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%% Cell type:markdown id:b146bc52 tags:
# About
In this notebook, we replace the ExactGP inference and log marginal
likelihood optimization by using sparse stochastic variational inference.
This serves as an example of the many methods `gpytorch` offers to make GPs
scale to large data sets.
$\newcommand{\ve}[1]{\mathit{\boldsymbol{#1}}}$
$\newcommand{\ma}[1]{\mathbf{#1}}$
$\newcommand{\pred}[1]{\rm{#1}}$
$\newcommand{\predve}[1]{\mathbf{#1}}$
$\newcommand{\test}[1]{#1_*}$
$\newcommand{\testtest}[1]{#1_{**}}$
$\newcommand{\dd}{{\rm{d}}}$
$\newcommand{\lt}[1]{_{\text{#1}}}$
$\DeclareMathOperator{\diag}{diag}$
$\DeclareMathOperator{\cov}{cov}$
%% Cell type:markdown id:f96e3304 tags:
# Imports, helpers, setup
%% Cell type:code id:193b2f13 tags:
``` python
##%matplotlib notebook
%matplotlib widget
##%matplotlib inline
```
%% Cell type:code id:c11c1030 tags:
``` python
import math
from collections import defaultdict
from pprint import pprint
import torch
import gpytorch
from matplotlib import pyplot as plt
from matplotlib import is_interactive
import numpy as np
from torch.utils.data import TensorDataset, DataLoader
from utils import extract_model_params, plot_samples
torch.set_default_dtype(torch.float64)
torch.manual_seed(123)
```
%% Cell type:markdown id:460d4c3d tags:
# Generate toy 1D data
Now we generate 10x more points as in the ExactGP case, still the inference
won't be much slower (exact GPs scale roughly as $N^3$). Note that the data we
use here is still tiny (1000 points is easy even for exact GPs), so the
method's usefulness cannot be fully exploited in our example
-- also we don't even use a GPU yet :).
%% Cell type:code id:f1a9647d tags:
``` python
def ground_truth(x, const):
return torch.sin(x) * torch.exp(-0.2 * x) + const
def generate_data(x, gaps=[[1, 3]], const=None, noise_std=None):
noise_dist = torch.distributions.Normal(loc=0, scale=noise_std)
y = ground_truth(x, const=const) + noise_dist.sample(
sample_shape=(len(x),)
)
msk = torch.tensor([True] * len(x))
if gaps is not None:
for g in gaps:
msk = msk & ~((x > g[0]) & (x < g[1]))
return x[msk], y[msk], y
const = 5.0
noise_std = 0.1
x = torch.linspace(0, 4 * math.pi, 1000)
X_train, y_train, y_gt_train = generate_data(
x, gaps=[[6, 10]], const=const, noise_std=noise_std
)
X_pred = torch.linspace(
X_train[0] - 2, X_train[-1] + 2, 200, requires_grad=False
)
y_gt_pred = ground_truth(X_pred, const=const)
print(f"{X_train.shape=}")
print(f"{y_train.shape=}")
print(f"{X_pred.shape=}")
fig, ax = plt.subplots()
ax.scatter(X_train, y_train, marker="o", color="tab:blue", label="noisy data")
ax.plot(X_pred, y_gt_pred, ls="--", color="k", label="ground truth")
ax.legend()
```
%% Cell type:markdown id:4ad60c5e tags:
# Define GP model
The model follows [this
example](https://docs.gpytorch.ai/en/stable/examples/04_Variational_and_Approximate_GPs/SVGP_Regression_CUDA.html)
based on [Hensman et al., "Scalable Variational Gaussian Process Classification",
2015](https://proceedings.mlr.press/v38/hensman15.html). The model is
"sparse" since it works with a set of *inducing* points $(\ma Z, \ve u),
\ve u=f(\ma Z)$ which is much smaller than the train data $(\ma X, \ve y)$.
See also [the GPJax
docs](https://docs.jaxgaussianprocesses.com/_examples/uncollapsed_vi) for a
nice introduction.
