{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sequential Monte Carlo\n", "\n", ":::{post} Oct 19, 2021\n", ":tags: SMC \n", ":category: beginner\n", ":::" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on PyMC v4.0.0b6\n" ] } ], "source": [ "import arviz as az\n", "import numpy as np\n", "import pymc as pm\n", "import pytensor.tensor as pt\n", "\n", "print(f\"Running on PyMC v{pm.__version__}\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "az.style.use(\"arviz-darkgrid\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sampling from distributions with multiple peaks with standard MCMC methods can be difficult, if not impossible, as the Markov chain often gets stuck in either of the minima. A Sequential Monte Carlo sampler (SMC) is a way to ameliorate this problem.\n", "\n", "As there are many SMC flavors, in this notebook we will focus on the version implemented in PyMC.\n", "\n", "SMC combines several statistical ideas, including [importance sampling](https://en.wikipedia.org/wiki/Importance_sampling), tempering and MCMC. By tempering we mean the use of an auxiliary _temperature_ parameter to control the sampling process. To see how tempering can help let's write the posterior as:\n", "\n", "$$p(\\theta \\mid y)_{\\beta} \\propto p(y \\mid \\theta)^{\\beta} \\; p(\\theta)$$\n", "\n", "When $\\beta=0$ we have that $p(\\theta \\mid y)_{\\beta=0}$ is the prior distribution and when $\\beta=1$ we recover the _true_ posterior. We can think of $\\beta$ as a knob we can use to gradually _fade up_ the likelihood. This can be useful as in general sampling from the prior is easier than sampling from the posterior distribution. Thus we can use $\\beta$ to control the transition from an easy to sample distribution to a harder one.\n", "\n", "A summary of the algorithm is:\n", "\n", "1. Initialize $\\beta$ at zero and stage at zero.\n", "2. Generate N samples $S_{\\beta}$ from the prior (because when $\\beta = 0$ the tempered posterior is the prior).\n", "3. Increase $\\beta$ in order to make the effective sample size equals some predefined value (we use $Nt$, where $t$ is 0.5 by default).\n", "4. Compute a set of N importance weights $W$. The weights are computed as the ratio of the likelihoods of a sample at stage $i+1$ and stage $i$.\n", "5. Obtain $S_{w}$ by re-sampling according to $W$.\n", "6. Use $W$ to compute the mean and covariance for the proposal distribution, a MVNormal.\n", "7. For stages other than 0 use the acceptance rate from the previous stage to estimate n_steps.\n", "8. Run N independent Metropolis-Hastings (IMH) chains (each one of length n_steps), starting each one from a different sample in $S_{w}$. Samples are IMH as the proposal mean is the of the previous posterior stage and not the current point in parameter space.\n", "9. Repeat from step 3 until $\\beta \\ge 1$.\n", "10. The final result is a collection of $N$ samples from the posterior\n", "\n", "The algorithm is summarized in the next figure, the first subplot shows 5 samples (orange dots) at some particular stage. The second subplot shows how these samples are reweighted according to their posterior density (blue Gaussian curve). The third subplot shows the result of running a certain number of IMH steps, starting from the reweighted samples $S_{w}$ in the second subplot, notice how the two samples with the lower posterior density (smaller circles) are discarded and not used to seed new Markov chains.\n", "\n", "![SMC stages](smc.png)\n", "\n", "\n", "SMC samplers can also be interpreted in the light of genetic algorithms, which are biologically-inspired algorithms that can be summarized as follows:\n", "\n", "1. Initialization: set a population of individuals\n", "2. Mutation: individuals are somehow modified or perturbed\n", "3. Selection: individuals with high _fitness_ have higher chance to generate _offspring_.\n", "4. Iterate by using individuals from 3 to set the population in 1.\n", "\n", "If each _individual_ is a particular solution to a problem, then a genetic algorithm will eventually produce good solutions to that problem. One key aspect is to generate enough diversity (mutation step) in order to explore the solution space and hence avoid getting trap in local minima. Then we perform a _selection_ step to _probabilistically_ keep reasonable solutions while also keeping some diversity. Being too greedy and short-sighted could be problematic, _bad_ solutions in a given moment could lead to _good_ solutions in the future.