Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

False warning #71

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

False warning #71

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
2b8b742
cleaning up tests
JackStat Nov 2, 2015
7f09ca4
added tests for no warnings #59
JackStat Nov 2, 2015
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
59 changes: 55 additions & 4 deletions tests/testthat/test_basic.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -73,6 +73,23 @@ test_that("gaussian works", {
# Base the validation tests on observed discrepancies
expect_true(cor(data2$Y, f.predict) > 0.990)
expect_true(sd(data2$Y-f.predict) < sigma)

# No warning
expect_that(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
n.cores=1)
, not(gives_warning()))
})

test_that("coxph works", {
Expand Down Expand Up @@ -141,6 +158,22 @@ test_that("coxph works", {
#plot(data2$f,f.predict)
# Use observed sd
expect_true(sd(data2$f - f.predict) < 0.4)

# No warning
expect_that(gbm(Surv(tt,delta)~X1+X2+X3, # formula
data=data, # dataset
weights=w,
var.monotone=c(0,0,0), # -1: monotone decrease, +1: monotone increase, 0: no monotone restrictions
distribution="coxph",
n.trees=3000, # number of trees
shrinkage=0.001, # 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
cv.folds = 5, # do 5-fold cross-validation
n.minobsinnode = 10, # minimum total weight needed in each node
keep.data = TRUE, n.cores=1)
, not(gives_warning()))
})

test_that("bernoulli works", {
Expand Down Expand Up @@ -203,6 +236,22 @@ test_that("bernoulli works", {

# Base the validation tests on observed discrepancies
expect_true(sd(f.new - f.1.predict) < 1.0)

# No warning
expect_that(gbm(Y~X1+X2+X3,
data=data,
weights=w,
var.monotone=c(0,0,0),
distribution="bernoulli",
n.trees=3000,
shrinkage=0.001,
interaction.depth=3,
bag.fraction = 0.5,
train.fraction = 0.5,
cv.folds=5,
n.minobsinnode = 10,
n.cores=1)
, not(gives_warning()))
})


Expand All @@ -215,8 +264,8 @@ test_that("relative influence picks out true predictors", {
cls <- rep(c(0, 1), ea=500) # Class
X <- data.frame(cbind(X1, X2, cls))
mod <- gbm(cls ~ ., data= X, n.trees=1000, cv.folds=5,
shrinkage=.01, interaction.depth=2, n.cores=1)
ri <- relative.influence(mod, sort.=TRUE, scale.=TRUE)
shrinkage=.01, interaction.depth=2, n.cores=1, distribution = 'bernoulli')
ri <- relative.influence(mod, sort.=TRUE, scale.=TRUE, n.trees = 1000)

wh <- names(ri)[1:5]
res <- sum(wh %in% paste("V", 51:55, sep = ""))
Expand All @@ -237,12 +286,14 @@ test_that("Conversion of 2 factor Y is successful", {

set.seed(32479)
g1 <- gbm(y ~ ., data = data.frame(y = NumY, PredX)
, distribution = 'bernoulli', verbose = FALSE)
, distribution = 'bernoulli', verbose = FALSE
, n.trees = 50)
rig1 <- relative.influence(g1, n.trees=10)

set.seed(32479)
g2 <- gbm(y ~ ., data = data.frame(y = FactY, PredX)
, distribution = 'bernoulli', verbose = FALSE)
, distribution = 'bernoulli', verbose = FALSE
, n.trees = 50)
rig2 <- relative.influence(g2, n.trees=10)

expect_equal(rig1, rig2)
Expand Down