We have the same hyper parameters as before
* $\ell$ = `model.covar_module.base_kernel.lengthscale`
* $\sigma_n^2$ = `likelihood.noise_covar.noise`
* $s$ = `model.covar_module.outputscale`
* $m(\ve x) = c$ = `model.mean_module.constant`
plus additional ones, introduced by the approximations used:
* the learnable inducing points $\ma Z$ for the variational distribution
$q_{\ve\psi}(\ve u)$
* learnable parameters of the variational distribution $q_{\ve\psi}(\ve u)=\mathcal N(\ve m_u, \ma S)$: the
variational mean $\ve m_u$ and covariance $\ma S$ in form a lower triangular
matrix $\ma L$ such that $\ma S=\ma L\,\ma L^\top$
%% Cell type:code id:444929a3 tags:
``` python
class ApproxGPModel(gpytorch.models.ApproximateGP):
def __init__(self, Z):
# Approximate inducing value posterior q(u), u = f(Z), Z = inducing
# points (subset of X_train)
variational_distribution = (
gpytorch.variational.CholeskyVariationalDistribution(Z.size(0))
)
# Compute q(f(X)) from q(u)
variational_strategy = gpytorch.variational.VariationalStrategy(
self,
Z,
variational_distribution,
learn_inducing_locations=True,
)
super().__init__(variational_strategy)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RBFKernel()
)
def forward(self, x):
mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
likelihood = gpytorch.likelihoods.GaussianLikelihood()
n_train = len(X_train)
# Start values for inducing points Z, use 10% random sub-sample of X_train.
ind_points_fraction = 0.1
ind_idxs = torch.randperm(n_train)[: int(n_train * ind_points_fraction)]
model = ApproxGPModel(Z=X_train[ind_idxs])
```
%% Cell type:code id:5861163d tags:
``` python
# Inspect the model
print(model)
```
%% Cell type:code id:98032457 tags:
``` python
# Inspect the likelihood. In contrast to ExactGP, the likelihood is not part of
# the GP model instance.
print(likelihood)
```
%% Cell type:code id:7ec97687 tags:
``` python
# Default start hyper params
print("model params:")
pprint(extract_model_params(model))
print("likelihood params:")
pprint(extract_model_params(likelihood))
```
%% Cell type:code id:167d584c tags:
``` python
# Set new start hyper params (scalars only)
model.mean_module.constant = 3.0
model.covar_module.base_kernel.lengthscale = 1.0
model.covar_module.outputscale = 1.0
likelihood.noise_covar.noise = 0.3
```
%% Cell type:markdown id:6704ce2f tags:
# Fit GP to data: optimize hyper params
Now we optimize the GP hyper parameters by doing a GP-specific variational inference (VI),
where we optimize not the log marginal likelihood (ExactGP case),
but an ELBO (evidence lower bound) objective
Now we optimize the GP hyper parameters by doing a GP-specific variational
inference (VI), where we don't maximize the log marginal likelihood (ExactGP
case), but an ELBO ("evidence lower bound") objective -- a lower bound on the
marginal likelihood (the "evidence"). In variational inference, an ELBO objective
shows up when minimizing the KL divergence between
an approximate and the true posterior
$$
\max_\ve\zeta\left(\mathbb E_{q_{\ve\psi}(\ve u)}\left[\ln p(\ve y|\ve u) \right] -
D_{\text{KL}}(q_{\ve\psi}(\ve u)\Vert p(\ve u))\right)
p(w|y) = \frac{p(y|w)\,p(w)}{\int p(y|w)\,p(w)\,\dd w}
= \frac{p(y|w)\,p(w)}{p(y)}
$$
with respect to
$$
\ve\zeta^* = \text{arg}\min_{\ve\zeta} D\lt{KL}(q_{\ve\zeta}(w)\,\Vert\, p(w|y))
$$
to obtain the optimal variational parameters $\ve\zeta^*$ to approximate
$p(w|y)$ with $q_{\ve\zeta^*}(w)$.
In our case the two distributions are the approximate
$$q_{\ve\zeta}(\mathbf f)=\int p(\mathbf f|\ve u)\,q_{\ve\psi}(\ve u)\,\dd\ve u\quad(\text{"variational strategy"})$$
and the true $p(\mathbf f|\mathcal D)$ posterior over function values. We
optimize with respect to
$$\ve\zeta = [\ell, \sigma_n^2, s, c, \ve\psi] $$
with
$$\ve\psi = [\ve m_u, \ma Z, \ma L]$$
the parameters of the variational distribution $q_{\ve\psi}(\ve u)$.
