{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "llE7btqvKM3m"
},
"source": [
"# High-dimensional Bayesian workflow, with applications to SARS-CoV-2 strains\n",
"\n",
"This tutorial describes a workflow for incrementally building pipelines to analyze high-dimensional data in Pyro. This workflow has evolved over a few years of applying Pyro to models with $10^5$ or more latent variables. We build on [Gelman et al. (2020)](https://arxiv.org/abs/2011.01808)'s concept of *Bayesian workflow*, and focus on aspects particular to high-dimensional models: approximate inference and numerical stability. While the individual components of the pipeline deserve their own tutorials, this tutorial focuses on incrementally combining those components.\n",
"\n",
"The fastest way to find a good model of your data is to quickly discard many bad models, i.e. to iterate.\n",
"In statistics we call this iterative workflow [Box's loop](http://www.cs.columbia.edu/~blei/papers/Blei2014b.pdf).\n",
"An efficient workflow allows us to discard bad models as quickly as possible.\n",
"Workflow efficiency demands that code changes to upstream components don't break previous coding effort on downstream components.\n",
"Pyro's approaches to this challenge include strategies for variational approximations ([pyro.infer.autoguide](https://docs.pyro.ai/en/stable/infer.autoguide.html)) and strategies for transforming model coordinate systems to improve geometry ([pyro.infer.reparam](https://docs.pyro.ai/en/stable/infer.reparam.html)).\n",
"\n",
"#### Summary\n",
"\n",
"- Great models can only be achieved by iterative development.\n",
"- Iterate quickly by building a pipeline that is robust to code changes.\n",
"- Start with a simple model and [mean-field inference](https://docs.pyro.ai/en/dev/infer.autoguide.html#autonormal).\n",
"- Avoid NANs by intelligently [initializing](https://docs.pyro.ai/en/dev/infer.autoguide.html#module-pyro.infer.autoguide.initialization) and [.clamp()](https://pytorch.org/docs/stable/generated/torch.clamp.html)ing.\n",
"- [Reparametrize](https://docs.pyro.ai/en/dev/infer.reparam.html) the model to improve geometry.\n",
"- Create a custom variational family by combining [AutoGuides](https://docs.pyro.ai/en/dev/infer.autoguide.html) or [EasyGuides](https://docs.pyro.ai/en/dev/contrib.easyguide.html).\n",
"\n",
"#### Table of contents\n",
"- [Overview](#Overview)\n",
"- [Running example: SARS-CoV-2 strain prediction](#Running-example)\n",
"1. [Clean the data](#Clean-the-data)\n",
"2. [Create a generative model](#Create-a-generative-model)\n",
"3. [Sanity check using mean-field inference](#Sanity-check)\n",
"4. [Create an initialization heuristic](#Create-an-initialization-heuristic)\n",
"5. [Reparametrize the model](#Reparametrize)\n",
"6. [Customize the variational family: autoguides, easyguides, custom guides](#Customize)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1Sj_aC_4KM3p"
},
"source": [
"## Overview \n",
"\n",
"Consider the problem of sampling from the posterior distribution of a probabilistic model with $10^5$ or more continuous latent variables, but whose data fits entirely in memory.\n",
"(For larger datasets, consider [amortized variational inference](http://pyro.ai/examples/svi_part_ii.html).) Inference in such high-dimensional models can be challenging even when posteriors are known to be [unimodal](https://en.wikipedia.org/wiki/Unimodality) or even [log-concave](https://arxiv.org/abs/1404.5886), due to correlations among latent variables.\n",
"\n",
"To perform inference in such high-dimensional models in Pyro, we have evolved a [workflow](https://arxiv.org/abs/2011.01808) to incrementally build data analysis pipelines combining variational inference, reparametrization effects, and ad-hoc initialization strategies. Our workflow is summarized as a sequence of steps, where validation after any step might suggest backtracking to change design decisions at a previous step.\n",
