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Additional params for xgbTree #308

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merged 7 commits into from
Nov 8, 2015
Merged

Additional params for xgbTree #308

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b816628
Changed installation of xgboost back from Github version
terrytangyuan Nov 5, 2015
a1ce3c7
Added gamma param to xgbTree
terrytangyuan Nov 5, 2015
afa0bdb
Added colsample_bytree to xgbTree
terrytangyuan Nov 5, 2015
fdb7006
Added min_child_weight to xgbTree
terrytangyuan Nov 5, 2015
639e761
Give the params in random search wider range to tune for xgbTree
terrytangyuan Nov 6, 2015
3647c36
Better range for gamma in xgbTree
terrytangyuan Nov 6, 2015
5c63e49
Changed gamma default in grid search of xgbTree
terrytangyuan Nov 6, 2015
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44 changes: 33 additions & 11 deletions models/files/xgbTree.R
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Original file line number Diff line number Diff line change
@@ -1,31 +1,44 @@
modelInfo <- list(label = "eXtreme Gradient Boosting",
library = c("xgboost", "plyr"),
type = c("Regression", "Classification"),
parameters = data.frame(parameter = c('nrounds', 'max_depth', 'eta'),
class = rep("numeric", 3),
parameters = data.frame(parameter = c('nrounds', 'max_depth', 'eta',
'gamma', 'colsample_bytree',
'min_child_weight'),
class = rep("numeric", 6),
label = c('# Boosting Iterations', 'Max Tree Depth',
'Shrinkage')),
'Shrinkage', "Minimum Loss Reduction",
'Subsample Ratio of Columns',
'Minimum Sum of Instance Weight')),
grid = function(x, y, len = NULL, search = "grid") {
if(search == "grid") {
out <- expand.grid(max_depth = seq(1, len),
nrounds = floor((1:len) * 50),
eta = .3)
eta = c(.3, .4),
gamma = 0,
colsample_bytree = c(.6, .8),
min_child_weight = c(1))
} else {
out <- data.frame(nrounds = sample(1:1000, size = len*10, replace = TRUE),
max_depth = sample(1:10, replace = TRUE, size = len),
eta = runif(len, min = .001, max = .6))
eta = runif(len, min = .001, max = .6),
gamma = runif(len*10, min = 0, max = 10),
colsample_bytree = runif(len*5, min = .3, max = .7),
min_child_weight = sample(0:20, size = len*5, replace = TRUE))
out$nrounds <- floor(out$nrounds)
out <- out[!duplicated(out),]
}
out
},
loop = function(grid) {
loop <- ddply(grid, c("eta", "max_depth"),
loop <- ddply(grid, c("eta", "max_depth", "gamma", "colsample_bytree", "min_child_weight"),
function(x) c(nrounds = max(x$nrounds)))
submodels <- vector(mode = "list", length = nrow(loop))
for(i in seq(along = loop$nrounds)) {
index <- which(grid$max_depth == loop$max_depth[i] &
grid$eta == loop$eta[i])
grid$eta == loop$eta[i] &
grid$gamma == loop$gamma[i] &
grid$colsample_bytree == loop$colsample_bytree[i] &
grid$min_child_weight == loop$min_child_weight[i])
trees <- grid[index, "nrounds"]
submodels[[i]] <- data.frame(nrounds = trees[trees != loop$nrounds[i]])
}
Expand All @@ -37,7 +50,10 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
y <- ifelse(y == lev[1], 1, 0)
dat <- xgb.DMatrix(as.matrix(x), label = y)
out <- xgb.train(list(eta = param$eta,
max_depth = param$max_depth),
max_depth = param$max_depth,
gamma = param$gamma,
colsample_bytree = param$colsample_bytree,
min_child_weight = param$min_child_weight),
data = dat,
nrounds = param$nrounds,
objective = "binary:logistic",
Expand All @@ -46,7 +62,10 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
y <- as.numeric(y) - 1
dat <- xgb.DMatrix(as.matrix(x), label = y)
out <- xgb.train(list(eta = param$eta,
max_depth = param$max_depth),
max_depth = param$max_depth,
gamma = param$gamma,
colsample_bytree = param$colsample_bytree,
min_child_weight = param$min_child_weight),
data = dat,
num_class = length(lev),
nrounds = param$nrounds,
Expand All @@ -56,7 +75,10 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
} else {
dat <- xgb.DMatrix(as.matrix(x), label = y)
out <- xgb.train(list(eta = param$eta,
max_depth = param$max_depth),
max_depth = param$max_depth,
gamma = param$gamma,
colsample_bytree = param$colsample_bytree,
min_child_weight = param$min_child_weight),
data = dat,
nrounds = param$nrounds,
objective = "reg:linear",
Expand Down Expand Up @@ -149,5 +171,5 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
sort = function(x) {
# This is a toss-up, but the # trees probably adds
# complexity faster than number of splits
x[order(x$nrounds, x$max_depth, x$eta),]
x[order(x$nrounds, x$max_depth, x$eta, x$gamma, x$colsample_bytree, x$min_child_weight),]
})
Binary file modified pkg/caret/inst/models/models.RData
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4 changes: 3 additions & 1 deletion release_process/update_pkgs.R
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ libs <- c(libs, "knitr", "Hmisc", "googleVis", "animation",
"desirability", "gsubfn", "gtools", "impute", "Matrix", "proxy", "plyr",
"reshape", "rJava", "SparseM", "sqldf", "XML", "lubridate", "dplyr", "GA",
"aroma.affymetrix", "remMap", "cghFLasso", "RCurl", "QSARdata", "reshape2",
"mapproj", "ggmap", "ggvis", "SuperLearner", "subsemble", "caretEnsemble", "xgboost")
"mapproj", "ggmap", "ggvis", "SuperLearner", "subsemble", "caretEnsemble")
libs <- unique(libs)


Expand All @@ -39,6 +39,8 @@ if(length(diffs) > 0) print(diffs)
###################################################################
## Install the files. This might re-install caret so beware.

devtools::install_github('dmlc/xgboost',subdir='R-package')

install.packages(libs, repos = "http://cran.r-project.org",
type = "source",
dependencies = c("Depends", "Suggests", "Imports"))
Expand Down