{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Priors and Posteriors" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import some helper functions (please ignore this!)\n", "from utils import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Context:** If there's one thing we learned from the chapter on model selection and evaluation is that we should not blindly trust our models. Models are complicated and require a robust and diverse toolkit for responsible evaluation in their intended context. For safety-critical applications of ML, like the ones from the IHH, we must take additional precautions to ensure responsible use. We therefore adopt the following philosophy:\n", "1. **Finite information $\\rightarrow$ uncertainty.** We're often asked to make decisions without all the information necessary for certainty. We ask the same of our models: given a finite data set and an incomplete understanding of the phenomenon we're modeling, we ask models to make predictions for data they have never encountered. Therefore, for responsible use in safety-critical contexts, our models must have some way of quantifying the limits of their \"knowledge.\"\n", "2. **Not making choices $\\rightarrow$ a choice will be made for you.** If we avoid making explicit choices in the design of our model, a choice will still be made for us---and it might not be the choice we want. For example, without explicitly choosing what's important to us, we might get a model with the highest accuracy for a task for which minimizing false negatives is most important. *It's therefore better to make your choices explicitly.* Making assumptions explicit is especially important for uncertainty quantification. \n", "\n", "**Challenge:** To satisfy our new modeling philosophy, we need (1) a way to quantify uncertainty, and (2) a way to understand how uncertainty depends on our modeling choices. How can we do that with the tools we have? As we show here, we can't. We will then expand our DGM to create models that quantify uncertainty (Bayesian models) and introduce a new way of fitting ML models called Bayesian inference.\n", "\n", "**Outline:** \n", "* Motivate the need for uncertainty\n", "* Introduce a new modeling paradigm based on Bayes' rule\n", "* Provide intuition for this modeling paradigm\n", "* Implement this modeling paradigm in `NumPyro`\n", "* Gain intuition how different models have different uncertainty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Data:** To help make the concepts concrete, we'll return to our regression data, in which we wanted to predict telekinetic ability from age. Let's load the data in:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeGlowTelekinetic-Ability
Patient ID
9030.6077290.604085-0.020933
25438.5313570.613645-0.070165
28321.8794140.8292120.140791
4452.9490040.9811200.261027
46130.2374460.688329-0.027250
1529.5624830.796853-0.033701
31615.2839750.8395460.344510
4892.6884880.9294220.268031
1594.1293710.8938130.422464
15315.1941820.8324830.375658
24133.3912470.676760-0.028127
25032.3637400.711121-0.078376
39020.6993660.6830750.176542
28951.3702300.472696-0.153246
17124.9837840.7036570.028212
\n", "
" ], "text/plain": [ " Age Glow Telekinetic-Ability\n", "Patient ID \n", "90 30.607729 0.604085 -0.020933\n", "254 38.531357 0.613645 -0.070165\n", "283 21.879414 0.829212 0.140791\n", "445 2.949004 0.981120 0.261027\n", "461 30.237446 0.688329 -0.027250\n", "15 29.562483 0.796853 -0.033701\n", "316 15.283975 0.839546 0.344510\n", "489 2.688488 0.929422 0.268031\n", "159 4.129371 0.893813 0.422464\n", "153 15.194182 0.832483 0.375658\n", "241 33.391247 0.676760 -0.028127\n", "250 32.363740 0.711121 -0.078376\n", "390 20.699366 0.683075 0.176542\n", "289 51.370230 0.472696 -0.153246\n", "171 24.983784 0.703657 0.028212" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import a bunch of libraries we'll be using below\n", "import pandas as pd\n", "import matplotlib.pylab as plt\n", "import numpyro\n", "import numpyro.distributions as D\n", "import jax\n", "import jax.numpy as jnp\n", "\n", "# Load the data into a pandas dataframe\n", "csv_fname = 'data/IHH-CTR-CGLF-regression-augmented.csv'\n", "data = pd.read_csv(csv_fname, index_col='Patient ID')\n", "\n", "# Print a random sample of patients, just to see what's in the data\n", "data.sample(15, random_state=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Why We Need Uncertainty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The MLE is Over-Confident.** In safety-critical contexts, like those from the IHH, it's important that our ML models don't just fit the observed data well; they should also communicate with us the limits of their \"knowledge.\" Let's illustrate what we mean. Consider the regression data below:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(5.5, 3.5))\n", "\n", "plt.scatter(data['Age'], data['Telekinetic-Ability'], color='black', alpha=0.5, marker='x', label='Data')\n", "plt.axvspan(78, 93, alpha=0.5, color='red', label='No Data')\n", "\n", "plt.xlabel('Age')\n", "plt.ylabel('Telekinetic Ability')\n", "plt.title('What should the model do where there\\'s no data?')