"\n",
"1. Clean the data.\n",
"2. Create a generative model.\n",
"3. Sanity check using MAP or mean-field inference.\n",
"4. Create an initialization heuristic.\n",
"5. Reparameterize the model, evaluating results under mean field VI.\n",
"6. Customize the variational family (autoguides, easyguides, custom guides).\n",
"\n",
"The crux of efficient workflow is to ensure changes don't break your pipeline. That is, after you build a number of pipeline stages, validate results, and decide to change one component in the pipeline, you'd like to minimize code changes needed in other components. The remainder of this tutorial describes these steps individually, then describes nuances of interactions among stages, then provides an example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running example: SARS-CoV-2 strain prediction \n",
"\n",
"The running example in this tutorial will be a model [(Obermeyer et al. 2022)](https://www.medrxiv.org/content/10.1101/2021.09.07.21263228v2) of the relative growth rates of different strains of the SARS-CoV-2 virus, based on [open data](https://docs.nextstrain.org/projects/ncov/en/latest/reference/remote_inputs.html) counting different [PANGO lineages](https://cov-lineages.org/) of viral genomic samples collected at different times around the world. There are about 2 million sequences in total.\n",
"\n",
"The model is a high-dimensional regression model with around 1000 coefficients, a multivariate logistic growth function (using a simple [torch.softmax()](https://pytorch.org/docs/stable/generated/torch.nn.functional.softmax.html)) and a [Multinomial](https://pytorch.org/docs/stable/distributions.html#multinomial) likelihood. While the number of coefficients is relatively small, there are about 500,000 local latent variables to estimate, and plate structure in the model should lead to an approximately block diagonal posterior covariance matrix. For an introduction to simple logistic growth models using this same dataset, see the [logistic growth tutorial](logistic-growth.html)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "njvgAMszKM3q"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using CPU\n"
]
}
],
"source": [
"from collections import defaultdict\n",
"from pprint import pprint\n",
"import functools\n",
"import math\n",
"import os\n",
"import torch\n",
"import pyro\n",
"import pyro.distributions as dist\n",
"import pyro.poutine as poutine\n",
"from pyro.distributions import constraints\n",
"from pyro.infer import SVI, Trace_ELBO\n",
"from pyro.infer.autoguide import (\n",
" AutoDelta,\n",
" AutoNormal,\n",
" AutoMultivariateNormal,\n",
" AutoLowRankMultivariateNormal,\n",
" AutoGuideList,\n",
" init_to_feasible,\n",
")\n",
"from pyro.infer.reparam import AutoReparam, LocScaleReparam\n",
"from pyro.nn.module import PyroParam\n",
"from pyro.optim import ClippedAdam\n",
"from pyro.ops.special import sparse_multinomial_likelihood\n",
"import matplotlib.pyplot as plt\n",
"\n",
"if torch.cuda.is_available():\n",
" print(\"Using GPU\")\n",
" torch.set_default_tensor_type(\"torch.cuda.FloatTensor\")\n",
"else:\n",
" print(\"Using CPU\")\n",
"smoke_test = ('CI' in os.environ)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZWFmW8HAKM3s"
},
"source": [
"## Clean the data \n",
"\n",
"Our running example will use a pre-cleaned dataset.\n",
"We started with Nextstrain's [ncov](https://docs.nextstrain.org/projects/ncov/en/latest/reference/remote_inputs.html) tool for preprocessing, followed by the Broad Institute's [pyro-cov](https://github.com/broadinstitute/pyro-cov/blob/master/scripts/preprocess_nextstrain.py) tool for aggregation, resulting in a dataset of SARS-CoV-2 lineages observed around the world through time."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "DlWE50HCKM3s"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"counts: Tensor of shape (27, 202, 1316) on cpu\n",
