcYXQggTgBHApsALy1i+tNIz4KSVRoxx7/YkB7YltdH+d/GIEEI3YENSU5c3OmhekVQld0aZ+RUubL1bsexVzgq83bfOPq8OIVxdZn5rhxAKNymD7dOnqRV4uxePHwaMAUaRajYZbGq/mH1W6rdjZPZZqb+O9swnz/1KbVfLbQ80bOfrScGm64EjY4y+AKBj1Wq7b05qLnlMCOGYCmnGhRAg1YK5bRnLV9vU+lz/McqX2cFyu9SEfThJSaGp2ifKjNuN9IaKx2KMCzpoXmOyzy3KpCkMm9iKZattarndHwcur/AHqZPZwvfWLF+tV+vjnRDCOsCDpGDTiQabcnN/9vmxrH+VBiGE/qQmTvOAJzpgPnnuV2q7Wm57Qgg9gJtIwaZrgCMMNnWKWm33iVS+hk/Opvlr9n1iW1ZIrVLL471imT3ru60QpJq4jGVLqwQDTlJyE/A+8LkQwraFgSGEXsCPs69/LE4QQhgYQhiV1VJo17xIHUcvBr6W3YgWp/lJ9vUv1ayYWlSz7R5jvCHGeHy5v2yS6UXDmlTZVrvV9HgPIaxHCjaNAI6LMV7S/lUSQIzxdeAeYAPgqyWjzwX6AtfEGD8ACCF0z7briPbMJ1PNuV85qeW2z24ybwUOIgUYjil9xbo6Rq22e4zx+Rau4YVaM9/Jhj2f0+oqU+Nz/V2kmqofDyHsU5Lm+6TmeQ/GGCcjibr6epuVSwAhhE+Rbhjmk4I704ADSX2r3AR8trgfhhDC0cCVwNUxxqPbM68szRnAr7JpbwM+AD5Oqub7JLCngYf81Xq7V8hTPfBWjHGdZU2r6tRyu2f9O2xAanb19wpZvCrGOLE967iqym4oHiO9Seh2YCywPbAnqVnETjHGqdm0GwATgEkxxg2qnU9Rmk+RwzlA1anVtg8hXAkcTQo4/oHyHRU/EGN8IJcVVRO1POYr5OcBUmfiI2OM49u9giqrxuf6XUiBqkL/q5OA0aTarFOAXWKMy2rOJ60SrOEkZbL29bsDDwGfBk4BFgFnAJ9ry01CNfOKMf4a2J/UweChwJeAJcAPMNjUYWq93VUbNd7uG2Sf2wBnV/jbAFUle2K9LXAV6abh66TaZL8FdmzNDWO18/EcUFs13PYbZp9rkK7Z5Y7pPapbKy1LLY951U6Nz/WPZGluJp3zTwU2Ai4BtjbYJDWyhpMkSZIkSZJyZQ0nSZIkSZIk5cqAkyRJkiRJknJlwEmSJEmSJEm5MuAkSZIkSZKkXBlwkiRJkiRJUq4MOEmSJEmSJClXBpwkSZIkSZKUKwNOkiRJkiRJypUBJ0mSJEmSJOXKgJMkSZIkSZJyZcBJkiRJkiRJuTLgJEmSJEmSpFwZcJIkSZIkSVKuDDhJkiRJkiQpVwacJEmSJEmSlKtutc6AJEkruxDCA8DuMca6WuelVAjhHOBsYM8Y4wNFw+uBB2OMe5RMPxT4ObA3MIz08GpwjHFG5+RYkiRJKwIDTpKkVVoWWCm2FJgJvAhcBVwdYyydplPy1FKAKoQwEVgf2DDGOLFzcgak3+RjwJ+B8UA9ML/aoFoI4WjgyjKj5gDjgJuAC2KMc6vNcNFv1VrnxhjPqXZ5HSmEcBVwVMngecBE4C7gZzHGKWXSPQDsXjJ4DvAacDNwfoxxXgjhS8DFwI0xxsOWkZfvAj8GfhNjPL2t65KXEMKmwOeADwMfAdbNRnWPMS6uYn5dgVOAY4GRpN/3CeDHMcbHKqRZDfgB8ClSIHYqcDfwgxjj/9qaB0mSVgYGnCRJSs7NPrsDGwMHk27QtwVOrlWmamhToEmQJ4TQA9gH+FeM8Ysl49q7vBeA27L/uwBDgQOAnwCfCCHsGWNcUuW8LwAGlQw7mhSEupoUrCn2QJXL6Uy3A89n/68FfBI4A/h0CGGbGOPUCukK61sHrAMcQvqNDwoh7AJcD/wK+FQIYY0Y4/vlZhJCqCMFZAAubffatM/HScGeJaQg5XygVzUzytbrL8ChQAR+D6wGHAY8FEL4dIzx9pI0qwOPAZsA92XpRwHHAPuFEHaMMb5RTX4kSVqRGXCSJAkordESQtgZeAg4KYTwqxjjhJpkrEZijK+WGTyUFAx6uwMW+XyZbTCIVNNs1+zvgWpmHGO8oHRYCGEPUsDpquKmhCuQ22KMVxW+hBB6kWrhbEUKkJ5bId1VJU0nvwc8B2wHfCHGeHUI4c/ACcCRwK8rzGcvYCPgsRjjy+1blXa7C3gceDGrpTWRttVoK/Y5UrDpMWDvGON8gBDCRcAjwKUhhPtijLOL0vyUFGw6P8Z4RmFgCOFU4DfAH4BPVJkfSZJWWAacJEkqI8b4aAjhVWAzYBugScAphLA98E1gF1INiHeBO0nNsToiILNMWRDlflKw4Z/Aj4DRpCDRY8B3Y4zPtHJeTfpwKrmJPyqEUGjWdTVFTbxKmig26wOqLWKMM0IIT5OaSA2pdj5tUdRkbQSwHynwMhJ4Msa4R1ETwGOKAz5F6Sv1fdUN+BIpiLMZqQwWgcuBP8QYl7Yn3zHG+SGE60gBp9FtSPdOCOEW4CukoNPVwCWk9T6eygGnE7LPS6rOdE5ijDHH2X0l+/xeIdiULePpEMINwBGkgNSVACGEvtmwD0h9oRX7PfA14OMhhI2s5SRJWtX4ljpJkior9Ee0qHhgCOEY4FFgX1KA5wLgGdIN+jMhhPU6MY/lbE+qDbQAuJBUA2Rv4OEQwq5VzvMCUm0NSM3fzs3+bss+J2Xjzi36u6rKZQEQQhhICp4sJdXC6Uy/IQXs/pP9/2i1MwohdAf+TtoWg0jN1i4hlcN+Rwry5KHs/tqGdIW+w/6/vTsPs6Mq8Mf96aSTkIRsLCEJKJAQKoCCssq+GUYEBBSVcQARcURHEUGd8ecI7sOAflXUcQEUBRyUHWWNIFtYwzpsxY5IEgIh+77074/b3abT3Ul3Uklned/n6ed2V9U5darr3q57P33OqXGp/b63a+zp10LjELKjUpvr7PIVaukaqCiKXkn2Sm0o6V1tbHJj4+NBSyzbM0nvJGOX6vWUxhDxlsYfD6y2tQCw5tPDCQDaUBTFfkmKJPOTPLDE8m1Tm1T55dQmyX5tiXUHJRmTWkBx9Ops71Lel+TzZVn+tGlBURRHphYO/booiqKzPWrKsvxRURRbJflCWg9/u6ZpiNpKTLb9rsY75iW1IGazJIcnGZDk1LIsn1/BelfUzkneXdFQyq+lNs/QT5Oc1jQXVePk1L9KclJRFFcsPTdQZxRF0TvJcY0/3t2JckNTm8cpSe5fYtX5qQ0F+1Rah20nJOmV5IKOTuZeFMVRqU3q3VFT2xoKuYptk6R7khfbmWz8ucbHbZdY1jR52bPt1NlWGQBYLwicACDJEmHHkpOG1yX5UlmWE5bY9DON23xhybApScqyvK0oiuuSHFEURb+lezysRs+nFhY0K8vy2qIo7khtIvR9k9zRFQ1bhp0av5b2v6lNxLy6nVNF2FQURbfU5lSamOSLS058XpbloqIozkhtcul/SW0i8I46qjEATJLBqYVzb0tt3rGfL6PciY3h4JKThg9MLVS9bIntLk1ybpIPF0XxhbIspy2x7pONj50ZTndUWt9db1leSa1X3eo0oPFxWjvrm5YPXMkyALBeEDgBQM3S8680JPlkWZa/WWr5no2P+xdF0dZcOYNT6yWxbZKHqm1ih93VTg+m21MLnN6dNS9w+m1Zlic2/VAUxWZJ3ptab7HDi6I4oCzLh1djex5Y/iYdsm2SjVPr6fKf7dzNb05qdwXsjCMbv5Y0JslhZVkua0jdkqHPrMZ2XZnk/y1ZrizL6UVR/DG1MOxjaQyxiqLYK8kOqc1p9XhHG9t4bk/s6PZrqBZDD1dhGQBYJwicACBJWZZ1SfMkwHumNpnzL4qieKUsyyV72Gzc+Pjl5VS54Uo0pyFJXVEU3ZYx9K1pHsa21r/eTpmJjY8D2lm/xijL8vUklzYOFTs/yX+lNixtdZm4/E06pOn5MjKtQ80ldfb58omyLC9qHJY3PLX5pj6aWjB08jLKHdiJu/L9KrXA6eT8o9dUU93nd7K9a4Om3kjtvT76L7XdipYBgPWCwAkAllCW5awkfymK4ogkDyf5beOcR01z1TR/wCzLcvoqasa01IbgbJzkjaVXFkVRl9qd8ZJkahvlN2un3iFL1L+2aJpXaPfVvN/2eqQ0BXyt3kMVRTGwje2bftdXl2X5wTbWr5TGIXrPFUXxsSRbJflkURTXlWV5XQV131cUxeNJdi6KYufUekN9JMn0tBx+t1xryRxOzydZlGR4URT1bczjNLLxccn5mprukNfeHE1tlQGA9YK71AFAGxqHC52f2jw3X1xi1X2Njyt6t7eOeKzxcc921u+YpG+Sl9sJvfZpnDtoaQc0Pq6KO74tORF2lQY1Pq4p71mmND6+rY11u7ax7JnUQsH3NN6tbpVo7An3hcYfz6nwPDT1ZDo5taF1fZP8vjGY7YyjUuvh1dGv01ay3Z1WluW8JPck6ZO2X9+HNj4u2ePxvtSGRO5dFEW/JTdufA0e0vjjX6ttLQCs+daUN28AsCb6TpK5Sb5UFEVT8PHT1G47/8PGO9a1UBRFz6IoVjaMuqjx8VtL95ppvHX7OUttt7SRST67VLkjU5u/6fm0fcv3lTW58fHtVVXYGJo0hSi3L7XugKIoGoqiuH3pcqvYuNR6OX2sKIo+S7Rno/zjvDRr7CXzkyRDk5zXOESwhaIohhZFsf3KNqwsy/uT/Dm1O6edsLL1NboktUDlX/KP51Snh9OVZXliWZZ1nfjaqqL2t6koircXRTFqyXPYqGno4HeKothgie13S23I4hupzXnVdFwzk1ycWhD3jaXq+lxqvc5uLsvyxUoPAADWAobUAUA7yrJ8rSiKX6YWenwlyVfLsnymKIqTkvw6yZNFUdyU2nCZHqmFLfum9qF01Ers+repzVd0bJJnG+98NzG1IXbvb9zPHUnObqf8TUl+UBTFoan1ltomtbuRzU1tIvT25oVaGbcm+XCSq4qiuCG1kOKVsiwv7mD5dy1xp8CkNvn6QamFJ2+m9vtfUtM/zdq6ff0qU5blhKIoLk1yfJJHi6K4PrV5et6f2h3i3t1GsW+ndge+U1K7g+FtSV5L7RhHJtk7ydeSPFVBE89McliSs4qiuLQsy/krU1lZllOLorg8tQBrxyQPrebJ25erKIpNknx/iUWbND5eWBRF09DIs8uyfGaJbX6XWgB7YFqGmZel9lo5JskjRVH8KbXX3UdTuxnAp9roVfj/pdZ78PSiKN6V2oTz26U2qfukJP+2EocHAGstPZwAYNn+K8nsJKc23jktZVlekmSX1G4dv2NqPRmOSy3YuSJL9S7qrLIsG1IbvnR8ksdTG47076kFUK827m904xCgttyf2gfgXo3bHpraMKD9yrK8c2XatgwXpPa7GpBaOPTtJJ/sRPmd0nJI1SdSm0fpx0l2KsuyXGr7dzY+dmouoYp8KrWAo09qYcL+Sc5LrRdQK413fzsqtdCmTHJ4kjOSvC+192JfT+25tNLKsnwkydVJtkzy6SrqTG3y8La+X1NsmNrd95q++jYuP2GJZUPaLtpS42vvn5OcnlqY+fnUAqg7U3v9XNtGmcmpDX89L7W/AWck2SPJb5LsUpblCyt6YACwNqtraHCXVgBYFxRFcUBqc8V8syzLb3Rta1atoiiuSm0i8eEr24sHAIDq6eEEAKxVGu/St2+S7wubAADWTOZwAgDWKo3Dnjbt6nYAANA+PZwAAAAAqJQ5nAAAAAColB5OAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApeq7ugGwpClTpjR0dRs6ol+/fkmSGTNmdHFLWJ2c9/WXc79+Gvj2t6du5sw0bLhhpv7tb13dHFYjr/n1k/O+flrXzvugQYPquroN0EQPJwCANtTNnNniEQCAjhM4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlarv6gYAACxtzK0NXbLf0QfXdcl+AQDWNXo4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFCp+q5uAACw5hpza8MKlx19cF2FLQEAYG2ihxMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApk4YDAKvEykw4DgDA2k0PJwAAAAAqJXACAAAAoFICJwAAAAAqJXACAAAAoFICJwAAAAAqJXACAAAAoFICJwAAAAAqJXACAAAAoFL1Xd0AAIA1xZhbG5q//0g7y5dl9MF1FbcIAGDtpIcTAAAAAJXSwwkAoCId7QnVFr2jAIB1icAJAGANIKwCANYlhtQBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVqu/qBgAArEleeOHB3H3npfn+iBGZX1eX4fPnZ8QD12TX3Y5MXV1dp+ubOfOt/PXWC1OWYzNz5lvZcMONUozaOwcdfHL69h203Ha89tpTWbhwQTbddMvsuttRy2zHwoUL8/vf/z433nhjxo8fn969e2ennXbKSSedlKIo2t3XmDFjcu211+bZZ5/NrFmz0q9fvxRFkQ9+8IPZf//92y23ePHiXHfddbnpppvy4osvZt68eRk0aFBGjRqVY489Nu9617s6/HsCANYtAicAgEbjHrwu1117durqumXPuXPTd/HijO3TJ09e+9959dUn8sEP/Wen6ps6dWJ+9YtPZcaMN7PJpltmu+33y8SJz+eB+6/KM8/cnU9/+vz0HzB4me3Yevgu6dWrT154/oFct4x2LFy4MKeddlrGjRuXQYMGZZ999smbb76ZO+64I/fcc0++//3vZ4899mhV7vvf/36uuOKKdOvWLTvttFM23njjTJw4Mffff3/uv//+HH/88fm3f/u3VuVmzZqVM844I48++mg23HDD7Ljjjundu3cmTpyYu+++O9tuu63ACQDWYwInAIAkU94anz//6fvp1q17TvzEefnKV96bJHm9e/ccueu+eeTh6zNy5Hvyzh3f2+E6r7nqe5kx483stvvROeIDX05dXV0aGhryp+vOzYMPXJ1rrv6vnHDiD5fZjq22fneSZPr0N3LBr05ptx0XX3xxxo0bl+233z4/+clP0rdv3yTJLbfckjPPPDPf+MY3cuWVV6ZPnz7NZZ566qlcccUV6dOnT37+85+36AX1wAMP5PTTT88ll1ySww47LFtttVWL/Z155pl59NFHc+ihh+bLX/5yi3qnTZuWadOmdfj3BACse8zhBACQ5J57/pBFixZk112PbA55kmSzRYtyyD99Nkly150Xd7i+8ePLvPDCg+nTZ0AOff8XmofB1dXV5dD3fyF9+gzIc8/dl4kTn+9QO/r337TddixcuDCXXXZZkuTLX/5yc9iUJIccckj22muvTJkyJX/6059alHvkkUeSJAcccECrIXe77757dtlllzQ0NOSpp55qse7OO+/M2LFjs9122+XrX/96i7ApSQYMGJC3v/3tHftFAQDrJD2cAGAt8573vCdDhgzJlVdemUsvvTTXX399Jk6cmEGDBuWwww7LJz7xidTX12fChAk5//zzc//992fGjBnZaqut8ulPfzp77713m/VOnjw5l1xySe65555MnDgx9fX12WTTkdljjw+12avnlZcfyxNP3JaXX3ok06dPyrx5s9Ov/ybZZsTu2e+Aj2fQoKGtylx4wWfz8kuP5PQvXZU3Jr2UO+/4XSZMeC51dcnb3vbOvHf0p7P5FttV/jvriGeeuStJ8o53Htxq3ajt9kt9fc9MmPBspk19PQMGbrb8+p6+u1Z21D7p0aNXi3U9evTKqFH75OGHr8/TT92ZIUO2Wal2PP7445k2bVqGDRuW7bZr/ft773vfm3vuuSd33XVXPvrRjy7Rjh7LPY6kFiAt6aqrrkqSfOxjH0u3bv5/CQC05h0CAKylzjzzzPzmN7/JFltskd133z2zZs3KhRdemHPOOSevvvpqTjrppIwbNy477LBDtttuuzz77LP5yle+koceeqhVXU8//XSOO+64/O///m/mz5+fPfbYI9tvv30mjH82f/zD13PTjT9pVeamm36SBx+4Ot2712fLLXfKtsVe6d6tPuPGXZuf/+zEvPHGK+22/cEHrs4lF38p8+bPzsht90j/AZvl+efvz4UXfDZvvPFylb+mDpk7d1amTpmQJBk6rPXk2vX1PTJ48PAkycSJz3Wozqbthm0+qs31TftZsofTirbjuedq348a1fa+mnovNW3XZLfddku3bt1y++23pyzLFuseeOCBPPTQQxk6dGh23XXX5uULFy5s7hm1++6758UXX8z555+fs88+O7/4xS/y8MMPt9kGAGD9oocTAKyFJk6cmF69euWPf/xjNt100yTJ66+/nhNOOCF//vOf8/jjj+fAAw/M6aefnvr62uX+yiuvzLnnnpsLL7wwu+yyS3Nds2bNyr//+79nypQpOeOMM/KhD32oudfKFVe9nksu/lLG3v37jBy5R0Zss3tzuQMPPClbvG2H9Onzj94vDQ0NGffgNbnu2nNyw/U/zMdP/FGb7b/3nj/kX447J8WofZrLNc1rdNedl7SaFPsH5x6dqVMndup3dOBBn8xBB5/coW2nTq2FPBts0C+9evVpc5v+AwZn/PhnMqUxEFqeaY3t7d+/9aTgTfUlaQ6YVqYdEyfW9jV4cNv7alo+bdq0zJ49u3kI3NZbb51TTz015513Xk466aTsuOOO2WSTTTJhwoQ88cQT2XHHHXPmmWemV69/9NAaP358893orrnmmvzyl7/M4sWLm9dfdNFF2XvvvfPtb3+71VA7AGD9IXACgLXU6aef3hw2Jclmm22W973vffnDH/6QefPm5Ytf/GJz2JQkRx11VH71q1/l8ccfz8KFC5vX/fnPf86kSZNy5JFH5sMf/nCLfQwYMDhHHfXV/OLnJ+X++69qEThtW+zVqk11dXXZbfej8+gjN+aF5x/MvHmz0qtX31bb7bnXR5vDpqZyB7/3U3nwgavz0oute2Dt8I4DM3tW5yahHjJ0ZIe3nT9vdpKkZ88N2t2mZ8/eSZJ582d3qM55y6mzVxv1rWg7Zs+ufb/BBm2X6927d4ttlwyCjj322Gy22Wb5zne+09xzKUn69euXXXfdNRtttFGLuqZPn978+POf/zwHH3xwTj755AwePDiPPfZY/vu//ztjx47NOeeck2984xvtHgcAsG4TOAHAWqi+vr5FL6UmW2yxRZJkl112aTU/T/fu3TN06NA888wzmTp1ajbZZJMkyX333ZckOfDAA9vc17DNR6Vnzz75+6tPtlo3c+ZbKZ+5O5MmvZx5c2dm8eJFSZIZM99KQ8PiTJ789wxrY2jYyG3f02pZ376D0rt3/8yYMbnVuvcdemqbbatKQxpq3zRO7N3eVlXW2VZtK9qOhoaGxmLLKtdGTQ0N+fGPf5zLLrsshx12WI477rgMHTo0EyZMyMUXX5xf//rXufvuu/OLX/yiOaRatGhR8+P222+f7373u8317bXXXjnnnHNy4okn5uabb87JJ5/c/JwEANYvAicAWAttvPHGLXovNWnqydLe0Kqm9fPnz29eNmFCbWjWaaedtsx9Llw4r8XPD9x/VW684cdZuHB+OyWSefNmtbl8wIC2J93u1atP5syZvsx2rIg33ng5d93R+g5zu+z6gWy51U7NvbDmz5vTbh0L5s+ttbFnx4aJNW3XXp1t1bei7WgKg+bMabvc3LlzW22bJDfccEMuu+yy7L333vn617/evHzrrbfOmWeemSlTpuTee+/NpZdemk996lOtyn/gAx9ota+iKLLddtvlqaeeyiOPPCJwAoD1lMAJANZCy+vJ0pmeLk29Y/bbb79suOGGLdZNaGe6otf+/nT+/Kfvp2fP3jns8DMyfMQu6ddv4/ToURvS9cc/nJn/e3xMu52C6uo6d9+Sm248r9ND6kZtv1+2337/JMnMGW/lkUduaLXNVsN3zpZb7ZSBA4ckSebOnZH58+c0D1tb0vTpbyRJ87bLM3DgkEyY8GymT5/U5vqm5QMHDWlRZkXaMXRo7Y6Akya1va+m5f37928RGN14441Janexa8vo0aNz77335oEHHmgOnIYNG9Zqv0sbOnRonnrqqUye3Lq3GgCwfhA4AcB6brPNNssrr7ySY489NjvvvHOLdWNubTsxevLJv6ahoSHvHX1Kdt2tdS+Xtyb/vdI2PvnEXzs9afjAQUObA6eth++cb3/33na33WCDDTNw0NBMnTIh48eX2Wqrd7VYv3Dhgrz++gtJOj431JCh2+bpp+/M+PFlm+ublg8Zss1Kt2PkyNr3S99prknT8qbtmjQFUUsHjU2aljfN25Qkffv2zRZbbJG///3vLZYvadq0Wji45NxRAMD6ReAEAOu597znPXnggQdy++23twqc2jNndi1oaGto3KRJL2XChGcrbeMZX7660vraMmrUPrnv3svz5BO3tQp6ymfuzsKF8zNk6MgO93AaNWqf/PW2C1I+MzYLF85PfX3P5nULFszLM0/fXdtuu/1Wuh077rhj+vfvn9deey3PPPNMRo0a1aLcX/7ylyS1XmxL2njjjfO3v/0tTz75ZPbdd99Wx/DEE08kad2Tad99983//u//5qGHHsro0aNbrJs1a1ZzwLV0OwCA9Ufn+rMDAOucI488MoMHD84VV1yR3//+91m4cGGL9Q0NDXn5pUdSPjO2edkmm26ZJBn34LVZuHBB8/KZM9/KVVd8u3ny8LXJnnt+JN271+fBB67Jyy8/2rx8UvfuufnmnyVJ9t33uFblrrj8m/nxDz+a++69vMXyYZsXGT58l8yaNSU33vDj5qGLDQ0NtSGCs6dmm232yNCleky1144Z099stx319fU59thjkyTnnntuZs36x9xZY8aMydixYzNw4MAcfvjhLcodcMABSZLLLrssjz76aIt148aNyx/+8IckrYfcHXvssenVq1f+/Oc/55577mlevmDBgvzgBz/IjBkzMnLkyOy