{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bayesian regression with linear basis function models\n", "\n", "This article is an introduction to Bayesian regression with linear basis function models. After a short overview of the relevant mathematical results and their intuition, Bayesian linear regression is implemented from scratch with [NumPy](http://www.numpy.org/) followed by an example how [scikit-learn](https://scikit-learn.org/stable/) can be used to obtain equivalent results. It is assumed that you already have a basic understanding probability distributions and [Bayes' theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem). For a detailed mathematical coverage I recommend reading chapter 3 of [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) (PRML) but this is not necessary for following this article. \n", "\n", "## Linear basis function models\n", "\n", "Linear regression models share the property of being linear in their parameters but not necessarily in their input variables. Using non-linear basis functions of input variables, linear models are able model arbitrary non-linearities from input variables to targets. Polynomial regression is such an example and will be demonstrated later. A linear regression model $y(\\mathbf{x}, \\mathbf{w})$ can therefore be defined more generally as\n", "\n", "$$\n", "y(\\mathbf{x}, \\mathbf{w}) = w_0 + \\sum_{j=1}^{M-1}{w_j \\phi_j(\\mathbf{x})} = \\sum_{j=0}^{M-1}{w_j \\phi_j(\\mathbf{x})} = \\mathbf{w}^T \\boldsymbol\\phi(\\mathbf{x}) \\tag{1}\n", "$$\n", "\n", "where $\\phi_j$ are basis functions and $M$ is the total number of parameters $w_j$ including the bias term $w_0$. Here, we use the convention $\\phi_0(\\mathbf{x}) = 1$. The simplest form of linear regression models are also linear functions of their input variables i.e. the set of basis functions in this case is the identity $\\boldsymbol\\phi(\\mathbf{x}) = \\mathbf{x}$. The target variable $t$ of an observation $\\mathbf{x}$ is given by a deterministic function $y(\\mathbf{x}, \\mathbf{w})$ plus additive random noise $\\epsilon$. \n", "\n", "$$\n", "t = y(\\mathbf{x}, \\mathbf{w}) + \\epsilon \\tag{2}\n", "$$\n", "\n", "We make the assumption that the noise is normally distributed i.e. follows a Gaussian distribution with zero mean and precision (= inverse variance) $\\beta$. The corresponding probabilistic model i.e. the conditional distribution of $t$ given $\\mathbf{x}$ can therefore be written as\n", "\n", "$$\n", "p(t \\lvert \\mathbf{x}, \\mathbf{w}, \\beta) = \n", "\\mathcal{N}(t \\lvert y(\\mathbf{x}, \\mathbf{w}), \\beta^{-1}) =\n", "\\sqrt{\\beta \\over {2 \\pi}} \\exp\\left(-{\\beta \\over 2} (t - y(\\mathbf{x}, \\mathbf{w}))^2 \\right) \\tag{3}\n", "$$\n", "\n", "where the mean of this distribution is the regression function $y(\\mathbf{x}, \\mathbf{w})$. \n", "\n", "## Likelihood function\n", "\n", "For fitting the model and for inference of model parameters we use a training set of $N$ independent and identically distributed (i.i.d.) observations $\\mathbf{x}_1,\\ldots,\\mathbf{x}_N$ and their corresponding targets $t_1,\\ldots,t_N$. After combining column vectors $\\mathbf{x}_i$ into matrix $\\mathbf{X}$, where $\\mathbf{X}_{i,:} = \\mathbf{x}_i^T$, and scalar targets $t_i$ into column vector $\\mathbf{t}$ the joint conditional probability of targets $\\mathbf{t}$ given $\\mathbf{X}$ can be formulated as\n", "\n", "$$\n", "p(\\mathbf{t} \\lvert \\mathbf{X}, \\mathbf{w}, \\beta) = \n", "\\prod_{i=1}^{N}{\\mathcal{N}(t_i \\lvert \\mathbf{w}^T \\boldsymbol\\phi(\\mathbf{x}_i), \\beta^{-1})} \\tag{4}\n", "$$\n", "\n", "This is a function of parameters $\\mathbf{w}$ and $\\beta$ and is called the *likelihood function*. For better readability, it will be written as $p(\\mathbf{t} \\lvert \\mathbf{w}, \\beta)$ instead of $p(\\mathbf{t} \\lvert \\mathbf{X}, \\mathbf{w}, \\beta)$ from now on. The log of the likelihood function can be written as \n", "\n", "$$\n", "\\log p(\\mathbf{t} \\lvert \\mathbf{w}, \\beta) = \n", "{N \\over 2} \\log \\beta - \n", "{N \\over 2} \\log {2 \\pi} - \n", "\\beta E_D(\\mathbf{w}) \\tag{5}\n", "$$\n", "\n", "where $E_D(\\mathbf{w})$ is the sum-of-squares error function coming from the exponent of the likelihood function.