R: implementation of a Bayesian Network classifier using package bnlearn

bnlearn_tan_example.R

library(data.table)
library(bnlearn)

df <- fread("file.csv", sep="|", verbose=TRUE)

cols <- colnames(df, 3:24)
df <- as.data.frame(df)
df_temp <- data.frame(apply(df, 2, as.factor))
df <- df_temp
rm(df_temp)

drops <- c(cols[1], cols[25])
training.set = subset(df, Partition==1)
training.set <- training.set[ , !(names(training.set) %in% drops)]
test.set = subset(df, Partition==2)
test.set <- test.set[ , !(names(test.set) %in% drops)]
cols2 = colnames(training.set)

tan <- tree.bayes(training.set, training = c('target'), explanatory = cols2[2:length(cols2)])

score(tan, training.set)


fitted1 <- bn.fit(nb, training.set, method = "bayes")

pred1 <- predict(fitted, test.set, method="parents")
pred2 <- predict(fitted, test.set, method="bayes-lw")

table(test.set[, 'target'], pred1)
table(test.set[, 'target'], pred2)

# Structure specification

e = empty.graph(cols2)

adj = matrix(0L, ncol = 23, nrow = 23, dimnames = list(cols2, cols2))
adj[cols2[1],] = 1L
adj[cols2[2],] = 1L
diag(adj) = 0L

amat(e)=adj

fitted3 <- bn.fit(e, training.set, method='bayes')
pred3 <- predict(fitted2, "target", test.set, method="bayes-lw")
table(test.set[, 'target'], pred3)

Hi what csv are you using for your input? Cab you attach the file.csv?