\n", "\n", "For the SMC version implemented in PyMC we set the number of parallel Markov chains $N$ with the draws argument. At each stage SMC will use independent Markov chains to explore the _tempered posterior_ (the black arrow in the figure). The final samples, _i.e_ those stored in the trace, will be taken exclusively from the final stage ($\\beta = 1$), i.e. the _true_ posterior (\"true\" in the mathematical sense).\n", "\n", "The successive values of $\\beta$ are determined automatically (step 3). The harder the distribution is to sample the closer two successive values of $\\beta$ will be. And the larger the number of stages SMC will take. SMC computes the next $\\beta$ value by keeping the effective sample size (ESS) between two stages at a constant predefined value of half the number of draws. This can be adjusted if necessary by the threshold parameter (in the interval [0, 1])-- the current default of 0.5 is generally considered as a good default. The larger this value, the higher the target ESS and the closer two successive values of $\\beta$ will be. This ESS values are computed from the importance weights (step 4) and not from the autocorrelation like those from ArviZ (for example using az.ess or az.summary). \n", "\n", "Two more parameters that are automatically determined are:\n", "\n", "* The number of steps each Markov chain takes to explore the _tempered posterior_ n_steps. This is determined from the acceptance rate from the previous stage.\n", "* The covariance of the MVNormal proposal distribution is also adjusted adaptively based on the acceptance rate at each stage.\n", "\n", "As with other sampling methods, running a sampler more than one time is useful to compute diagnostics, SMC is no exception. PyMC will try to run at least two **SMC _chains_** (do not confuse with the $N$ Markov chains inside each SMC chain).\n", "\n", "Even when SMC uses the Metropolis-Hasting algorithm under the hood, it has several advantages over it:\n", "\n", "* It can sample from distributions with multiple peaks.\n", "* It does not have a burn-in period, it starts by sampling directly from the prior and then at each stage the starting points are already _approximately_ distributed according to the tempered posterior (due to the re-weighting step).\n", "* It is inherently parallel." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solving a PyMC model with SMC\n", "\n", "To see an example of how to use SMC inside PyMC let's define a multivariate Gaussian of dimension $n$ with two modes, the weights of each mode and the covariance matrix." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "n = 4\n", "\n", "mu1 = np.ones(n) * (1.0 / 2)\n", "mu2 = -mu1\n", "\n", "stdev = 0.1\n", "sigma = np.power(stdev, 2) * np.eye(n)\n", "isigma = np.linalg.inv(sigma)\n", "dsigma = np.linalg.det(sigma)\n", "\n", "w1 = 0.1 # one mode with 0.1 of the mass\n", "w2 = 1 - w1 # the other mode with 0.9 of the mass\n", "\n", "\n", "def two_gaussians(x):\n", " log_like1 = (\n", " -0.5 * n * pt.log(2 * np.pi)\n", " - 0.5 * pt.log(dsigma)\n", " - 0.5 * (x - mu1).T.dot(isigma).dot(x - mu1)\n", " )\n", " log_like2 = (\n", " -0.5 * n * pt.log(2 * np.pi)\n", " - 0.5 * pt.log(dsigma)\n", " - 0.5 * (x - mu2).T.dot(isigma).dot(x - mu2)\n", " )\n", " return pm.math.logsumexp([pt.log(w1) + log_like1, pt.log(w2) + log_like2])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initializing SMC sampler...\n", "Sampling 4 chains in 4 jobs\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", " \n", " 100.00% [100/100 00:00<00:00 Stage: 6 Beta: 1.000]\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " " ] } ], "source": [ "with pm.Model() as model:\n", " X = pm.Uniform(\n", " \"X\",\n", " shape=n,\n", " lower=-2.0 * np.ones_like(mu1),\n", " upper=2.0 * np.ones_like(mu1),\n", " initval=-1.0 * np.ones_like(mu1),\n", " )\n", " llk = pm.Potential(\"llk\", two_gaussians(X))\n", " idata_04 = pm.sample_smc(2000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see from the message that PyMC is running four **SMC chains** in parallel. As explained before this is useful for diagnostics. As with other samplers one useful diagnostics is the plot_trace, here we use kind=\"rank_vlines\" as rank plots as generally more useful than the classical \"trace\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Estimated w1 = 0.907'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "