inst/examples/example-S4VM.R

In RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

library(RSSL)
library(dplyr)
library(ggplot2)
library(tidyr)

set.seed(1)
df_orig <- generateSlicedCookie(100,expected=TRUE)
df <- df_orig %>% add_missinglabels_mar(Class~.,0.95)
g_s <- SVM(Class~.,df,C=1,scale=TRUE,x_center=TRUE)
g_s4 <- S4VM(Class~.,df,C1=1,C2=0.1,lambda_tradeoff = 3,scale=TRUE,x_center=TRUE)

labs <- g_s4@labelings[-c(1:5),]
colnames(labs) <- paste("Class",seq_len(ncol(g_s4@labelings)),sep="-")

# Show the labelings that the algorithm is considering
df %>%
  filter(is.na(Class)) %>% 
  bind_cols(data.frame(labs,check.names = FALSE)) %>% 
  select(-Class) %>% 
  gather(Classifier,Label,-X1,-X2) %>% 
  ggplot(aes(x=X1,y=X2,color=Label)) +
  geom_point() +
  facet_wrap(~Classifier,ncol=5)

# Plot the final labeling that was selected
# Note that this may not correspond to a linear classifier
# even if the linear kernel is used.
# The solution does not seem to make a lot of sense,
# but this is what the current implementation returns
df %>% 
  filter(is.na(Class)) %>% 
  mutate(prediction=g_s4@predictions) %>% 
  ggplot(aes(x=X1,y=X2,color=prediction)) +
  geom_point() +
  stat_classifier(color="black", classifiers=list(g_s))