"features: Tensor of shape (1316, 2634) on cpu\n",
"lineages: list of length 1316\n",
"locations: list of length 202\n",
"mutations: list of length 2634\n",
"sparse_counts.index: Tensor of shape (3, 57129) on cpu\n",
"sparse_counts.total: Tensor of shape (27, 202) on cpu\n",
"sparse_counts.value: Tensor of shape (57129,) on cpu\n",
"start_date: datetime\n",
"time_step_days: int\n"
]
}
],
"source": [
"from pyro.contrib.examples.nextstrain import load_nextstrain_counts\n",
"dataset = load_nextstrain_counts()\n",
"\n",
"def summarize(x, name=\"\"):\n",
" if isinstance(x, dict):\n",
" for k, v in sorted(x.items()):\n",
" summarize(v, name + \".\" + k if name else k)\n",
" elif isinstance(x, torch.Tensor):\n",
" print(f\"{name}: {type(x).__name__} of shape {tuple(x.shape)} on {x.device}\")\n",
" elif isinstance(x, list):\n",
" print(f\"{name}: {type(x).__name__} of length {len(x)}\")\n",
" else:\n",
" print(f\"{name}: {type(x).__name__}\")\n",
"summarize(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6euQcq4SKM3s"
},
"source": [
"## Create a generative model\n",
"\n",
"The first step to using Pyro is creating a generative model, either a python function or a [pyro.nn.Module](https://docs.pyro.ai/en/dev/nn.html#pyro.nn.module.PyroModule). Start simple. Start with a shallow hierarchy and later add latent variables to share statistical strength. Start with a slice of your data then add a [plate](https://docs.pyro.ai/en/stable/primitives.html#pyro.primitives.plate) over multiple slices. Start with simple distributions like [Normal](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.Normal), [LogNormal](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.LogNormal), [Poisson](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.Poisson) and [Multinomial](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.Multinomial), then consider overdispersed versions like [StudentT](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.StudentT), [Gamma](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.Gamma), [GammaPoisson](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.GammaPoisson)/[NegativeBinomial](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.NegativeBinomial), and [DirichletMultinomial](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.DirichletMultinomial). Keep your model simple and readable so you can share it and get feedback from domain experts. Use [weakly informative priors](http://www.stat.columbia.edu/~gelman/presentations/weakpriorstalk.pdf)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S3XpW4EzKM3t"
},
"source": [
"We'll focus on a multivariate logistic growth model of competing SARS-CoV-2 strains, as described in [Obermeyer et al. (2022)](https://www.medrxiv.org/content/10.1101/2021.09.07.21263228v2). This model uses a numerically stable `logits` parameter in its multinomial likelihood, rather than a `probs` parameter. Similarly upstream variables `init`, `rate`, `rate_loc`, and `coef` are all in log-space. This will mean e.g. that a zero coefficient has multiplicative effect of 1.0, and a positive coefficient has multiplicative effect greater than 1.\n",
"\n",
"Note we scale `coef` by 1/100 because we want to model a very small number, but the automatic parts of Pyro and PyTorch work best for numbers on the order of 1.0 rather than very small numbers. When we later interpret `coef` in a volcano plot we'll need to duplicate this scaling factor."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "gGdvwzs9KM3t"
},
"outputs": [],
"source": [