\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we were to make a prediction for a patient of age 85, what should the model do? The model wasn't trained on any such patients. Does the trend keep going down with age? Doing so would ignore the point at the very right of the plot, treating it as an *outlier*. Or maybe the trend should go up after age 80? It's impossible for us to know because we haven't observed data about such patients. In cases such as these, it's important that our model alert us about its uncertainty. \n", "\n", "Especially in recent years, there has been more and more research dedicated to developing models that can reliably quantify uncertainty. As an example, a recent paper evaluated how confident different deep learning models are on a medical imaging task. In the paper, the authors evaluated models for predicting whether patients had COVID or not from X-ray scans. Here's what they found:\n", "\n", "\n", "```{figure} _static/figs/cats-vs-covid.png\n", "---\n", "width: 100%\n", "name: cats-vs-covid\n", "align: center\n", "---\n", "\n", "ML models can be over-confident about wrong predictions. Figure adapted from [this paper](https://www.semanticscholar.org/paper/Can-Your-AI-Differentiate-Cats-from-Covid-19-Sample-Mallick-Dwivedi/c100c33afbb2efa5197f7d6042022e1227c5e298).\n", "```\n", "As you can see from the figure, the model makes predictions that aren't only incorrect, *but also overconfident*. And it does this for inputs (like the cat) for which it should really just communicate \"I don't know.\" \n", "\n", "How can a model know when it \"doesn't know\"? One way to do this is to alert us when many possible models fit the data reasonably well, but behave differently away from the data (i.e. on previously unseen inputs, like the cat). Unfortunately, the learning algorithm we've used so far---the MLE---doesn't provide us with a way to do this. The MLE gives a *single* model. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ensembling.** If the MLE gives us a single model, why not use it to fit a whole *ensemble* of models? We could rely on the imperfections of the optimizer to give us a diversity of models. Remember that, especially for more expressive models, optimization tends to get stuck in local optima. What if we were to collect an ensemble of models, all fit with the MLE to data, but each optimized from a different random initialization? Because each model would get stuck in a different local optima, each *might* behave differently than the others away from the data. What's nice about this approach is that it's easy to implement: we already have all the tools we need! Let's see what ensembling a neural network regression model looks like:\n", "\n", "```{figure} _static/figs/example_nn_ensemble_regression.png\n", "---\n", "width: 100%\n", "name: nn_ensemble_regression\n", "align: center\n", "---\n", "\n", "Ensembling 10 neural networks of different sizes.\n", "```\n", "\n", "As you can see, with ensembling, we managed to get a more diverse set of functions (though in this case, not all that diverse---they all have roughly the same trend). Ensembling can be quite effective in practice, but it suffers from one main shortcoming when it comes to safety-critical contexts: it makes implicit assumptions that are difficult to understand. Specifically, we relied on the imperfections of our black-box optimizer to find us a diverse set of models. What kind of models will the optimizer give us, however? Do these models have an *inductive bias* that's appropriate for our task? \n", "\n", "The need for explicit assumptions motivates us to find an alternative way of fitting our models, leading us to the *Bayesian approach*. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is Uncertainty?\n", "\n", "To design a system that captures uncertainty, we first need to know what it means. To do this, let's think about the uncertainty we encounter in everyday scenarios. Maybe these will help us formalize uncertainty mathematically.\n", "\n", "**Total Certainty.** When you have lots of data, and you have a *mechanistic* understanding of the system, you have certainty. By mechanistic understand, we mean that you can characterize the system mathematically (e.g. you can predict how quickly an object will fall because you have an equation for gravity).\n", "> Example: You're asked to predict whether the sun will rise tomorrow. Of course, you know the sun will rise tomorrow, and you're absolutely certain about it (if you have reason to believe the sun will not rise tomorrow, please do let the teaching staff know so they can head to the course bunker). So what makes you sure the sun will rise tomorrow? There are two reasons you will likely think about. (1) You have an abundance of observational data---the sun has risen every day of your life. (2) You have a model (or inductive bias)---you know that day and night are created by the earth's rotation around its axis. Because you understand the mechanical properties of the system, you know that certain predictions just don't make sense---like the sun can't rise twice in the span of 24 hours." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Aleatoric Uncertainty.** When you have lots of data from a *noisy* system, you can be certain about the probability of an outcome, but not about the actual outcome. As an example, having observed 100 flips of a fair coin, you can say with fair certainty that the coin will land heads 50\\% of the time. But you will never be able to predict whether the next flip will be heads with any greater accuracy. We call this type of uncertainty, *aleatoric uncertainty*. This is uncertainty that's due to the inherent stochasticity in the system. \n", "> You're asked to predict whether it will rain at Wellesley next week. Having lived in New England for a little while, you scoff at the possibility of getting this prediction correct. New England weather is notoriously unpredictable. So what makes you uncertain about our prediction? You have an abundance of experiential data suggesting that weather is difficult to predict (how many times have you stood outside in the rain, while your phone's weather app says it's sunny?). As a result, you're certain we can't make a good prediction.\n", "\n", "All models we've worked with so far quantify aleatoric uncertainty. For example, in our regression and classification models, our observation error captures aleatoric uncertainty. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Epistemic Uncertainty.** When you don't have enough data, and you also don't have a mechanistic understanding of the system, you have epistemic uncertainty. In this case, we'd ideally like to have a diversity of possible models that fit the data. \n", "> Your colleague for the IHH is getting married on Venus next week. Your friend, who is also attending the wedding, asked you to predict the weather on Venus next week (so you can choose your outfit). Having forgotten all of your astrophysics knowledge (or having never learned it), you actually don't know what the weather on Venus is like in general. As a result, what makes you uncertain is: (1) a lack of observations or experiential knowledge, and (2) a model (or domain knowledge) about Venus's climate.\n", "\n", "None of our models so far have been able to capture epistemic uncertainty. In the regression case, epistemic uncertainty would be uncertainty over the *parameters* of the model. Epistemic uncertainty indicates that many potential models could explain the observed data, but we don't know which one is the \"right\" one. We can reduce our uncertainty by observing more data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Visualizing Regions of Uncertainty.** Returning to our original data of Telekinetic Ability vs. Age, we see that:\n", "* Where we've observed data, there's *aleatoric uncertainty*: there's \"noise\" around the trend. No matter how good your model is, it will only be able to predict the trend, not the noise around it.\n", "* Where we haven't observed data, there's *epistemic uncertainty*: we don't know what's the appropriate model behavior. In addition to epistemic uncertainty, there's still aleatoric uncertainty. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(5.5, 3.5))\n", "\n", "plt.scatter(data['Age'], data['Telekinetic-Ability'], color='black', alpha=0.5, marker='x', label='Data')\n", "plt.axvspan(78, 93, alpha=0.5, color='red', label='Epistemic Uncertainty', zorder=-10)\n", "plt.axvspan(0, 78, alpha=0.3, color='blue', label='Aleatoric Uncertainty', zorder=-10)\n", "\n", "plt.xlabel('Age')\n", "plt.ylabel('Telekinetic Ability')\n", "plt.title('Epistemic vs. Aleatoric Uncertainty')\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Conclusion.** We need some way of capturing epistemic uncertainty. To do this, we'll next extend our DGM toolkit, as well as introduce a different way of fitting models, called *Bayesian inference*. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "````{admonition} Exercise: Identify Type of Uncertainty\n", "In each of the scenarios below, identify the sources of uncertainty (i.e. why you can't be certain about the outcome---be creative!), and categorize them into aleatoric vs. epistemic. Explain your reasoning.\n", "1. A new tree was planted outside your window. You want to predict how tall it will be in one year. \n", "2. You want to predict your grade on your next CS exam.\n", "3. You just met someone at a party. You want to predict their mood tomorrow.\n", "````" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Bayesian Modeling Paradigm\n", "\n", "**Capturing Epistemic Uncertainty.** So let's go back to the drawing board and rethink how we've been fitting models this whole time. So far, our approach has been finding the *single* model that maximizes the probability of our observed data: $\\theta^\\text{MLE} = \\mathrm{argmax}_\\theta \\log p(\\mathcal{D}; \\theta)$. But isn't what we're actually interested is the *distribution* of models given the data, $p(\\theta | \\mathcal{D})$? In other words, conditioned on the data we've observed so far, we want to know which models (represented by their parameters, $\\theta$) are likely to fit the data well. In this new paradigm, we hope that:\n", "1. $p(\\theta | \\mathcal{D})$ will capture a diversity of models with different inductive biases.