A Quick Guide to dgpsi

dgpsi provides a flexible toolbox for Gaussian process (GP), deep Gaussian process (DGP) and linked (D)GP emulations. In this guide, we shows how to use the package to emulate a step function with a three-layered DGP structure. There are other examples showing the functionality of the package in Articles, and how to customize a DGP structure. A comprehensive reference of available functions is documented in Reference.

Load the package

dgpsi in R provides an initialization function init_py() that helps us set up and initialize the required Python environment automatically. We just need to run init_py() every time after the package is loaded to initiate the underlying Python environment.

## Connecting to Python ... done
## Importing required Python modules ... done
## The Python environment for 'dgpsi' is successfully loaded.

Set up the step function

We first define the step function:

f <- function(x) {
  if (x < 0.5) return(-1)
  if (x >= 0.5) return(1)

Then we generate ten training data points:

X <- seq(0, 1, length = 10)
Y <- sapply(X, f)


We now build and train a DGP emulator with three layers:

m <- dgp(X, Y, depth = 3)
## Auto-generating a 3-layered DGP structure ... done
## Initializing the DGP emulator ... done
## Training the DGP emulator: 
## Iteration 500: Layer 3: 100%|██████████| 500/500 [00:04<00:00, 123.37it/s]

The progress bar displayed shows how long it takes to finish the training. We are able to change some default settings. E.g., we can set verb = 2 so a summary table of the initialized DGP emulator will be printed out before we proceed to training. This is particularly useful if a customized structure is provided to struc and we want to check it before the training is implemented. Note that if we want to train the DGP emulator m for additional iterations, we can simply do m <- continue(m) instead of rebuilding the DGP emulator.

The trained DGP emulator can be viewed using the summary() function:

## +-----------+----------+------------------+-----------------+---------------+-------------------+------------+-------------------+
## | Layer No. | Node No. |       Type       | Length-scale(s) |   Variance    |      Nugget       | Input Dims | Global Connection |
## +-----------+----------+------------------+-----------------+---------------+-------------------+------------+-------------------+
## |  Layer 1  |  Node 1  | GP (Squared-Exp) |     [0.285]     | 1.000 (fixed) | 1.000e-06 (fixed) |    [1]     |        No         |
## |  Layer 2  |  Node 1  | GP (Squared-Exp) |     [1.773]     | 1.000 (fixed) | 1.000e-06 (fixed) |    [1]     |        [1]        |
## |  Layer 3  |  Node 1  | GP (Squared-Exp) |     [2.339]     |     0.475     | 1.000e-06 (fixed) |    [1]     |        [1]        |
## +-----------+----------+------------------+-----------------+---------------+-------------------+------------+-------------------+
## 1. 'Input Dims' presents the indices of GP nodes in the feeding layer whose outputs feed into the GP node referred by 'Layer No.' and 'Node No.'.
## 2. 'Global Connection' indicates the dimensions (i.e., column indices) of the global input data that are used as additional input dimensions to the GP node referred by 'Layer No.' and 'Node No.'.

The summary table gives key information about the trained DGP emulator. Note that in the auto-generated emulator we have nugget terms fixed to 1e-6 for all GP nodes because we are emulating deterministic step function (i.e., we would like our emulator to interpolate training data points). The prior scales (i.e., variances) for GP nodes in the first and second layers are fixed to 1.0 while that for the GP node in the final layer is estimated due to its attachment to the output. For further information on how to change the default settings to construct and train a DGP emulator, see ?dgp.

At this point, you could use write() to save the emulator m to a local file and then load it using read() next time when you would like to make predictions from the emulator.


After we have the emulator, we can validate it by drawing the validation plots. There are two types of validation plots provided by the package. The first one is the Leave-One-Out (LOO) cross validation plot:

## Initializing ... done
## Post-processing LOO results ... done
## Plotting ... done

The second validation plot is the Out-Of-Sample (OOS) validation plot that requires an out-of-sample testing data set. Here we generate an OOS data set that contains 10 testing data points

oos_x <- sample(seq(0, 1, length = 200),10)
oos_y <- sapply(oos_x, f)

for the validation:

## Initializing ... done
## Post-processing OOS results ... done
## Plotting ... done


Once the validation is done, we can make predictions from it. We generate 200 data points from the step function over \([0,1]\)

test_x <- seq(0, 1, length = 200)
test_y <- sapply(test_x, f)

for predictions:

m <- predict(m, x = test_x)

The predict() function returns an updated DGP emulator m that contains a slot named results that gives the posterior predictive means and variances at testing positions. We can extract these information and plot the emulation results to check the predictive performance of our constructed DGP emulator:

mu <- m$results$mean # extract the predictive means 
sd <- sqrt(m$results$var) # extract the predictive variance and compute the predictive standard deviations
# compute predictive bounds which are two predictive standard deviations above and below the predictive means
up <- mu + 2*sd 
lo <- mu - 2*sd

plot(test_x, mu, type = 'l', lty = 2, lwd = 1.5, col = 'black', xlab = 'x', cex.axis = 1.3, cex.lab = 1.3, ylab = 'y', ylim = c(-1.5,1.5))  # predictive means 
polygon(c(test_x, rev(test_x)), c(up,rev(lo)), col = 'grey80', border = F) # predictive bounds
lines(test_x, test_y, type = 'l', col = "#D55E00", lwd = 2) # Underlying truth
lines(test_x, mu, type = 'l', lty = 2, lwd = 1.5, col = 'black')
lines(X, Y, type = 'p', pch = 16, cex = 1, col = "#0072B2") # Training data points