predictCluster <- function(df_test, models, index) {
model_s <- models[[index]]$model_s
scores <- rxPredict(model_s, df_test)
}
predictLabel <- function(df_test, df_scores, models) {
# get the highest score to determine the predictive model to use.
id <- max.col(df_scores)
df_test <-
cbind(df_test, id) %>%
mutate(key = row_number())
model_index <- unique(id)
pred <- NULL
for (j in model_index) {
df <- filter(df_test, id == j)
model_c <- models[[j]]$model_c
if (is.numeric(model_c)) {
# since there is only one class within the cluster.
pred <- rbind(pred, data.frame(Class_prob=rep(model_c, nrow(df)), key=df$key))
} else {
result <- rxPredict(model_c, df)
pred <- rbind(pred, mutate(result, key=df$key))
}
}
pred <-
mutate(pred, Class=ifelse(Class_prob > 0.5, 1, 0)) %>%
arrange(key)
pred
}
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