DexGroves

library("devtools")
install_github("DexGroves/gbm")
library("gbm")

# Data generation from gbm manual ----------------------------------------------
set.seed(112358)
N     <- 1000
X1    <- runif(N)
X2    <- 2*runif(N)
X3    <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])

DexGroves

library("gbm")

plot_gbm_contour <- function(gbm_obj, i.var, ...){
  ##############################################################################
  # Plot a 3D contour plot for a gbm's partial dependencies.                   #
  # Args:                                                                      #
  #   gbm_obj: The gbm object.                                                 #
  #   i.var:   Character vector of variables to show.                          #
  #   ...:     Additional args passed to wireframe.                            #
  # Returns:                                                                   #

DexGroves

attempt_plot_gbm <- function(from_github){
  # Try to make a simple partial dependency plot with a toy gbm
  if (from_github){
    devtools::install_github("gbm-developers/gbm")
  }
  else{
    install.packages("gbm", repos = "http://cran.rstudio.com/")
  }
  library("gbm")
  N <- 1000

DexGroves

benchmark_scoring <- function(repetitions, rows, n.trees){
  # Score a n.trees gbm on rows, repetitions times, and time it.
  library("gbm")
  y        <- seq(1, 2, length.out = rows)
  number_x <- runif(rows)
  factor_x <- sample(letters, replace = TRUE, size = rows)
  factor_2 <- sample(LETTERS, replace = TRUE, size = rows)

  g <- gbm(y ~ number_x + factor_x,
           data = data.frame(y, number_x, factor_x),

DexGroves

handle_progressbar <- function(recurse_i, num) {
  # Sort out the progress bar, and some messages while we're at it.            
  if (recurse_i == 1) {
    if (num <= 10) {
      message("Number is very large! This may take some time.")
    } else {
      message("Number is VERY large! This may take some time.")
    }
    pb <<- txtProgressBar(min = 0, max = num - 4, style = 3)
  } else {

DexGroves

contrast_matrix <- function(object, data, ...) {
  # Make a model matrix with one column per factor level, rather than base hacks
  object_vars <- all.vars(object)
  factor_vars <- colnames(data)[sapply(data, is.factor) & 
                               colnames(data) %in% object_vars]
  model.matrix(object = object, 
               ..., 
               data = data,
               contrasts.arg = lapply(data[, factor_vars],
                                      contrasts,

DexGroves

library("Rcpp")
library("microbenchmark")

simulate_throws <- cppFunction('
  double simulate_throw(NumericVector start, int nthrows, int trials) {
      
      int trials_so_far = 0;
      int final_throw_successes_so_far = 0;
      int hits_so_far = 0;
      int nstart = start.size();

DexGroves

library("igraph")
library("stringi")
library("magrittr")


generate_bridge_df <- function(nrow, ncol) {
  this_layer <- generate_layer(ncol)
  first_connections <- data.frame(from = "Northside", 
                                  to = get_layer_vertices(this_layer))
  all_bridges <- rbind(first_connections, this_layer)

DexGroves

library("data.table")
library("xgboost")
library("Matrix")


generate_data <- function(N) {
  data.table(
    response = as.numeric(runif(N) > 0.8),
    int1 = round(rnorm(N, 3, 3)),
    int2 = round(rnorm(N, 3, 3)),

DexGroves

library("data.table")
library("xgboost")
library("Matrix")


generate_data <- function(N) {
  data.table(
    response = as.numeric(runif(N) > 0.8),
    int1 = round(rnorm(N, 3, 3))
  )