4446tfl8AwPpBDycAWM/17ds3P/jBD3L66afnvPPOy6WXXpoRI0akf//+ee65NzJ58quZOfOt7LnXR1KM2jtJsvMuh+Weey7Ls8/ekx/+v2PytrftkAUL5uXllx5N//6bZLvt9svTT9/ZxUfWORttvEUOO/z0/Om6c/ObCz+XcvPN02fx4tzTp09mvvVadnrX+7LjToe0Kjdt2ut5882/Zfbs1nNMHf3Br+VXv/zXPHD/VXnpxYez2ZBt8vrE5/PGGy+nX79NctTRX11uO4YP3zU9e/XOC88/mHnzZrXbjuOPPz7jxo3Lww8/nGOOOSY777xzJk+enEcffTT19fU566yz0rdv3xZlPvjBD+buu+/Ogw8+mM985jPZfvvtM2TIkEyYMCFPPfVUklqvqEMPPbRFuc022yxf+9rX8s1vfjNnnHFGtt9++2yyySYpyzITJ07MRhttlG9961udvmseALDuEDgBABk5cmQuueSSXH755bnzzjvzxBNPZOHChenTZ6MMHrx19tn3uLzjnQc3b9+7d/+c8plf5y9jfpEXXxiX8pmx2bDfxtll1yNy4EGfzI3X/6jrDmYl7Lb70dloo81z152X5LFn78uCuroMnz8/I47+j+y2+9Gdrm/goKH57Od+m9tuvSBlOTZPP3VH+m44KLvtfnQOOvjkbLjhRsttx9///mQWLVqQTTbZMrvudmS77ejRo0d+/OMf59JLL81NN92Uu+66K717985+++2Xk046qc3hbT169MiPfvSjXHvttbnlllvywgsv5JlnnkmfPn3y7ne/O+9///tz2GGHpVu31p3iDznkkAwbNiy/+93v8vjjj6csy2y66aY55phj8vGPfzybbrppp39fAMC6o66pezesCaZMmbJWPCH79euXJJkxY0YXt4TVyXlff63P5769ScPXBx/58MbN3//x8jX7bmujD9aTqErr82t+fea8r5/WtfM+aNAgFwTWGHo4AQCs5VYmGBRWAQCrgknDAQAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKhUfVc3AABYtcbc2tDVTQAAYD2jhxMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFCp+q5uAAAAXWfMrQ0rXHb0wXUVtgQAWJfo4QQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFRK4AQAAABApQROAAAAAFSqvqsbAAAs25hbG7q6CQAA0Cl6OAEAAABQKYETAAAAAJUSOAEAAABQKYETAAAAAJUSOAEAAABQKYETAAAAAJUSOAEAAABQKYETAAAAAJUSOAEAAABQKYETAAAAAJUSOAEAAABQKYETAAAAAJWq7+oGAMD6YMytDV3dBAAAWG30cAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAACpV39UNAABg7TTm1oYVLjv64LoKWwIArGn0cAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUvVd3QAAANY/Y25tWKnyow+uq6glAMCqoIcTAAAAAJXSwwkAOmhle2QAAMD6Qg8nAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUgInAAAAAColcAIAAACgUvVd3QAAWJ3G3NrQ6TK9N5i/CloCAADrLj2cAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACAStV3dQMAAKCzxtzasMJlRx9cV2FLAIC26OEEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUSuAEAAAAQKUETgAAAABUqr6rGwAAnTXm1oaubgIAALAMejgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVqu/qBgAA657Jb76aO+74bV544cHMmjklvXv3z/ARu+aAAz+RTTfdskN1vPHGK/mfn56QhQvnZ4u37ZBPn3JBq22mT5uU66//UZ5/7v7U1dVl5Lbvyfvff1r69d+k1bbz58/JeT/65/Trv0n+9dPnp66urlPHdNUV384jj9yQAw/6ZA46+OR2t/vBuUdn6tSJOemTP8vWw3duXn7brRfkr7dd2GLbHj16pVevDbPRxptn881H5R3vfG/e/vZ3tlv317+2Z5Lk9C9dlUGDhnaq/QAAq5PACQCo1EsvPZxLfvflzJ8/OxttvEWKUXvnrbdey+OP3Zynn7o9Hz/xx9lyq52WWcfixYtzzdXfy6JFC5a5zcW/+1ImTnwuWw/fJUnyxP/dmrcm/z2nfPY3rQKlv952YWbMeDMfO+6/Ox02VWmjjTbPllvWjn/R4oWZPXtaJk54Ln975fHce88fs/XWO+eDx3w9AwcO6bI2AgCsLIETAFCZ+fPn5o+XnZn582dnn33/JaMP+Wy6dauN4H/44etz9ZXfyR8u+8+cdvrl6dlzg3bruf++K/K3Vx7PbrsfnQcfuLrNbZ5++s5MnPhcDjzo5Bx08CeTJH8Z88vccftFefrpO7P99vs3bztp0ku5954/ZLfdj86wYUWFR9x5W265Uz54zNdbLX/xxYdyw59/mJdeejgXnP+ZnPKZC7Phhht1QQsBAFaeOZwAgMo89dTtmTlzcjbe+G0ZfchnmsOmJNl558Myarv9MmPGm3nk4evbrWPKW+PzlzG/yLbFXnnnO9/b7nYTxpdJkl13+0Dzst13P7rFuiZ/vu776d27f947+tMrdFyrw/Dhu+RfTzk/gwcPz7SpE3PD9T/q6iYBAKwwgRMAUJnxrz2TJNlq63enW7furdaPGLFrkuTpp+5ot45rrvmvJHX5wAe+ssx9zZk9PUnSu3f/5mW9+wxIksyePa152aOP3pSXXno4/3To57PBBht27EC6SM+evXPoYV9Ikjz5xG2ZPv2NLm4RAMCKETgBAJVZMH9ukpYh0JJ696ktnzDh2TbXj3vwurz4wriMPuSUDBi42TL3NWBQbY6jN954uXlZ0/dNE2rPnTszN9/402y11bvyrne9r8PH0ZW22Wb39OkzMIsXL8pLLz3c1c0BAFghAicAoDJ9+g5MkkydOqHN9VPeqi2fPXta5s2b3WLd9GmTcvNNP8kWb9shu+/xoeXua9tt90xdXbfcdMN5mTZtUqZNm5SbbjgvdXXdMnLb2t3c/jLml5k9e2oO/8CXV+KoVr+hQ0cmSSa9/lIXtwQAYMWYNBwAqMzWw3fOnXf8Ns+W92TGjMnp12/j5nWLFi3MI4/8Y+6m+fNmp1evPs0/X3ftOVmwYG6OPOo/Wsz91J4hQ7bJHu85Jvfd+8d8/5wjm5fvvc8/Z7PNRmT8+DIPPnB19tzro9lss+HN6xcsmJfu3Xt0aB9t+ettF+avt124QmU7qim4mzNn+irdDwDAqiJwAgAqM2LEbtnibTvk768+md9edFqOOOJLGTJ0ZKZMGZ9bbvpZprw1PnV13dLQsDh1SwQ+jz12c8pybPY/4MQMGbJNh/d32OFfzPARu+aF5x9Ikowc+Z4Uo/ZOQ0ND/nTdudmw38Y58KDaHeyefvqu3HLTT/Pmm39LfX3P7PCOg3L4EV/KBhv07dQxDhkysrkHUluefPKvmT9/TqfqXFpDQ0OSpC51K1UPAEBXETgBAJWpq6vLP//z93LpJf+e8eOfyQXnn9K8rnv3+rz/sNNyw/U/TF1dXfME3rNmTckNf/5RNt7k7dn/gBM7vc/ttts32223b4tl4x68Nn9/9cl89J+/m169+mTixOdz2e+/mi233CmjD/lM3nrrtdz6l19l0aIF+eix3+nc/rbfLwcdfHK761966eGVDpzmNE563jTnFQDA2kbgBABUqv+Awfn0Zy7Mc8/ek5dffjRz58zMgIGb5Z3vfG8WLlqQhoaGbLzx21Jf3zNJ8sorj2f27Knp2at3fvfbL7aoa+6cmUmSNya9lAsv+GyS5Ljjv99iKN7SZs+eljG3/DzbbLNH3vGOg5Ikd991aerre+Wf/+Xs9O7dr1b33Jm54/aLMnr0Kdlo4y0q/z2sqIaGhuZJ1QcvMRQQAGBtInACACrXrVu3FKP2STFqnxbL7xl7WZJk+IhdW5WZOmVCpk5pe7LxefNm5+WXHkmSLF68aJn7vvmmn2bBgrk5/IgzmpdNev3FbLrpls1hU5K87W3vSJK8PumlNSpweuH5BzJ79rR069Y9W2/17q5uDgDAChE4AQCrxYIFc3PffVekrq4uu+12VPPy7bffP9/+7r1tlnnpxYfz6wv/LVu8bYd8+pQLlruPv/3t//LIw9dn/wNOzMabvK15eV1dt8xfMLfFtvMX1Ia9datbc27aO3/+nNx4w3lJknfuODr9+m/SxS0CAFgxa847LABgnfD66y9kwVLhzuzZ03LZ/34tU956LbvtfnSGDtu28v0uXrwof7ru3AwcODT77X9Ci3WbbTY8b77xcl792xON2y7Oow/fmLq6ujVm2NpLLz2cX/3yXzNp0osZOGhoDn3/qV3dJACAFaaHEwBQqbF3/T5PPXV7hg4r0q/fJpkzZ3peefmxLFgwNzu848C8/7AvLr+SFXD/fVdk4oTnctzx56ZHjw1arNtnv+Py2GM356LfnJrhI3bL1CkTMnHic3nXuw/NoEFDV0l72vPKK4/lqiu+nSRZtHhh5syengkTnsvMmZOTJMOH75IPfujr6dt30GptFwBAlQROAEClRm2/X2bOfCsTJz6fV//2RHr16pO3b7ljdt31A3nHOw9eJfucMWNybv3L+Rk1at9W80YlyeDBW+f4j/+//OWWX+S5Z+/NBhtsmD32+FAOed/nVkl7luWtt17LW2+9liTp0aNXevXaMBttNCzv3PHgvPOdo/O2t79jtbdpfTPm1oZlru+9wfwkyZy5rbcbfXDdKmkTAKxr6hoaln3BhdVpypQpa8UTsl+/2qSzM2bM6OKWsDo572uO5X1YrFrvDWq9ZebMnbucLVmXfOTDGzd//8fLJ3dhS1jdlvWaFzitu1zn10/r2nkfNGiQP1KsMczhBAAAAEClBE4AAAAAVErgBAAAAEClBE4AAAAAVErgBAAAAEClBE4AAAAAVErgBAAAAEClBE4AAAAAVErgBAAAAEClBE4AAAAAVErgBAAAAEClBE4AAAAAVErgBAAAAEClBE4AAAAAVKq+qxsAAABrizG3Nqxw2dEH11XYEgBYs+nhBAAAAEClBE4AAAAAVMqQOgC6xMoMSwEAANZsejgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVEjgBAAAAUCmBEwAAAACVqu/qBgAAwPpgzK0NK1x29MF1FbYEAFY9PZwAAAAAqJTACQAAAIBKCZwAAAAAqJTACQAAAIBKCZwAAAAAqJTACQAAAIBKCZwAAAAAqJTACQAAAIBKCZwAAAAAqFR9VzcAgLXTmFsburoJAADAGkoPJwAAAAAqpYcTwHpMLyUAAGBV0MMJAAAAgEoJnAAAAAColMAJAAAAgEoJnAAAAAColMAJAAAAgEoJnAAAAAColMAJAAAAgEoJnAAAAAColMAJAAAAgEoJnAAAAAColMAJAAAAgEoJnAAAAAColMAJAAAAgEoJnAAAAACoVH1XNwAAAFi2Mbc2rHDZ0QfXVdgSAOgYPZwAAAAAqJTACQAAAIBKCZwAAAAAqJTACQAAAIBKCZwAAAAAqJTACQAAAIBKCZwAAAAAqJTACQAAAIBKCZwAAAAAqJTACQAAAIBK1Xd1AwBYOWNubejqJgAAALSghxMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlRI4AQAAAFApgRMAAAAAlarv6gYArK1ee+21nH/++XnwwQczY8aMDB48OAcddFA+8YlPpHfv3p2ub+HChfn973+fG2+8MePHj0/v3r2z00475aSTTkpRFK22nzx5cu69995cfc3YvP76i5k29fXU1XXLpptume3fcWD23PPD6dFjgzb3NXfuzNx15yV56qnbM3XKhPTo0SvDNt8ue+99bEZuu2en2w7A2uOtt97KBRdckLFjx+att97KRhttlH322Scnn3xyBg0a1On6Ghoacs011+Saa67JK6+8kp49e2a77bbLCSeckF122aXV9jNnzsy9996bsWPH5rnnnsuECROyePHibL755tl3333zsY99LP37929V7qijjsrEiROX255rrrkmQ4YM6fRxAFCtuoaGhq5uAzSbMmXKWvGE7NevX5JkxowZXdwSVqclz3tZlvnMZz6T2bNnpyiKbLHFFnnyySczceLEbLPNNvnlL3+Zvn37drjuhQsX5rTTTsu4ceMyaNCg7LzzznnzzTfz2GOPpUePHvn+97+fPfbYo0WZs846KzfffHO6deueoUO3zaCNNs+c2dPy6qtPZP78ORm82fCc9MmfpW/fgS3KzZz5Vi741SmZPPnV9O07KFu8bYfMmzszr776ZBYtWpD3Hfr57L3Px1b697Uu6b1BLbibM3duF7eE1ekjH964+fs/Xj65C1vC6rauveZHH1zX/P3EiRPzqU99Km+88Ua23HLLbLPNNnn++efzyiuvZPDgwbngggsyePDgTtX/zW9+MzfeeGP69OmT3XffPbNnz85DDz2UxYsX52tf+1oOP/zwFtv/4he/yEUXXZS6uroMHz48W221VWbPnp0nn3wy06dPz+DBg/M///M/2WKLLVqUO++88zJ16tQ22/DCCy+kLMsMGzYsV155Zerq6trcblm8v1s/rWvnfdCgQZ1/8sMqoocTQCctWrQoZ555ZmbPnp3PfvazOeGEE5IkCxYsyH/8x39k7Nix+elPf5p///d/73CdF198ccaNG5ftt98+P/nJT5rDqltuuSVnnnlmvvGNb+TKK69Mnz59mssMGDAgp5xySgYMOiIbbrhR8/Lp0ybl4t99KRMnPpcbrv9RPvyRb7TY1zVX/1cmT341w0fsmn/+2H9lgw02TJK89toz+d1Fp+Xmm36arbfeJcM2b92rCoC123e/+9288cYbOfroo/OVr3wldXV1aWhoyDnnnJOrr7463/ve9/KjH/2ow/XddNNNufHGGzNs2LD88pe/zKabbpokeeSRR/L5z38+55xzTnbdddcWPY569+6d4447Lh/84AczbNiw5uXTp0/Pf/7nf+aBBx7It771rfzqV79qsa9TTz213XZ8/vOfT5IceuihKxQ2AVA9czgBdNJdd92VV155JSNGjMjxxx/fvLxHjx756le/mu7du+dPf/pTpk2b1qH6Fi5cmMsuuyxJ8uUvf7lFz6hDDjkke+21V6ZMmZI//elPLcqdfvrpOfHEE1uETUnSf8DgHPGBLyVJnnryr1m4cEHzumlTX0/5zN3p1q17jjzqP5rDpiTZfPNR2f+AE9PQ0JA77/xdB38bAKwtyrLMgw8+mAEDBuS0005rDmbq6upy2mmnZcCAAbnvvvvy3HPPdbjOSy65JEnyuc99rjlsSpJ3v/vdOfLIIzN//vzma1yTj3/84/nc5z7XImxKkv79++frX/96kuTxxx/v0PC5JJk0aVIeeuih1NXV5f3vf3+H2w7AqiVwAuiku+66K0ly0EEHtfov6iabbJJ3vetdWbhwYe65554O1ff4449n2rRpGTZsWLbbbrtW69/73ve22G9HDBm6bZJk4cL5mTP7H8HX+PFlkmTgoKHZaKPNW5UbPmLXJMlzz97bIqgCYO015taGjLm1IRf9tnYdGTFin9x5d8/m5WNubcidd/fMiBH7JEl+c9GdLda1Z+LEiXn++efTq1ev7Lvvvq3Wr8j1a9NNN22eR+qNN97oUJmbbropixcvzk477ZTNN299bQOgawicADqp6T+/o0aNanN90wTfHf0PcdX1Jclbb/09SdK9e4/07vOPiVfnL5iTJOnTu/VkrEnSp8+A2nbz52Ty5L91eH8ArPkmTqxdR4Zt3vb1ZuiwonG75ztU37PPPpskGT58eHr06NFqfdP167XXXsusWbM6VOf06dMzffr0JMnGG2+8nK1rbrjhhiTJYYcd1qHtAVg9BE4AndTUxb+9SVWblk+YMKHS+qZNm5bZs2d3qM6xd/8+STJy2z1TX9+zeXnfvrX/Gk+Z0nbbpkwZ3/z91Ha2AWDtNG1q7XrTv3/b15v+A2rLO/r3f3nXrz59+mTDDTdsse3y/OEPf8iiRYsycuTIVkPu2vLUU0/l5ZdfTq9evXLQQQd1aB8ArB4CJ4BOagp9Nmi8i9HSevfu3WK7qurraJ1P/N+tefSRG1Nf3zOjDzmlxbotttgh9fU9M2vWlDzzdOshDuMevLb5+3nzOtZ+ANYOTX/Xe/Zs+3rTq2ftejNvfjXXr6Rz18THH388v/vd71JXV7fMCcKX1NS76YADDujU3WEBWPUETgArqL274DQ0tD/fxbK2r+KuOq/9/elcfdV3kySHHX56Bg/eusX6DTbom/fs+eEkyVVXfiePPz4mc+ZMz5QpE3LzTT/LY4/elG7duje2xyUCYF3SkMbrU3vXr87WV+H1a/z48fnqV7+aBQsW5IQTTshuu+223DILFizImDFjksRk4QBroPqubgDAmuZb3/pWq2U77bRTjjvuuCS1IQLTp0/PnDlz2iw/b9685u06omm79uqbO3duq23bMmnSS/ndb0/P/PlzctDBJ2fX3Y5sc7v3jj4lM2ZMzmOP3pTL/3Bmi3Xv3vmwvPnmK3n1b0+kd+9+HWo/AGuHXj1r15D589q+3iyYP7fFdsuzvOtX8o9r2LKuX2+++WZOPfXUTJ48OUcccUROOeWUdrdd0tixYzNt2rRsuummHQqoAFi9BE4AS2nqnr+0psBpyJAhmT59eiZNmpSRI0e22m7SpEnN23XE0KFDW5Rrr77+/fu3+4Z98uRXc9GvT83s2VOzz77/kgMP+mS7++vevT7HfPis7LnnR1KWYzNj+pvp3ad/tt12z2y19btz9vdq/yUevNnwDrUfgLXDwIFDMmHCs5k+ve3rTdPygYOquX7NmTMnM2bMSNL+NXHKlCn5/Oc/n7///e8ZPXp0vvrVr3a4x1TT9frQQw9Nt2565QKsaQROAEu57777lrl+5MiRefbZZ1OWZfbee+9W68uybN6uI5q2ayrX2fqmTJmQ31z4+cyY8Wb22OND+af3fa5D+918i+2y+RbbtVg2YfyzmTVrSjbZdMv0779ph+oBYO0wZOi2efrpOzN+fNvXm6blQ4Zs02L5mFvbHmw3dUptu+effzE33jw/9fUt71T38svPJEmGDRvW5vxK06dPz6mnnpqXXnop+++/f84666wOB0dTp07NPffck8RwOoA1lX8FAHTSPvvskyS57bbbWq2bPHlyHn300XTv3j177bVXh+rbcccd079//7z22mt55plnWq3/y1/+kiTZb7/9Wq2bNGlSfvPrz2fatNezyy5H5LAjzujMobRy112XJEl23/3olaoHgDXPqFG161f5zNgsXDi/xboFC+blmafvrm23XevrTVsGDhqazTYbkQUL5qUsx7Za/8T/3Zqk7evXrFmzctppp+W5557Le97znnznO99JfX3H/xd+yy23ZOHChdlhhx2y1VZbdbgcAKuPwAmgk/bdd9+8/e1vz/PPP5+LL764efnChQtz9tlnZ+HChTniiCMycODAFuUuv/zyfPSjH803v/nNFsvr6+tz7LHHJknOPffczJo1q3ndmDFjMnbs2AwcODCHH354i3JNwxCmvPVadtzpkHzgqP/o0DCEqVMnZubMt1osW7RoYf4y5pf5v8fHZMiQkdl9jw916HcBwNpj2OZFhg/fJbNmTcmNN/y4edLvhoaG3HTjeZk9e2q22WaPDB3askftU0/enh//8KP5zYWte9Dus++/JEluvumnmTH9zeblL7/8aMY9eG169OiRj3zkIy3KzJ07N2eccUaeeuqp7LLLLjn77LPTo0fL3lHL0zScTu8mgDWXIXUAnVRfX59vfetb+exnP5uf/exnue2227L55pvniSeeyMSJEzNixIh87nOt35RPnTo1r7zySjbaaKNW644//viMGzcuDz/8cI455pjsvPPOzb2l6uvrc9ZZZ7UajnD22WfnlVdeabyrXF2uabw73dL+6dDPp2/fgc0/v/jiQ7n26v/KsGFFBgwcksWLFubVV5/IzJlvZdNNt8rxH/9Bund3eQBYFx39wa/lV7/81zxw/1V56cWHs9mQbfL6xOfzxhsvp1+/TXLU0V9tVWbuvFl5882/teoVlSQ7vet9ee65+/L4Y7fkxz86NiO22S3z583Jiy+OS0PD4nzlK/+RYcOGtSjzi1/8Io8++miSZMCAATnnnHPabOsJJ5zQZu+ll156Kc8880x69uyZ0aNHd/6XAMBq4RMFwAoYNWpUfvvb3+aCCy7Igw8+mOeffz6DBw/Occcdl5NOOqnDd6hr0qNHj/z4xz/OpZdemptuuil33XVXevfunf322y8nnXRSRo0a1arM9OnTkySLFy/K44/d3G7dBx58covAadiwIju846D8/dUn8/rrL6Rbt+7ZeOO3Z+99Ppb37Pnh1Nf37FTbAVh7DBw0NJ/93G9z260XpCzH5umn7kjfDQdlt92PzkEHn5wNN2z9T5FlqauryzEf/ka23HKnjHvw2jz37L3p3r1Htt565+y7//E58sjdW5Vpun4lbQ9Pb3LYYYe1GThdf/31SWpD3Pv379+p9gKw+tQ1daWFNcGUKVPWiidkv36128U33XmF9cOaet7bm8yV6vTeYIMkyZzG23uzfvjIhzdu/v6Pl0/uwpawunnNV2f0wR2749yaYE29zrNqrWvnfdCgQWvPi451njmcAAAAAKiUwAkAAACASgmcAAAAAKiUwAkAAACASrlLHUBFTN4NAABQo4cTAAAAAJUSOAEAAABQKYETAAAAAJUSOAEAAABQKYETAAAAAJVylzoAAGCVWJk7uI4+uK7ClgCwugmcAJawrDfGvTeYnySZM3fF3zwDAB0jrAJYuxlSBwAAAEClBE4AAAAAVErgBAAAAEClBE4AAAAAVMqk4QCskAkTXujqJqw2G/TqlSSZO29eF7eErrI+Pd9Ze1/zQ4eO6Oo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\n", - " \n", - " 100.00% [6000/6000 00:04<00:00 Sampling chain 1, 0 divergences]\n", - "
