Reputation: 147
I am trying to export XGboost model to pmml
format using library(pmml)
.
This is the simple code that I use and the error that keeps showing.
dev_samp = data.matrix(mtcars[,c(4,6,9)])
set.seed(123)
bst <- xgboost(data=as.matrix(dev_samp[,1:2]),
label=dev_samp[,3],
max_depth=2,
eta=0.2,
nrounds=2,
colsample_bytree = 0.5,
lambda = 0.3,
objective = "binary:logistic",
eval_metric = "error")
# Export to PMML:
pmod <- pmml(bst,input_feature_names = colnames(as.matrix(dev_samp[,1:2])),
output_label_name = colnames(dev_samp)[3])
Error in pmml.xgb.Booster(bst, input_feature_names = colnames(as.matrix(dev_samp[, :
Input feature names required at present version. Try using colnames function on Matrix, matrix or xgb.DMatrix$data
Outside of this function the code colnames(as.matrix(dev_samp[,1:2]))
and colnames(dev_samp)[3]
works.
EDIT:
Thank you for your answer. Unfortunately I am restricted from direct install from GitHub.
So what I did is this:
library(xgboost)
library(r2pmml)
#mtcars$wt=as.integer(mtcars$wt*1000)
#mtcars$hp=as.integer(mtcars$hp*2)
dev_samp = data.matrix(mtcars[,c(4,6,9)])
set.seed(123)
bst <- xgboost(data=as.matrix(dev_samp[,1:2]),
label=as.integer(dev_samp[,3]),
max_depth=2,
eta=0.2,
nrounds=2,
colsample_bytree = 0.5,
lambda = 0.3,
objective = "binary:logistic",
eval_metric = "error")
fmap=data.frame(seq_along(bst$feature_names)-1, bst$feature_names, "q")
fmap[,2] = as.factor(fmap[,2])
fmap[,3] = as.factor(fmap[,3])
fmap[,1] = as.integer(fmap[,1])
pmod <- decorate.xgb.Booster(bst, "mtcars.pmml",
fmap = fmap,
response_name = "prediction",
response_levels = c("0", "1"),
missing = "")
r2pmml(pmod, "XGBoostModel2.pmml")
But the last line r2pmml(pmod, "XGBoostModel2.pmml")
returns error:
Error in .convert(tempfile, file, converter, converter_classpath, verbose) :
The JPMML-R conversion application has failed (error code 1). The Java executable should have printed more information about the failure into its standard output and/or standard error streams
Upvotes: 0
Views: 693
Reputation: 4926
Alternatively, if correctness and conversion speed matter to you, then you can use the R2PMML package for converting R language XGBoost models to PMML.
library("r2pmml")
r2pmml(bst, "xgboost.pmml", fmap = as.fmap(dev_samp[,1:2]), response_name = colnames(dev_samp)[3])
Be sure to use the latest R2PMML package version (direct install from GitHub repository). The CRAN version is currently a bit outdated (does not provide the as.fmap(..)
utility function).
Upvotes: 1