In addition, we perform a stochastic
optimization by using a deep learning type mini-batch loop, hence
"stochastic" variational inference (SVI). The latter speeds up the
optimization since we only look at a fraction of data per optimizer step to
calculate an approximate loss gradient (`loss.backward()`).
%% Cell type:code id:f39d86f8 tags:
``` python
# Train mode
model.train()
likelihood.train()
optimizer = torch.optim.Adam(
[dict(params=model.parameters()), dict(params=likelihood.parameters())],
lr=0.1,
)
loss_func = gpytorch.mlls.VariationalELBO(
likelihood, model, num_data=X_train.shape[0]
)
train_dl = DataLoader(
TensorDataset(X_train, y_train), batch_size=128, shuffle=True
)
n_iter = 200
history = defaultdict(list)
for i_iter in range(n_iter):
for i_batch, (X_batch, y_batch) in enumerate(train_dl):
batch_history = defaultdict(list)
optimizer.zero_grad()
loss = -loss_func(model(X_batch), y_batch)
loss.backward()
optimizer.step()
param_dct = dict()
param_dct.update(extract_model_params(model, try_item=True))
param_dct.update(extract_model_params(likelihood, try_item=True))
for p_name, p_val in param_dct.items():
if isinstance(p_val, float):
batch_history[p_name].append(p_val)
batch_history["loss"].append(loss.item())
for p_name, p_lst in batch_history.items():
history[p_name].append(np.mean(p_lst))
if (i_iter + 1) % 10 == 0:
print(f"iter {i_iter + 1}/{n_iter}, {loss=:.3f}")
```
%% Cell type:code id:dc39b5d9 tags:
``` python
# Plot scalar hyper params and loss (ELBO) convergence
ncols = len(history)
fig, axs = plt.subplots(
ncols=ncols, nrows=1, figsize=(ncols * 3, 3), layout="compressed"
)
with torch.no_grad():
for ax, (p_name, p_lst) in zip(axs, history.items()):
ax.plot(p_lst)
ax.set_title(p_name)
ax.set_xlabel("iterations")
```
%% Cell type:code id:c4907e39 tags:
``` python
# Values of optimized hyper params
print("model params:")
pprint(extract_model_params(model))
print("likelihood params:")
pprint(extract_model_params(likelihood))
```
%% Cell type:markdown id:4ae35e11 tags:
# Run prediction
%% Cell type:code id:629b4658 tags:
``` python
# Evaluation (predictive posterior) mode
model.eval()
likelihood.eval()
with torch.no_grad():
M = 10
post_pred_f = model(X_pred)
post_pred_y = likelihood(model(X_pred))
fig, axs = plt.subplots(ncols=2, figsize=(14, 5), sharex=True, sharey=True)
fig_sigmas, ax_sigmas = plt.subplots()
for ii, (ax, post_pred, name, title) in enumerate(
zip(
axs,
[post_pred_f, post_pred_y],
["f", "y"],
["epistemic uncertainty", "total uncertainty"],
)
):
yf_mean = post_pred.mean
yf_samples = post_pred.sample(sample_shape=torch.Size((M,)))
yf_std = post_pred.stddev
lower = yf_mean - 2 * yf_std
upper = yf_mean + 2 * yf_std
ax.plot(
X_train.numpy(),
y_train.numpy(),
"o",
label="data",
color="tab:blue",
)
ax.plot(
X_pred.numpy(),
yf_mean.numpy(),
label="mean",
color="tab:red",
lw=2,
)
ax.plot(
X_pred.numpy(),
y_gt_pred.numpy(),
label="ground truth",
color="k",
lw=2,
ls="--",
)
ax.fill_between(
X_pred.numpy(),
lower.numpy(),
upper.numpy(),
label="confidence",
color="tab:orange",
alpha=0.3,
)
ax.set_title(f"confidence = {title}")
if name == "f":
sigma_label = r"epistemic: $\pm 2\sqrt{\mathrm{diag}(\Sigma_*)}$"
zorder = 1
else:
sigma_label = (
r"total: $\pm 2\sqrt{\mathrm{diag}(\Sigma_* + \sigma_n^2\,I)}$"
)
zorder = 0
ax_sigmas.fill_between(
X_pred.numpy(),
lower.numpy(),
upper.numpy(),
label=sigma_label,
color="tab:orange" if name == "f" else "tab:blue",
alpha=0.5,
zorder=zorder,
)
y_min = y_train.min()
y_max = y_train.max()
y_span = y_max - y_min
ax.set_ylim([y_min - 0.3 * y_span, y_max + 0.3 * y_span])
plot_samples(ax, X_pred, yf_samples, label="posterior pred. samples")
if ii == 1:
ax.legend()
ax_sigmas.set_title("total vs. epistemic uncertainty")
ax_sigmas.legend()
```
%% Cell type:markdown id:60abb7ec tags:
# Let's check the learned noise
%% Cell type:code id:d8a32f5b tags:
``` python
# Target noise to learn
print("data noise:", noise_std)
# The two below must be the same
print(
"learned noise:",
(post_pred_y.stddev**2 - post_pred_f.stddev**2).mean().sqrt().item(),
)
print(
"learned noise:",
np.sqrt(
extract_model_params(likelihood, try_item=True)["noise_covar.noise"]
),
)
```
%% Cell type:code id:0637729a tags:
``` python
# When running as script
if not is_interactive():
plt.show()
```
......
BLcourse2.3/03_one_dim_SVI.py
+ 26
− 6
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......@@ -25,6 +25,8 @@
# $\newcommand{\predve}[1]{\mathbf{#1}}$
# $\newcommand{\test}[1]{#1_*}$
# $\newcommand{\testtest}[1]{#1_{**}}$
# $\newcommand{\dd}{{\rm{d}}}$
# $\newcommand{\lt}[1]{_{\text{#1}}}$
# $\DeclareMathOperator{\diag}{diag}$
# $\DeclareMathOperator{\cov}{cov}$
......@@ -108,6 +110,9 @@ ax.legend()
# 2015](https://proceedings.mlr.press/v38/hensman15.html). The model is
# "sparse" since it works with a set of *inducing* points $(\ma Z, \ve u),
# \ve u=f(\ma Z)$ which is much smaller than the train data $(\ma X, \ve y)$.
# See also [the GPJax
# docs](https://docs.jaxgaussianprocesses.com/_examples/uncollapsed_vi) for a
# nice introduction.
#
# We have the same hyper parameters as before
#
......@@ -183,16 +188,31 @@ likelihood.noise_covar.noise = 0.3
# # Fit GP to data: optimize hyper params
#
# Now we optimize the GP hyper parameters by doing a GP-specific variational inference (VI),
# where we optimize not the log marginal likelihood (ExactGP case),
# but an ELBO (evidence lower bound) objective
# Now we optimize the GP hyper parameters by doing a GP-specific variational
# inference (VI), where we don't maximize the log marginal likelihood (ExactGP
# case), but an ELBO ("evidence lower bound") objective -- a lower bound on the
# marginal likelihood (the "evidence"). In variational inference, an ELBO objective
# shows up when minimizing the KL divergence between
# an approximate and the true posterior
#
# $$
# \max_\ve\zeta\left(\mathbb E_{q_{\ve\psi}(\ve u)}\left[\ln p(\ve y|\ve u) \right] -
# D_{\text{KL}}(q_{\ve\psi}(\ve u)\Vert p(\ve u))\right)
# p(w|y) = \frac{p(y|w)\,p(w)}{\int p(y|w)\,p(w)\,\dd w}
# = \frac{p(y|w)\,p(w)}{p(y)}
# $$
#
# with respect to
# $$
# \ve\zeta^* = \text{arg}\min_{\ve\zeta} D\lt{KL}(q_{\ve\zeta}(w)\,\Vert\, p(w|y))
# $$
#
# to obtain the optimal variational parameters $\ve\zeta^*$ to approximate
# $p(w|y)$ with $q_{\ve\zeta^*}(w)$.
#
# In our case the two distributions are the approximate
#
# $$q_{\ve\zeta}(\mathbf f)=\int p(\mathbf f|\ve u)\,q_{\ve\psi}(\ve u)\,\dd\ve u\quad(\text{"variational strategy"})$$
#
# and the true $p(\mathbf f|\mathcal D)$ posterior over function values. We
# optimize with respect to
#
# $$\ve\zeta = [\ell, \sigma_n^2, s, c, \ve\psi] $$
#
......
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