\n", "2. We can make our assumptions clear, and we can specify what type of inductive biases are appropriate for our task.\n", "\n", "And assuming we could compute this distribution, $p(\\theta | \\mathcal{D})$, how would we actually use it for fitting a model? The process goes something like this:\n", "1. **Prior:** Prior to having observed data, we express our beliefs about possible sets of parameters, $\\theta$. Our beliefs don't have to be correct, just reasonable. We then encode our beliefs into a distribution, $p_\\theta(\\cdot)$, called the \"prior.\" For example, we scientifically believe that as age increases, glow decreases. As such, we force the slope to be negative by setting $p_\\theta(\\cdot)$ to be a Normal distribution centered at some negative number. \n", "2. **Likelihood:** Having observed data, we can score how well any set of parameters, $\\theta$, from the prior fits the data by evaluating $p(\\mathcal{D} | \\theta)$, the joint data likelihood. This distribution is the *very same* distribution we've worked with in all previous chapters.\n", "3. **Posterior Update:** Post observing data, we *update* our beliefs about $\\theta$. We do this by computing $p(\\theta | \\mathcal{D})$. Observing data will help us reduce the initial uncertainty from the prior, honing in on a set of parameters that could explain the data well. As we show in a bit, this posterior update will depend on both the prior and the likelihood distributions. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Bayesian Models.** By having a prior distribution $\\theta$ to encode our beliefs, we now treat $\\theta$ as a *random variable*. This means that our generative process will now include an additional line, in which we sample $\\theta$ from the prior. For example, for Bayesian regression, our generative process is:\n", "\\begin{align}\n", "\\theta &\\sim p_\\theta(\\cdot) \\quad (\\text{prior}) \\\\\n", "y_n | x_n, \\theta &\\sim p_{Y | X}(\\cdot | x_n, \\theta) = \\mathcal{N}(\\mu(x_n; \\theta), \\sigma^2) \\quad (\\text{likelihood}) \\\\\n", "\\end{align}\n", "\n", "We can similarly depict our Bayesian model using a directed graphical model as follows:\n", "
\n", "
\n", " \n", "
\n", "
\n", "\n", "As you can see, the difference between the depiction of the non-Bayesian and the Bayesian regression is that $\\theta$ is in a circle, indicating its a random variable. Next, the circle is *white* (not filled in), indicating that $\\theta$ is not observed. Our goal will be to infer it given the data. \n", "\n", "Before defining $p(\\theta | \\mathcal{D})$, let's walk through an example to show you what this process looks like." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Illustration: Bayesian Regression.** Let's see what the Bayesian modeling paradigm looks like for regression, visually. We'll use the generative process from above, setting $\\sigma$ as a constant (so we can ignore it). We've picked an expressive function, $\\mu(x_n; \\theta)$, that will be fun to visualize---its details aren't important. \n", "\n", "Given our generative process, our goal is to sample the posterior,\n", "\\begin{align}\n", "p(\\theta | \\mathcal{D}) &= p(\\theta | x_1, \\dots, x_N, y_1, \\dots, y_n).\n", "\\end{align}\n", "For intuition, we can visualize posterior samples $\\theta \\sim p(\\theta | \\mathcal{D})$ by plotting the *functions* they represent, $\\mu(x_n; \\theta)$. The plot below shows samples from the posterior as the number of points, $N$, increases. \n", "\n", "```{figure} _static/figs/example_online_bayesian_regression.png\n", "---\n", "width: 100%\n", "name: bayesian-update-example\n", "align: center\n", "---\n", "\n", "Samples from the posterior of a Bayesian regression model, capturing epistemic uncertainty.\n", "```\n", "\n", "In the above plot, $N = 0$ represents our *prior*. The functions drawn from our prior illustrate our beliefs about which functions are appropriate for the data. In this specific case, our prior functions don't exhibit any strong trends; overall, the functions don't increase/decrease as age increases---they just wiggle about. However, the functions are incredibly smooth---another prior may have drawn more jagged functions. Whether this prior is appropriate for our task is up to you to decide. \n", "\n", "Next, we see what happens as we start observing data. As $N$ increases, you can see our prior distribution getting \"filtered out\" by the likelihood. By this, we mean that our posterior will sample functions that are both likely under the prior *and* likelihood. It therefore keeps samples from the prior that also go *through the data* to ensure the likelihood is high. As you can see, in regions of the input space near our observed data, the posterior is quite certain about the trend; it knows the function must pass close to the observed data. But as we move away from the observed data, the posterior maintains a diversity of possible functions. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model-Fitting via Bayes' Rule" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Bayes' Rule.** But what is $p(\\theta | \\mathcal{D})$, exactly? How can we possibly write down a distribution of models that fit the data well by hand? To avoid specifying this distribution by hand, we will use *Baye's rule* to write down $p(\\theta | \\mathcal{D})$ in terms of what we already know how to specify: the joint data likelihood, $p(\\mathcal{D} | \\theta)$, and the prior. \n", "\n", "Let's derive Bayes' rule in general before applying it to our problem. Recall from the chapter on joint probability that a joint distribution over two random variables, $A$ and $B$, can be factorized as follows:\n", "\\begin{align}\n", "p_{A, B}(a, b) &= p_{B | A}(b | a) \\cdot p_A(a) \\quad (\\text{Option 1}) \\\\\n", "&= p_{A | B}(a | b) \\cdot p_B(b) \\quad (\\text{Option 2})\n", "\\end{align}\n", "This means we can also equate the two factorizations:\n", "\\begin{align}\n", "p_{B | A}(b | a) \\cdot p_A(a) &= p_{A | B}(a | b) \\cdot p_B(b)\n", "\\end{align}\n", "Diving both sides by $p_A(a)$, we get:\n", "\\begin{align}\n", "p_{B | A}(b | a) &= \\frac{p_{A | B}(a | b) \\cdot p_B(b)}{p_A(a)} \\quad \\text{(Bayes' Rule)}\n", "\\end{align}\n", "This is Bayes' rule. What's cool about it is that relates $p_{B | A}(b | a)$ to $p_{A | B}(a | b)$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Bayesian Inference.** Using Bayes' rule in the context of our problem, let's treat $\\mathcal{D}$ *and* $\\theta$ as random variables. We can now relate $p(\\theta | \\mathcal{D})$, which we don't know how to specify, to $p(\\mathcal{D} | \\theta)$, which we do know how to specify:\n", "\\begin{align}\n", "\\underbrace{p(\\theta | \\mathcal{D})}_{\\text{posterior}} &= \\frac{\\overbrace{p(\\mathcal{D} | \\theta)}^{\\text{likelihood}} \\cdot \\overbrace{p(\\theta)}^{\\text{prior}}}{\\underbrace{p(\\mathcal{D})}_{\\text{normalizing const.}}}\n", "\\end{align}\n", "When used as a model-fitting paradigm, each term in Bayes' rule has a special name. We'll now define each:\n", "* **Likelihood:** This is the data joint likelihood, which we've previously maximized as part of the MLE.\n", " > For example, suppose we're fitting a linear regression model to predict an intergalactic being's glow given age. Our model is then:\n", " > \\begin{align}\n", " p(\\mathcal{D} | \\theta) &= \\prod\\limits_{n=1}^N p(\\mathcal{D}_n | \\theta) \\\\\n", " &= \\prod\\limits_{n=1}^N p_{Y | X}(y_n | x_n, \\theta) \\\\\n", " &= \\prod\\limits_{n=1}^N \\mathcal{N}(y_n | \\underbrace{\\theta_0 + \\theta_1 \\cdot x_n}_{\\mu(x_n; \\theta)}, \\sigma^2)\n", " \\end{align}\n", " > where $\\theta = \\{ \\theta_0, \\theta_1 \\}$ is the slope and intercept, and $\\sigma$ is observation noise variance (which we fix as a constant for now). \n", "* **Prior:** This is the distribution of models we're willing to consider *before having observed any data*. The prior allows us to specify our model's *inductive bias*.\n", " > Continuing with the above example, we know that in general, glow decreases with age. We can encode this belief into the inductive bias of the model by selecting an appropriate prior distribution---one for which the slope, $\\theta_1$, is likely negative. As an example, we could select, $\\mathcal{N}(-1, 0.1)$. In this way, $\\theta_1$ is most likely to be near $-1$. We can similarly encode our belief into the intercept, $\\theta_0$, saying we believe it should be positive: $\\mathcal{N}(1, 0.1)$. \n", " > Putting these together, we get the following prior distribution over our model parameters:\n", " > \\begin{align}\n", " p_\\theta(\\cdot) = p_{\\theta_1}(\\cdot) \\cdot p_{\\theta_0}(\\cdot) = \\mathcal{N}(-1, 0.1) \\cdot \\mathcal{N}(1, 0.1)\n", " \\end{align}\n", " In contrast to the ensembling approach, prior specification makes our assumptions about uncertainty explicit and easier to interrogate. \n", "* **Posterior:** This is the distribution of interest. It's called a posterior because it determines the distribution of likely models, $\\theta$, *after having observed data*. The posterior balances information from both the prior and the likelihood. \n", "* **Normalizing Constant:** This is a constant that turns the whole fraction into a valid probability density function (i.e. a function that integrates to 1). To compute $p(\\mathcal{D})$, we use the law of total probability:\n", " \\begin{align}\n", " p(\\mathcal{D}) &= \\int\\limits \\underbrace{p(\\mathcal{D} | \\theta) \\cdot p(\\theta)}_{\\text{numerator of Bayes' rule}} d\\theta\n", " \\end{align}\n", " Notice that the law of total probability tells us to integrate the numerator of Bayes' rule over the support of $\\theta$. In doing so, when we divide by it, the whole fraction integrates to $1$. In general, marginalizing out a variable via the law of total probability is intractable---there's no analytic solution to it, and approximating it is computationally too expensive. For now, we won't worry about how to compute this integral (or how to *avoid* computing it)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Computational Efficiency.