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\n", - "text/plain": [ - "
" - ] - }, - "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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xpkLL20xSivEIYEUPyf0kxnhON/l4LRcppRjfBnyklyTnxRjHdzjnXLyOS1aOOIcQJgPvJRmfOQrYA7gjxji1l/Nsl6sgjRjbJldfSnEege1y1aQU49uwXa6qpppMDyFMBH4GbAF+ArwKnA58CzgeOLvApHYDrs38/a/AGuDNBeQ/AngQOBSYD3wH6EMywDYZ+Bzg4HWOtOOW42skE0bqQcox+xmwD/AIcAfQCrwb+ChwTghhQozx94V+l2aS9rUWQrgGuBB4HvgvYABwDnBPCOHTMcb/LPS7NIsyxqzotDJt2h+AYcBDwP0k/8k+FbgKmBpCOC7GuLnU79eo0oxb5pwR2B8pStoxy2FfpEApx83+SDdCCCNJ/l2GAb8ElgLHAhcA7w8hHB9jfKUS6ZTzd6LR1Ftc7DeWJsU4XwFMAZ4G7iOJcQDOAM4IIVwQY/x2N9n9Engyz/bHeitnM0rzWs54imTwONfibvLxWi5SijGeTXLjdD7TgENI/k+Yj9dxkcoVZ5IJ8aOAv5FcZ6MKyNt2uQpSjLFtchWleS1n2C5XWIoxno3tclU1zWR6CGEISQWwAzgxxvhYZvtXgHnA5BDCOTHGOwtIbhPJU3hPxhhXhxAuBWb2kn9/4Bckg58TY4x35+zvC/y9uG/V+NKOW05ZTiR5iu98kokH5VEDMfsWMCvG+GJOuf4d+CrwPWB0EV+pKaQdt8xdzxcCzwLHZO/aCyFcTfIE5jUhhF/FGFeW8v0aUTljVmJaXyDpKF4aY7ysQ1p9gTnAyST/yf7BTn/ZBpJ23OyPFC/tmOWcfyL2RQpSA3GzP9K9G0naj8/EGK/Pbgwh/AfJ7/dXgU+UO50y93UaUd3ExX7jTkklzsADwDdijH/smEgI4b0kN/hdHUK4K8a4Ok9es2OMtxVQJiXSinHWkzHGSwspqNdyyVKJcYxxNnkmZEIIewJfJFml7LZu8vI6Ll654vxZkkmZZ0ieeOzy1HFHtstVlUqMsU2utrTinGW7XHmpxNh2ufqa6Z3pk0meDrkz2xEAiDFuIbnrA+CThSQUY9wWY7y/m0alO9NIlmi4LnfgOpPmjhhjWxHpNYu04wa0dyZvA+a61EWvUo1ZjPEbuQPXGd8ANgNvDSG8sdD0mkja11q2U/HV2GH5m0wH7QZgIPDPRaTXDMoWsxLTOiTz2alNi8mSb/dmftynwPybSdpxsz9SvLRjBtgXKUGqcbM/kl8I4RCSFUxWkrTvHc0EWoBpIYTdKpBOOX8nGkodxsV+YwnSjHOM8bbcQfvM9t+SrHA0ABhb+LdRPilfy6XwWi5SjcZ4GjAY+HmM8eUCjlcvyhmfGONvYozLi/g/nu1yFaQZY9vk6kn5Wi6F13KRajTGtssV0kyT6SdnPh/Is+9hkicpx4bkPZSV8H8zn7eFEEaEED4ZQvhyCOGfmnEgrQhpxy3r28BeJEtzqme1ErNcbSRLrEJyh606SztuPeV/f84xSpQzZqWktSTz+Q8dDw4h7AKcRvJ087wC8m42acfN/kjx0o5Zln2R4tRK3HI1e38k+285J8bYaRWMGONGYAGwK8n7OcudTtp9nVpWb3Gx31iaNOPck+yrZVq72T8mhPCvIYQvhRCmheSdlMqvFmK8Xwjh4yGEf898vq2AfLyWC1cLMc71L5nP7/VwjNdxccpdz5aSt+1yZaUZ457YJpdXLcTZdrmyaiHGuWyXK6SZJtND5nNZ7o4YYyuwgmTZ+0Ny95fJMSTvmjkNWE6y/MPXgB8Cq0II0yuUb71LO26EEM4EPgJ8Lsb4l0rl00BSj1k3zgb2ABbGGNdXOe96kFrcMnfn7Q/8rZun2ZdnPg8vd951rpwxKyWtq4AIXBFCmBtCuDqEcB3JJPs7gfPy3e2s1ONmf6R4acfMvkhpUo9bN5q9P9Ltv2VGoW1+KenUah+1FtRNXOw37pQ045w/oRAOAsaTTM483M1hF5C8OuPrJK8PWhlCuCmEMKi39JtQLcR4AnATyZKmNwFPhRB+E0I4sFMGXsulqoUYv55ICO8meW3NshhjT8vReh0Xp2z1bDnztl0uqzRjnJdtckXUQpxtlyurFmLczna5spppMn1o5vO1bvZnt+9Z7owzd+sNAfoDV5P8oh4EvBGYTvKEys0hBO/s6Sq1uAGEEN4EfBe4P8Z4SyXyaECpxiyfEMLBwPUkd1ZeWK1860yacau535k6Uc5/t6LTijG+RHJn5S9I7sT8PPAZko7kfwO/LiDfZpRa3OyPlCzVa82+SMlSjVs+9keA8sWllHTsb3SvnuJiHEuXZpy7yPRL7iBZMvTSjsuJZqwAPk3St9wN2A/4R5JlND8O3NpLOZtRmjHeBFwBvINkJZ29eP19nycCc3OWOPVaLk1NXcfAxzKf/9XNfq/j0tTT+IzXcmlq6t/NNrli0oyz7XJ11Nq/m+1yBfVLuwDFCCGsJBn0LdQdMcapBR7bJ/NZifdO9O3w+bMY4xc77Pt+CGF3kqU7/40GXBK3juMGScXTn9eXx2gKdR6zTkIIw0iWotkHmBFjfKQa+aahkeLWjYZ7j3MdxaxLWiGEESTvSx8MfIDXly6aCHwTmBhCeHeMcUUZ8q8pdRy3pu2P1HHMoEn7IlD3ceukmfojO6lccSklnVro69SqeoyLcSxe1eIcQugLzAKOB34CXJN7TObdrb/tsGkTcFcIYSHwFPB/QgjfiDE+tZPlbSYVi3HmRttLco57OIRwKvA74F3AecB1RebltVycal7HQ0kG4LcBt+U7xuu4YtLss9guV0fVYmybnKqKxdl2uWZU81q2Xa6wuppMB54lWZq0UC92+Hv2LpCh+Q4keVKr43FlE2PcFELYBgwgeYIv1y9IBq+PLXfeNaIu4xZC+DBwOvCRGOML5U6/xtVlzHJlBq7nkdxtdUGM8cZK55myeo1bb3n3dpdfPauVmJWS1m0kSwcdFWP8U2bbBuC7mWWBrgVmAucWkH+9qcu4NXl/pC5j1uR9EajTuOVqwv5IT8oVl1LSqZk+ag2qp7g0c79xZ6UZ53aZQfsfkrz24r+BqTHGggcYY4z/G0K4D/gnYBzJwJ8SNRHjjmKMrSGEm0kG7cfx+qC913JpainGU0lupr4zxvhyL/l14nXcq1oen7FdLo+a6JfaJldcTcS5I9vlsqulGNsuV1hdTabHGMfvzOkk73A9HHi8444QQj/gYJIlF5/biTx6y380sD7PvuzSKYMrlHeq6jhuR2c+bw8h3J5n//4hhGwHY69Geu9lHcesY17DgbnAKJInwBp+4Lpe4xZjbAkhvEByTQ3P826ewzKf3b1/pm7VUMyKSiuEsAfJ8lCvdphI7+g3mc93FPhd6kq9xq3DOU3XH6njmDVtXwTqOm4d9zddf6QXMfPZ3TvjCm3zS0mnJvqoNapu4tLM/cYySDPOQHtMf0QyaP8j4MMxxh295JfP2sznbj0e1XxSj3E3usTLa7lktRTj7KpJ3+0lr+54HXev3NdgsXnbLldemjEGbJOrJPU4d8N2uXxqKca2yxXWTO9Mzy5X+v48+8aR3LXxSIxxa4Xyn5v5fGuefdltKyuUdz1LM26/B27p5g8ky2Bkf67U7009SvtaI4RwAMmSJaOATzhwXZC049ZT/qflHKNEOWNWbFoDMp9DQggD8pyzT+ZzWwF5N5s04wb2R0qRZszsi5Qu7WvN/kh+2ZutTg0hdPq/aOZGreOBzcDCCqSTdl+nltVbXOw3libNOJPpM/6UZND+B8C0EgftIXmaCprz5peepBrjHhyX+cyNl9dy8WoixiGEdwFHActijA8V8wU68DruXrmvwWLYLldHmjG2Ta6eVOPcA9vl8qmJGNsuV0czTab/FHgZOCeE8M7sxsxStP8v8+N3Op4QQhgaQhiVeaJkZ32X5M69z2YG1jrm/9XMj3eWIZ9Gk1rcYow/iTGel+9P5pB1HbZt3pm8Gkyq11oI4UCSgeuRwEdjjN/b2TSbRNp15E2Zz4tCCHt1yGMEMINkkuj7ZcinkZQzZkWlFWN8BfgzyQo3X8nJYxBwcebHuShXanHLsD9SvDSvNfsipUv1WrM/kl+M8VlgDjCCpH3v6DKSu+B/EGNsAQgh9M/EZOTOpJNRSp3ZFOowLvYbS5BmnEMIA0leJzOR5Aawf44x/r2n8oYQ3pNnW58QwpeBd5P83jzQUxrNJuUYvyvfTbYhhJOBz2Z+/GHObq/lIqVcX3f0scxnj/0br+PSlCvOJbJdroI0Y2ybXD0px9l2uQpSrq87sl2ugj5tbQW/BqPuhRAmkXQKtpAMFL8KnEHy/sKfAv/Y8b0gIYRzSSqI22OM5+ak9SWSp0wAxpDc+fEIsDyz7Xcxxptzzvkc8M1MvrOBFuB9JMtA/AE4yYHQrtKOWzdlagNeiDEe0NuxzSjNmIUQVpA0YI8Dv+qmiLfFGFeW9u0aV9rXWgjhm8DngOcz+Q0ApgBvBD4dY/zPsnzRBlLmmBWb1inAvSRx+gNJfAeT3K16EPAMcFxm4l0dpBm3zDn2R4qUdsy6KZN9kV6kXEfaH+lGZuDgEWAY8EuSm7PeBZxEsvzd2GzbkRm4WQGsijGOKDWdDudMogzXXyOqt7jYbyxNWnEOIXwfOJdkgO5GIN919lDHJ2ky7dwyYBHwAsm7KI8nWUlnE3BmjHFOif8UDSvFGD8EHAk8RHJdArwNODnz96/EGLMTcR3z8VouUpr1dea8IcCLQH9g/9jDe1m9jktXxjhPAiZlftyX5P99zwHzM9tejjF+Ps85tssVllaMbZOrK8U4P4TtclWkWV9nzrNdrpJmejKdGONskne8Pgx8CPg0sJ2kgjinyIGT9wMfyfw5KrNtbIdtJ+TJ/z+ADwJPAZNJ7hjZAVyCA9fdSjtuKl7KMRuR+XwHMLObPyNQF2lfazHGC0k69GtI6scPA0uA0+2s5VfOmBWbVozx18AxJHey7gd8iiR+LcDXgWOcSM8vzbhlzrE/UqS0Y6bSpBy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1JhYBolS8rW0s0crjXTPn8FL04zXnBPL5tuJ1Op+Hq28w9Wl9tQcq5WpCbYOHKpUyN5R8s7paS+toKaWxRfoGV62v10oNOKU8YOPd5IBD7Q5UWjddMac7QsBp5IjbGHLhWCLlPq1LkJsfTgS+Xtx5qWx7S/JBGOB/afZxCq6us51zyRer26WUFiMX9r3Jd5jWIEdsO4mIT5LvoAD8DFgh5T77yxfrAzguIvavs72fkA+kU8k/ssWL/Xo3uVndz+eQL91xIbkCslaRrqXJF5rvkE+M36izT3sXaZ9KPqGtmHK/+CHkH24iD/Zde9I4kXwAfZr8VIaFUu6bOoQcXPgBudUNKaX/pZSGkU8iAFfX+Y7+18R+Xwq8h/x9fgJYLKW0JLmi9G/yj/mGiFiuskBlu+QgKHQeF+AjNO//yOM7bFKkZVHyxVAlWPcH8gn9AnKLrCHkQMLG5LtVq5ErhdUDtn2afFE+BTgAWDR1jBkxnFzxmzUmRy9/F6UqAkl7kSsykMfKGltn1r7+3b9Avju9WdW6FyZXvv9M/g3/Jno3vtTqVf+PrzO9YVnoStGy6Spy5e0OYP2iZVIlT94iV4B+2sVqvkuuBOxOLi9Lkve5Yvvitas7Pm+S73rvBiyX8lglQ8hl7mzyCelXRYuTnlqefBfvUmClojwvR8ddmS9FRFcPo7iYHPiu5O3i5CDEW+T9PLzOMn1ZFnrrMxHxXERMj4g3IuLuiPhG5EEj69mAfJEA+a7gbIpzbaWivGHl82K/1u9q2ULlTvuGNZ9X3ndn2eWrj7slOoNcyXxvyuMSLEY+x71ODkbNNuhub467KaWri/PEPcVqjqo5xmxZtYl3yBe0nyLXgxYpyuRQchDgTeBzkZ8a028i4u8RMTEipkbEsxFxeRF4a6SZ77ey7MsNjvPVy1bPD02U6RqVp+6tH3XGTWnCzsXr2zS+QOyuMsrKEeTxi/Ygl+HFyb+BSeRjWaXlyhXAysW5YwVyS7424OxGF4TklqZHkx/GsVSRtneR834Z8vHzcnL9adNi+pJ0dA35YtGCodaDxTrXKfZ5KPk3uBe5ZcQWdLQi66muzsMrksdT+T86zjOLk8fu6VKT9f6KOdUBunMe7svz1t7F39eAJYuysg55DKJB5JadazdYdnlyt9Gfk/N2aXI5ruTNV6L+UCt9WRZ6otLiqKvBkSu/k9r9aMnxroRzeFn65ZozpXRUcR6u2LLmPFx9o70v66s9USlXXcVaqo+/s8rWAKqjVc6l7+1iPd3SFyPXX0o+cB5ILoSVjNu/anpDxR2Tr5BPFrsVQSwgN00kF5LxwNXkE8CVdVbTXW8B76scJFJKM8kjvv+AXJn9GFU/lmI/vl+8vSql9OWqtI0lH1SXI1cgfhARv6kE14qKbqXLzRGpqhtTSunhiPgADaLrPfQ8sE/q6Ks5GTi9aAFxIvDNiPhp8TlFpbpysXpAymPtVNI1g/yEhN3JQZtDIuKk1NHVsNJi6DsppTurlnuL3CLjkRL2Z44i4r3kx5gCfDql9KeqtNwfESPIAb0VyWXru/2RLvIg2bsXUejKXZLKE1B+SD4wn1NzoAR4tDgg30cORu5LR6WokueXppQuryxQ3Gn9L3BeH+xHj0REdSR/ELn1VRu5bP6A3MKvdpk+/92nlOo2aQX+WeT3A+QD/o7k4E5PVIIa7eRKZa2uykJXvk2+kHgG+FDq6P/9FjlPIFf0DomIRo/eXbhYdtbvsWa+rYrXh2kgpTSKOi1TU0r/JfcfX5J8fDuYng9QuSi5UntQkS+k3BXk2OJ4+lng5Ii4MtV//OsL5P2rHPPeAi6K3P/9S+Tj+P/VpLvXZSE6uir2xi5FXtazDvmu8pvkY8N2xd+REbFXmn1w5+oWPF11o6hMq55/STq6tfZ02er33Vm2Mv/rXczbGwuTf1NPw6yK+Y0RMZHcEmFEROyQUrqrapneHne7pfh9frjO5+OBn0XEBHId6Ahquq9GR5ee3phTF/Bt6LgDvEbx95mI+ClwdJ3fVTPf7xyXTSlNLY7nS9O5bDVTpqvX/2xRDpYkH99u7mJdPVFpnfB05VjcW82UlSpLAXunlKqPUTdGxOnk8+xxwO0ppVljcKSUXouIz5C7n65EPsb8rcG6T0op/bRq2X9HxOHF/PuSb/CtVaS5sk8/jPzUpV3JN4M61QNTSrPdACiO2X+IiEfJF5sHRcTXe5LHRWuIyo2JR+osuwhwZUqpMqYKKaVp5HpJV+vtdb2/RsM6QOShNiotwLs6D/dlHWYpcl2++iL/meJm4UPkFlXfomO8x2qLkruAVufNeHLe7Ezet49Sc/HbTFmI2YdP6LaUUm1A7jnyxX3doExxnbRe8bZ2jM5WHe+aPYeXpT+vObulmfpqdHQx7Y2TU0onVb1/rnhdIiJWS/UbVVSXueqyNVDqaJV659ZdrKdbyu5SB7mJ5DTgI9HRzWMncmTx/jTnJyAdWKTrhuqLzhq/IweLNorGI7t3x68aRKRvKF7XjM5dVTYlXxRAvnCup1J4h9NxIQf5xLsA+QueLeiW8pgRZbRwOrPyw69xFvl7WZLcZ7Ni5yKtY6p/+DVpe5bcrHMwHXf4ILc4gb47kHXXx4rX+6uDTRUppVfo6NvaZRPykv0s1el/HvmpFJW7lmfVToc86CgdFzvV39dAyfOurFj1txwdd3CWJN8ZHVJnmf783c+m+M3cWrzdvqt5KyJioYjYMCIuIFemIFdI63VLq1sW5rD+tqr1/qRB5fsCcsCljY7fQa0/Vgeb6qjkZTMBgUr/727lXR0/bhBMqjwVZh1yEKCesxoc824oXuvdZW+oG2XhDbp+zHxXf9PrrO8B8phsq5Pv8i5D/p18gRyAXR34Y+SxtqpVn5u6KluVcrN4SctWL9+dZestX4bf1guwppRup6M10qzfRJPH3bJUfifbxOyPGp5O78tVvS7g08iB1h3JrSGWJl8Ybl6VjqOo05Kb5r7f7ixbvXyZ5bJapW5X5rlimeJ1XKMZIuIXEfFynb9je7itrspKxd9rgk0Vf636f7auXsXF373F20bHx+nU/53cTS5bAD+vBJtqjJzDuusqzv2Pksvppt1ZJiIWjzyGzF/oaOHUqBv26T1JT2FTel/vr9ZVHWAFOlrX9Oo83Js6TI0p5NYfteudRkdr44920XqqUZfCG4vXsstCb4+Vr9RZV6W14qcjP7Gu1iHkVlwAg4pzSUWrjndlHiub0Z/XnGXpqr76Jr0vV7UPnrqdjjrfbONaRx5r6qtVH1V3Hx0odbTaG0m9VnoLp5TS+Ij4A7li91FycKXSna7L1k2F7YrXjxVRzkYqfWZXI9+p6Y16LRGgcz/wpekYd6jSv/K1VDxJoFZKKUXEC+Qm0pvRcVKvLHtng7sf0PM7EvWMapCuiRHxIHncn83ouBir5PfKNS1Tai1VvFYfjG8hRz1PLVqoXAvc29ML6xJU8raru8O3kSvW60XEYpVoex/7e4PPt6BjILl/ROdhmqpVTmrVef5H8oFr74j4Pbk70R0NAqctUXv3qAgObUEe3PQIYLuI2DHlwVQr+uV3HxHrk1u97Ei+y784HQGxiq6eMndiRDS6+3Evef/qaVQWurIWHb+7umU75QFyR5G7Itb2/+7utitNaRteRMGsAUuPJHfNiyJttRdCvXlC3wwajFuRUnoqIip34jcj32mtNafjeKOx+HpVFlI53W2r1zfbWCfFRdwvI+I+crlaidz94ttVszXT3a962d7cKa4s3+u7zCUY1cW0O8jHlOrfRDPH3W6L3EXps+Q60LvJQYqFamZbhFwuZ11cpvzkmGGUJOUxI46s+aydHODcKyJ+W6Tx2xHxfzWBg2a+3zKWLcM48ph3fdGdsytLk2+21JqtQt/bslLl3w3SUD24a6ObDZUL77rHR/IF4aTaD4tzzuvkMVB7te6i1fkh5ODMStS/CdXVueT2Ln6/P08p/arO51OpGm6gB5qp91fr6jxcXUbndB5utg7TyP1d1Isr1yZLk39TtTcF3+jiRuGczsO9Kgupc/eqZv2kSMMS5DExv0Kuk1TG2D2TXE+p1D+rr+Fadbxr9hxellH1Puyja85u6219NaV0Bh1DvzQlpfRq5AHjvwIcUbRa/Tn5+LgxOQC+Jh1lq165gtbW0SrHo6bPo33RpQ5yYOnjwAERcQ058DSD7nWDqUTRFqd7EdlF5zxLQ7OdTCFH9KtOZtUDzlYi3I0G4Kp4nnziqR47oPJ/V83b5rTe7uhqHZVp1emq5PdC1K8k1arO71PJd0v3Il9oHwHMjIjRwPXA+Q3ufpWtO99Lpdl0G/mH0x8Bp0YDMFdHinuU5ymlOyLiu+RugXsWf0TEE+RuA79MKT3Vu+T2jaI57B+Ki+cnyHerjqLz3cI+/91HHofhUjpXGiaQW01Vtr0YXTxBj1xuKncx3i6Wf5xc3q9KuVtuPY3KQleqf6fdKduNxiqZ07YXLl7rtb4BICI2JAdtq8vrJHIlvp18/BhK13nXyOtFy5JGXiCXj0b7V/c4Tsdd+NnOcyWVhT6X8pNEriLftNmTzgGn6rtpQ2icD5XfSvX8b9aZ3t1lq993Z9l6y5eht+c66Pm5rlsiDwT9Zzoq1ZB/I6/RUZmsbHsxyu9m2BPfJNfTFiOPdXFd1bRmvt/uLFs9vVG57GmZrlX5/de7gO2tSkuyRkEaUkqfBD5ZeR8Rd1HnTnpJZaXRTZdZg5l30R2lMs+CDaZ3dUPn7TnM03DdkZ/K9OWqj2aQ87UyKP8yxXJdHXvH0XG+mkHOmwfIQw00unE7toubvV1ppt5fravz8MJV/3d1Hu7L81Z3jqeQ9682uNTodwodv8O+KgtNS/lJ2h8j3zTfkM4tBCEHcH9FHp9sWk2LnlYd75o9h5elP685u6WP66s99Q1yUKlSf/t2zfTzyOOwBp3HnRsodbTK73ehiFigl8dQoO8CTn8i/0B3JUfilwR+n1LqTuWq0s3vqHp3fgeIhec8S6/09QC19dZfye/re3rnvjjo7h356W/7ku+4bE6OaG9LHuB5RJp97JG+0lffS281eoJNJc/HFd1neiSl9P3IT3Pbj9zcdFty//P1gaMi4tCUUndaE/arlNIrEfEncmX8E3QOOPXp7z7ywLHnkysvV5PvLDycOj929fvkCkVXv8Mzavpod1evn2ZUaKZsz2nbb5BbVizdxTy/Jp+8HyCfMO9OeWwtACJiN3IlrS+OYaWus8Sy0F/+QQ44rVXzefXNi5XpGFi0VuUuXvXF4URy8HQxur4bXm/ZyrY37eay9Zbva12d63p13O2mE8gBhNfJLdL+lKoeJVx0jaoEpVtatlIe5+g18sVAvbK1Kb37fl+sM72TokvK0l0sW1m+J2W6ViUoVGbr38qQEOtExKINujl311xTVspStF7+Mvmc9H3yoOP/qe5OHRF3kp/81dU+fyQ1HguvkVaeg+e0/eousUtTJ/DQ4vNW6eWvxLJQipTSX4pAxVfIT+Rakfy9jCQ/0KYyDlrtDd1WHe+aPYf3h1KvOXuglfXVTlJKbxXjq32M3BNhI3JLqyfIv+c/0DFUSnXZGih1tMp5dHwzwSboo4BTSmlmcVf2K3SMwXFZNxd/hRzpqzt4W4tV7lDMaWT7VWvmr/6/qy+/jLEGViY/5aur9Venq9L0udf5nVK6l6IJcTHm1Z7k1k+rk8eY2bLx0qV4jVxmhncxT+U7aae1d5Whqrl5RAxLjR+X2VDRx/kU4JSiYvpe8jgCOwL/FxGdKq8DyH+L19oLnL7+3e9Ovvv3GHlg+XoHzu7cbelP1b/T4XTuJlGt3vGmJ14nB5waNXlfndzc/W1gr1T/EavN5N1yEbFQF62c6h23mtFUWYiI39G5VUJPfKToOtUTjZpGP1F81kauxMxWWY2IBci/K6h6Uk5KqT0iHid3M6v39KCKyu/xsZrPHyM//ro7y77WzZtNPdWdc2m9c12vj7vdUBkj6ssppavqTO+qXG1HHqeuN+7pZeW9Udlq5vutlJVhEbFsg+7eG9aZH5oo03VUjmdllr1Rxesg4APklq291euyMher7PMFKaVGD5cYaPvdTL2/u6rL6FA6npxcra/rMN29NinrPNxUWZhDd6wuNeqOl1J6njpPVSu2V3mqZ23XyJYc70o4h5el3685u9JsfbUYa6+n4+1VnFF0yeukCKJeQ52HP0TEVnS0wr23epkBUkcr7TzaF4OGV1RaWCxIbv76hy7mrVb5Me8ZEY2a+jZSOQD3VdTygeJ1saKQzCYi1iM3q62ev/r/HboYdK+3Tz6a4zoiYgk6+qJXp+vvHbPUfWxpj6SUJheVp88VH20enQde74vvqLI/O3WRt7sWr0/20/hNXbmfjjuXTUf4U0pvF3f79iA3RV6Mzo+97+vfRU9Ufhszaj7v6999pTL4cL2KWlFudq39vMX+Q0cT213qzVBURnYu3j5Qb55uqFRy1mwwfVZFusHJG3KT4N5akNxKbzYRsQ4dFeHe7l+tZsvCMnQeGL8nf7Xjs3RH5VwzpvrDYnyVyiNrGw1wvTUdYyGMrJlWGRes7rKRn5xUeRRuo2U3iohGFyrvb7BsWbo6X1amVZeZZo+7PTnOPNhgele/k0oXg9789bjFVkRUj280pmZyM9/vXXQc3xvtb2XZl+hoNVRGmQZmPbGs0o2jjKf/VtxNx5O2jovGg3l3RzNlZW7V5T5HfprzOvWmtVAz9f5uKYIUlQu6OZ2H+6oOs2Xxu6mncjwdDzzbxDaqNVsWenus7HFQLiJWoeN49Juaya083jVzDi9Lf19zVm6ONDoPN1tfXZzel6veDMx+cPE6KqVUO+TOQKijrVG8Nn0e7bOAU0rpn8BJ5MHWvtpgFPt6LiFX7Fam/tNTZomI2jvylWZpS9XOW5KHgMpTcWr7YVacVLyOIT9eueJ35P1aBdi/dqFiX75QQhqPiTzyfa2vkgefnEh+mkfFSDpanfykqwpUbX432E7F1OK1jc4XWZXvaOkulu2pylOFNgL2rp0YESvSkbe/LXG7vVKcZCrjZXynSF9dETG4GOuh8r6rPJ9OR7Pt6ubfff276JaIWJqOA2dtpayvf/eVR4Jv3CAoeTiwdlfb7W/FXZFKi4ejGlQGDyMfU9rp4SPcq1QG7N6iwfRK3q0YESvUToyITeh4HHVvfavB91IpC0/RuwFf62mqLKSUdk4ptfXyb1T1uroIkFemv5uO8WDqPdq9Uvn9TNR/cmPlTt0/U0q1d0+vLF7Xj4g96ix7OPk3NZXZW3KMJLe4WwD4WoN0Vyp1V9RZdxn2i4jalpJExI50jJkz645iM8fdQnfOXZWytUnthGJ9xzdaMKU0qolytXOd7c3pBsOPitep5PEuqvX6+00pTSA/UATga0VQvHrZxeg4H/8mzf50ymbKdMVm5FZIkyjvuFE5Jh9XvN0KOLd2/3qg12VlLtZwnws/YmDcGKv2EL2v9/dEpeXrnM7DfVWHWZQ8tmYnEbEwHceAa+v8XnurqbLQxLGyR+WruCb6OblH0D2pZoywFh/vmjmHl6XfrjkLczoPN1VfTSmd1ETZOqnReuuJiG3JdXio/5THgVBHq/RQurOLebqlL1s4kVI6OaV0bOrBeDIppcfpeDTnyRFxXnWlMvIjUEdExGXM3jztKXKkeamI+CglKw4U3yne7h0R50bxqOqIWDbyAHifKqZ/p/ouRErpOeCi4u0vIuLASkuO4gfwJ/KPs1mrA9dHxBrFuheNiK8BlSdrnZqqxh1Iuf/3l8kXrCOAv0TE1pUTWlHx3jwiTmH2gQIfiYgfRcSWlQNORLQVd4Eqj6UdnVKqfupG5e7gDpGfbNe0lNKd5PwDuCgiPlY5iEXE5uSD3VByU86flrHNEhxH7h++EnBPROxbnNiB3LIjIr5KvhtSXQG5NCJ+HREfKO4gVOZfgxy0WYR88Kk+OHTrdxERO0dEe/G3c7M7WLXewRGxBbmVY+UE0Omxxf3wu/8ruYxvDJxTBL+IiCUj4uvkgfsGzJP+qvyI3I97ZeDmiPw0g4hYOCIOByrjXV2Y6jwivpvuKl7f0+Dk/zh5QNQ24OrIrY6IiAUj4iPkRzE3MyDlFPKd2QsrFYSIWDoiTiU/OQbgpBIrugOpLOwfEddExF6Rn6pCkZaliu/3dnLA/lXqPznll8Bz5Kfr3BR5DAoiYomIOI2OljyzXSillB6kIwB/cUR8qFh2UEQcSO4WDfCTVNM9t7iBdFLx9uiIOKZy/CoqUdeT6xd3p5Ruqt12RJxUOdZ0lTlzMB34Y+SuaETEAhGxJx2B11tTSrVPP+ztcRc6zl0fiYhGwe3KY8nPiohZLW4jYktyBbA/n5g2KiK+FREbV50P2yLiPRFxPR2BzFNTStVjyDT9/ZLrGzPIQZmLI2K5YtnVyUH01cmtJU6ts2yvy3SVSiX5npTSbGPnRMTFRfkb08U66ir293vF2y8C90bEJ2p+v4MiYqOI+B71H+kOA6us9JfKPn8+Ig6pqjeuHhGXkOvPXT6lrb81U+/vocp5uNEQFH193poAfD8ijoo85hBFHexGYAPy4MGnNLH+WgOqLETED4s65hLF+7bit/hH8jAhE+kYx6lWS453zZzDi/lGFcfBUQ32qzv685oTOs7DB7aovtojEbFLRBwdEWtVnYeHRsSXyQ+NGAz8KqX0l9plW1lHK+ZrI4/LDAM94NSEb5AjypCffPZMREyMiHF0REv3p+YRhyl3lapEBK+NiPERMab4+1gZCUspXU3HuFRfAl6NiDfIFwSVpy2cklKqFzE8mjwA7KLk4MCkiBgPPExunfPFEpJ4KLmZ3LNFfk0gtzIbRD5xnFZnn35fLDedfOF3LzAl8qNvp5GbfH6T2SPKK5BbINxXzD+W/LSMfwDvIjcRPqxmmVHAM+QuACkiXq36jlal9w4k34kaSg5IvBkRE4u0v4t84to31e9f3e9SSmOAD5IHdluLfFJ6MyJej4hp5CDKT8hNiqsvyhYBDiIH2CZExLiImExu5rwfuYXT51NVf9z++F1Ui4iXq//IAbDR5MEf24Hvp5RuqLNon/3uiztDZxfzfAkYV/xu3yD/JkYCv2h+78uVUnqGXPGaRu4690SRH5PIT01ZmJz2rzaxmfvJJ/bF6OieV52Gd8jj8b1TTH+q+G29SW4x8laT23+N/N0fDLxcfC9j6RhL4byUUm0z9l4bYGVhEHkwyRuBsUV5H0s+Xv2KfDz7L7B7Smm2sTNSSlPJrTrHklt1PBr50bvjga+Tf2/fqleZKRwO/BNYlhzQnEwOcF5CHlfgJjoqjrXb/jl50MsFyMGwSRExiXynfk1ymfpETzKjh44l58/dxXbfBH5P7kr1NPlx87VpHkPvjruQx6GcTj6OvR4RLxTHmLuq5vkO+by3GvlcNyUi3iSfIzeh48K0PwwnB6z/DUwtzueTya1L9ynm+RkdwZNOmvl+U35QyOHkLowHkOtJ48kXVu8v0vHRPirTAB8uXq/uYp5eSymdSN6/CeQAwdXk3++bkQdinwo8Qh4YfFHyHeRf1axmIJWV/nIxuX45GLiQvM/jyOXiQPKx5uGWpa6BJuv93XUNuWzvGLO3ruyP89aN5OPn2RR1S3Jd/QPkeuXBRX2kLBczsMrCZ8h1zInFsWoq+bc4gtwV7v0ppbrdilp8vOv1Obwk/XnNCXlcYMh1zjcj4rniPHxGse6+rq/21HDgLPJvaVqRR2PJN4uXIO/PEV0s38o62vbk7+A58nV9UwZkwCnlMWmOIFfsLifv7ELkzP0vOTL3WToqTdW+QG6alsgXY8OLv970rWyUvu+QHyN8I7nCsDi5AP0eeF9KqW6XoJRHyd+Z/Ej7J4uPp5ErK1sx+2B0vUnbdeTxXm4mnyRmkpuUf5k8YG3dx7anlH5NHpjubHIEeSa5qd5Y8l32Y+noy1mxNzmv7yZX4BcnH0AeJt8J2Sil1OmEUUS3dyNX3l8gXzBUvqNeD2JfHMi3JT/t5X7y3YaFyBcQZxdpaTp/y5RSGk1+stw3yQeASeQf91TyPpwKbFnThPc48oX4n8gHi4XIB/ZnyE9m2CylVG+A/u78LirNeafQ3ACDtf2aZxZpvQzYPqX03XoL9fXvPqX0NfLYYg+STzqDyUHKr5IvUOr+NlotpfQH8gXI+eQm+4uSv6O7yPvzgdTEuGTFHdxK68tPNpjnenLF4FZyOV2Q/P2cAbyHfEep11JKZwN7AXeQz0vTyJWQ/VNKX2pm3Q22N1DKwu3ki9I/0TE2xpLk88ptRXo2Tik1HBekqOxuTK7A/Idc/seSzwEjUkoN70qnlCaSB0A/jnyeaCfnx73A58mDbjbMi5TS58iB7tvJFbrB5L7+PwQ2TbOPSVBROdbc32B6dzxNboV0EbmSO4j8+zgT2CI1eBx8L4+7FBcbIyiC/eSB9ofTMWYEKaX/kM/ll5MvRgeRLxyuKNbZVZCkbF8nHzP+Rb4oXZJcCU/kPNsmpfTlrloONvH9klK6hHxO/i25dfEQ8mDIFxXL1nbjq16212U6civJnYv0NupCXyl/oxutZ05SSheQ60RfI5eJ58nf9+LkIPpfyL/ttVJK+9fm1QArK/0i5QdDvI9cP/wPuTzOJJ9X9kwpfb+FyetSb+v9PVj/GHLQaAh1hoYo5unL81Y7eSDvr5FbiSxEvvFxE7Bdqj+wfa8NwLLwffJ3OYZ8vKmcD74DrJ9S6vKCu1XHuybP4WUcB/vzmrOy3OHkYOBMcsB+OFUtQvu6vtpDd5F71jxIrjcsWmz/KmDXlNLhqU4r3IoW1tGg43rgoq7qCd3V1t5eVi8FSXOziPgF+QB2Zkqpt09p0Fwo8qCCY8gn55VT98fca2abO5NPgs+llNbo6+1p4IiIJ8iVzT1Tg+bcUm8UXRXOIXdT+Hyd6YPJF9KLkSvcA65FjeZPRYvsa4CbU0r1xmzpi22eRG4hcUlK6aD+2KZaLyKGkVtuTQXWTCm9ModFNB8pzpPPk1tWrZVSqvfkzB4ZkC2cJLXETuSTz+mtToj6V3GX45fkrq4Hz2F2qdciD9YdwAMGm1SmYoyMo8h3gH/UYLbNya1TrjfYpAHmd+RWDB+qjOUj9ZGditefG2xSHQeQe6hcWEawCQw4SQIiYnlyN5NfevKZb32f3OT2m8XdDakv7Fi81h07SGrCp8lP6vpFyg9qqadS/gZs9y3Nn4rxZ75FHvC4q0HxpWbtSB66wBvM6iTykxaPIzdAKK2e5kWFpMoYWAPtccTqRymlV4snX7ybPC7NmNamSPOilNI1eKxR32gHTiY/sauulNLpeJGlASql9MfiKV+LRcTgrsZnkXorpXQkcGSr06EBaWXyg5gencMYTz1iwEkDRkQcSx4orttSSsP6KC37kQd664kty2p6KLVCMdji9a1OhyT1VErp8lanQWpWSuknrU6DpPlTSul54KSy12vASQPJ4uQ+owPBEHqelkF9kRBpXpRSGoUtXSRJaomU0kn0wcWlJFXzKXWSJEmSJEkqlYOGS5IkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKtXgVidAkiR1iIhRwE4ppbZWp6VWRJwEnAjsklIaVfV5O3BHSmnnmvmHAacCuwErkW90DU0pje+fFEuSJKlVDDhJktRNRWCl2jvABOBh4GLgkpRS7Tz9kqauAlQRMQYYDqyZUhrTPykDcp68H7gSeBpoB6aVFVSLiCeBdYG/p5S2ay6ps9bZ0+/v4JTSxWVsu2yVfK75eDL5u7geODOl9Gad5caQy0tFOzAReAy4AvhlSmlmRPwI+BZwWkrpm3NIy/nAYcDRKaWze7M/ZYiIrYB9gU2B9wArAi+klFbt5fqGAMcBnyTn2URgFHBiSunxBsusCnwP+CCwLPAScANwckppXG/SIUnSQGTASZKknju5eF0QWId8AbsTsAXwpVYlqoU2AKZUfxARCwEjgL+mlD5TM63pDUbELuRgUzuwbURsnFJ6pOkVd3y31b4KLAX8FBhfM+2hErbZ1y4BxgBtwMrAPsBJwF4RsW1KaXqD5Sr7OwhYE/gIsC25xdpHgPPJwZaDIuI7KaUZ9VYSEYsB+wFvAZeVsUNN+DRwFDADeJwccOqViFgYuBXYHrifnF+rAR8HPhwRu6aU/lGzzNrAPcAKwI3AE8BWRZo+GBHbp5TG9jZNkiQNJAacJEnqoZTSSdXvI2J74G/AERFxZkrp2ZYkrEVSSk/U+XgYuQvdi3202c8Vr6cB3yzef6XZldZ+twARcRA54HR2P7cQK8vFNV0gjyO3ytsM+BQ5IFVPp/2NiI2A0cC+EbFTSumOiPgrObC4J/C7Buv5JLAE8JsBEEy5mLy/j6aUpveiRVu1r5GDTdcC+6WU3gGIiKvJLZYuiohNKp8X/o8cbPpKSuncyocRcRZwNPBD4AtNpEmSpAHDgJMkSU1KKd0dEU8AGwKbA50CThGxNfB1YAdgGeAV4BZyF5q+Csh0KSJ2Bm4nt+j5M/B9YEtykOge4PiU0v3dXFenMZxqumR9NiI+W/x/CfDZmuUqZhsDqovtLUtuVfYU8B3gIGD/iPhGSmlad9ZRhqouawuTW/p8BlgDuDKldFCjMa+KZdcgl5NLUkoH1UxblNziZT86WnH9GzgnpXRls+lOKY2NiBuAI8jfeaOAU+1yjxb7vDu5Vc4dwK/IAafDaRxwOrx4/VXvU12OlNJDZawnItroCAx9ozqolFK6MSLuBN5LLh+3F8usRe5iOgY4r2aVJ5KDpgdExDEppcllpFOSpFbyKXWSJJWjMh5Rp25FEXEwcDf5Iv124Gxy95vDgPsjYvV+TGM9W5PHnHmLfBH8R3KXqTsj4r29XOfZ5O5FAP8iB7VOphinBniumHZy1d/FPVj/Z8lBnotTSjPJ4woNJXdlaoXryMGbe8j7/u/erigilgbuAn4EvA1cRA4ILQ/8JiJ+0GRaK+qW1x4sVwkW3gi8Cry/XlmOiI3JZezJlNIdvUnoALU2sDp5v+q1aPxj8bpr1WeV//9S0+qJlNIk8nFiUWCbktMqSVJL2MJJkqQmRcSOQADTgfuqPl8P+CW5RcNOKaUXqqbtSh7/5afk1jqt8kHgyymln1U+iIi96egSFLUXx3OSUjq7aMFzFPBQTTe1G4rWVcPrdV/rpsPIA7ZfWrz/Nbl70+dozRhBw4GNU0qvl7Cus8mDWX8zpXRa5cOIWIT8nXw7Iq5tpqVORCxPHscJcnCru8ttRMcg5P8ASCnNiIiLgW8Ah5DHhqpWad10fg+2cxC5pVh3jWnBwO2VgciebDD9qeJ1vR4u8/5imZFNpU6SpAHAgJMkST1UdJWCzoOGtwHHppReqpr1i8U8R1UHmwBSSrdFxO+BPSNiiaKFQys8TR5XZpaiS9Ad5ODCe8ldpwaEIri3AbmVyPMAKaVHIuIBYIeI2KDR08H60AllBJuKroL7A/dXB5sAUkrTIuKbwAfIA18/1INVH1QE+SqDhu9Lfjrab8lPq2vkqxExns6Dhg8Brk8p3Vk13/nkLqMHR8T3qsYyWrjYn+l0s9teJb3M/nS9rtxBz1rIlWGp4nVCg+mVz5duchlJkuZaBpwkSeq5E2vetwOHppR+XfP5tsXrThGxZZ31rEC+mF8P+Ge5Sey2Oxu0YBpFvuh/DwMo4ERHi5navL6YPAj24eTWTv3pvjnP0i1bkstDe1VQs9qCxesGPVzvZ+t8dnFK6eA5LHdU8doOvEkeaPxy4BfVM6WUni7GdtqFHBCrdCf7KHnMsqtTSq91N7HdHctrgKvtethXy0iSNGAZcJIkqYdSSm0w63Hv2wIXAr+IiOdSSrdVzbps8fr1Oaxy8SaS0w60RcQCXXR9q4zZWG/6Kw2Webl4XarB9H4XEUOBjwHjyd3Lqv0GOAM4MCK+lVJ6qx+T9vKcZ+mWSnnZsvhrpKflZZeU0qiIWJAcrPoJudXTMymlrsaEWrMHT+X7FTngdBgdAafDitdud6ebi1RaIzX6fSxZM19vl5Ekaa5lwEmSpF4qniT114jYE3gAuKQY82hKMcusC8yU0sQ+SsYEchecZYHZWpEUT9Napng7vs7yKzZY77Cq9Q8UBwKLFH9TI6LePMuSW9b8pr8SlVJq1CKlEuCrV99aus5nlbz+SUqp9FZaKaUZwMNFeX0MODkibk4pPVjC6n8HvE7uIroiOSi2M/AMcFsXy81mLhnDKRWv6zWYvm7xWj1eU2+WkSRprmXASZKkJqWUHo6I88mPST8a+GEx6V5gc/I4SDf30eb/Re76ti3w+zrT3wUsRr4orxf02qFB66idi9cyghG13gaIiEEppbd7sFylO92VwJQ605cit4A6nH4MOHVhXPG6Wp1pW9T57D5ykKq3TwfslpTSlGI8qKuA04ARJaxzekRcAhxD7sI3lNxF7PwuAnKNHMTAH8PpGeC/wHoRsWadJ9XtXrxWB9tuL17fX/ubi4glgO2BqeTjhiRJc70F5jyLJEnqhh8A04Bji65fAD8jP3b+J8UT6zqJiIUiotngwsXF6/ciYuma9S9MDihUz1drXeCImuX2Jl/wPw3cWW+hJo0tXlfv7gIRsR2wEfB4SunTKaXDav+A/YDngJ2r8zsiDoqI9uJpav2pMrbTwREx6yZfRKwGfLd25pTSq8AVwBYRcUL1MlXLrh0Ra5aQtt+Sx2R6X0TsUsL6oKPr3OHkoNEMehEISintnFJq68HfziWlv64iz9cvuiRW0thOx1hWp0XEAlXz700OGj5G1fhnKaVngL+QW28dWbOZk8mB4UuLlpOSJM31bOEkSVIJUkovRMQvyQMtfwP4VkrpiYg4BLgIeDQi/kTuLrMgOdjyXnI3uPWb2PQl5IGaPwk8WTz57mVy17IPFdu5AzilwfJ/As6MiN3JraXWIT+NbBp5IPRG40I1YyTwceB3EXELuVXHcymly7pY5nPF6wWNZkgpvRMRvwZOIgc9KmNnVYIBM5tJdE+llP4REX8DdgTui4jbyF0Y9wT+TP2WT18iBwG/BxwQEXeRx9lamTz+0pbAp4DaFjU9TVt7RJxIfkrdD4Htmllfsc5Utb8A16WUGo0R1hIRsT5wXM3HQ2uCkcfWPHVwJDCc/KS+MVWfnwXsQW5V94+IGEn+vX2c3ALvkDq/nyOAe4BzImI34HFga/L4V08Cx/d65yRJGmBs4SRJUnl+TL7Q/Eoxjg0ppcvJ3equIHdv+xL5UfHrANdS07qop4qWFp8GDiC3WNkH+CY5APW/YnsjuhhE+x/k7nMLF/PuTu4GtGNK6W/NpK0LF5DzailycO77wKGNZo6IpcgX8dOBS+ew7ovI3dI+GxELFZ9tUrxe1USae2tv8v6uCnyZ/NS/b5C/o9kU3R53KuZ9nTwe1dfIAYlJ5C6bt5aRsJTSDeSnI25bjOtUhl81+H+gGEbu8lf5A1i05rNuDcpe/KbeRw4OLk3+bkaQB7TfMqX0jzrLPEPuTnkxOdB0DLA2cA6wbUppbO0ykiTNrdra233yqiRJ85uI2Jk8pszJKaWTWpuavhURDwAzU0pbtTotkiRJ8wu71EmSpHlW0Trq3eSWQpIkSeonBpwkSdI8K6U0ARjU6nRIkiTNbxzDSZIkSZIkSaVyDCdJkiRJkiSVyhZOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVINbnQCpv4wbN6690bQlllgCgEmTJvVbeuZ35nn/Mr/7n3ne/5ZefXXa3nyT9sUXZ/x//9vq5MzzLOP9zzzvX+Z3/zPPmzd06NC2VqdBqrCFkyRJmie0vflmp1dJkiS1jgEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVanCrEyBJZbl1ZPus/0fs1tbClEiSJEnS/M0WTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlGtzqBEhSX7h1ZHun9yN2a2tRSiRJkiRp/mMLJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSDW51AiSpP9w6sn3W/yN2a2thSiRJkiRp3mcLJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUg1udQIkqb/dOrK90/sRu7W1KCWSJEmSNG8y4CRJTRo9ejSXX345jz/+ONOnT2f48OHsu+++7L333rS19SyYdffdd/PII4/w2GOP8fjjjzNx4kSGDRvGDTfc0KP13HTTTfzgBz8A4IADDuC4447r0fKSJEmS1AwDTpLUhBtvvJFTTjmFBRZYgM0335xFF12U++67j1NOOYV///vfnHDCCT1a34knnsibb77ZVJrGjh3LOeecQ1tbG+3t7XNeQJIkSZJKZsBJknrpxRdf5IwzzmDQoEGce+65vOc97wHgtdde4/Of/zw333wz22yzDSNGjOj2OnfZZRdWX311NthgAxZZZBEOO+ywHqfrtNNOY/r06ey+++7ccsstPV5ekiRJkpplwEnSXKt2LKYy1tOT8ZyuuuoqZsyYwcc+9rFZwSaA5ZdfniOPPJLjjz+eyy67rEcBp+OPP37W/2PGjOn2chV//etfueOOO/jyl7/MlClTery8JEmSJJXBgJM0H9hmm20YNmwY1113HVdccQU333wzL7/8MkOHDuXDH/4wBx98MIMHD+all17i/PPP5x//+AeTJk1ijTXW4POf/zzbb7993fWOHTuWyy+/nHvuuYeXX36ZwYMHs+666/LRj360bpDloYce4rbbbuPBBx/ktddeY/LkySy33HJstdVWfPazn2XllVeebZkvfvGLPPjgg/zud79jzJgxXHLJJTz11FMArLzyJrxvxOdZZdUNys2wbrrzzjsB2G233WabtuOOO7Lwwgvz5JNP8sorr7Diiiv2eXomTJjAmWeeSUTwyU9+kosuuqjPtylJkiRJ9SzQ6gRI6j/f/e53+fWvf82qq67KVlttxeTJk7nwwgs57bTT+N///schhxzC/fffz0YbbcQGG2zAk08+yTe+8Q3++c9/zrauxx9/nP33358rr7yS6dOns/XWW7Phhhvy5JNPcsIJJ3DuuefOtsy5557L9ddfz+DBg9l8883ZaaedGDx4MDfeeCMHHXQQzz33XMO033DDDRxzzDFMnTqVbbbZhhVXXJGnn/4HF15wBK+9NqbMbOqWyZMn89JLLwEQEbNNX3DBBVlzzTUBZgXI+tpZZ53FxIkT+fa3v82gQYP6ZZuSJEmSVI8tnKT5xMsvv8zCCy/Mb3/7W5ZffnkAXnnlFQ488EBuuukmHn74YXbZZRe+9rWvMXhwPjRcd911nH766Vx44YVsvvnms9Y1efJkvvnNbzJu3DiOOeYYPvrRj7LAAjl+/eqrr3LsscdyxRVXsPXWW7PVVlvNWu7QQw9lo402YqmllmKJJZYAYOLEidxwww2ceuqp/OQnP+Hss8+um/6rr76a008/nR122AGA9vZ2vnLUaYy+73ru/NvlfOSj3+k0/5mn78v48S/3KI922fVQRux2eLfmrQSbllhiCRZddNG686ywwgo88cQTs+btS3fffTd//vOf+cxnPlM3ACZJkiRJ/cmAkzQf+drXvjYr2ASw4oor8sEPfpCrr76at956i6OPPnpWsAlgn3324Ve/+hUPP/wwM2fOnDXtpptu4tVXX2Xvvffm4x//eKdtrLDCCnzrW9/i4IMP5rrrrusUcNpuu+1mS1NbWxv77rsvt9xyC/fddx+TJ09mscUWm22+T3ziE7OCTZXldnvf4Yy+73qe/c/sLbA22ngXpkye0IPcgWErrdvteSvjIy2yyCIN5xkyZEinefvK5MmTOeWUU1hllVU4/PDuBcwkSZIkqS8ZcJLmE5VubLVWXXVVADbffHMWXHDBTtMGDRrESiutxBNPPMH48eNZbrnlALj33nuB/ES1etZff30WXXRRHn300dmmvfHGG9x111288MILvPnmm0ydOnXW5++88w7PP/983RY622677WyfLbbYUIYMWZJJk8bONu2Du3+lbtrK0t6eBxpva2s8yHhlnr52zjnn8Nprr/HTn/60ywCYJEmSJPUXA07SfGLZZZft1HqpotIKZ4UVVqi7XGX69OnTZ31W6SL21a9+tcttvvXWW53eX3fddZxzzjmzfV5t8uTJdT9vNOj2wgsvytSpE7tMR2+MGTOGSy+9dLbP99prLzbddNNZ3egqAbN6KvvZqMtdGe6//35uvPFGPvShD7H11lv32XYkSZIkqScMOEnzia5a4nRnerVKy50dd9yRxRdfvFvLPP7445xxxhkMGTKEb33rW+y0004sv/zyzJgxA4ATTjiBW2+9dbZWQePG59e7/97G0KHtjNite+n80x/P6XGXuvU33JERu+0M5Cfw3XLLLbPNs9lmm7Hpppuy0korATBp0iSmTp06KzBX7dVXXwVg2LBhPUpHT1SelPf000/zxS9+sdO0SmDwL3/5C0888QSrr746X//61/ssLZIkSZJUYcBJUo+tuOKKPPfcc3zyk59ks80269Yyt912G+3t7Xzxi19k7733njVoeCXg9Pzzz5eaxkcfub3Hg4YvPXQlYGcgdzGsdB2sZ/HFF2ellVbipZdeIqXEpptu2mn6jBkz+M9//gPAuut2f2yo3nryyScbTnvllVd45ZVXmDix/JZgkiRJklSPASdJPbbNNttw3333MWrUqG4HnCrBjnpd45599tkuAya9cczXry91ffXssMMOXHPNNYwcOXK2gNOdd97JW2+9xXrrrdenLZyOPvpojj766LrTzj//fC688EIOOOAAjjvuOCC3yJIkSZKkvrZAqxMgae6z9957s8IKK3Dttdfym9/8hpkzZ3aa3t7ezoMPPsjdd98967Phw4cDcMMNN8xq1QR5sPDvf//7vP322/2T+BLtt99+DB48mBtuuIGHHnpo1uevv/465513HgD777//bMudfPLJ7LffflxzzTX9lVRJkiRJ6le2cJLUY4stthhnnnkmX/va1zjnnHO44oorWHvttVlyySV57bXX+N///scbb7zBfvvtx/bbbw/AHnvswVVXXcU999zDRz/6UTbddFPeeustRo8ezfLLL89OO+3EHXfc0eI9g1tHdh5Dqqsxo1ZddVWOOeYYTjvtNI488ki22GILhgwZwujRo5k8eTK7774773//+2db7uWXX+a5555j/Pjxs0276KKLZgXqKgO1jx07lkMPPXTWPIcccsisfJUkSZKkgciAk6ReWXfddbn88su55ppr+Nvf/sYjjzzCzJkzWWaZZVhrrbXYf//9ed/73jdr/iWXXJJf//rX/OIXv2D06NGMGjWK5Zdfnr322otDDz2Un/zkJy3cm97bd999WWWVVbjssst49NFHmTFjBsOHD2efffZh33337fH6nn/+eR599NFOn82YMaPTZ+PGjWs63ZIkSZLUl9pqnwglzavGjRvXsLBXBrB2fJv+090876rFUe20vtDdp+INdJbx/mee97+hyywz6/9xb7zRwpTMHyzj/c8871/md/8zz5s3dOjQeaPyqnmCYzhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKNbjVCZCknrh1ZHurkyBJkiRJmgNbOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSjW41QmQpFq3jmxvdRIkSZIkSU2whZMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklWpwqxMgSQPZrSPbZ/0/Yre2FqZEkiRJkuYetnCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlGtzqBEiaP/3xT9MBmDqtnRG7tbU4NZIkSZKkMhlwktRyt45sb3USJEmSJEklskudJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSOYaTJHVT7VhTDnYuSZIkSfXZwkmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSrV4FYnQJLmVreObJ/1/4jd2lqYEkmSJEkaWGzhJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKNbjVCZCkecGtI9s7vR+xW1uLUiJJkiRJrWcLJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpXIMJ0n9onaMoyGLtCghkiRJkqQ+ZwsnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSrV4FYnQJLmRbeObJ/1/4jd2lqYEkmSJEnqf7ZwkiRJkiRJUqls4SSpz1S38pEkSZIkzT9s4SRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKNbjVCZA0d/rvf//LJZdcwujRoxk3bhxLLrkkW2yxBYcccgjDhw/v1jpefXUMZ525HzNnTmfV1Tbi81+4YLZ5Jk54lZtvPpunn/oHbW1trLveNnzoQ19liSWXm23e6dOncs7Zn2KJJZfjc58/n7a2th7t0++u/T4PPngLu+x6KLvudljD+c48fV/Gj3+ZQw49jzXX2mzW57eNvIDbb7uw07wLLrgwZ5+1OKussgobbLAB73vf+9hkk00arnubbbbJafnd71h55ZV7lH5JkiRJGigMOEnqsQceeIBjjz2WKVOmsOqqq7L99tvz4osv8uc//5lRo0bx05/+lE033bTLdbzzzjv89uqTefvtGV3Oc9mlx/Lyy0+x5lqbA/DIv0fyxtjn+cIRv54toHT7bRcyadLrfHr/U3scbCrTMsuswvDh7wbg7XdmsuiQiTz55JM8/PDDXH311Wy22WZ897vfZdiwYS1LoyRJkiT1JQNOknpk2rRpnHDCCUyZMoXPfOYzHHnkkSywQO6de9NNN/GDH/yA73znO1x77bXAwg3Xc/fdVzFmzENsudW+jL7v+rrzPP7433j55afYZdfD2HW3QwH4662/5I5RF/P4439jww13mjXvq68+y9/vuZott9qXlVeO8na4F4YPfzcf+dgJs96P2C0Hv/75z39y1lln8cADD/CFL3yBCy+8kGWXXbZVyZQkSZKkPuMYTpJ65Pbbb2fs2LGsttpqHHHEEbOCTQB77LEHO+64I6+//jo33XRTw3WMe+NF/njLz9hgg/eyySbvazjfSy8mALbYcq9Zn2211b6dplXc9PszGDJkSd434vO92q/+sPnmm3PBBRew1lpr8fLLL/PTn/601UmSJEmSpD5hwElSj6SUAz3vec97GDRo0GzTt9hiCwDuuOOOhuu44YYf09bWxkc/+u0utzV1ykQAhgxZctZnQxZdCoApUybM+uyhh/7Es88+wAd2/zKLLLJ4N/ekNYYMGcJRRx0FwMiRI3nttddanCJJkiRJKp8BJ0k9MnXqVACWXHLJutOXWioHhJ588sm60+8f/Xv+88z97L77l1h6aNdjGC1VTH/ttTGzPqv8P3ToSgBMm/Ymf/7jz1hjjU3ZdNMPdns/+tOtI9s7/W299dYsvfTSvP322zzwwAOtTp4kSZIklc6Ak6QeWXrppQF46aWX6k5/8cUXAZgwYQJvvTWl07SJE17lz386l1VX24jttt9vjttab71taWtbgD/dcg4TJrzKhAmv8qdbzqGtbQHWXW9bII/pNGXKePbY6+tN7FX/W3fddQH4z3/+0+KUSJIkSVL5HDRcUo9sttlmXHLJJdxzzz2MHTu206DXM2fO5Oabb571fvpbU1h44UVnvf/9jacxY8Y09t7nuE5jPzUybNg6bL3Nx7j377/ljNP2nvX59jt8ihVXXJsXX0yMvu96tt1uP1Zcca1Z02fMeItBgxbs1jbquf22C7n9tgt7tWx3VQJ3EydO7NPtSJIkSVIrGHCS1CNbbbUVG220EY8++ihHHXUUxx57LOuttx4vvPAC5513Hi+++CILLLAA77zzDm1VAZ9//evPpHQ3O+18EMOGrdPt7X14j6NZa+0teObp+wBYd91tiPW3p729nT/8/nQWX2JZdtk1P8Hu8cfv5C9/+hmvv/5fBg9eiI023pU99jyWRRZZrEf7OGzYuqy00roNpz/66O1Mnz61R+us1d7eDkBbW1tT65EkSZKkgciAk6QeaWtr48c//jHf+MY3eOKJJ/jCF74wa9rgwYM5+uijOeuss2hra5s1gPfkyeO45aazWXa51dlp54N6vM0NNngvG2zw3k6f3T/6Rp7/36Ps96kfsvDCi/Lyy09z1W++xfDh72bE+7/IG2+8wMi//oq3357Bfp/8Qc+2t+GO7LrbYQ2nP/vsA00HnCZMyIOeNxoLS5IkSZLmZgacJPXYCiuswEUXXcQ999zDgw8+yJtvvsmKK67I+973PmbOnEl7ezurrbYagwcvBMBzzz3MlCnjWWjhIVx6ydEAs7q7TZmcu5S99uqzXHjBEQDsf8AZnbri1ZoyZQK3/uXnrLPO1my88a4A3HXnFQwevDCf+swpDBmyBJAHFL9j1MWMGPEFlll21b7JjF5ob2+fNaj62muv3eLUSJIkSVL5DDhJ6pUFFliAHXbYgR122KHT51dddRUAW2yxxWzLjB/3EuPH1R9s/K23pjDm2QcBeOedt7vc9p//9DNmzJjGHnseM+uzV1/5D8svP3xWsAlgtdU2BuCVV58dUAGn++67jwkTJjBo0CDe8573tDo5kiRJklQ6A06SSjNt2jQuufQa2traWGnlfWZ9vuGGO/H9H/6907xDFlkEgMceu4eLLjySVVfbiM9/4YI5buO///03Dz5wMzvtfBDLLrfarM/b2hZg+oxpneadPiN3e1ugbeA8kHP69Kn89Kc/BWDEiBEst9xyLU6RJEmSJJVv4FyFSZprPPPMM0yb1jm4M2HCBI4//njGvfECW261LyutvF7p233nnbf5w+9PZ+mlV2LHnQ7sNG3FFdfi9dfG8L//PlLM+w4PPfBH2traWKHqCXat9OyzD/CrX36O//znP6y00kocddRRrU6SJEmSJPUJWzhJ6rErrriCUaNGEREsv/zyTJw4kYceeohp06ax0ca78KEPH90n2/3Hvdfy8ktPsf8Bp7Pggot0mrbDjvvzr3/9mYt//RXWWntLxo97iZdffopN37M7Q4eu1CfpaeS55/7F7679PgBvvzOTqVMm8tJLT/Hmm2OB3N3whBNOYOjQof2aLkmSJEnqLwacJPXYTjvtxBtvvMEzzzzDI488wqKLLsq73vUu9t57b95h1z7Z5qRJYxn51/NZf/33EuvvMNv0FVZYkwM+exZ//csveOrJv7PIIouz9dYf5f0f/FKfpKcrb7zxAm+88QIACy64MAsvvDjLLLMym7xrNzbZZASHHLxJv6dJkiRJkvpTW3t7e6vTIPWLcePGNSzsSyyRB5qeNGlSv6VnXnXryO4dUypjOE2t6Zo3PxixW1u/b9My3v/M8/43dJllZv0/7o03WpiS+YNlvP+Z5/3L/O5/5nnzhg4d2v8VTakBx3CSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlGtzqBEjS/ObWke2z/h+xW1sLUyJJkiRJfcMWTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFINbnUCJM3dbh3Z3uokSJIkSZIGGFs4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcpBwyX1mAOFS5IkSZK6YgsnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUqsGtToAkzc9uHdne6f2I3dpalBJJkiRJKo8tnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJVqcKsTIEnqcOvI9ln/j9itrYUpkSRJkqTes4WTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQ+pU7SHFU/OU2SJEmSpDmxhZMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpHDRckgao6sHaR+zW1sKUSJIkSVLP2MJJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVAacJEmSJEmSVCoDTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSjW41QmQNDDdOrK91UmQJEmSJM2lbOEkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUjuEk9ZMXXniB888/n9GjRzNp0iRWWGEFdt11Vw4++GCGDBnSo3U98cQTjB49mscff5zHHnuMl19+GYDf/e53rLzyynNc/vHHH+eqq67iwQcfZNy4cSy++OKsuuqq7LTTTuy///692j/1rdoxtUbs1tbtZUePHs2VV17JI488wvTp0xk+fDj77rsve++9N21t3V8PwN13380jjzzCY489xuOPP87EiRMZNmwYN9xwQ8Nlxo8fz5133jlrmaeffpqZM2dy6KGHcvjhh/do+5IkSZLmDgacpH6QUuKLX/wiU6ZMISLYdNNNefTRR7n00ku55557+OUvf8liiy3W7fVddNFF/O1vf+tVWq644grOO+88ADbccEPe/e53M27cOJ555hluuOEGA07zmBtvvJFTTjmFBRZYgK233pqFFlqI++67j1NOOYV///vfnHDCCT1a34knnsibb77Zo2X+9a9/8cMf/rBHy0iSJEmauxlwkvrY22+/zXe/+12mTJnCEUccwYEHHgjAjBkzOO6447j77rv52c9+xje/+c1ur3PjjTdm7bXXZoMNNmD99dfnoIMO4o033pjjciNHjuTcc89l2LBhnH766ay77rqd0plS6vkOasB68cUXOeOMMxg0aBAXXnghW2yxBZMmTeK1117j85//PDfffDPbbLMNI0aM6PY6d9llF1ZffXU22GADFllkEQ477LA5LrPMMsvwkY98ZFZ5/eMf/8hvfvObZnZNkiRJ0gBnwEnqY3feeSfPPfcca6+9NgcccMCszxdccEG+9a1vsffee/OHP/yBL3zhCyy11FLdWmclaNUT06dP58wzz2SBBRbg1FNP7RRsAhg0aBAbbrhhj9ergeuqq65ixowZfOxjH2OLLbaY9fnyyy/PkUceyfHHH89ll13Wo4DT8ccfP+v/MWPGdGuZTTbZhE022WTW+7/85S/d3p4kSZKkuZODhkt97M477wRg1113nW28nOWWW45NN92UmTNncs899/RpOu644w7eeOMNttpqKyKiT7elgaFS9nbbbbfZpu24444svPDCPPnkk7zyyiv9nTRJkiRJ8zgDTlIfe+qppwBYf/31606vBH8q8/WV0aNHA7DVVlsxefJkrr/+ek4//XTOOussbrrpJqZOndqn21f/mjx5Mi+99BJA3QDjggsuyJprrgn0fdmTJEmSNP+xS53UxypPkFthhRXqTq98XgkO9JVnn30WgLfeeotPfvKTvPbaa52m//znP+fUU09l44037tN0qH9UytMSSyzBoosuWneeFVZYgSeeeKLPy54kSZKk+Y8tnKQ+NmXKFAAWWWSRutOHDBnSab6+MnHiRAAuuOACBg0axE9+8hNGjhzJlVdeyS677MLYsWM55phjGDt2bJ+mQ/1jTuUO+q/sSZIkSZr/GHCS+knt+E0V7e3t/bL9t99+e9b2zjjjDLbddlsWW2wx1lxzTX70ox+x3nrrMWHCBK677rp+SY/6VqVcNSp31fNIkiRJUtnsUic16Xvf+95sn7373e9m7733BmDRRRdl4sSJDcdIeuutt2bN15cq699oo41YZ511Ok1ra2tjr7324owzzuCBBx7o03SoHGPGjOHSSy+d7fO99tqLTTfddNb33dXYXP1V9iRJkiTNfww4SU265ZZb6n5eCTgNGzaMiRMn8uqrr7LuuuvONt+rr746a76+tNJKK/Hkk0+y0kor1Z2+8sorA9ilbi4xduzYumVvs802Y9NNN531PU+aNImpU6eyxBJLzDZvf5U9SZIkSfMfA05Sk+69994up6+77ro8+eSTpJTYfvvtZ5ueUpo1X1+KCO64445ZYznVmjBhAtAxro8Gts0337zLsrf44ouz0kor8dJLL5FSmm3Q+hkzZvCf//wH6PuyJ0mSJGn+4xhOUh/bYYcdALjttttmmzZ27FgeeughBg0axHbbbden6dhxxx0BePzxx+sOEv3Pf/4TgPXXX79P06H+Uyl7I0eOnG3anXfeyVtvvcV6661nCydJkiRJpTPgJPWx9773vay++uo8/fTTXHbZZbM+nzlzJqeccgozZ85kzz33ZOmll+603DXXXMN+++3HySefXEo61llnHbbffnsmTJjAWWedxcyZM2dNu+uuu7jllj/S1rYAK6+6L7eOdDDpecF+++3H4MGDueGGG2YFFAFef/11zjvvPAD233//2ZY7+eST2W+//bjmmmv6La2SJEmS5i12qZP62ODBg/ne977HEUccwXnnncdtt93GKquswiOPPMLLL7/M2muvzZe+9KXZlhs/fjzPPfccyyyzzGzT7r77bi666KJZ7yvd5I477jgWXHBBALbffnsOOeSQTst9+9vf5nOf+xw33XQT999/PxHB66+/zmOPPUZ7ezsf3P3LrLKKLZzmFauuuirHHHMMp512GgcffDBbb701Cy20EKNHj2by5MnsvvvuvP/9759tuZdffpnnnnuO8ePHzzbtoosu4u677wZg+vTpQG6pd+ihh86a55BDDpmt+2j19MrYUb///e87dQu88MILe7+zkiRJkgYUA05SP1h//fW55JJLuOCCCxg9ejRPP/00K6ywAvvvvz+HHHJIj58SNm7cOB599NHZPn/yySdn/T98+PDZpi+77LJcfPHFXHzxxdxxxx3cc889DBkyhK233pr1N/gUa6+zVc93TgPavvvuyyqrrMKVV17Jv//9b6ZPn87w4cPZZ5992HfffXu8vueff362sjdjxoxOn40bN2625eqV19dee43XXnutx2mQJEmSNPC1tbfbdUbzh3HjxjUs7JUneE2aNKnf0jPQ9Hc3uiGLLALA1GnT+nW784oRu7X1aH7LeP8zz/vf0KoWoePeeKOFKZk/WMb7n3nev8zv/meeN2/o0KE9qyRKfcgxnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcpBwyVpLlQ95lZPx3OSJEmSpL5mCydJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSOWi4NB+rHnhac6/a79FBxCVJkiS1mi2cJEmSJEmSVCoDTpIkSZIkSSqVXeqk+Yhd6CRJkiRJ/cEWTpIkSZIkSSqVASdJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaVy0HBJPfbSS880vY5FFl4YgGlvvdX0uuZVK620dq+Wqzc4