caret slowdown issue

caret-slowdown-issue.R

library(caret)
library(readr)
library(ROCR)

set.seed(123)

d <- read_csv("https://raw.githubusercontent.com/szilard/teach-data-science-UCLA-master-appl-stats/master/wk-06-ML/data/airline100K.csv")

N <- nrow(d)
N <- 10000
idx <- sample(1:N, 0.6*N)
d_train <- d[idx,]
d_test <- d[-idx,]


#library(doMC)
#registerDoMC(cores = parallel::detectCores())



system.time({
  mds <- train(dep_delayed_15min ~ ., data = d_train, 
               method = "glmnet",
               trControl = trainControl(number = 1, verboseIter = TRUE),
               tuneGrid = data.frame(alpha = 1, lambda = 0))
})

#  user  system elapsed 
# 10.963   0.044  11.004

system.time({
  mds <- train(dep_delayed_15min ~ ., data = d_train, 
               method = "glmnet",
               trControl = trainControl(number = 1, search = "random", verboseIter = TRUE),
               tuneLength = 1)
})


phat <- predict(mds, new = d_test, type = "prob")[,"Y"]   ## error new cats
phat <- predict(mds, new = d_train, type = "prob")[,"Y"]

rocr_pred <- prediction(phat, d_train$dep_delayed_15min)
performance(rocr_pred, "auc")@y.values[[1]]





X <- Matrix::sparse.model.matrix(dep_delayed_15min ~ . - 1, data = d)
X_train <- X[idx,]

library(glmnet)

system.time({
  md <- glmnet( X_train, d_train$dep_delayed_15min, family = "binomial", lambda = 0)
})

#user  system elapsed 
#  0.092   0.000   0.092

phat <- predict(md, newx = X_train, type = "response")

rocr_pred <- prediction(phat, d_train$dep_delayed_15min)
performance(rocr_pred, "auc")@y.values[[1]]