README.md
In CollinErickson/GauPro: Gaussian Process Fitting

README

GauPro
Overview
Installation
Example in 1-Dimension
Factor data: fitting the diamonds dataset
- Constructing a kernel
Using kernels
Combining kernels

GauPro

This package allows you to fit a Gaussian process regression model to a dataset. A Gaussian process (GP) is a commonly used model in computer simulation. It assumes that the distribution of any set of points is multivariate normal. A major benefit of GP models is that they provide uncertainty estimates along with their predictions.

You can install like any other package through CRAN.

install.packages('GauPro')

The most up-to-date version can be downloaded from my Github account.

# install.packages("devtools")
devtools::install_github("CollinErickson/GauPro")

This simple shows how to fit the Gaussian process regression model to data. The function gpkm creates a Gaussian process kernel model fit to the given data.

library(GauPro)

n <- 12
x <- seq(0, 1, length.out = n)
y <- sin(6*x^.8) + rnorm(n,0,1e-1)
gp <- gpkm(x, y)
#> * Argument 'kernel' is missing. It has been set to 'matern52'. See documentation for more details.

Plotting the model helps us understand how accurate the model is and how much uncertainty it has in its predictions. The green and red lines are the 95% intervals for the mean and for samples, respectively.

gp$plot1D()

Factor data: fitting the `diamonds` dataset

The model fit using gpkm can also be used with data/formula input and can properly handle factor data.

In this example, the diamonds data set is fit by specifying the formula and passing a data frame with the appropriate columns.

library(ggplot2)
diamonds_subset <- diamonds[sample(1:nrow(diamonds), 60), ]
dm <- gpkm(price ~ carat + cut + color + clarity + depth,
           diamonds_subset)
#> * Argument 'kernel' is missing. It has been set to 'matern52'. See documentation for more details.

Calling summary on the model gives details about the model, including diagnostics about the model fit and the relative importance of the features.

summary(dm)
#> Formula:
#>   price ~ carat + cut + color + clarity + depth 
#> 
#> Residuals:
#>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
#> -6589.09  -217.68    37.85  -165.28   181.42  1619.37 
#> 
#> Feature importance:
#>   carat     cut   color clarity   depth 
#>  1.5497  0.2130  0.3275  0.3358  0.0003 
#> 
#> AIC: 1008.96 
#> 
#> Pseudo leave-one-out R-squared       :   0.901367 
#> Pseudo leave-one-out R-squared (adj.):   0.8427204 
#> 
#> Leave-one-out coverage on 60 samples (small p-value implies bad fit):
#>  68%:     0.7        p-value:   0.7839 
#>  95%:    0.95        p-value:   1

We can also plot the model to get a visual idea of how each input affects the output.

plot(dm)

In this case, the kernel was chosen automatically by looking at which dimensions were continuous and which were discrete, and then using a Matern 5/2 on the continuous dimensions (1,5), and separate ordered factor kernels on the other dimensions since those columns in the data frame are all ordinal factors. We can construct our own kernel using products and sums of any kernels, making sure that the continuous kernels ignore the factor dimensions.

Suppose we want to construct a kernel for this example that uses the power exponential kernel for the two continuous dimensions, ordered kernels on cut and color, and a Gower kernel on clarity. First we construct the power exponential kernel that ignores the 3 factor dimensions. Then we construct

cts_kernel <- k_IgnoreIndsKernel(k=k_PowerExp(D=2), ignoreinds = c(2,3,4))
factor_kernel2 <- k_OrderedFactorKernel(D=5, xindex=2, nlevels=nlevels(diamonds_subset[[2]]))
factor_kernel3 <- k_OrderedFactorKernel(D=5, xindex=3, nlevels=nlevels(diamonds_subset[[3]]))
factor_kernel4 <- k_GowerFactorKernel(D=5, xindex=4, nlevels=nlevels(diamonds_subset[[4]]))

# Multiply them
diamond_kernel <- cts_kernel * factor_kernel2 * factor_kernel3 * factor_kernel4

Now we can pass this kernel into gpkm and it will use it.

dm <- gpkm(price ~ carat + cut + color + clarity + depth,
           diamonds_subset, kernel=diamond_kernel)
dm$plotkernel()

A key modeling decision for Gaussian process models is the choice of kernel. The kernel determines the covariance and the behavior of the model. The default kernel is the Matern 5/2 kernel (Matern52), and is a good choice for most cases. The Gaussian, or squared exponential, kernel (Gaussian) is a common choice but often leads to a bad fit since it assumes the process the data comes from is infinitely differentiable. Other common choices that are available include the Exponential, Matern 3/2 (Matern32), Power Exponential (PowerExp), Cubic, Rational Quadratic (RatQuad), and Triangle (Triangle).