/JBFpgOww/ZNJUmSJEmSusWAkzSP64sn0331K1uVvk7N7uprxrY6CZIkSZLUKwacJGk+Uh2AHLFbWwtTIkmSJGle5hhOkiRJkiRJKpUBJ0mSJEmSJJXKLnWSeuzsc+5reh0OGt56teN72cVOkiRJUlkMOEnqsd4+Pa3akEUWAWDqtGlNr0uSJEmSNLAYcJLmAX3xJDpJkiRJknrLgJMkCeg6cGl3O0mSJEk9YcBJGqC6Gl/HFk3qb9VlzuCTJEmSpDkx4CS1UE8CRwaZJEmSJElzi7b2di9ipZNPPrkd4MQTT7TpRj8xz/uX+d3/zPP+Z573L/O7/5nn/cv87n/muTRvWaDVCZAkSZIkSdK8xYCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqXxKnSRJkiRJkkplCydJkiRJkiSVyoCTJEmSJEmSSmXASZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkq1eBWJ0DqSxGxHfAdYBtgEeBp4CLg3JTS291cx7rAR4APAOsCKwLjgHuBs1NKt3ex7GeBI4ENgbeBB4EzUko39XafBrKS8ntB4AhgU+A95LxbEDg8pXRBg2UOAn7dxWq/mFL6Rff2Yu7SqjyvWtYy3sP87s265vUyHhGrAt8DPggsC7wE3ACcnFIa15frKfM7nVu0Ir8jYg3g2S5Wd3VK6ZPd3om5TBl5HhEfA3YiH6vfDSwBXJFS2n8Oy1nG+yG/LePN5XlELAvsC3wY2ARYBZgO/Jt8/vt1SumdBsvOd2VcmlsYcNI8KyL2Bq4DpgFXA28AewI/AbYHPt7NVX0f2A94DLilWE8AewF7RcRRKaVz6mz/DOAY4HngfGAh4JPAHyLiyymln/V+7waeEvN7MeDs4v9XgJeB1bq57I3AQ3U+v7+by89VWp3nlvFe53cz65rnynhErA3cA6xA3r8ngK2Ao4APRsT2KaWxfbGeMr/TuUUr87vwL/JFaK1Her43c4ey8px8Qf1u4E3ycXf9bmzbMt6P+V2wjPcuzz8O/JwcqLod+C/5Ju9HgAuA3SPi4yml9pptz3dlXJqbGHDSPCkiliRfAL8N7JxSur/4/ATgNuBjEfHJlNJV3Vjdn4BTU0oP1mxjJ+BW4PSIuCal9FLVtO3IF+LPAFtW7uxExOnAP4EzIuKmlNKYJnd1QCg5v6cAHwIeSim9FBEnASd2Myk3pJQu7mn650atznPLeO/zu8l1zYtl/P/IFylfSSmdW/kwIs4CjgZ+CHyh7PWU/Buam7Qkv6s8lFI6qdepnzuVledHkwMfT5Nb3jRsYV2s3zLej/ldxTJe6GGeP0m+mXtzdUumiPg2cB/wUXLw6bqqafNrGZfmGo7hpHnVx4DlgasqJx+AlNI08h0rgC92Z0UppYtrg03F53cAo8itOrarmVw5qf6wuhlxcfF9HrAwcHB3tj+XKDO/p6eU/lgdwFNdrc5zyzi9y++S1zVXi4i1gPcDY8jlptqJwGTggIhYrA/WM999Dy3O7/lSmXmVUro9pfRUbQuPLljGO+vr/J4vlZXnKaXbUkp/qO02l1J6Gah0Gd+5ZrH5roxLcxsDTppX7Vq8/qnOtL+RW3RsFxELN7mdGcXrzB5s/48188wL+iu/52TTiPhqRBwXEQcU4wnMq1qd55bxDj3N72bWNa+V8Upe/KXORcYk4G5gUfK4HGWvp9W/oVZoZX5XrBwRn4+Ibxev7+rpTsxlysrzZrZtGadf8rvCMl4oMc97U9+eV8u4NFcx4KR5VRSvT9ZOSCnNJA/qOBhYq9cbiBgO7EY+mf2t6vPFyAMdvtmgxchTxet6vd32ANTn+d1NR5H77P8YuBQYExG/iIhF+ni7rdCyPLeMd9aL/G5mXfNaGW+YF4XulqXerGegHLf6Uyvzu2IEubXCD4vXf0XE7RGx+hy2ObcqK89L3bZlvE/PT5bxzprK84gYDBxYvK0NLM2PZVyaqxhw0rxqqeJ1QoPplc+X7s3KizslV5C7DZ1U8/SNPt32ANXqfX4W+DK54rEYsDLwCXLz7s+Tn1Qyr2llnrf6+26FMve5N+uaV8t4Wfnam/VYjmfXl/k9hfwQjs2BocVfZVycnYGR82gXPI/V/auV+2wZr6/ZPD8F2Bi4JaX0537etqQmOWi4BqyIGAMM78Eic3w0cJW24rXH/fIjYhBwGfnJF1cDZ/R0Hb3ddl8aqPndHcV4WndUfTQFuCYi7iU/LeZTEXFqSulffbH93pqb87ybLOO9M9u65tYyXoKy8rU36xkIv6H+1mf5nVJ6FfhuzXx/i4j3A3cBWwOHAT9tcttzm1aWM8t4iSzjDTVT5/4K+QElTwAH9Oe2JZXDgJMGsmfIjzjtrher/q/c0Viq3ozAkjXzdUsRbLqc/IjV3wL71xlMck7bntPdmFYZcPndrJTS/yLiFuAzwI7kC/OBZG7Nc8v47HqS36Wtay4o43NSVl70Zj0D8rjVx1qZ33WllGZGxAXki/Edmfcuxgfysdoy3g8s472ucx9JzqvHgN1SSm/017YllceAkwaslNJuzSwObEHuL/7P6glFX/A1yQMP/qe7KyyW+w052PQb4MCU0tuzbTilyRHxArBKRKxUZ4ybdYvXRn3dW2Kg5XeJXiteB1wz9rk1zy3jTed32d/dgC3j3ZCK10Zje3S3LPVmPQP5uNVXWpnfXZmby/CclJ1XPd22ZbyzVp2fLOM9yPOI+Cp5vMJHyMGmV7vY9vxWxqW5imM4aV51W/H6wTrTdiQ/LeOelNJb3VlZRCwEXEsONl0KHFAv2NTN7e9eM8+8oNT8LtnWxeu8VtlodZ5bxjv0NL/L/u7m5jJ+e/H6/ojoVCeJiCXIXZenAvf2wXpa/RtqhVbmd1cqT6+aG8vwnJSdVz1hGa/SD/ndFct4N/M8Ir5JDjY9BOzSRbAJ5s8yLs1VDDhpXnUt8DrwyYjYovJh8SSnHxRvf169QEQsFRHrR8RKNZ8vDFwP7A1cCBxc+9jXOn5RvB4fEUOr1rUGcCTwFvDrnu7UAFZafvdGRLy3zmdtEfEtYNsibfUemTs3a2meYxkHep3fvVnXPFnGU0rPAH8B1iCXm2onk1sDXJpSmgwQEQsWebp2M+sp9Ph7mNu1Mr8jYuvi5k0nEbErcHTx9vLe7dnAVVae95JlvLM+zW/LePN5HhEnkAcJ/ye5ZdPrc9j8fFfGpblNW3u7Y6hp3hQR+5BPRNOAq4A3gL3IT3m6FvhE9fhLEXEQ+QL5kpTSQVWf/xo4iHxC+z/qDzw4KqU0qmb7ZwJfA54vtrcQsB+wLPDllNLPmt7JAaSs/C6mHQesX7zdFHg3cA8dj9a9K6V0QdX87eSm2qOBF8h9+bcnP9VkCrBvSukvZe3rQNHKPC+WsYz3Pr97uq55towXFx33ACsANwKPk1tt7ULe5+1SSmOLedcgP7HvuZTSGr1dT9Uy+9CD72Fe0Kr8johRwEbAKPIxA+BdwK7F/yeklCoXiPOUEvN8H2Cf4u0w4APkFjN3Fp+9nlI6ts4ylvF+yG/LeHN5HhGfBS4G3gbOpf64S2NSShfXbHsf5rMyLs1NHMNJ86yU0g0RsRNwPPBRYBHgafIF8jk9OPmsWbwux+xPH6k2qmb7x0TEw8CXgM8B7wAPAKenlG7q7n7MLUrMb8hNo3eq+Wy74q+iOvhxBrAVuVK3DDmv/wucB5yVUpoXm7C3Os8t403kdy/WNc+W8ZTSM8Wd6e+Ry+GHgJeAc4CTGwwUW8p6Sv4NzRVamN+XAfsCW5K73S4IvEJ+AMfPUkp3Mo8qK8/JNwM+W/PZWsUfwHNAp4CTZbxf89sy3lyeV+rbg4CvNpjnDnJQqnrb810Zl+YmtnCSJEmSJElSqRzDSZIkSZIkSaUy4CRJkiRJkqRSGXCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJVqcKsTIEmSOkTEKGCnlFJbq9NSKyJOAk4Edkkpjar6vB24I6W0c838w4BTgd2Alcg3uoamlMb3T4olSZLUKgacJEnqpiKwUu0dYALwMHAxcElKqXaefklTVwGqiBgDDAfWTCmN6Z+UATlP3g9cCTwNtAPTehtUi4iDgF/XmfQm8BRwLXB2SmlKbxNclVfddXJK6aTebq8vRcTFwGdrPp4KjAH+CJySUnqtznKjgJ1qPn4TeBK4DvhJSmlqRHwO+CXw25TSfnNIy/HAD4CfppS+2tN9KUtEbAB8EtgUeA+wWjFpwZTSzP5aV0RsAnwd2AVYgXwceRy4MKV0aU/SIUnSQGXASZKknju5eF0QWAfYl3yBvgXwpVYlqoU2ADoFeSJiIWAE8NeU0mdqpjW7vX8BNxT/LwAMA/YEfgh8MCJ2SSm93ct1nw0sXfPZQeQg1CXkYE21Ub3cTn+6EXio+H9F4EPA14CPRsTmKaWxDZar7G8bsCrwEXIe7x0ROwC/Ac4E9omI5VJKr9dbSUS0AYcUb89vem+a8wHgu8Db5CDlNGCR/lxXETi9gPybuYmcx0sDG5O/GwNOkqR5ggEnSZJ6qLZFS0RsD/wNOCIizkwpPduShLVISumJOh8PIweDXuyDTT5U5ztYmtzS7L3F36jerDildHbtZxGxMzngdHF1V8K5yA0ppYsrbyJiEeBe4N3kAOnJDZa7uKbr5HeAB4GtgE+nlC6JiCuBw4EDgbMarGdXYC3gnpTSo83tStP+CPwdeLhopTWGnrVoa2pdEbENOdj0CPDBlNLLNdMX7GVaJEkacAw4SZLUpJTS3RHxBLAhsDnQKeAUEVuTu8/sACwDvALcQu6O1RcBmTkqgii3k4MNfwa+D2xJDhLdAxyfUrq/m+vqNIZTzYX3ZyOi0q3rEqq6eNV0UZxtDKieSCmNj4jR5G5Ny/d2PT1R1WVtbeDD5MDLusA/Uko7V3UBPLg64FO1fKOxrwYDnyMHcTYk19cScCHwfymld5pJd0ppWkRcQQ44bdmD5V6KiN8BXyQHnS4BfkXe78NoHHA6vHj9Va8TXZKUUmrxuk4DBgH71wabinXOaDphkiQNEAacJEkqR2U8ok4XjBFxMLkb0VvA74H/kYMShwF7RsQ2KaX/9mdCa2wNfAv4K3AeuYvgR4AdI+L9KaU7e7HOs4E1gKPo3P3tIXL3oYPIAanqljVjerGdWSJiKXLw5B1yK5z+9FNyq6qbyYHE3nbnq7Rw+QO5u1Yid1ubRh7r51zy93VAk+mFBuW1B8tVxg67PyIeBN4TEdunlO6unjkilgX2IY9RdE3vkzv3i4hVyeXkfuDRiNiFHKBuJ/82bm82mChJ0kBiwEmSpCZFxI5AANOB+6o+X488qPIY8iDZL1RN2xW4lRys2Lc/01vjg8CXU0o/q3wQEXuTg0QXRUT09CI4pXR2RKxBDjjVdn+7odJFrYnBtjctnpgHuUXWisAewFLAV1JKT/dyvb21GfCekrpSHk8ONv0M+GplLKqIGERuIXRIRFybUrqxtxuIiCHA/sXbu3qw3ErkYCTAP6omnQ/8H7kl0901ix0ILAxc0N3B3CNiH/JA3N01vl5XyAGo0prsKeA2YOea6f+OiI+0oPxKktQnDDhJktRDVcGO6kHD24BjU0ovVc36xWKeo6qDTQAppdsi4vfkVk5LpJQm9X3K63qaHCyYJaV0Y0TcQR4I/b3AHa1IWBfeXfzVupJ8Id/fTisj2BQRC5DHVHoZOLp64POU0tsRcQxwMPAZ8kDg3bVPEQCE/ES0PchdD/8G/LyL5Q4qgoPVg4YvTQ6qXlU13xXA6cDHI+KolNKEqmmHFq896U63D7M/Xa8rz5Fb1Q10KxSvnwBeJ+fnSHIX0BPJLddujohNUkrTW5NESZLKY8BJkqSeO7HmfTtwaErp1zWfb1u87hQR9cbKWYE8nst6wD/LTWK33dmgBdMocsDpPQy8gNMlKaWDKm8iYkXgfeTWYntExM4ppQf6MT33zXmWblkPWJbcAuY7DZ7mN5X8VMCe2Lv4q3Yr8OE5jBlUHfSZXKTrOuCs6uVSShMj4rfkYNinKYJYEbEdsBF5TKuHu5vY4rs9qLvzz0UGVb0ellK6qXg/sRjnbAPyky4/Sg6eSpI0VzPgJElSD6WU2gAiYjFyUOlC4BcR8VxKqbqFzbLF69fnsMrFm0hOO9AWEQt00fVtgeK13vRXGixTGdB4qSbS1i9SSq8AVxRdxc4HfkzultZfZhv8uZcq5WVdZg9qVutpeTk4pXRx0S1vLfIA8fuRA0OHdbHcLj14Kt+vyAGnw+hoNVVZ9/k9TO+8alzx+hZ5rK9ZUkrtEXEjOeC0FQacJEnzAANOkiT1UkppMvDXiNgTeAC4pBjzqDJWTaVr0VIppYl9lIwJ5G5OywKv1U6MiDbyk/EAxtdZfsUG6x1Wtf65RWVcoa36ebvtDT6vBPhmq29FxNJ15q/k9fUppY/Umd6UooveUxHxafKg7odGxO9TSr8vYd33RsTDwGYRsRm5NdQngIl07n43R/PwGE6Vp9pNahAcrgSkhvRTeiRJ6lMLzHkWSZLUlaK70PnkcW6Orpp0b/H63j7c/L+K120bTH8XsBgwpkHQa4di7KBaOxevffHEt+qBsMs0tHgdKPWbSgBhtTrTtqjz2RPkoOA2xdPq+kQR7DiqeHtaid9DpSXTYeSudYsBvykCsz2xD7mFV3f/vtpkuvvLw+Sxm5YruoHW2rh4HdNvKZIkqQ8NlAqZJElzux+QH19/bERUAh8/Iz92/ifFE+s6iYiFIqLZYNTFxev3alvNRMTCwGk189VaFziiZrm9yeM3PQ3c2WT66hlbvK5e1gqLoEkliDKqZtrOEdEeEaNql+tj95NbOX06IhatSs8ydHwvs6SUZgLnAisB5xRdBDuJiJUiYsNmE5ZS+gdwE/npigc2u77C5eQxpj5DR5nqcXe6lNJBKaW2HvytUVL664qI1SNi/ervsDeK7/eXxdvTqgO9EbEJedyqmcC1zWxHkqSBwi51kiSVIKX0QkT8khz0+AbwrZTSExFxCHAR8GhE/Al4kvzkutXJLZ9eA9ZvYtOXkMcr+iTwZPHku5fJXew+VGznDuCUBsv/CTgzInYnt5Zah/z0rGnkgdAbjQvVjJHAx4HfRcQt5CDFcymly7q5/KZVTwqEPPj6ruTgyevk/K9WubCf2esU90JK6aWIuIL89LGHIuJmYEny9/I38oDstb5PfgLfF8hPMLwNeIG8j+sC2wPHA4+VkMTvAh8GToyIK5p9MlpKaXxEXEMOYL0L+Gc/D94+RxGxHHBG1UfLFa8XRkSla+QpKaUnqua5lByA3YWqYGYv1/UjYDdyHm1SBEGXJw8UvghwTErp6d7tnSRJA4stnCRJKs+PgSnAVypdZlJKlwObkx8d/y7yY+/3Jwd2rqWmdVFPpZTayd2XDiB32dkH+CY5APW/YnsjUkpvNVjFP8jd5xYu5t0duA3YMaX0t2bS1oULyHm1FDk49H3g0B4s/246d6k6mDyO0k+Bd6eUUs38mxSvPRpLqCSHk4MSiwJHkgMX55BbAc2mePrbPuSARAL2AI4BPkiut51ALktNSyk9CFwPDAc+X8Y6yYOH1/t/oFic/PS9yt9ixecHVn02rP6iza+rGN9tN+BkOsrEXsA9wIdSSmf1ZqckSRqI2trbG41zKUmS5lURsTNwO3BySumk1qamb0XE78gDia/VbCseSZIkdY8tnCRJ0jyreErfe4EzDDZJkiT1H8dwkiRJ86yiy+HyrU6HJEnS/MYWTpIkSZIkSSqVYzhJkiRJkiSpVLZwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKNbjVCZD6y7hx49obTVtiiSUAmDRpUr+lZ35nnvcv87v/mef9b+nVV6ftzTdpX3xxxv/3v61OzjzPMt7/zPP+ZX73P/O8eUOHDm1rdRqkCls4SZKkeULbm292epUkSVLrGHCSJEmSJElSqQw4SZIkSZIkqVQGnCRJkiRJklQqA06SJEmSJEkqlQEnSZIkSZIklcqAkyRJkiRJkkplwEmSJEmSJEmlMuAkSZIkSZKkUhlwkiRJkiRJUqkMOEmSJEmSJKlUBpwkSZIkSZJUKgNOkiRJkiRJKpUBJ0mSJEmSJJXKgJMkSZIkSZJKZcBJkiRJkiRJpTLgJEmSJEmSpFIZcJIkSZIkSVKpDDhJkiRJkiSpVINbnQBJkiRJmlvdOrJ9ts9G7NbWgpRI0sBiCydJkiRJkiSVyhZOkiRJ/9/enYdZVR34wv4VljIJCCqK0agobpzSiRJJG2ckbTq2Y2zj15rEqyaxM6lJ2zej6Wub5Iq5SUjspBO8GTWaiGZwiggKqCHihFeE4wQYIwgKKDOC9f1RQxgKqYJddaqK930enlO1h7XWXrWqivOrtdcGKJFZTwBmOAEAAABQMoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKWqrXYDAAAAOotx4+uq3QSATsEMJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFS11W4AAABAR3TnXauzYmVdtZsB0CkJnAAAgG3euPHrB0s9e6yuUksAuga31AEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKWyaDgAAEAb23BR8iQZOaKmCi0BaB9mOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQqtpqNwAAAKC9jRtfV+0mAHRpZjgBAAAAUCqBEwAAAAClEjgBAAAAUCqBEwAAAAClEjgBAAAAUCpPqQMAAOhgFi5cmDFjxuSBBx7IwoULM2DAgBx11FG58MIL079//1aVNWfOnEyZMiXTp0/PjBkz8uKLL6auri7XXnttDj/88M2e/8ILL+SGG27In//857z66qvp0aNH9thjjxx++OH59Kc/vaWXCHRxAicAAIAOZN68ebnooouyYMGC7L333jnmmGPy7LPPZuzYsZk8eXLGjBmTgQMHtri8W265JTfddNMWtWXcuHG58sors3r16hxwwAE5+OCDs3Tp0syaNSs33nijwAnYJIETAABAB3LVVVdlwYIFOf3003P55ZenpqYmdXV1ufrqq3Prrbfm61//er7zne+0uLz99tsv5557boYOHZoDDzwwX/3qVzN9+vTNnvfkk0/ma1/7Wnr37p1vf/vbG82GevLJJ1t7acA2ROAEAADQQVQqlUydOjX9+vXLJZdckpqamiRJTU1NLrnkkkyYMCFTpkzJM888kyFDhrSozFNOOWWL2nL11Vdn7dq1+fKXv9zsrXeHHHLIFpULbBsETgAAQOne8573ZPfdd8/YsWNz/fXX5/bbb8+8efPSv3//fOADH8j555+f2trazJ07Nz/+8Y/z5z//OUuWLMk+++yTj3/843nve9/bbLmvvvpqfvnLX+bBBx/MvHnzUltbmyFDhuTMM8/MyJEjNzr+8ccfz4QJE/LYY49l/vz5Wb58eXbZZZe87W1H5JjjPpL+/QdtdM51Y/41s2c9li9+6fb85S8zM2nizzN37jOpqUn22uvQnDjy43nbngeW3mdJMnny5CTJ0Ucfne7du6+3r3v37jn66KNz2223ZfLkyS0OnLbEk08+maeffrrplj6A1hI4AQAAbearX/1qHnzwwRx22GF5+9vfnscffzzXXXddFixYkPPOOy8f+9jHsv322+fggw/Oa6+9lmnTpuXyyy/P6NGjN5pVM2PGjFx22WVZtGhRBg0alOHDh2fFihWZPn16vvKVr2TmzJkbrSn0ve99L08//XT233//vPOd70ySPP/883n44d9l+vR7c9HHf5Rdd9272bZP+dPNuffen2a33ffPkAOGZ/782Xn22T9nzpxpufiTP8muu+5Ten8988wzSZKhQ4c2u78oitx2221Nx7WVqVOnJkmOOOKIrFq1KhMmTMhTTz2Vurq67LvvvjnxxBPTr1+/Nm0D0LkJnAAAgDYxb968dO/ePb/+9a+z6667JklefvnlfPjDH85tt92WJ554Iscff3wuu+yy1NbWvzUZO3ZsRo0aleuuu269wGnZsmX593//9yxatCif+9zncuaZZ6Zbt25Jkvnz5+fzn/98rr/++gwfPjxHHHFE03kXXHBBDj744PXCkbq6unzjm7fm97+7Onfc/u185KPfabb9kybdkH859+oUQ49qOu8Pvx+VqQ/dmsmTfpkzzvzyesd/a9TpWbx4Xov75ytfqm/fRRddtF6fJdnkouCN2+fOndvierbErFmzktTPqvrwhz+cOXPmrLf/v/7rv3LFFVeY/QRsksAJAABoM5dddllT2JQku+22W0466aTcdNNNWbVqVS699NKmsClJTjvttPzoRz/KE088kTVr1jTtu+222zJ//vyceuqpOeuss9arY+DAgfnCF76Q888/P2PHjl0vcDryyCM3alNNTU3efcTpefyxO/Pcs1OzatWydO/ee6Pjjj76nKawqfG8ESdelKkP3ZpZzz+y0fEHH3J8li97rcV9M2hQcsABB6y3bfny5UmSHj16NHtOz5491zuurbz++utJkl/96lfp3bt3rrzyygwfPjyvv/56brzxxtx888358pe/nJ/+9KcZPHhwm7YF6JwETgAAQJuora1tdrHpPffcM0ly+OGHZ/vtt19v33bbbZdBgwZl5syZWbx4cXbZZZckyZQpU5Ikxx9/fLN1DR06NL169Wr26WsLFy7M/fffn1mzZmXp0qVZu3Zt5s5NlixdmLq6N/Pqqy9mjz2Kjcs8cON1pHr37p+ePftmyZJXN9p30vs/02zbNmXkiJqNttXV1SVJ02Lhm9rf1tauXdv0esUVVzStqdW3b998/vOfz/z58zNp0qT84he/yBVXXNEubQI6F4ETAADQJnbeeef1Zi81apyls6nbxhr3r169umlb4y1kl1xyyVvWuWrVqvU+Hzt2bEaPHr3R9vXPWdbs9p122r3Z7d2798qKFa+/ZTu2VK9evZIkK1asaHZ/43U0HtdWGsvfZZddml3A/bTTTsukSZPy6KOPtmk7gM5L4AQAALSJTc3Saen+dTXO7DnmmGOy4447tuicGTNm5JprrknPnj3zhS98IcOGDcvOO++cHj16ZNz4uvz6pq/m/z0xLtnEpKGamm4tbl+S3HXn6FbdUvenB5Jjjz02xx57bNO2QYMG5emnn878+fObPadx++67Nx+GlWXQoEHrvW5q/6uvbjzTCyAROAEAAJ3Abrvtljlz5uRDH/pQDjvssBadM2HChNTV1eXiiy/OqaeeutH+ha++WGobpz95b6sWDX/ssfrgZt3AaciQIZk4cWIqlUqz5zRuHzJkyNY1djOKov4Ww8a1nDb02mv1wVpbz7QCOi+BEwAA0OG95z3vyUMPPZT77ruvxYFTY1iy2267bbRv/vxZmTv36VLb+Ll/u7VVxze3htNRRx2VMWPG5P7778/q1auzww47NO1btWpVJk+enCRt/nS4I488MrW1tXnxxRczb968jWZUPfJI/aLpjcEUwIZaN0cUAACgCk499dQMHDgwN998c2644YasWbNmvf11dXV57LHH8sADDzRt23vvvZMkv/3tb/PGG280bV+4cGFuufnKvPnm2vZpfCsMHTo0w4YNy6JFi/Ld73636VbCurq6jB49OosXL87w4cM3muF033335eyzz86nPvWpUtrRr1+/nHbaaVm7dm2++c1vZtmyv61z9eSTT+ZXv/pVkuTMM88spT6g6zHDCQAA6PB69+6db33rW7nssssyevToXH/99dlvv/3St2/fLFiwIH/5y1+ycOHCnH322U2LXJ988sm58cYb8+CDD+bMM8/MIYccklWrVuWxxx5Lr1675MADj8mMGZOqfGUb+9KXvpSLLrooY8eOzSOPPJL9998/zz77bGbPnp1dd901X/ziFzc6Z+nSpZkzZ06zi6PPnDkzo0aNavp81qxZSZJRo0ald+/eSepnKl1++eXrnffJT34yTz31VKZMmZKzzjorBx98cJYsWZInn3wya9asyVlnnZXjjjuuxCsHuhKBEwAA0CkMGTIkv/zlL/Ob3/wmkyZNago+BgwYkMGDB+fcc8/NiSee2HR8375985Of/CQ//OEPM3Xq1Nx///3Zeeedc8opp2Tw/v8jd97+nepdzFsYNGhQfvazn2XMmDF54IEHMnHixPTv3z+nn356LrroogwYMKBV5S1btizTp0/faPvs2bObPl731r1GPXv2zA9+8INcf/31ufvuu/PQQw9lu+22y6GHHpozzjgjI0eObPW1AduOmsYpmtDVLVq0aJODvU+fPkmSJUuWtFt7tnX6vH3p7/anz9tf/3XegC1auLCKLdk2GOPtT5+Xa9z4t34f1LNHjyTJipUr26wNG67h1Fybmlvnqasyxrde//79t50BQ4dnDScAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASlVb7QYAAABsi8aNr6t2EwDajBlOAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqWqr3QAAAIC2NG58XbWbALDNMcMJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFIJnAAAAAAoVW21GwAAAEDzxo2v2+wxI0fUtENLAFrHDCcAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUtdVuAAAAQFnGja+rdhMAiBlOAAAAAJRM4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJRK4AQAAABAqQROAAAAAJSqttoNAAAA2FLjxtdVuwkANMMMJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFS11W4AAABAS4wbX1ftJgDQQmY4AQAAAFAqgRMAAAAApRI4AQAAAFAqgRMAAAAApRI4AQAAAFAqgRMAAAAApRI4AQAAAFAqgRMAAAAApRI4AQAAAFAqgRMAAAAApRI4AQAAAFCq2mo3AAAAgC03bnzdRttGjqipQksA/sYMJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFS11W4AAADAhsaNr6t2EwDYCmY4AQAAAFAqgRMAAAAApRI4AQAAAFAqazgBAAB0Mc2tgTVyRE0VWgJsq8xwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASlVb7QYAAADQ9saNr1vv85EjaqrUEmBbYIYTAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQKoETAAAAAKUSOAEAAABQqtpqNwAAAGDc+LpqNwGAEpnhBAAAAECpBE4AAAAAlErgBAAAAECpBE4AAAAAlErgBAAAAECpPKUOAADo0F595S+ZOPFnee65qVm2dFF69uybwfsNy3HHn59dd927RWUsWDAn//X9D2fNmtXZc6+D8/FPjNnomNdfm5/bb/9Onn3mz+nWrVuK4sj8w0mfTp++u2x07OrVKzL6O+ekT99d8rGP/zg1NTWtuqZbbr4yjz12R44/4YKcMOLCTR73rVGnZ/HiefkfF1ybfQcf1rR9wvgxuXfCdesdu/323dO9+44ZsPPb8ra3Dc0hh56Yt7/90E2W/Z73vKe+Lbfckj322KNV7QfYHIETAADQYc2a9Wh++fN/y+rVyzNg5z1TDH1vFi78a56Y9sfMeOq+fOSj383e+/zdW5bx5ptv5re3fj1r177xlsf84uefz7x5z2TfwYenW7dumTbt7ixYMCef+NefbBQo3TvhuixZ8kr+v3P/d6vDpjINGPC27L13/fWvfXNNli9/LfPmPpMX5jyRPz346+y772E544NfyU477V61NgLbJoETAADQIa1evTK/vvGrWb16eY46+l8y8n3/mm7d6lcFefTR23Pr2P/MTTd+OZdc9pvssEOPTZbz5yk354U5T+TdR5yeqQ/d2uwxM2ZMyrx5z+T4Ey7MCSMuSM8ePXLXndfmnnvGZMaMSTnooGObjp0/f1b+9OBNefcRp2ePPYpyL7qV9t7773LGB7+y0fbnn38kd9z27cya9WjG/PjifOLi67LjjgOq0EJgW2UNJwAAoEN66qn7snTpq9l5570y8n0XN4VNSXLYYR/I0AOPyZIlr+SxR2/fZBmLFr6Ue8b9MAcUR+bQQ0/c5HFzX6okSYa9+5SmbX9/5Fnr7Wt02++vSc+efXPiyI9v0XW1h8GDD8/HPvHjDBw4OK8tnpc7bv9OtZsEbGMETgAAQIf00l9nJkn22fdd6dZtu43277ffsCTJjKcmbrKM3/72G0lqcsopl79lXSuWv54k6dmzb9O2Xr36JUmWL3+tadvjj9+VWbMezT+8/9Pp0WPHll1IleywQ8+8/wOfTZJMf3JCXn99QZVbBGxLBE4AAECH9MbqlUnWD4HW1bNX/fa5c59udv/DU3+f5597OCPf94n022m3t6yrX//6NY4WLJjdtG3+y7OSJP37D0qSrFy5NH+88/vZZ5935p3vPKnlF1JF++9/RHr12ilvvrk2s2Y9Wu3mANsQgRMAANAh9eq9U5Jk8eK5ze5ftLB++/Llr2XVquXr7Xv9tfn5413fy557HZwjhp+52boOOODvU1PTLXfdMTqvvTY/ixe/nN///lupqemWIQf8fZLknnH/neXLF+fkU/5tK66q/Q0aNCTJ3wI0gPZg0XAAAKBD2nfwYZk08Wd5uvJglix5NX367Ny0b+3aNXnssb+t3bR61fJ0796r6fPf/+7qvPHGypx62v9cb+2nTdl99/0z/D0fzJQ//TrXXH1q0/b3HnVOdtttv7z0UiVTH7o1f3/k2dltt8FN+994Y1W22277FtXRnHsnXJd7J1y3Ree2VGNwt2LF621aD8C6BE4AAECHtN9+786eex2cF/8yPT/76SX5p3/6fHYfNCSLFr2Uu++6NosWvpSamm6pq3szNesEPtOm/TGVygM59riPZvfd929xfR84+dIM3m9Ynnv2odRut12Koe/NvoPfnbq6uvzh96OyY5+dc/wJFyRJZsyYnLvv+n5eeeWF1NbukIMPOSEn/9Pn06NH71Zd4+67D2magdSc6dPvzerVK1pV5obq6uqSJDWp2apyAFpD4AQAAHRINTU1Oeecr+f6X/57XnppZsb8+BNN+7bbrjb/+IFLcsft305NTU3TAt7Lli3KHbd9Jzvv8vYce9xHW13ngQcenQMPPDo9e/RIkqxYuTIPT/1dXvzL9Jx9zlXp3r1X5s17Njfe8IXsvfffZeT7Ls7ChX/N+Ht+lLVr38jZH/rP1tV30DE5YcSFm9w/a9ajWx04rWhY9LxxzSuA9iBwAgAA2tW48XUtPrZvv4H5+MXX5ZmnH8zs2Y9n5Yql6bfTbjn00BOzZu0bqaury84775Xa2h2SJHPmPJHlyxdnh+498/OfXbpeWStXLE2SLJg/K9eN+dckybnnXbPerXgbWr78tYy7+wfZf//hOeSQE5Ik90++PrW13XPOv3wzPXv2qS975dJMvO+nGTnyExmw854t74w2VldX17So+sB1bgUEaGsCJwAAoEPr1q1biqFHpRh61HrbH3zgxiTJ4P2GbXTO4kVzs3hR84uNr1q1PLNnPZYkefPNtW9Z9x/v+n7eeGNlTv6nzzVtm//y89l1172bwqYk2WuvQ5IkL8+f1aECp+eefSjLl7+Wbt22y777vKvazQG2IQInAACg03njjZWZMuXm1NTU5N3vPq1p+0EHHZsrr/pTs+fMev7R/N/rPpk99zo4H//EmM3WMXv2tDz26O059riPZudd9mraXlPTLavfWLnesavfqL/trVtNx3kQ+OrVK3LnHaOTJIe+Y2T69N2lyi0CtiUd56chAADABl5++bm8sUG4s3z5a7nxV1/KooV/zbuPOD2D9jig9HrffHNtbhn7jey006Acc+yH19u3226D88qC2fnLC082HPtmHn/0ztTU1HSY29ZmzXo0P/rvj2X+/OezU/9Bef8/fqbaTQK2MWY4AQAAHdYDk2/IU0/dl0F7FOnTZ5esWPF65syeljfeWJmDDzk+//iBSzdfyJbUe/9NeemlSs49b1S2377HevuOOubcTJv2x/z0J5/J4P3encWL5mbevGfyzne9P/37D2qT9mzKnDnTcsvNVyZJ1r65JiuWv565c5/J0qWvJkkGDz48Z5z5lfTu3b9d2wUgcAIAADqsoQcdk6VLF2bevGfzlxeeTPfuvfL2vd+RYcNOySGHjmiTOpcseTV//OMPcvDBx260blSSDBy4b877yP/JPXf/MM88/af06LFjhg8/M+876VNt0p63snDhX7Nw4V+TJNtv3z3du++YAQP2yKHvGJFDDx2Zvd5+SLu3CSBJaurqWv6ECOjMFi1atMnB3qdP/YKPS5Ysabf2bOv0efvS3+1Pn7e//gMGNH28aOHCKrZk22CMt7+u1OeteUpdtfTsUT+racXKlZs5smsZOaKmanV3pTFeLf3796/eFxA2YA0nAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVLXVbgAAAAAdw7jxdRttGzmipgotATo7gRMAANBmmgswAOj63FIHAAAAQKnMcAIAAGCTNpyl5hY7oCXMcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVAInAAAAAEolcAIAAACgVLXVbgAAANB1jBtfV+0mANABmOEEAAAAQKkETgAAAACUSuAEAAAAQKkETgAAAACUSuAEAAAAQKkETgAAAACUSuAEAAAAQKkETgAAAACUSuAEAAAAQKkETgAAAACUSuAEAAAAQKkETgAAAACUqrbaDQAAAKDzGDe+bqNtI0fUVKElQEdmhhMAAAAApTLDCQAA2CLNzXQBgMQMJwAAAABKJnACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKJXACAAAAoFQCJwAAAABKVVvtBgAAAB3fuPF11W4CAJ2IwAkAAICt0lwgOXJETRVaAnQUAicAAOhg1qxZkxtuuCF33nlnXnrppfTs2TPDhg3LJz7xiey5556tKmvx4sWZPHlynnrqqcyYMSPPPvts1qxZkwsuuCAXXXTRJut/5JFH8uCDD+aJJ57IvHnz8vrrS9On7y4ZPPjwvPeof8nAgfuUcKVsSxYuXJgxY8bkgQceyMKFCzNgwIAcddRRufDCC9O/f/9WlTVnzpxMmTIl06dPz4wZM/Liiy+mrq4u1157bQ4//PC3PHfq1Kn51a9+lenTp2fFihUZOHBgjj322Jx//vnZcccdt+YSgXUInAAAoANZs2ZNLrnkkjz88MPp379/jjrqqLzyyiu55557MnHixFxzzTUZPnx4i8ubNm1arrrqqla14dFHH81nP/vZJMluu+2WQw89NK+82i0vvVTJo4/cliem3Z1/PvvKHHjQMa0ql23XvHnzctFFF2XBggXZe++9c8wxx+TZZ5/N2LFjM3ny5IwZMyZ9+vRpcXm33HJLbrrppla34/rrr8/3v//91NXV5aCDDsrAgQMzc+bMXH/99Zk4cWJ+9KMfZcCAAa0uF9iYwAkAADqQX/ziF3n44Ydz0EEH5Xvf+1569+6dJJk0aVIuv/zyfO1rX8vYsWPTq1evFpU3YMCAnHHGGTnwwAMzdOjQ3Hnnnbnhhhve8pxu3bplxIgROeecc3LIIYckqb9l6s031+aecf+dyZN+kVvGXplL97k5vXr127oLZptw1VVXZcGCBTn99NNz+eWXp6amJnV1dbn66qtz66235utf/3quu+66Fpe333775dxzz83QoUNz4IEH5qtf/WqmT5/+lufMmDEj1157bbp165ZvfOMbOeaY+sB0zZo1GTVqVH73u9/lm9/8Zq6++uqtulagnsAJAAA6iDVr1uTGG29Mkvzbv/1bU9iUJB/4wAdy2223ZdKkSfnDH/6Qs88+u0VlHnrooTn00EObPr/77rs3e86wYcMybNiwjbZ367ZdRr7v4sx4amJeeeWFVCoP5F3v+scWtYNtV6VSydSpU9OvX79ccsklqampX9uppqYml1xySSZMmJApU6akUqmkKIoWlXnKKae0uh233HJL3nzzzZx88slNYVOS1NbW5tJLL83999+fSZMmZdasWdl3331bXT6wvm7VbgAAAFDviSeeyGuvvZY99tgjBx544Eb7TzrppCTJ5MmT27tpTWpqarLb7vsnSV5/fUHV2kHn0Thejz766HTv3n29fd27d8/RRx+dJJkwYUKbtqNSqSRJs2Fqjx49moLZ++67r03bAdsKgRMAAHQQzzzzTJJk6NChze4/6KCD1juuWha++mKSpM+OO1e1HXQOmxvXjbOaGgOhtrJixYokSd++fZvd369f/e2hTz/9dJu2A7YVAicAAOgg5s2blyQZOHBgs/t32223JMlrr72W5cuXt1u71vX8cw9n7tynU1u7Q4Yc8PdVaQOdy+bGdeP2l156qU3bsdNOOyVJ5s6d2+z+xvo3tR9oHYETAAB0EI0hUo8ePZrdv+5C4dUInJYtW5xbb6l/4t1RR5+bPn3McGLzNjeue/bsmSRZtmxZm7bjsMMOS5L84Q9/SF1d3Xr7XnjhhTz66KNJqvO9BV2RwAkAADqIxjfBjYsqdyRr1qzOjTd8IYsXz8s++74rxx1/frWbRCexuXG9YfjTVj74wQ+mT58+mTlzZr785S9n1qxZWbZsWaZOnZrLLrus6biO+P0HnZHACQAAOojGGUyNa81saN3t6852amtr1qzJTb/6cmbPfjyDBh2Qfzn36my3nQde0zKbG9erVq1KkvWeytgWdt1111x99dXp379/xo8fn3POOScjRozIpz/96SxbtiwXXnhhkk2v8QS0jt8SAADQQQwaNChJMn/+/Gb3v/zyy0nq3xC3V+C0du3afO1rX8vMmZOz68B985Hzv5sePXZsl7rpGgYNGpSnn356k+O6cXvj+G9L73rXuzJ27NhMmDAhTz/9dNasWZN9990373vf+zJu3LgkyeDBg9u8HbAtEDgBAEAHMWTIkCSbflrXU089td5xba2uri5XXXVV7rnnngzYec+cf/7o9O69U7vUTdcxZMiQTJw4cZPjunF749Pq2lqvXr1y8sknb7T9oYceSpIMGzasXdoBXZ1b6gAAoIN4xzvekb59++avf/1rZs6cudH+u+66K0lyzDHHtEt7rr766txxxx3Zfffdc/7/+F769N2lXeqlaznqqKOSJPfff39Wr1693r5Vq1Zl8uTJSZITTjih3dvW6Pnnn88DDzyQAQMG5Nhjj61aO6ArETgBAEAHUVtbmw996ENJklGjRq331K4777wzEydOzE477bTR7Iz58+fn7LPPztlnn73J25Zaa/To0bn11luz66675tprr81OO+1eSrlse4YOHZphw4Zl0aJF+e53v9u0SHhdXV1Gjx6dxYsXZ/jw4Rk6dOh659133305++yz86lPfaq0tsyYMSNvvvnmetuee+65XH755Vm7dm0+97nPZYcddiitPtiWuaUOAAA6kPPOOy8PP/xwHn300Xzwgx/MYYcdlldffTWPP/54amtrc8UVV2y0uPKaNWsyZ86cpo83dMEFFzR93BhI/f73v8+UKVOatl933XVNH0+aNCk33HBDkmSPPfbIddddl7lzN27r0IOOyUEHmQ1C88aN/9vT54497ouZPftjGTt2bB555JHsv//+efbZZzN79uzsuuuu+eIXv7jR+UuXLs2cOXOaFhVf18yZMzNq1Kimz2fNmpWkPqht/P4oiiKXX375eud94QtfyNq1azN48OD069cvc+fOzfTp05Mkn/nMZzJixIitv3AgicAJAAA6lO233z7f/e53c/311+euu+7K5MmT07Nnz5xwwgm5+OKLs9dee7W6zMY31OtasGBBFixY0Ozxr7/+etPH06ZNy7Rp05o9bqf+gwROtMhO/QflZz/7WcaMGZMHHnggEydOTP/+/XP66afnoosuyoABA1pV3rJly5od17Nnz276uLmZSmeccUYmT56cmTNnZtmyZenfv39OPPHEnHPOOTnwwANbfV3AptU0TmeErm7RokWbHOx9+vRJkixZsqTd2rOt0+ftS3+3P33e/vqv82Zl0cKFVWzJtsEYb3/V7vN1Z6tsC3r26JEkWbFyZZVb0nWMHFHzlvurPca7gv79+791J0M7soYTAAAAAKVySx0AALCRbW1GEwDlMsMJAAAAgFIJnAAAAAAolVvqAAAAaHPN3aa5uYXEgc7LDCcAAAAASiVwAgAAAKBUAicAAAAASiVwAgAAAKBUAicAAAAASuUpdQAAAFTFuk+u69ljdZLkqPdWqzVAmcxwAgAAAKBUAicAAAAASuWWOgAA2IasewsTALQVM5wAAAAAKJXACQAAAIBSuaUOAABoM3PnPlftJmyRHt27J0lWrlpV5ZZ0fIMG7VftJgAdkMAJAAC6sGqv2XTJZ46oav20vZt+82q1mwB0QG6pAwAAAKBUZjgBAEAXUe3ZTFCGDcfxyBE1VWoJsDXMcAIAAACgVGY4AQAAbeY7ox+qdhO2iEXDAbaOwAkAAGgznfUJZj179EiSrFi5ssotoblbRd1mBx2fwAkAAIBORQgFHZ/ACQAAOoE771qdJFmx0sLgAHR8AicAAAC6HLOgoLoETgAA0ME090a5Z48qNAQ6kea+b7bkvOZCKeEVtJ7ACQAAgG1CS0KpLQ2ugPXV1NX5ZoL/+I//qEuSK664wp8p2ok+b1/6u/3p8/anz9uX/m5/+rx96e/2p8+ha+lW7QYAAAAA0LUInAAAAAAolcAJAAAAgFIJnAAAAAAolcAJAAAAgFJ5Sh0AAAAApTLDCQAAAIBSCZwAAAAAKJXACQAAAIBSCZwAAAAAKJXACQAAAIBSCZwAAAAAKJXACQAAAIBS1Va7AdCWiqI4MsmXk7wnSY8kzyb5v0m+V6lU1rawjCFJzkjyD0mGJNktyaIkU5J8p1Kp3PsW534kySeTHJRkbZLHklxTqVRu29Jr6shK6u/tk/xrkncmeVfq+277JBdVKpUxmzjno0l+8hbFXlypVH7YsqvoXKrV5+uca4y3sr+3pKyuPsaLotgzyf9KclKSnZPMTfLbJP9RqVQWtWU5ZX5NO4tq9HdRFPskmfUWxd1UqVQ+1OKL6GTK6POiKD6Y5NjU/6z+uyR9klxfqVTO3cx5xng79LcxvnV9XhTFzklOT/KBJIcmeVuS1Un+X+p///2kUqm8uYlzt7kxDp2FwIkuqyiKU5OMTbIyyU1JFib5pyTfTvLeJGe1sKgrk5yd5KkkdzSUUyQ5JckpRVF8tlKpjG6m/muSfC7Ji0l+nGSHJB9K8oeiKD5dqVS+v+VX1/GU2N+9k3yn4eOXk8xLslcLz/1dkseb2f5wC8/vVKrd58b4Fvf31pTV5cZ4URT7JXkwycDUX9/MJEck+WySk4qieG+lUnm1Lcop82vaWVSzvxtMS/2b0A092fqr6RzK6vPUv6H+uyRLU/9zd2gL6jbG27G/GxjjW9bnZyX5QeqDqnuTvJD6P/KekWRMkvcXRXFWpVKp26DubW6MQ2cicKJLKoqib+rfAK9NclylUnm4YftXkkxI8sGiKD5UqVRubEFxdyX535VK5bEN6jg2ybgko4qi+E2lUpm7zr4jU/9G/Lkk7278y05RFKOSPJLkmqIobqtUKrO38lI7hJL7e3mSf0zyeKVSmVsUxdeSXNHCpvy2Uqn8tLXt74yq3efG+Jb391aW1RXH+H+l/k3KZyqVyvcaNxZF8X+SXJrkqiSfKLuckr+HOpOq9Pc6Hq9UKl/b4tZ3TmX1+aWpDz6eTf3Mm03OsG4o3xhvx/5ehzHeoJV9/nTq/5h7+7ozmYqi+GKSh5Kcmfrwaew6+7bVMQ6dhjWc6Ko+mGTXJDc2/vJJkkqlsjL1f7FKkotbUlClUvnphmFTw/aJSe5L/ayOIzfY3fhL9ap1pxE3vPm+Nkn3JOe3pP5Oosz+Xl2pVO5cN8CjWdXuc2M8W9bfJZfVqRVFMTjJ+5LMTv24WdcVSZYlOa8oit5tUM4293Wocn9vk8rsq0qlcm+lUnlmwxkeb8EYX19b9/c2qaw+r1QqEyqVyh82vG2uUqnMS9J4y/hxG5y2zY1x6GwETnRVJzS83tXMvkmpn9FxZFEU3beynjcaXte0ov47NzimK2iv/t6cdxZFcUlRFP+zKIrzGtYT6Kqq3efG+N+0tr+3pqyuNsYb++LuZt5kLEnyQJJeqV+Xo+xyqv09VA3V7O9GexRF8fGiKL7Y8PqO1l5EJ1NWn29N3cZ42qW/GxnjDUrs8y35/3ZXHePQqQic6KqKhtenN9xRqVTWpH5Rx9okg7e4gqLYO8mI1P8ym7TO9t6pX+hw6SZmjDzT8HrAltbdAbV5f7fQZ1N/z/43kvw8yeyiKH5YFEWPNq63GqrW58b4+ragv7emrK42xjfZFw1aOpa2pJyO8nOrPVWzvxuNTP1shasaXqcVRXFvURRv30ydnVVZfV5q3cZ4m/5+MsbXt1V9XhRFbZIPN3y6YbC0LY5x6FQETnRV/RpeX9vE/sbtO21J4Q1/Kbk+9bcNfW2Dp2+0ad0dVLWveVaST6f+Px69k+yR5J9TP73746l/UklXU80+r/b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" - ] - }, - "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", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "288px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/case_studies/bayesian_ab_testing_introduction.ipynb