** Unfortunately for us, for most models, Bayesian inference is *intractable*, meaning there exists no efficient algorithm for posterior sampling. As a result, we will have to resort to approximations. This is the main drawback of Bayesian inference. Approximate Bayesian inference is fascinating, but unfortunately, we will not get to study it here. We will, however, learn how to use some approximate inference algorithms. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayesian Models in `NumPyro`\n", "\n", "**The Model.** As you may have expected, writing out explicitly all parts of the model in math will now allow us to translate into `NumPyro`. And using some wizardry, `NumPyro` will do the heavy lifting for us, sampling from the model's posterior.\n", "\n", "The process of describing a Bayesian in `NumPyro` will actually not differ much from the process of writing its coding its non-Bayesian counterpart. We'll therefore start with the non-Bayesian version using the syntax you're already familiar with, and then we'll show you how to make it Bayesian. \n", "\n", "**Review: Non-Bayesian Linear Regression.** For the specific model we'll implement, we'll use a univariate linear regression model. Here's the non-Bayesian version." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def univariate_linear_regression(N, x, y=None):\n", " slope = numpyro.param(\n", " 'slope',\n", " jnp.array([-1.0]),\n", " constraint=C.real,\n", " )\n", "\n", " intercept = numpyro.param(\n", " 'intercept',\n", " jnp.array([1.0]),\n", " constraint=C.real,\n", " )\n", "\n", " std_dev = numpyro.param(\n", " 'std_dev',\n", " jnp.array(1.0),\n", " constraint=C.positive,\n", " )\n", "\n", " with numpyro.plate('data', N):\n", " mu = numpyro.deterministic('mu', slope * x / 100.0 + intercept)\n", " p_y_given_x = D.Normal(mu, std_dev)\n", " numpyro.sample('y', p_y_given_x, obs=y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above model, we use `numpyro.deterministic`. Notice how `cs349_sample_generative_process` returns variables created with `numpyro.param` and `numpyro.sample`? Recall that `numpyro.deterministic`, allows you to save all other variables. In this case, since we're interested in visualizing *epistemic uncertainty*, we want to visualize $\\mu(\\cdot; \\theta)$. This new primitive allows us to save it. When calling `cs349_sample_generative_process`, we'll now be able to see a new variable called `mu`.\n", "\n", "For completeness, let's also fit the non-Bayesian model using the MLE to our IHH data:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████| 10000/10000 [00:00<00:00, 17780.91it/s, init loss: 566.6539, avg. loss [9501-10000]: -382.8940]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] } ], "source": [ "NUM_ITERATIONS = 10000\n", "\n", "# Define an optimizer; here we chose the \"Adam\" algorithm\n", "optimizer = numpyro.optim.Adam(step_size=0.01)\n", "\n", "# Pick a random generator seed for the optimizer\n", "key_optimizer = jrandom.PRNGKey(seed=0)\n", "\n", "# Fit the model via the MLE\n", "result = cs349_mle(\n", " univariate_linear_regression, \n", " optimizer, \n", " key_optimizer, \n", " NUM_ITERATIONS,\n", " len(data), \n", " jnp.array(data['Age']), \n", " y=jnp.array(data['Telekinetic-Ability']),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the fitted model, we can make predictions as follows:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Make predictions for a set of test inputs\n", "x_test = jnp.linspace(0.0, 100.0, 100)\n", "\n", "# Make predictions\n", "samples = cs349_sample_generative_process(\n", " result.model_mle, \n", " jrandom.PRNGKey(seed=0), \n", " len(x_test), \n", " x_test,\n", ")\n", "\n", "y_pred = samples['mu']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how `samples` now contains `mu`, saved from `numpyro.deterministic`.\n", "\n", "Finally, let's plot its loss and trend against the data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n", "\n", "# Plot the loss\n", "axes[0].scatter(jnp.arange(NUM_ITERATIONS), result.losses)\n", "axes[0].set_xlabel('Optimization Step')\n", "axes[0].set_ylabel('Loss')\n", "axes[0].set_title('Convergence of MLE')\n", "\n", "# Plot the trend of the regression\n", "axes[1].plot(\n", " x_test, \n", " y_pred, \n", " color='blue', \n", " alpha=0.5,\n", " label=r'$\\mu(\\cdot; \\theta)$',\n", ")\n", "\n", "# Plot the data\n", "axes[1].scatter(\n", " data['Age'], data['Telekinetic-Ability'], \n", " color='black', marker='x', alpha=0.5, label='Data',\n", ")\n", "\n", "axes[1].set_xlabel('Age')\n", "axes[1].set_ylabel('Telekinetic Ability')\n", "axes[1].set_title('Non-Bayesian Linear Regression')\n", "\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, our gradient optimizer converged, and our model fits the data as well as any linear model can fit a non-linear trend.