andremonaco / cheapml Goto Github PK

Hi Andre,
Thanks for putting up this code. I an have a few problems and I thought I would check with you before looking further into them

1. Xy seems to have changed. It now requires something like

Xy(task = "regression")
recipe <- Xy(task = "regression" ) %>%
       add_linear(p = 2, family = xy_normal()) 
simulate(recipe)

I have not figured it out yet. I just used an older version of R with an older Xy
2) There seems to be a problem or a change with raply. Here is a small snippet showing what happens.

f<-function(){
    list(runif(10), data.frame(matrix(0,2,2)))
    }
temp <- plyr::raply(2,  f() )
> temp
     1          2     
[1,] Numeric,10 List,2
[2,] Numeric,10 List,2

This can we fixed or at least worked around by changing you code to

trees<-lapply (c(1:n_trees), f<-function(x){sprout_tree(formula = formula,  feature_frac = feature_frac, data = data )})

3. the prediction is very poor

source("../algorithms/reg_rf.R")
mod3 <- reg_rf(formula = eq, data = model_df,n_trees=10,feature_frac=0.63)
plot(mod3$fit , model_df$y)
cor(mod3$fit, model_df$y) #0.01

I think this is because the fits coming out of sprout_tree are for the boostrapped data set and not the original data set.
I can see a fix for this but I thought I woudl check with you first in case I was missing something.

Bye

Thanks for sharing your code (reg_tree.R), this helped me save some time. If I understand your code correctly, I think below line 69 you may want to insert the following line:

y <- this_data[, as.character(formula)[2]]

Otherwise I think the sse is not calculated correctly.
Thanks.