"def model(dataset):\n",
" features = dataset[\"features\"]\n",
" counts = dataset[\"counts\"]\n",
" assert features.shape[0] == counts.shape[-1]\n",
" S, M = features.shape\n",
" T, P, S = counts.shape\n",
" time = torch.arange(float(T)) * dataset[\"time_step_days\"] / 5.5\n",
" time -= time.mean()\n",
" strain_plate = pyro.plate(\"strain\", S, dim=-1)\n",
" place_plate = pyro.plate(\"place\", P, dim=-2)\n",
" time_plate = pyro.plate(\"time\", T, dim=-3)\n",
"\n",
" # Model each region as multivariate logistic growth.\n",
" rate_scale = pyro.sample(\"rate_scale\", dist.LogNormal(-4, 2))\n",
" init_scale = pyro.sample(\"init_scale\", dist.LogNormal(0, 2))\n",
" with pyro.plate(\"mutation\", M, dim=-1):\n",
" coef = pyro.sample(\"coef\", dist.Laplace(0, 0.5))\n",
" with strain_plate:\n",
" rate_loc = pyro.deterministic(\"rate_loc\", 0.01 * coef @ features.T)\n",
" with place_plate, strain_plate:\n",
" rate = pyro.sample(\"rate\", dist.Normal(rate_loc, rate_scale))\n",
" init = pyro.sample(\"init\", dist.Normal(0, init_scale))\n",
" logits = init + rate * time[:, None, None]\n",
" \n",
" # Observe sequences via a multinomial likelihood.\n",
" with time_plate, place_plate:\n",
" pyro.sample(\n",
" \"obs\",\n",
" dist.Multinomial(logits=logits.unsqueeze(-2), validate_args=False),\n",
" obs=counts.unsqueeze(-2),\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FSwBD5tNKM3u"
},
"source": [
"The execution cost of this model is dominated by the multinomial likelihood over a large sparse count matrix."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "Hf2Qui8UKM3u"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"counts has 57129 / 7177464 nonzero elements\n"
]
}
],
"source": [
"print(\"counts has {:d} / {} nonzero elements\".format(\n",
" dataset['counts'].count_nonzero(), dataset['counts'].numel()\n",
"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qA0ZvbCsKM3u"
},
"source": [
"To speed up inference (and model iteration!) we'll replace the `pyro.sample(..., Multinomial)` likelihood with an equivalent but much cheaper `pyro.factor` statement using a helper `pyro.ops.sparse_multinomial_likelihood`. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "fCbB8bN2KM3v"
},
"outputs": [],
"source": [
"def model(dataset, predict=None):\n",
" features = dataset[\"features\"]\n",
" counts = dataset[\"counts\"]\n",
" sparse_counts = dataset[\"sparse_counts\"]\n",
" assert features.shape[0] == counts.shape[-1]\n",
" S, M = features.shape\n",
" T, P, S = counts.shape\n",
" time = torch.arange(float(T)) * dataset[\"time_step_days\"] / 5.5\n",
" time -= time.mean()\n",
"\n",
" # Model each region as multivariate logistic growth.\n",
" rate_scale = pyro.sample(\"rate_scale\", dist.LogNormal(-4, 2))\n",
" init_scale = pyro.sample(\"init_scale\", dist.LogNormal(0, 2))\n",
" with pyro.plate(\"mutation\", M, dim=-1):\n",
" coef = pyro.sample(\"coef\", dist.Laplace(0, 0.5))\n",
" with pyro.plate(\"strain\", S, dim=-1):\n",
" rate_loc = pyro.deterministic(\"rate_loc\", 0.01 * coef @ features.T)\n",
" with pyro.plate(\"place\", P, dim=-2):\n",
" rate = pyro.sample(\"rate\", dist.Normal(rate_loc, rate_scale))\n",
" init = pyro.sample(\"init\", dist.Normal(0, init_scale))\n",
" if predict is not None: # Exit early during evaluation.\n",
" probs = (init + rate * time[predict]).softmax(-1)\n",
" return probs\n",
" logits = (init + rate * time[:, None, None]).log_softmax(-1)\n",
"\n",
" # Observe sequences via a cheap sparse multinomial likelihood.\n",
" t, p, s = sparse_counts[\"index\"]\n",
" pyro.factor(\n",
" \"obs\",\n",
" sparse_multinomial_likelihood(\n",
" sparse_counts[\"total\"], logits[t, p, s], sparse_counts[\"value\"]\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UPtf7TPSKM3v"
},
"source": [
"## Sanity check using mean field inference\n",
