context('s3 objects')
test_that('it can diff a trivial s3 object', {
x <- list(1)
class(x) <- 'boo'
y <- x
x[[1]] <- 2
expect_diff(x, y)
})
test_that('it can diff an lm object', {
lmo <- lm(Sepal.Width ~ Sepal.Length, iris)
iris2 <- iris; iris2[1, 1] <- 7
lmo2 <- lm(Sepal.Width ~ Sepal.Length, iris2)
expect_diff(lmo, lmo2)
})
# Test diffing of GBM objects
local({
some_gbm_data <- function(N = 1000) {
# Drawn from the documentation for ?gbm
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])
X4 <- factor(sample(letters[1:6],N,replace=TRUE))
X5 <- factor(sample(letters[1:3],N,replace=TRUE))
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)
# introduce some missing values
X1[sample(1:N,size=500)] <- NA
X4[sample(1:N,size=300)] <- NA
data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)
}
run_gbm <- function(data) {
# Drawn from the documentation for ?gbm
require(gbm)
gbm(Y ~ X1 + X2 + X3 + X4 + X5 + X6,
data = data, # dataset
var.monotone = rep(0, 6), # -1: monotone decrease,
# +1: monotone increase,
# 0: no monotone restrictions
distribution = "gaussian", # see the help for other choices
n.trees = 1000, # number of trees
shrinkage = 0.05, # 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 total weight needed in each node
cv.folds = 3, # do 3-fold cross-validation
keep.data = TRUE, # keep a copy of the dataset with the object
verbose = FALSE, # don't print out progress
n.cores = 1 # use only a single core (detecting #cores is
) # error-prone, so avoided here)
}
test_that('it can diff a GBM object', {
# TODO: (RK) Re enable for travis
#gbmo <- run_gbm(some_gbm_data())
#gbmo2 <- run_gbm(some_gbm_data())
#expect_diff(gbmo, gbmo2)
})
})
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