\n", "\n", "**Bayesian Linear Regression.** The main difference between the non-Bayesian and the Bayesian versions of the model is that we no longer have fixed parameters. Our parameters are now random variables with distributions (or priors). As such, instead of using the primitive `numpyro.param`, we will use `numpyro.sample`. For example, instead of describing `slope` as follows:\n", "```\n", "slope = numpyro.param(\n", " 'slope',\n", " jnp.array([-1.0]),\n", " constraint=C.real,\n", ")\n", "```\n", "we instead write:\n", "```\n", "p_slope = D.Normal(-1.0, 0.1)\n", "slope = numpyro.sample('slope', p_slope)\n", "```\n", "In this example, we specified that the prior distribution for `slope` is a Gaussian, centered at $-1.0$. Lastly, notice that, unlike in `numpyro.sample('y', p_y_given_x, obs=y)`, when sampling the slope we *do not* pass in `obs=`. This is because we have not *observed* slope in the data---this tells `NumPyro` we'd like to *infer* it.\n", "\n", "Replacing all fixed parameters with random variables yields the following model:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def univariate_bayesian_linear_regression(N, x, y=None):\n", " # Prior for the slope\n", " p_slope = D.Normal(-1.0, 0.1)\n", " slope = numpyro.sample('slope', p_slope)\n", "\n", " # Prior for the intercept\n", " p_intercept = D.Normal(-1.0, 0.1)\n", " intercept = numpyro.sample('intercept', p_intercept)\n", "\n", " # Inverse gamma is a standard prior for the observation noise variance\n", " # To get the standard deviation, we just take the square-root\n", " p_var = D.InverseGamma(3.0, rate=0.5)\n", " var = numpyro.sample('var', p_var)\n", " std_dev = jnp.sqrt(var)\n", "\n", " # Same as in the non-Bayesian version\n", " with numpyro.plate('data', N):\n", " mu = numpyro.deterministic('mu', slope * x / 100.0 + intercept)\n", " p_y_given_x = D.Normal(mu, std_dev)\n", " numpyro.sample('y', p_y_given_x, obs=y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** In these examples, we divide $x$ by $100.0$ when computing `mu` since Age is on a range from 0 to 100, which is quite large. This will numerically help our inference later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayesian Inference in `NumPyro`\n", "\n", "**Posterior Sampling.** Now that we have our Bayesian model, let's fit it by sampling from the posterior. We will do this using a helper function we've created, `cs349_bayesian_inference`. This function uses a class of algorithm called *Markov Chain Monte Carlo* (MCMC). We will not get into how such algorithms work, but there are a few things you should know about them:\n", "1. They are iterative.\n", "2. Given an infinite number of iterations, they will eventually draw samples from the model's posterior.\n", "3. Since we cannot run them for an infinite number of iterations, we have to take a few precautions. First, it takes MCMC a while to start drawing samples from the posterior. As a result, we toss out the first batch of samples drawn in what's called a \"warmup\" phase. Second, there are some diagnostics you can run to assess the quality of the samples. \n", "\n", "Our helper function takes in the following arguments:\n", "* A Bayesian `NumPyro` model.\n", "* A random generator key, used by the inference algorithm.\n", "* The number of warmup steps we'd like the algorithm to run for. To ensure your model converges, you should make this number as large as you can tolerate! \n", "* The number of samples to draw (after the warmup phase). \n", "* Finally, we pass in the arguments of the model. \n", "\n", "Putting all of this together, we get:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "sample: 100%|█████████████████████████████████████████████████| 25000/25000 [00:02<00:00, 10689.18it/s, 3 steps of size 4.04e-01. acc. prob=0.93]\n" ] } ], "source": [ "posterior_samples = cs349_bayesian_inference(\n", " univariate_bayesian_linear_regression, \n", " jrandom.PRNGKey(seed=0), \n", " 20000, \n", " 5000, \n", " len(data), \n", " jnp.array(data['Age']), \n", " y=jnp.array(data['Telekinetic-Ability']),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our helper function returns a dictionary, `posterior_samples`, the posterior samples. Have a look:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"intercept\" has shape = (5000,)\n", "\"mu\" has shape = (5000, 500)\n", "\"slope\" has shape = (5000,)\n", "\"var\" has shape = (5000,)\n" ] } ], "source": [ "for k, v in posterior_samples.items():\n", " print('\"{}\" has shape = {}'.format(k, v.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the $0$th dimension of all of the shapes is the number of samples drawn. \n", "\n", "**Visualizing the Posterior.