DGP Classification using Stochastic Imputation

Load packages and data

We start by loading required packages,

library(dgpsi)
library(dplyr)

We now load the iris data set,

data(iris)

and do a min-max normalization on its four input variables.

iris <- iris %>%
  mutate(across(1:4, ~ (. - min(.)) / (max(.) - min(.))))

Before building a classifier, we set a seed with set_seed() from the package for reproducibility

set_seed(9999)

and split a training data set and a testing data set:

test_idx <- sample(seq_len(nrow(iris)), size = 30)

train_data <- iris[-test_idx, ]
test_data <- iris[test_idx, ]

X_train <- as.matrix(train_data[, 1:4])
Y_train <- as.matrix(train_data[, 5])

X_test <- as.matrix(test_data[, 1:4])
Y_test <- as.matrix(test_data[, 5])

Construct and train a DGP classifier

We consider a three-layer DGP classifier, using a Matérn-2.5 kernel in the first layer and a squared exponential kernel in the second layer:

m_dgp <- dgp(X_train, Y_train, depth = 3, name = c('matern2.5', 'sexp'), likelihood = "Categorical")

## Auto-generating a 3-layered DGP structure ... done
## Initializing the DGP emulator ... done
## Training the DGP emulator: 
## Iteration 500: Layer 3: 100%|██████████| 500/500 [00:31<00:00, 15.63it/s]
## Imputing ... done

We set likelihood = "Categorical" since the DGP classifier is essentially a DGP emulator with a categorical likelihood.

Validation

We are now ready to validate the classifier via validate() at 30 out-of-sample testing positions:

m_dgp <- validate(m_dgp, X_test, Y_test)

## Initializing the OOS ... done
## Calculating the OOS ... done
## Saving results to the slot 'oos' in the dgp object ... done

Finally, we visualize the OOS validation for the classifier:

plot(m_dgp, X_test, Y_test)

## Validating and computing ... done
## Post-processing OOS results ... done
## Plotting ... done

By default, plot() displays true labels against predicted label proportions at each input position. Alternatively, setting style = 2 in plot() generates a confusion matrix:

plot(m_dgp, X_test, Y_test, style = 2)

## Validating and computing ... done
## Post-processing OOS results ... done
## Plotting ... done