h2o.gbm histogram_type="Random" doesn't seem to be random

[exalate-issue-sync]

I think there may be an issue but not sure. Here is a minimal example. If histogram_type = "Random" shouldn't the root node split threshold vary if ran multiple times?

{code:r}library(data.table) #1.12.8
library(zipcode) #1.0
library(h2o) #3.30.0.4
data("zipcode")
zipcode <- data.table(zipcode)[state=="WY"] #choosing a squarish state

Code to produce loss ratio data

loss_indicator <- sample(0:1,10000,replace=TRUE,prob=c(0.1,0.9))
loss_ratio <- data.table(
earned_premium = runif(10000,0,10000),
losses = runif(10000,0,3500) * loss_indicator,
zip = sample(as.numeric(zipcode$zip),10000,replace=TRUE)
)
loss_ratio[,lr:=losses/earned_premium]
#########
loss_ratio <- merge(
loss_ratio,
zipcode[,.(zip=as.numeric(zip),latitude,longitude)],
by="zip"
)
h2o.init()
lr.hex <- as.h2o(loss_ratio)
split_points <- numeric(length = 100)
for (i in 1:100) {
terr_gbm1 <- h2o.gbm(
x=c("latitude","longitude"),
y="lr",
training_frame = lr.hex[lr.hex$earned_premium>0,],
distribution = "tweedie",
tweedie_power = 1.5,
nbins_top_level = 2,
nbins=2,
histogram_type = "Random",
stopping_rounds = 3,
score_tree_interval = 3,
stopping_tolerance = 1e-5
)
first_tree1 <- h2o.getModelTree(terr_gbm1,1)
split_points[i] <- first_tree1@thresholds[1]
}

should be a variety of split thresholds?

unique(split_points){code}

[h2o-ops]

JIRA Issue Migration Info

Jira Issue: PUBDEV-7880
Assignee: Michal Kurka
Reporter: Joseph Granados
State: Resolved
Fix Version: 3.32.0.3
Attachments: N/A
Development PRs: Available

Linked PRs from JIRA

#5171