\n", "\n", "$$\n", "E_D(\\mathbf{w}) = \n", "{1 \\over 2} \\sum_{i=1}^{N}(t_i - \\mathbf{w}^T \\boldsymbol\\phi(\\mathbf{x}_i))^2 = \n", "{1 \\over 2} \\lVert \\mathbf{t} - \\boldsymbol\\Phi \\mathbf{w} \\rVert^2 \\tag{6}\n", "$$\n", "\n", "Matrix $\\boldsymbol\\Phi$ is called the *design matrix* and is defined as\n", "\n", "$$\n", "\\boldsymbol\\Phi = \n", "\\begin{pmatrix}\n", "\\phi_0(\\mathbf{x}_1) & \\phi_1(\\mathbf{x}_1) & \\cdots & \\phi_{M-1}(\\mathbf{x}_1) \\\\ \n", "\\phi_0(\\mathbf{x}_2) & \\phi_1(\\mathbf{x}_2) & \\cdots & \\phi_{M-1}(\\mathbf{x}_2) \\\\\n", "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", "\\phi_0(\\mathbf{x}_N) & \\phi_1(\\mathbf{x}_N) & \\cdots & \\phi_{M-1}(\\mathbf{x}_N)\n", "\\end{pmatrix} \\tag{7}\n", "$$\n", "\n", "## Maximum likelihood\n", "\n", "Maximizing the log likelihood (= minimizing the sum-of-squares error function) w.r.t. $\\mathbf{w}$ gives the maximum likelihood estimate of parameters $\\mathbf{w}$. Maximum likelihood estimation can lead to severe over-fitting if complex models (e.g. polynomial regression models of high order) are fit to datasets of limited size. A common approach to prevent over-fitting is to add a regularization term to the error function. As we will see shortly, this regularization term arises naturally when following a Bayesian approach (more precisely, when defining a prior distribution over parameters $\\mathbf{w}$). \n", "\n", "## Bayesian approach\n", "\n", "### Prior and posterior distribution\n", "\n", "For a Bayesian treatment of linear regression we need a prior probability distribution over model parameters $\\mathbf{w}$. For reasons of simplicity, we will use an isotropic Gaussian distribution over parameters $\\mathbf{w}$ with zero mean:\n", "\n", "$$\n", "p(\\mathbf{w} \\lvert \\alpha) = \\mathcal{N}(\\mathbf{w} \\lvert \\mathbf{0}, \\alpha^{-1}\\mathbf{I}) \\tag{8}\n", "$$\n", "\n", "An isotropic Gaussian distribution has a diagonal covariance matrix where all diagonal elements have the same variance $\\alpha^{-1}$ ($\\alpha$ is the precision of the prior). A zero mean favors small(er) values of parameters $w_j$ a priori. The prior is [conjugate](https://en.wikipedia.org/wiki/Conjugate_prior) to the likelihood $p(\\mathbf{t} \\lvert \\mathbf{w}, \\beta)$ meaning that the posterior distribution has the same functional form as the prior i.e. is also a Gaussian. In this special case, the posterior has an analytical solution with the following sufficient statistics \n", "\n", "\n", "\\begin{align*}\n", "\\mathbf{m}_N &= \\beta \\mathbf{S}_N \\boldsymbol\\Phi^T \\mathbf{t} \\tag{9} \\\\\n", "\\mathbf{S}_N^{-1} &= \\alpha\\mathbf{I} + \\beta \\boldsymbol\\Phi^T \\boldsymbol\\Phi \\tag{10}\n", "\\end{align*}\n", "\n", "\n", "$(9)$ is the mean vector of the posterior and $(10)$ the inverse covariance matrix (= precision matrix). Hence, the posterior distribution can be written as\n", "\n", "$$\n", "p(\\mathbf{w} \\lvert \\mathbf{t}, \\alpha, \\beta) = \\mathcal{N}(\\mathbf{w} \\lvert \\mathbf{m}_N, \\mathbf{S}_N) \\tag{11}\n", "$$\n", "\n", "For the moment, we assume that the values of $\\alpha$ and $\\beta$ are known. Since the posterior is proportional to the product of likelihood and prior, the log of the posterior distribution is proportional to the sum of the log likelihood and the log of the prior\n", "\n", "$$\n", "\\log p(\\mathbf{w} \\lvert \\mathbf{t}, \\alpha, \\beta) = \n", "-\\beta E_D(\\mathbf{w}) - \\alpha E_W(\\mathbf{w}) + \\mathrm{const.