b/examples/case_studies/bayesian_ab_testing_introduction.ipynb new file mode 100644 index 000000000..c7d997b3e --- /dev/null +++ b/examples/case_studies/bayesian_ab_testing_introduction.ipynb @@ -0,0 +1,2241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "15107005-e1a2-49e4-ac0f-00f6524ee7f6", + "metadata": {}, + "source": [ + "(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", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b48cad3d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:36.476575Z", + "iopub.status.busy": "2022-06-01T18:53:36.476139Z", + "iopub.status.idle": "2022-06-01T18:53:39.372124Z", + "shell.execute_reply": "2022-06-01T18:53:39.370978Z" + } + }, + "outputs": [], + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "30cc5507", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:39.376825Z", + "iopub.status.busy": "2022-06-01T18:53:39.376264Z", + "iopub.status.idle": "2022-06-01T18:53:39.388197Z", + "shell.execute_reply": "2022-06-01T18:53:39.387403Z" + } + }, + "outputs": [], + "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 {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?\"." + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a5376bb4", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:39.391621Z", + "iopub.status.busy": "2022-06-01T18:53:39.391347Z", + "iopub.status.idle": "2022-06-01T18:53:39.395688Z", + "shell.execute_reply": "2022-06-01T18:53:39.394570Z" + } + }, + "outputs": [], + "source": [ + "@dataclass\n", + "class BetaPrior:\n", + " alpha: float\n", + " beta: float" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a0c80bf2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:39.399745Z", + "iopub.status.busy": "2022-06-01T18:53:39.399180Z", + "iopub.status.idle": "2022-06-01T18:53:39.403589Z", + "shell.execute_reply": "2022-06-01T18:53:39.402642Z" + } + }, + "outputs": [], + "source": [ + "@dataclass\n", + "class BinomialData:\n", + " trials: int\n", + " successes: int" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b625c349", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:39.406921Z", + "iopub.status.busy": "2022-06-01T18:53:39.406669Z", + "iopub.status.idle": "2022-06-01T18:53:39.412545Z", + "shell.execute_reply": "2022-06-01T18:53:39.412035Z" + } + }, + "outputs": [], + "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, + "id": "4a103373", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:39.415596Z", + "iopub.status.busy": "2022-06-01T18:53:39.415337Z", + "iopub.status.idle": "2022-06-01T18:53:39.418295Z", + "shell.execute_reply": "2022-06-01T18:53:39.417874Z" + } + }, + "outputs": [], + "source": [ + "weak_prior = ConversionModelTwoVariant(BetaPrior(alpha=100, beta=100))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1d8702af", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:39.420933Z", + "iopub.status.busy": "2022-06-01T18:53:39.420683Z", + "iopub.status.idle": "2022-06-01T18:53:39.423573Z", + "shell.execute_reply": "2022-06-01T18:53:39.423001Z" + } + }, + "outputs": [], + "source": [ + "strong_prior = ConversionModelTwoVariant(BetaPrior(alpha=10000, beta=10000))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9e1e0769", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:39.427592Z", + "iopub.status.busy": "2022-06-01T18:53:39.427300Z", + "iopub.status.idle": "2022-06-01T18:53:43.647086Z", + "shell.execute_reply": "2022-06-01T18:53:43.645945Z" + } + }, + "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": { + "iopub.execute_input": "2022-06-01T18:53:43.652280Z", + "iopub.status.busy": "2022-06-01T18:53:43.651840Z", + "iopub.status.idle": "2022-06-01T18:53:44.243767Z", + "shell.execute_reply": "2022-06-01T18:53:44.242862Z" + } + }, + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a3d30bb9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-01T18:53:44.248078Z", + "iopub.status.busy": "2022-06-01T18:53:44.247715Z", + "iopub.status.idle": "2022-06-01T18:53:44.713458Z", + "shell.execute_reply": "2022-06-01T18:53:44.712846Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "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": [ + "
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" + ], + "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" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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" + ] + }, + "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", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " \n", + " 100.00% [24000/24000 00:09<00:00 Sampling 4 chains, 0 divergences]\n", + "
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", + "text/plain": [ + "
" + ] + }, + "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" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "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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", 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" + ] + }, + "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", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [24000/24000 00:10<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "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": [ + "
" + ] + }, + "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": { + "image/png": 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", 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" + ] + }, + "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": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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", 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" + ] + }, + "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", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " \n", + " 100.00% [6000/6000 00:14<00:00 Sampling chain 1, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "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": [ + "
" + ] + }, + "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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", 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" + ] + }, + "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", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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", + "text/plain": [ + "
" + ] + }, + "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", + "aesara: 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 aesara,xarray" + ] + }, + { + "cell_type": "markdown", + "id": "bed3eeaf-7907-4d47-89b7-8ed85838c42d", + "metadata": {}, + "source": [ + ":::{include} ../page_footer.md\n", + ":::" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 ('pymc-examples-ipRlw-UN')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "288px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "6d634b478020e8973f8932bbbe101d0b4067e4493dd3db1f2db4e2681c3b3de1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/references.bib b/examples/references.bib index ac8cde13f..aa5d70b8d 100644 --- a/examples/references.bib +++ b/examples/references.bib @@ -432,6 +432,12 @@ @article{spiller2013spotlights year = {2013}, publisher = {SAGE Publications Sage CA: Los Angeles, CA} } +@online{stucchio2015bayesian, + title = {Bayesian A/B Testing at VWO}, + author = {Stucchio, Chris}, + year = {2015}, + url = {https://vwo.com/downloads/VWO\_SmartStats\_technical\_whitepaper.pdf} +} @misc{szegedy2014going, title = {Going Deeper with Convolutions}, author = {Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich}, diff --git a/myst_nbs/case_studies/bayesian_ab_testing.myst.md b/myst_nbs/case_studies/bayesian_ab_testing_introduction.myst.md similarity index 96% rename from myst_nbs/case_studies/bayesian_ab_testing.myst.md rename to myst_nbs/case_studies/bayesian_ab_testing_introduction.myst.md index 6bf92ffc2..53c08e528 100644 --- a/myst_nbs/case_studies/bayesian_ab_testing.myst.md +++ b/myst_nbs/case_studies/bayesian_ab_testing_introduction.myst.md @@ -6,11 +6,20 @@ jupytext: format_version: 0.13 jupytext_version: 1.13.7 kernelspec: - display_name: Python 3 (ipykernel) + display_name: Python 3.8.10 ('pymc-examples-ipRlw-UN') language: python name: python3 --- +(bayesian_ab_testing_intro)= +# Introduction to Bayesian A/B Testing + +:::{post} May 23, 2021 +:tags: case study, ab test +:category: beginner, tutorial +:author: Cuong Duong +::: + ```{code-cell} ipython3 from dataclasses import dataclass from typing import Dict, List, Union @@ -38,7 +47,7 @@ plotting_defaults = dict( ) ``` -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. +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. #### What is A/B testing? @@ -478,7 +487,7 @@ class RevenueModel: 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. -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. +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. ```{code-cell} ipython3 c_prior = BetaPrior(alpha=5000, beta=5000) @@ -654,19 +663,33 @@ There are many other considerations to implementing a Bayesian framework to anal * 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? * 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? -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. +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). We also plan to create more PyMC tutorials on these topics, so stay tuned! ---- ++++ + +## Authors -Author: [Cuong Duong](https://github.com/tcuongd) (2021-05-23) +* Authored by [Cuong Duong](https://github.com/tcuongd) in May, 2021 ([pymc-examples#164](https://github.com/pymc-devs/pymc-examples/pull/164)) +* Re-executed by [percevalve](https://github.com/percevalve) in May, 2022 ([pymc-examples#351](https://github.com/pymc-devs/pymc-examples/pull/351)) -### References ++++ -* [Stucchio, C. (2015) _Bayesian A/B Testing at VWO_](https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf) +## References + +:::{bibliography} +:filter: docname in docnames +::: + ++++ + +## Watermark ```{code-cell} ipython3 %load_ext watermark %watermark -n -u -v -iv -w -p aesara,xarray ``` + +:::{include} ../page_footer.md +:::