JingyuHe
XBART.multinomial error during fit
While attempting to fit using XBART.multinomial on a test case I encounter the following error:
Error in r_to_json(obj$model_list$tree_pnt) : argument "tree_pnt" is missing, with no default
Code used for testing is below, I also encounter this error when attempting to use this test file: https://github.com/JingyuHe/XBART/blob/master/tests/test_multinomial_prh.R
#######################################################################
set parameters of XBART
get_XBART_params <- function(y) { XBART_params = list(num_trees = 30, # number of trees num_sweeps = 40, # number of sweeps (samples of the forest) n_min = 1, # minimal node size alpha = 0.95, # BART prior parameter beta = 1.25, # BART prior parameter mtry = 10, # number of variables sampled in each split burnin = 15, no_split_penality = "Auto" ) # burnin of MCMC sample num_tress = XBART_params$num_trees XBART_params$max_depth = 250 XBART_params$num_cutpoints = 50;
number of adaptive cutpoints
XBART_params$tau = var(y) / num_tress # prior variance of mu (leaf parameter) return(XBART_params) }
####################################################################### library(XBART)
set.seed(100) new_data = TRUE # generate new data run_dbarts = TRUE # run dbarts run_xgboost = FALSE # run xgboost run_lightgbm = FALSE # run lightgbm parl = TRUE # parallel computing
small_case = TRUE # run simulation on small data set verbose = FALSE # print the progress on screen
if (small_case) { n = 20000 # size of training set nt = 5000 # size of testing set d = 20 # number of TOTAL variables dcat = 10 # number of categorical variables
must be d >= dcat
(X_continuous, X_categorical), 10 and 10 for each case, 20 in total
} else { n = 100000 nt = 10000 d = 50 dcat = 0 }
#######################################################################
Data generating process
#######################################################################
Have to put continuous variables first, then categorical variables
X = (X_continuous, X_cateogrical)
####################################################################### if (new_data) { if (d != dcat) { x = matrix(runif((d - dcat) * n, -2, 2), n, d - dcat) if (dcat > 0) { x = cbind(x, matrix(as.numeric(sample(-2:2, dcat * n, replace = TRUE)), n, dcat)) } } else { x = matrix(as.numeric(sample(-2:2, dcat * n, replace = TRUE)), n, dcat) }
if (d != dcat) { xtest = matrix(runif((d - dcat) * nt, -2, 2), nt, d - dcat) if (dcat > 0) { xtest = cbind(xtest, matrix(as.numeric(sample(-2:2, dcat * nt, replace = TRUE)), nt, dcat)) } } else { xtest = matrix(as.numeric(sample(-2:2, dcat * nt, replace = TRUE)), nt, dcat) }
f = function(x) { sin(rowSums(x[, 3:4] ^ 2)) + sin(rowSums(x[, 1:2] ^ 2)) + (x[, 15] + x[, 14]) ^ 2 * (x[, 1] + x[, 2] ^ 2) / (3 + x[, 3] + x[, 14] ^ 2) #rowSums(x[,1:30]^2) #pmax(x[,1]x[,2], abs(x[,3])(x[,10]>x[,15])+abs(x[,4])*(x[,10]<=x[,15])) # }
to test if ties cause a crash in continuous variables
x[, 1] = round(x[, 1], 4) #xtest[,1] = round(xtest[,1],2) ftrue = f(x) ftest = f(xtest) sigma = sd(ftrue)
#y = ftrue + sigma*(rgamma(n,1,1)-1)/(3+x[,d]) #y_test = ftest + sigma*(rgamma(nt,1,1)-1)/(3+xtest[,d])
y = ftrue + sigma * rnorm(n) y <- as.numeric(y > 0) y_test = ftest + sigma * rnorm(nt) y_test <- as.numeric(y_test > 0) }
#######################################################################
XBART
categ <- function(z, j) { q = as.numeric(quantile(x[, j], seq(0, 1, length.out = 100))) output = findInterval(z, c(q, + Inf)) return(output) }
params = get_XBART_params(y) time = proc.time() fit = XBART.multinomial(y = as.matrix(y), num_class = 2, X = as.matrix(x), Xtest = as.matrix(xtest), p_categorical = dcat, num_trees = params$num_trees, num_sweeps = params$num_sweeps, max_depth = params$max_depth, Nmin = params$n_min, alpha = params$alpha, beta = params$beta, s = 1, kap = 1, mtry = params$mtry, verbose = verbose, num_cutpoints = params$num_cutpoints, parallel = TRUE, random_seed = 100, no_split_penality = params$no_split_penality)