} \\tag{12}\n", "$$\n", "\n", "where $E_D(\\mathbf{w})$ is defined by $(6)$ and \n", "\n", "$$\n", "E_W(\\mathbf{w}) = {1 \\over 2} \\mathbf{w}^T \\mathbf{w} \\tag{13}\n", "$$\n", "\n", "Maximizing the log posterior w.r.t. $\\mathbf{w}$ gives the [maximum-a-posteriori](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) (MAP) estimate of $\\mathbf{w}$. Maximizing the log posterior is equivalent to minimizing the sum-of-squares error function $E_D$ plus a quadratic regularization term $E_W$. This particular form regularization is known as *L2 regularization* or *weight decay* as it limits the magnitude of weights $w_j$. The contribution of the regularization term is determined by the ratio $\\alpha / \\beta$.\n", "\n", "### Posterior predictive distribution\n", "\n", "For making a prediction $t$ at a new location $\\mathbf{x}$ we use the posterior predictive distribution which is defined as\n", "\n", "$$\n", "p(t \\lvert \\mathbf{x}, \\mathbf{t}, \\alpha, \\beta) = \n", "\\int{p(t \\lvert \\mathbf{x}, \\mathbf{w}, \\beta) p(\\mathbf{w} \\lvert \\mathbf{t}, \\alpha, \\beta) d\\mathbf{w}} \\tag{14}\n", "$$\n", "\n", "The posterior predictive distribution includes uncertainty about parameters $\\mathbf{w}$ into predictions by weighting the conditional distribution $p(t \\lvert \\mathbf{x}, \\mathbf{w}, \\beta)$ with the posterior probability of weights $p(\\mathbf{w} \\lvert \\mathbf{t}, \\alpha, \\beta)$ over the entire weight parameter space. By using the predictive distribution we're not only getting the expected value of $t$ at a new location $\\mathbf{x}$ but also the uncertainty for that prediction. In our special case, the posterior predictive distribution is a Gaussian distribution\n", "\n", "$$\n", "p(t \\lvert \\mathbf{x}, \\mathbf{t}, \\alpha, \\beta) = \n", "\\mathcal{N}(t \\lvert \\mathbf{m}_N^T \\boldsymbol\\phi(\\mathbf{x}), \\sigma_N^2(\\mathbf{x})) \\tag{15}\n", "$$\n", "\n", "where mean $\\mathbf{m}_N^T \\boldsymbol\\phi(\\mathbf{x})$ is the regression function after $N$ observations and $\\sigma_N^2(\\mathbf{x})$ is the corresponding predictive variance\n", "\n", "$$\n", "\\sigma_N^2(\\mathbf{x}) = {1 \\over \\beta} + \\boldsymbol\\phi(\\mathbf{x})^T \\mathbf{S}_N \\boldsymbol\\phi(\\mathbf{x}) \\tag{16}\n", "$$\n", "\n", "The first term in $(16)$ represents the inherent noise in the data and the second term covers the uncertainty about parameters $\\mathbf{w}$. So far, we have assumed that the values of $\\alpha$ and $\\beta$ are known. In a fully Bayesian treatment, however, we should define priors over $\\alpha$ and $\\beta$ and use the corresponding posteriors to additionally include uncertainties about $\\alpha$ and $\\beta$ into predictions. Unfortunately, complete integration over all three parameters $\\mathbf{w}$, $\\alpha$ and $\\beta$ is analytically intractable and we have to use another approach.\n", "\n", "### Evidence function\n", "\n", "Estimates for $\\alpha$ and $\\beta$ can alternatively be obtained by first integrating the product of likelihood and prior over parameters $\\mathbf{w}$\n", "\n", "$$\n", "p(\\mathbf{t} \\lvert \\alpha, \\beta) =\n", "\\int{p(\\mathbf{t} \\lvert \\mathbf{w}, \\beta) p(\\mathbf{w} \\lvert \\alpha) d\\mathbf{w}} \\tag{17}\n", "$$\n", "\n", "and then maximizing the resulting *marginal likelihood* or *evidence function* w.r.t. $\\alpha$ and $\\beta$. This approach is known as [empirical Bayes](https://en.wikipedia.org/wiki/Empirical_Bayes_method). It can be shown that this is a good approximation for a fully Bayesian treatment if the posterior for $\\alpha$ and $\\beta$ is sharply peaked around the most probable value and the prior is relatively flat which is often a reasonable assumption. Integrating over model parameters or using a good approximation for it allows us to estimate values for $\\alpha$ and $\\beta$, and hence the regularization strength $\\alpha / \\beta$, from training data alone i.e. without using a validation set. \n", "\n", "The log of the marginal likelihood is given by\n", "\n", "$$\n", "\\log p(\\mathbf{t} \\lvert \\alpha, \\beta) = {M \\over 2} \\log \\alpha + {N \\over 2} \\log \\beta -\n", "E(\\mathbf{m}_N) - {1 \\over 2} \\log \\lvert \\mathbf{S}_N^{-1}\\rvert - {N \\over 2} \\log {2 \\pi} \\tag{18}\n", "$$\n", "\n", "where\n", "\n", "$$\n", "E(\\mathbf{m}_N) = {\\beta \\over 2} \\lVert \\mathbf{t} - \\boldsymbol\\Phi \\mathbf{m}_N \\rVert^2 +\n", "{\\alpha \\over 2} \\mathbf{m}_N^T \\mathbf{m}_N \\tag{19}\n", "$$\n", "\n", "For completeness, the relationship between evidence, likelihood, prior, posterior is of course given by Bayes' theorem\n", "\n", "$$\n", "p(\\mathbf{w} \\lvert \\mathbf{t}, \\alpha, \\beta) = \n", "{p(\\mathbf{t} \\lvert \\mathbf{w}, \\beta) p(\\mathbf{w} \\lvert \\alpha) \\over p(\\mathbf{t} \\lvert \\alpha, \\beta)} \\tag{20}\n", "$$\n", "\n", "#### Maximization\n", "\n", "Maximization of the log marginal likelihood w.r.t. $\\alpha$ and $\\beta$ gives the following implicit solutions.\n", "\n", "$$\n", "\\alpha = {\\gamma \\over \\mathbf{m}_N^T \\mathbf{m}_N} \\tag{21}\n", "$$\n", "\n", "and \n", "\n", "$$\n", "{1 \\over \\beta} = {1 \\over N - \\gamma} \\sum_{i=1}^{N}(t_i - \\mathbf{m}_N^T \\boldsymbol\\phi(\\mathbf{x}_i))^2 \\tag{22}\n", "$$\n", "\n", "where \n", "\n", "$$\n", "\\gamma = \\sum_{i=0}^{M-1} {\\lambda_i \\over \\alpha + \\lambda_i} \\tag{23}\n", "$$\n", "\n", "\n", "and $\\lambda_i$ are the [eigenvalues](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors) of $\\beta \\boldsymbol\\Phi^T \\boldsymbol\\Phi$. The solutions are implicit because $\\alpha$ and $\\gamma$ as well as $\\beta$ and $\\gamma$ depend on each other. Solutions for $\\alpha$ and $\\beta$ can therefore be obtained by starting with initial values for these parameters and then iterating over the above equations until convergence.\n", "\n", "#### Evaluation\n", "\n", "Integration over model parameters also makes models of different complexity directly comparable by evaluating their evidence function on training data alone without needing a validation set. Further below we'll see an example how polynomial models of different complexity (i.e. different polynomial degree) can be compared directly by evaluating their evidence function alone. The highest evidence is usually obtained for models of intermediate complexity i.e. for models whose complexity is just high enough for explaining the data sufficiently well. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation\n", "\n", "### Posterior and posterior predictive distribution\n", "\n", "We start with the implementation of the posterior and posterior predictive distributions. Function posterior computes the mean and covariance matrix of the posterior distribution and function posterior_predictive computes the mean and the variances of the posterior predictive distribution. Here, readability of code and similarity to the mathematical definitions has higher priority than optimizations." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "def posterior(Phi, t, alpha, beta, return_inverse=False):\n", " \"\"\"Computes mean and covariance matrix of the posterior distribution.