** Since we have such a small number of parameters---the slope, intercept, and observation noise variance---let's visualize their posterior distributions. Specifically, let's visualize the joint distribution of the slope and intercept (darker means higher probability):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(6.5, 5.0))\n", "plt.hist2d(\n", " posterior_samples['slope'], posterior_samples['intercept'], \n", " bins=25, density=True, cmap='BuPu',\n", ")\n", "plt.xlabel('Slope')\n", "plt.ylabel('Intercept')\n", "plt.title('Posterior Distribution of the Slope and Intercept')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the above distribution, you can see that the slope and intercept are *negatively correlated*; as slope increases, the intercept decreases. But in the prior, we sampled the slope and intercept from two *independent* Gaussians---how come they are correlated in the posterior? This is because once we observe data, we the posterior hones in on models that fit the data well. As a result, if the slope increases, the intercept has to decrease to maintain good fit (and vice versa). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Predicting from Posterior Samples.** Looking at `posterior_samples`, notice that the shape of `mu` is the number of samples by number of training points. However, we would like to make predictions for non-training points. How can we do that? Using another helper function we've created: `cs349_sample_predictive`. This function takes in:\n", "* The `NumPyro` model.\n", "* A random generator key.\n", "* The posterior samples, returned from `cs349_bayesian_inference`.\n", "* Arguments needed for the model. In this case, the arguments are the test points.\n", "\n", "The functions will then apply posterior samples to the new test inputs, giving us a collection of `mu`s. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"mu\" has shape = (5000, 100)\n", "\"y\" has shape = (5000, 100)\n" ] } ], "source": [ "predictive_samples = cs349_sample_predictive(\n", " univariate_bayesian_linear_regression, \n", " jrandom.PRNGKey(seed=0),\n", " posterior_samples,\n", " len(x_test),\n", " x_test,\n", ")\n", "\n", "for k, v in predictive_samples.items():\n", " print('\"{}\" has shape = {}'.format(k, v.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can go ahead and visualize our samples of `mu`:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(5, 2.5))\n", "\n", "x_test = jnp.linspace(0.0, 100.0, 100)\n", "\n", "# Iterate over posterior samples and plot them\n", "# Plot every 5th sample so as not to overwhelm \n", "for mu in predictive_samples['mu'][::5]:\n", " plt.plot(\n", " x_test, \n", " mu, \n", " color='blue', \n", " alpha=0.01,\n", " )\n", "\n", "# Plot the training data\n", "plt.scatter(\n", " data['Age'], data['Telekinetic-Ability'], \n", " color='black', marker='x', alpha=0.5, \n", ")\n", "\n", "plt.xlabel('Age')\n", "plt.ylabel('Telekinetic Ability')\n", "plt.title('Bayesian Linear Regression')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see from the figure, we have a distribution over different linear functions that fit our data best. Of course, none of them are particularly good, since the model is non-linear. But in the region where we have less data (age $> 80$), the model is more uncertain. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing Bayesian Regression Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "````{admonition} Exercise: Compare Posteriors of Different Bayesian Regression Models\n", "\n", "Your goal is to develop a Bayesian predictive model for Telekinetic Ability given Age. We'll work with a smaller version of the data for this problem: `data/IHH-CTR-CGLF-regression-subsampled.csv`.\n", "\n", "**Part 1:** Implement a Bayesian polynomial regression model. Please use your previous implementations of the non-Bayesian versions of these models as your starting point. Use a standard Gaussian, $\\mathcal{N}(0, 1)$, for the prior over all parameters. \n", "\n", "**Part 2:** Implement a 1-layer Bayesian neural network. Please use your previous implementations of the non-Bayesian versions of these models as your starting point. Use a standard Gaussian, $\\mathcal{N}(0, 1)$, for the prior over all parameters (except the observation noise variance, $\\sigma^2$). (For this exercise, please do not use `neural_network_fn`, which provided only for the chapter on Factor Analysis). \n", "\n", "**Part 3:** Given the IHH data above, perform inference on the following models:\n", "* Bayesian linear regression (polynomial regression of degree 1)\n", "* Bayesian polynomial regression of degree 5\n", "* Bayesian polynomial regression of degree 6\n", "* Bayesian neural network regression with 1 layer, 20 hidden nodes, and sigmoid activation\n", "* Bayesian neural network regression with 1 layer, 20 hidden nodes, and ReLU activation\n", "\n", "**Part 4:** Plot their epistemic uncertainty (i.e. the distribution of `mu`). \n", "\n", "**Part 5:** How does each of their epistemic uncertainty differ?\n", "\n", "**Part 6:** For every model, change the standard deviation of the Normal priors. What happens to the epistemic uncertainty as you increase/decrease the standard deviation of the prior?\n", "````" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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": "3.10.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }