tonglu

lift1 <- caret::lift(factor(test_data$dep_var) ~ model2.2.1_v3$predict(test_data), class = "1")
png("~/tmp/output/model2.2.1/validation_v3/lift_chart.png")             
xyplot(lift1)                                                           
dev.off()    

tonglu

# rlist
# http://cran.r-project.org/web/packages/rlist/rlist.pdf
default <- list(a = 1, b = 2)
new <- list(b = 3)
list.merge(default, new) #new would overwrite default
list.join(default, new, "b")

# package::ff
# http://cran.r-project.org/web/packages/ff/index.html
# doing complicated operations on disk

tonglu

git checkout somecommit   #revert back to an older commit
git stash                 #stash changes
git checkout -b new_branch #spin off to a new branch
git stash pop             # restore the changes to the new branch

git add . 
git commit -a -m
git push origin new_branch

tonglu

# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols:   Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#

tonglu

system(paste('s3cmd ls ', s3mpi:::s3path(), 'models/*', sep = ''), intern = TRUE)

tonglu

restoreLevels <- function(datum, model_varnames, model_levels, klasses) {
  #model_varnames = .GBM_model$var.names,
  #model_levels = .GBM_model$var.levels,
  #klasses = attr(.GBM_model$Terms, 'dataClasses')) {
  grab_levels <- function(colname) {
    level_index <- which(model_varnames == colname)
    if (length(level_index) == 0) return(NULL) 
    model_levels[[level_index]]
  }
  .datum <<- datum

tonglu

null_to_na <- function(obj) {
  df <- as.data.frame(t(as.data.frame(lapply(lapply(obj, lapply, function(x)ifelse(is.null(x), NA, x)), unlist))))
  rownames(df) <- NULL
  df
}

dst <- rubyread('defaults_source_tier')
dst2 <- null_to_na(dst)
colnames(dst2) <- c('loan_id', 'dep_var', 'source', 'tier', 'created_at')

tonglu

#http://stackoverflow.com/questions/1358003/tricks-to-manage-the-available-memory-in-an-r-session?rq=1
.ls.objects <- function (pos = 1, pattern, order.by,
                         decreasing=FALSE, head=FALSE, n=5) {
  napply <- function(names, fn) sapply(names, function(x)
    fn(get(x, pos = pos)))
  names <- ls(pos = pos, pattern = pattern)
  obj.class <- napply(names, function(x) as.character(class(x))[1])
  obj.mode <- napply(names, mode)
  obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
  obj.size <- napply(names, object.size)