{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(sampling_conjugate_step)=\n", "# Using a custom step method for sampling from locally conjugate posterior distributions\n", "\n", ":::{post} Nov 17, 2020\n", ":tags: sampling, step method\n", ":category: advanced\n", ":author: Christopher Krapu\n", ":::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Markov chain Monte Carlo (MCMC) sampling methods are fundamental to modern Bayesian inference. PyMC leverages Hamiltonian Monte Carlo (HMC), a powerful sampling algorithm that efficiently explores high-dimensional posterior distributions. Unlike simpler MCMC methods, HMC harnesses the gradient of the log posterior density to make intelligent proposals, allowing it to effectively sample complex posteriors with hundreds or thousands of parameters. A key advantage of HMC is its generality - it works with arbitrary prior distributions and likelihood functions, without requiring conjugate pairs or closed-form solutions. This is crucial since most real-world models involve priors and likelihoods whose product cannot be analytically integrated to obtain the posterior distribution. HMC's gradient-guided proposals make it dramatically more efficient than earlier MCMC approaches that rely on random walks or simple proposal distributions.\n", "\n", "However, these gradient computations can often be expensive for models with especially complicated functional dependencies between variables and observed data. When this is the case, we may wish to find a faster sampling scheme by making use of additional structure in some portions of the model. When a number of variables within the model are *conjugate*, the conditional posterior--that is, the posterior distribution holding all other model variables fixed--can often be sampled from very easily. This suggests using a HMC-within-Gibbs step in which we alternate between using cheap conjugate sampling for variables when possible, and using more expensive HMC for the rest. \n", "\n", "Generally, it is not advisable to pick *any* alternative sampling method and use it to replace HMC. This combination often yields much worse performance in terms of *effective* sampling rates, even if the individual samples are drawn much more rapidly. In this notebook, we show how to implement a conjugate sampling scheme in PyMC and compare it against a full-HMC (or, in this case, NUTS) approach. For this case, we find that using conjugate sampling can dramatically speed up computations for a Dirichlet-multinomial model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probabilistic model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To keep this notebook simple, we'll consider a relatively simple hierarchical model defined for $N$ observations of a vector of counts across $J$ outcomes::\n", "\n", "$$\\tau \\sim Exp(\\lambda)$$\n", "$$\\mathbf{p}_i \\sim Dir(\\tau )$$\n", "$$\\mathbf{x}_i \\sim Multinomial(\\mathbf{p}_i)$$\n", "\n", "The index $i\\in\\{1,...,N\\}$ represents the observation while $j\\in \\{1...,J\\}$ indexes the outcome. The variable $\\tau$ is a scalar concentration while $\\mathbf{p}_i$ is a $J$-vector of probabilities drawn from a Dirichlet prior with entries $(\\tau, \\tau, ..., \\tau)$. With fixed $\\tau$ and observed data $x$, we know that $\\mathbf{p}$ has a [closed-form posterior distribution](https://en.wikipedia.org/wiki/Dirichlet_distribution#Conjugate_to_categorical/multinomial), meaning that we can easily sample from it. Our sampling scheme will alternate between using the No-U-Turn sampler (NUTS) on $\\tau$ and drawing from this known conditional posterior distribution for $\\mathbf{p}_i$. We will assume a fixed value for $\\lambda$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementing a custom step method" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding a conjugate sampler as part of our compound sampling approach is straightforward: we define a new step method that examines the current state of the Markov chain approximation and modifies it by adding samples drawn from the conjugate posterior." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pymc as pm\n", "\n", "from pymc.distributions.transforms import simplex as stick_breaking\n", "from pymc.step_methods.arraystep import BlockedStep" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "RANDOM_SEED = 8927\n", "np.random.seed(RANDOM_SEED)\n", "az.style.use(\"arviz-darkgrid\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need a method for sampling from a Dirichlet distribution. The built in `numpy.random.dirichlet` can only handle 2D input arrays, and we might like to generalize beyond this in the future. Thus, I have created a function for sampling from a Dirichlet distribution with parameter array `c` by representing it as a normalized sum of Gamma random variables. More detail about this is given [here](https://en.wikipedia.org/wiki/Dirichlet_distribution#Gamma_distribution)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def sample_dirichlet(c):\n", " \"\"\"\n", " Samples Dirichlet random variables which sum to 1 along their last axis.\n", " \"\"\"\n", " gamma = np.random.gamma(c)\n", " p = gamma / gamma.sum(axis=-1, keepdims=True)\n", " return p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we define the step object used to replace NUTS for part of the computation. It must have a `step` method that receives a dict called `point` containing the current state of the Markov chain. We'll modify it in place.