These kernels only work on numeric data. For factor data, the kernel will default to a Latent Factor Kernel (LatentFactorKernel) for character and unordered factors, or an Ordered Factor Kernel (OrderedFactorKernel) for ordered factors. As long as the input is given in as a data frame and the columns have the proper types, then the default kernel will properly handle it by applying the numeric kernel to the numeric inputs and the factor kernel to the factor and character inputs.

Kernels are stored as R6 objects. They can all be created using the R6 object generator (e.g., Matern52$new()), or using the k_<kernel name> shortcut function (e.g., k_Matern52()). The latter is easier to use (and recommended) since R will show the function arguments and autocomplete.

The following table shows details on all the kernels available.

| Kernel | Function | Continuous/discrete | Equation | Notes | |-----------------------|-------------------------|---------------------------|--------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Gaussian | k_Gaussian | cts | | Often causes issues since it assumes infinite differentiability. Experts don’t recommend using it. | | Matern 3/2 | k_Matern32 | cts | | Assumes one time differentiability. This is often too low of an assumption. | | Matern 5/2 | k_Matern52 | cts | | Assumes two time differentiability. Generally the best. | | Exponential | k_Exponential | cts | | Equivalent to Matern 1/2. Assumes no differentiability. | | Triangle | k_Triangle | cts | | | | Power exponential | k_PowerExp | cts | | | | Periodic | k_Periodic | cts | $k(x, y) = \sigma^2 * \exp(-\sum(\alpha_i*sin(p * (x_i-y_i))^2))$ | The only kernel that takes advantage of periodic data. But there is often incoherance far apart, so you will likely want to multiply by one of the standard kernels. | | Cubic | k_Cubic | cts | | | | Rational quadratic | k_RatQuad | cts | | | | Latent factor kernel | k_LatentFactorKernel | factor | | This embeds each discrete value into a low dimensional space and calculates the distances in that space. This works well when there are many discrete values. | | Ordered factor kernel | k_OrderedFactorKernel | factor | | This maintains the order of the discrete values. E.g., if there are 3 levels, it will ensure that 1 and 2 have a higher correlation than 1 and 3. This is similar to embedding into a latent space with 1 dimension and requiring the values to be kept in numerical order. | | Factor kernel | k_FactorKernel | factor | | This fits a parameter for every pair of possible values. E.g., if there are 4 discrete values, it will fit 6 (4 choose 2) values. This doesn’t scale well. When there are many discrete values, use any of the other factor kernels. | | Gower factor kernel | k_GowerFactorKernel | factor | $k(x,y) = \begin{cases} 1, & \text{if } x=y \ p, & \text{if } x \neq y \end{cases}$ | This is a very simple factor kernel. For the relevant dimension, the correlation will either be 1 if the value are the same, or $p$ if they are different. | | Ignore indices | k_IgnoreIndsKernel | N/A | | Use this to create a kernel that ignores certain dimensions. Useful when you want to fit different kernel types to different dimensions or when there is a mix of continuous and discrete dimensions. |

Factor kernels: note that these all only work on a single dimension. If there are multiple factor dimensions in your input, then they each will be given a different factor kernel.

Kernels can be combined by multiplying or adding them directly.

The following example uses the product of a periodic and a Matern 5/2 kernel to fit periodic data.

x <- 1:20
y <- sin(x) + .1*x^1.3
combo_kernel <- k_Periodic(D=1) * k_Matern52(D=1)
gp <- gpkm(x, y, kernel=combo_kernel, nug.min=1e-6)
#> * nug is at minimum value after optimizing. Check the fit to see it this caused a bad fit. Consider changing nug.min. This is probably fine for noiseless data.
gp$plot()