\"\"\"\n", " S_N_inv = alpha * np.eye(Phi.shape[1]) + beta * Phi.T.dot(Phi)\n", " S_N = np.linalg.inv(S_N_inv)\n", " m_N = beta * S_N.dot(Phi.T).dot(t)\n", "\n", " if return_inverse:\n", " return m_N, S_N, S_N_inv\n", " else:\n", " return m_N, S_N\n", "\n", "\n", "def posterior_predictive(Phi_test, m_N, S_N, beta):\n", " \"\"\"Computes mean and variances of the posterior predictive distribution.\"\"\"\n", " y = Phi_test.dot(m_N)\n", " # Only compute variances (diagonal elements of covariance matrix)\n", " y_var = 1 / beta + np.sum(Phi_test.dot(S_N) * Phi_test, axis=1)\n", " \n", " return y, y_var" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example datasets\n", "\n", "The datasets used in the following examples are based on $N$ scalar observations $x_{i = 1,\\ldots,N}$ which are combined into a $N \\times 1$ matrix $\\mathbf{X}$. Target values $\\mathbf{t}$ are generated from $\\mathbf{X}$ with functions f and g which also generate random noise whose variance can be specified with the noise_variance parameter. We will use f for generating noisy samples from a straight line and g for generating noisy samples from a sinusoidal function." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "f_w0 = -0.3\n", "f_w1 = 0.5\n", "\n", "\n", "def f(X, noise_variance):\n", " '''Linear function plus noise'''\n", " return f_w0 + f_w1 * X + noise(X.shape, noise_variance)\n", "\n", "\n", "def g(X, noise_variance):\n", " '''Sinusoidial function plus noise'''\n", " return 0.5 + np.sin(2 * np.pi * X) + noise(X.shape, noise_variance)\n", "\n", "\n", "def noise(size, variance):\n", " return np.random.normal(scale=np.sqrt(variance), size=size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basis functions\n", "\n", "For straight line fitting, a model that is linear in its input variable $x$ is sufficient. Hence, we don't need to transform $x$ with a basis function which is equivalent to using an identity_basis_function. For fitting a linear model to a sinusoidal dataset we transform input $x$ with gaussian_basis_function and later with polynomial_basis_function. These non-linear basis functions are necessary to model the non-linear relationship between input $x$ and target $t$. The design matrix $\\boldsymbol\\Phi$ can be computed from observations $\\mathbf{X}$ and a parametric basis function with function expand. This function also prepends a column vector $\\mathbf{1}$ according to $\\phi_0(x) = 1$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def identity_basis_function(x):\n", " return x\n", "\n", "\n", "def gaussian_basis_function(x, mu, sigma=0.1):\n", " return np.exp(-0.5 * (x - mu) ** 2 / sigma ** 2)\n", "\n", "\n", "def polynomial_basis_function(x, power):\n", " return x ** power\n", "\n", "\n", "def expand(x, bf, bf_args=None):\n", " if bf_args is None:\n", " return np.concatenate([np.ones(x.shape), bf(x)], axis=1)\n", " else:\n", " return np.concatenate([np.ones(x.shape)] + [bf(x, bf_arg) for bf_arg in bf_args], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Straight line fitting\n", "\n", "For straight line fitting, we use a linear regression model of the form $y(x, \\mathbf{w}) = w_0 + w_1 x$ and do Bayesian inference for model parameters $\\mathbf{w}$. Predictions are made with the posterior predictive distribution. Since this model has only two parameters, $w_0$ and $w_1$, we can visualize the posterior density in 2D which is done in the first column of the following output. Rows use an increasing number of training data from a training dataset." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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