"\n",
"Mean field Normal inference is cheap and robust, and is a good way to sanity check your posterior point estimate, even if the posterior uncertainty may be implausibly narrow. We recommend starting with an [AutoNormal](https://docs.pyro.ai/en/latest/infer.autoguide.html#autonormal) guide, and possibly setting `init_scale` to a small value like `init_scale=0.01` or `init_scale=0.001`.\n",
"\n",
"Note that while MAP estimating via [AutoDelta](https://docs.pyro.ai/en/latest/infer.autoguide.html#autodelta) is even cheaper and more robust than mean-field `AutoNormal`, `AutoDelta` is coordinate-system dependent and is not invariant to reparametrization. Because in our experience most models benefit from some reparameterization, we recommend `AutoNormal` over `AutoDelta` because `AutoNormal` is less sensitive to reparametrization (`AutoDelta` can give incorrect results in some reparametrized models)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "Uhl_-vMIKM3v"
},
"outputs": [],
"source": [
"def fit_svi(model, guide, lr=0.01, num_steps=1001, log_every=100, plot=True):\n",
" pyro.clear_param_store()\n",
" pyro.set_rng_seed(20211205)\n",
" if smoke_test:\n",
" num_steps = 2\n",
"\n",
" # Measure model and guide complexity.\n",
" num_latents = sum(\n",
" site[\"value\"].numel()\n",
" for name, site in poutine.trace(guide).get_trace(dataset).iter_stochastic_nodes()\n",
" if not site[\"infer\"].get(\"is_auxiliary\")\n",
" )\n",
" num_params = sum(p.unconstrained().numel() for p in pyro.get_param_store().values())\n",
" print(f\"Found {num_latents} latent variables and {num_params} learnable parameters\")\n",
" \n",
" # Save gradient norms during inference.\n",
" series = defaultdict(list)\n",
" def hook(g, series):\n",
" series.append(torch.linalg.norm(g.reshape(-1), math.inf).item())\n",
" for name, value in pyro.get_param_store().named_parameters():\n",
" value.register_hook(\n",
" functools.partial(hook, series=series[name + \" grad\"])\n",
" )\n",
"\n",
" # Train the guide.\n",
" optim = ClippedAdam({\"lr\": lr, \"lrd\": 0.1 ** (1 / num_steps)})\n",
" svi = SVI(model, guide, optim, Trace_ELBO())\n",
" num_obs = int(dataset[\"counts\"].count_nonzero())\n",
" for step in range(num_steps):\n",
" loss = svi.step(dataset) / num_obs\n",
" series[\"loss\"].append(loss)\n",
" median = guide.median() # cheap for autoguides\n",
" for name, value in median.items():\n",
" if value.numel() == 1:\n",
" series[name + \" mean\"].append(float(value))\n",
" if step % log_every == 0:\n",
" print(f\"step {step: >4d} loss = {loss:0.6g}\")\n",
"\n",
" # Plot series to assess convergence.\n",
" if plot:\n",
" plt.figure(figsize=(6, 6))\n",
" for name, Y in series.items():\n",
" if name == \"loss\":\n",
" plt.plot(Y, \"k--\", label=name, zorder=0)\n",
" elif name.endswith(\" mean\"):\n",
" plt.plot(Y, label=name, zorder=-1)\n",
" else:\n",
" plt.plot(Y, label=name, alpha=0.5, lw=1, zorder=-2)\n",
" plt.xlabel(\"SVI step\")\n",
" plt.title(\"loss, scalar parameters, and gradient norms\")\n",
" plt.yscale(\"log\")\n",
" plt.xscale(\"symlog\")\n",
" plt.xlim(0, None)\n",
" plt.legend(loc=\"best\", fontsize=8)\n",
" plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "-z-UBawaKM3w"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 538452 latent variables and 1068600 learnable parameters\n",
"step 0 loss = 273.123\n",
"step 100 loss = 63.2423\n",
"step 200 loss = 44.9539\n",
"step 300 loss = 34.8813\n",
"step 400 loss = 30.4243\n",
"step 500 loss = 27.5258\n",
"step 600 loss = 25.4543\n",
"step 700 loss = 23.9134\n",
"step 800 loss = 22.7201\n",
"step 900 loss = 21.8574\n",
"step 1000 loss = 21.2031\n",
"CPU times: user 3min 4s, sys: 2min 48s, total: 5min 52s\n",
"Wall time: 1min 47s\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"