context("testing openmp parallelization")
test_that("gbm refuses to work with insane numbers of threads", {
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- factor(sample(letters[1:4],N,replace=T))
X4 <- ordered(sample(letters[1:6],N,replace=T))
X5 <- factor(sample(letters[1:3],N,replace=T))
X6 <- 3*runif(N)
mu <- c(-1,0,1,2)[as.numeric(X3)]
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N, 0, sigma)
## create a bunch of missing values
X1[sample(1:N,size=100)] <- NA
X3[sample(1:N,size=300)] <- NA
w <- rep(1,N)
data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)
# fit initial model
expect_error(gbmt(Y~X1+X2+X3+X4+X5+X6, # formula
data=data, # dataset
var_monotone=c(0,0,0,0,0,0), # -1: monotone decrease, +1: monotone increase, 0: no monotone restrictions
keep_gbm_data=TRUE,
cv_folds=10, # do 10-fold cross-validation
par_details=gbmParallel(num_threads=-1)),
"number of threads must be strictly positive",
fixed=TRUE)
})
test_that("gbm refuses to work with insane array chunk size - old api", {
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- factor(sample(letters[1:4],N,replace=T))
X4 <- ordered(sample(letters[1:6],N,replace=T))
X5 <- factor(sample(letters[1:3],N,replace=T))
X6 <- 3*runif(N)
mu <- c(-1,0,1,2)[as.numeric(X3)]
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N,0,sigma)
## create a bunch of missing values
X1[sample(1:N,size=100)] <- NA
X3[sample(1:N,size=300)] <- NA
w <- rep(1,N)
data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)
# fit initial model
expect_error(gbmt(Y~X1+X2+X3+X4+X5+X6, # formula
data=data, # dataset
var_monotone=c(0,0,0,0,0,0), # -1: monotone decrease, +1: monotone increase, 0: no monotone restrictions
keep_gbm_data=TRUE,
cv_folds=10, # do 10-fold cross-validation
par_details=gbmParallel(num_threads=1, array_chunk_size=0)),
"array chunk size must be strictly positive", fixed=TRUE)
})
test_that("gbm refuses to work with insane numbers of threads - old API", {
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- factor(sample(letters[1:4],N,replace=T))
X4 <- ordered(sample(letters[1:6],N,replace=T))
X5 <- factor(sample(letters[1:3],N,replace=T))
X6 <- 3*runif(N)
mu <- c(-1,0,1,2)[as.numeric(X3)]
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N,0,sigma)
## create a bunch of missing values
X1[sample(1:N,size=100)] <- NA
X3[sample(1:N,size=300)] <- NA
w <- rep(1,N)
data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)
# fit initial model
expect_error(gbm(Y~X1+X2+X3+X4+X5+X6, # formula
data=data, # dataset
var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease, +1: monotone increase, 0: no monotone restrictions
distribution="Gaussian", # bernoulli, adaboost, gaussian, poisson, coxph, or
# list(name="quantile",alpha=0.05) for quantile regression
n.trees=2000, # number of trees
shrinkage=0.005, # shrinkage or learning rate, 0.001 to 0.1 usually work
interaction.depth=3, # 1: additive model, 2: two-way interactions, etc.
bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best
train.fraction = 0.5, # fraction of data for training, first train.fraction*N used for training
n.minobsinnode = 10, # minimum number of obs needed in each node
keep.data=TRUE,
cv.folds=10, # do 10-fold cross-validation
par.details=gbmParallel(num_threads=-1)),
"number of threads must be strictly positive",
fixed=TRUE)
})
test_that("gbm refuses to work with insane array chunk size - old api", {
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- factor(sample(letters[1:4],N,replace=T))
X4 <- ordered(sample(letters[1:6],N,replace=T))
X5 <- factor(sample(letters[1:3],N,replace=T))
X6 <- 3*runif(N)
mu <- c(-1,0,1,2)[as.numeric(X3)]
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N,0,sigma)
## create a bunch of missing values
X1[sample(1:N,size=100)] <- NA
X3[sample(1:N,size=300)] <- NA
w <- rep(1,N)
data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)
# fit initial model
expect_error(gbm(Y~X1+X2+X3+X4+X5+X6, # formula
data=data, # dataset
var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease, +1: monotone increase, 0: no monotone restrictions
distribution="Gaussian", # bernoulli, adaboost, gaussian, poisson, coxph, or
# list(name="quantile",alpha=0.05) for quantile regression
n.trees=2000, # number of trees
shrinkage=0.005, # shrinkage or learning rate, 0.001 to 0.1 usually work
interaction.depth=3, # 1: additive model, 2: two-way interactions, etc.
bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best
train.fraction = 0.5, # fraction of data for training, first train.fraction*N used for training
n.minobsinnode = 10, # minimum number of obs needed in each node
keep.data=TRUE,
cv.folds=10, # do 10-fold cross-validation
par.details=gbmParallel(num_threads=2, array_chunk_size=0)),
"array chunk size must be strictly positive", fixed=TRUE)
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.