\n", "\n", "There is an extra complication here as PyMC does not track the state of the Dirichlet random variable in the form $\\mathbf{p}=(p_1, p_2 ,..., p_J)$ with the constraint $\\sum_j p_j = 1$. Rather, it uses an inverse stick breaking transformation of the variable which is easier to use with NUTS. This transformation removes the constraint that all entries must sum to 1 and are positive." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class ConjugateStep(BlockedStep):\n", " def __init__(self, var, counts: np.ndarray, concentration):\n", " self.vars = [var]\n", " self.counts = counts\n", " self.name = var.name\n", " self.conc_prior = concentration\n", " self.shared = {}\n", "\n", " def step(self, point: dict):\n", " # Since our concentration parameter is going to be log-transformed\n", " # in point, we invert that transformation so that we\n", " # can get conc_posterior = conc_prior + counts\n", " conc_posterior = np.exp(point[self.conc_prior.name + \"_log__\"]) + self.counts\n", " draw = sample_dirichlet(conc_posterior)\n", "\n", " # Since our new_p is not in the transformed / unconstrained space,\n", " # we apply the transformation so that our new value\n", " # is consistent with PyMC's internal representation of p\n", " point[self.name] = stick_breaking.forward(draw).eval()\n", "\n", " return point, [] # Return empty stats list as second element" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The usage of `point` and its indexing variables can be confusing here. This expression is necessary because when `step` is called, it is passed a dictionary `point` with string variable names as keys. \n", "\n", "However, the prior parameter's name won't be stored directly in the keys for `point` because PyMC stores a transformed variable instead. Thus, we will need to query `point` using the *transformed name* (hence, the `_log__` suffix) and then undo that transformation.\n", "\n", "To identify the correct variable to query into `point`, we need to take an argument during initialization that tells the sampling step where to find the prior parameter. Thus, we pass `var` into `ConjugateStep` so that the sampler can find the name of the transformed variable (`var.transformed.name`) later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulated data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll try out the sampler on some simulated data. Fixing $\\tau=0.5$, we'll draw 500 observations of a 10 dimensional Dirichlet distribution." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 10)\n" ] } ], "source": [ "J = 10\n", "N = 500\n", "\n", "ncounts = 20\n", "tau_true = 0.5\n", "alpha = tau_true * np.ones([N, J])\n", "p_true = sample_dirichlet(alpha)\n", "counts = np.zeros([N, J])\n", "\n", "for i in range(N):\n", " counts[i] = np.random.multinomial(ncounts, p_true[i])\n", "print(counts.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing partial conjugate with full NUTS sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't have any closed form expression for the posterior distribution of $\\tau$ so we will use NUTS on it. In the code cell below, we fit the same model using 1) conjugate sampling on the probability vectors with NUTS on $\\tau$, and 2) NUTS for everything." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sequential sampling (1 chains in 1 job)\n", "CompoundStep\n", ">ConjugateStep: [p]\n", ">NUTS: [tau]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5226dc9fa11e4f8d8ae5761bebd51d0f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 26527 seconds.\n", "Only one chain was sampled, this makes it impossible to run some convergence checks\n", "Initializing NUTS using jitter+adapt_diag...\n", "Sequential sampling (1 chains in 1 job)\n", "NUTS: [tau, p]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "51f002774dad471b9c0eecca302741d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 104 seconds.\n", "Only one chain was sampled, this makes it impossible to run some convergence checks\n" ] } ], "source": [ "traces = []\n", "models = []\n", "names = [\"Partial conjugate sampling\", \"Full NUTS\"]\n", "\n", "for use_conjugate in [True, False]:\n", " with pm.Model() as model:\n", " tau = pm.Exponential(\"tau\", lam=1, initval=1.0)\n", " alpha = pm.Deterministic(\"alpha\", tau * np.ones([N, J]))\n", " p = pm.Dirichlet(\"p\", a=alpha)\n", "\n", " if use_conjugate:\n", " # If we use the conjugate sampling, we don't need to define the likelihood\n", " # as it's already taken into account in our custom step method\n", " step = [ConjugateStep(model.rvs_to_values[p], counts, tau)]\n", "\n", " else:\n", " x = pm.Multinomial(\"x\", n=ncounts, p=p, observed=counts)\n", " step = []\n", "\n", " trace = pm.sample(step=step, chains=1, random_seed=RANDOM_SEED)\n", " traces.append(trace)\n", "\n", " # assert all(az.summary(trace)[\"r_hat\"] < 1.1)\n", " models.append(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the runtimes for the partially conjugate sampling are much lower, though this can be misleading if the samples have high autocorrelation or the chains are mixing very slowly. We also see that there are a few divergences in the NUTS-only trace." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to make sure that the two samplers are converging to the same estimates. The posterior histogram and trace plot below show that both essentially converge to $\\tau$ within reasonable posterior uncertainty credible intervals. We can also see that the trace plots lack any obvious autocorrelation as they are mostly indistinguishable from white noise." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "