Scalable Kernel Interpolation for Product Kernels (SKIP)

Overview

In this notebook, we’ll overview of how to use SKIP, a method that exploits product structure in some kernels to reduce the dependency of SKI on the data dimensionality from exponential to linear.

The most important practical consideration to note in this notebook is the use of gpytorch.settings.max_root_decomposition_size, which we explain the use of right before the training loop cell.

In [1]:
import math
import torch
import gpytorch
from matplotlib import pyplot as plt

# Make plots inline
%matplotlib inline
/home/jrg365/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py:465: UserWarning: matplotlibrc text.usetex option can not be used unless TeX is installed on your system
  warnings.warn('matplotlibrc text.usetex option can not be used unless '
/home/jrg365/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py:473: UserWarning: matplotlibrc text.usetex can not be used with *Agg backend unless dvipng-1.6 or later is installed on your system
  'your system' % dvipng_req)

Loading Data

For this example notebook, we’ll be using the elevators UCI dataset used in the paper. Running the next cell downloads a copy of the dataset that has already been scaled and normalized appropriately. For this notebook, we’ll simply be splitting the data using the first 80% of the data as training and the last 20% as testing.

Note: Running the next cell will attempt to download a ~400 KB dataset file to the current directory.

In [3]:
import urllib.request
import os.path
from scipy.io import loadmat
from math import floor

if not os.path.isfile('elevators.mat'):
    print('Downloading \'elevators\' UCI dataset...')
    urllib.request.urlretrieve('https://drive.google.com/uc?export=download&id=1jhWL3YUHvXIaftia4qeAyDwVxo6j1alk', 'elevators.mat')

data = torch.Tensor(loadmat('elevators.mat')['data'])
X = data[:, :-1]
X = X - X.min(0)[0]
X = 2 * (X / X.max(0)[0]) - 1
y = data[:, -1]

train_n = int(floor(0.8*len(X)))

train_x = X[:train_n, :].contiguous().cuda()
train_y = y[:train_n].contiguous().cuda()

test_x = X[train_n:, :].contiguous().cuda()
test_y = y[train_n:].contiguous().cuda()
In [4]:
X.size()
Out[4]:
torch.Size([16599, 18])

Defining the GP Model

We now define the GP model. For more details on the use of GP models, see our simpler examples. This model uses a GridInterpolationKernel (SKI) with an RBF base kernel. To use SKIP, we make two changes:

  • First, we use only a 1 dimensional GridInterpolationKernel (e.g., by passing num_dims=1). The idea of SKIP is to use a product of 1 dimensional GridInterpolationKernels instead of a single d dimensional one.
  • Next, we create a ProductStructureKernel that wraps our 1D GridInterpolationKernel with num_dims=18. This specifies that we want to use product structure over 18 dimensions, using the 1D GridInterpolationKernel in each dimension.

Note: If you’ve explored the rest of the package, you may be wondering what the differences between AdditiveKernel, AdditiveStructureKernel, ProductKernel, and ProductStructureKernel are. The Structure kernels (1) assume that we want to apply a single base kernel over a fully decomposed dataset (e.g., every dimension is additive or has product structure), and (2) are significantly more efficient as a result, because they can exploit batch parallel operations instead of using for loops.

In [5]:
from gpytorch.means import ConstantMean
from gpytorch.kernels import ScaleKernel, RBFKernel, ProductStructureKernel, GridInterpolationKernel
from gpytorch.distributions import MultivariateNormal

class GPRegressionModel(gpytorch.models.ExactGP):
    def __init__(self, train_x, train_y, likelihood):
        super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = ConstantMean()
        self.base_covar_module = ScaleKernel(RBFKernel())
        self.covar_module = ProductStructureKernel(
            GridInterpolationKernel(self.base_covar_module, grid_size=100, num_dims=1), num_dims=18
        )

    def forward(self, x):
        mean_x = self.mean_module(x)
        covar_x = self.covar_module(x)
        return MultivariateNormal(mean_x, covar_x)
In [6]:
likelihood = gpytorch.likelihoods.GaussianLikelihood().cuda()
model = GPRegressionModel(train_x, train_y, likelihood).cuda()

Training the model

The training loop for SKIP has one main new feature we haven’t seen before: we specify the max_root_decomposition_size. This controls how many iterations of Lanczos we want to use for SKIP, and trades off with time and–more importantly–space. Realistically, the goal should be to set this as high as possible without running out of memory.

In some sense, this parameter is the main trade-off of SKIP. Whereas many inducing point methods care more about the number of inducing points, because SKIP approximates one dimensional kernels, it is able to do so very well with relatively few inducing points. The main source of approximation really comes from these Lanczos decompositions we perform.

In [7]:
# Find optimal model hyperparameters
model.train()
likelihood.train()

# Use the adam optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# "Loss" for GPs - the marginal log likelihood
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)

training_iterations = 25
def train():
    for i in range(training_iterations):
        # Zero backprop gradients
        optimizer.zero_grad()
        with gpytorch.settings.use_toeplitz(False), gpytorch.settings.max_root_decomposition_size(30):
            # Get output from model
            output = model(train_x)
            # Calc loss and backprop derivatives
            loss = -mll(output, train_y)
            loss.backward()
        print('Iter %d/%d - Loss: %.3f' % (i + 1, training_iterations, loss.item()))
        optimizer.step()
        torch.cuda.empty_cache()

# See dkl_mnist.ipynb for explanation of this flag
with gpytorch.settings.use_toeplitz(True):
    %time train()
Iter 1/25 - Loss: 0.942
Iter 2/25 - Loss: 0.919
Iter 3/25 - Loss: 0.888
Iter 4/25 - Loss: 0.864
Iter 5/25 - Loss: 0.840
Iter 6/25 - Loss: 0.816
Iter 7/25 - Loss: 0.792
Iter 8/25 - Loss: 0.767
Iter 9/25 - Loss: 0.743
Iter 10/25 - Loss: 0.719
Iter 11/25 - Loss: 0.698
Iter 12/25 - Loss: 0.671
Iter 13/25 - Loss: 0.651
Iter 14/25 - Loss: 0.624
Iter 15/25 - Loss: 0.600
Iter 16/25 - Loss: 0.576
Iter 17/25 - Loss: 0.553
Iter 18/25 - Loss: 0.529
Iter 19/25 - Loss: 0.506
Iter 20/25 - Loss: 0.483
Iter 21/25 - Loss: 0.460
Iter 22/25 - Loss: 0.441
Iter 23/25 - Loss: 0.413
Iter 24/25 - Loss: 0.391
Iter 25/25 - Loss: 0.375
CPU times: user 1min 6s, sys: 26.8 s, total: 1min 33s
Wall time: 1min 48s

Making Predictions

The next cell makes predictions with SKIP. We use the same max_root_decomposition size, and we also demonstrate increasing the max preconditioner size. Increasing the preconditioner size on this dataset is not necessary, but can make a big difference in final test performance, and is often preferable to increasing the number of CG iterations if you can afford the space.

In [8]:
model.eval()
likelihood.eval()
with gpytorch.settings.max_preconditioner_size(10), torch.no_grad():
    with gpytorch.settings.use_toeplitz(False), gpytorch.settings.max_root_decomposition_size(30), gpytorch.fast_pred_var():
        preds = model(test_x)
In [9]:
print('Test MAE: {}'.format(torch.mean(torch.abs(preds.mean - test_y))))
Test MAE: 0.07745790481567383
In [ ]: