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move tests that are currently failing on CRAN/MKL to the test-depr-ba…
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…rk.R

file and skip_on_cran() for now until can test/reproduce locally
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@merliseclyde
merliseclyde committed Apr 17, 2023
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4 changes: 2 additions & 2 deletions DESCRIPTION
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Package: bark
Type: Package
Title: Bayesian Additive Regression Kernels
Version: 1.0.2
Date: 2023-04-06
Version: 1.0.4
Date: 2023-04-17
Authors@R: c(person("Merlise", "Clyde", email="clyde@stat.duke.edu",
role=c("aut","cre", "ths"),
comment=c("ORCID=0000-0002-3595-1872")
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15 changes: 12 additions & 3 deletions NEWS.md
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@@ -1,7 +1,16 @@
# bark 1.0.2
# bark 1.0.4

* archived on 3/31 due to unit test failure on M1 Mac with R-devel
(false-positive). No changes.
* archived on 3/31 due to unit test failure on MKL with R-devel gcc
skip this unit test on CRAN as test is for code that will be deprecated
shortly.

# bark 1.0.3

* failed MKL checks

# bark 1.0.3

* failed MKL checks

# bark 1.0.1

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16 changes: 10 additions & 6 deletions cran-comments.md
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@@ -1,10 +1,13 @@
# bark 1.0.3 Comments to CRAN
# bark 1.0.4 Comments to CRAN

Package archived on 3/31 due to CRAN check failiure on M1 Mac (R-devel) due
to unit test. Checks using mac-builder-M1mac with R-release and R-devel
currently do not any errors in the unit test that failed (no errors on any of
the platforms) so this may have been a false -positive with R-devel on the
M1 Mac?
Package archived on 3/31 due to CRAN check failiure on MKL (R-devel) due
to unit test.

Attempts to replicate the failed test in a docker container for
Debian with gcc-12, MKL latest (2023), and r-devel have been unsuccesful
(still passes the test). Skipping test on cran as it is for
code that will be deprected shortly and was there for internal development
(and passes on all other platforms with CI for r-devel, r-release).

Possible misspelled words in the DESCRIPTION are correct surnames

Expand All @@ -17,6 +20,7 @@ Possible misspelled words in the DESCRIPTION are correct surnames
- R-hub ubuntu-gcc-release (r-release)
- R-hub fedora-clang-devel (r-devel)
- R-hub linux-x86_64-rocker-gcc-san (r-devel)
- docker debian, gcc-12, intel-MKL-latest


## R CMD check results
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197 changes: 61 additions & 136 deletions tests/testthat/test-bark.r
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Expand Up @@ -4,156 +4,80 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later

test_that("new bark", {
test_that("test inputs", {

#regression
traindata <- sim_Friedman2(200, sd=125)
testdata <- sim_Friedman2(1000, sd=0)

# check main input argument types
# no formula (character string)
expect_error(bark("y ~ .", data=data.frame(traindata),
# check main input argument types
# no formula (character string)
expect_error(bark("y ~ .", data=data.frame(traindata),
testdata= data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification = FALSE,
common_lambdas = FALSE,
selection = FALSE,
printevery=10^10))

# train ata is not a dataframe
expect_error(bark(y ~ ., data=traindata,
testdata= data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification = FALSE,
common_lambdas = FALSE,
selection = FALSE,
printevery=10^10))

# testdata is not a dataframe
expect_error(bark(y ~ ., data=data.frame(traindata),
testdata=testdata,
nburn=10, nkeep=100, keepevery=10,
classification = FALSE,
common_lambdas = FALSE,
selection = FALSE,
printevery=10^10))

set.seed(42)
fit.bark.depc <- bark_mat(traindata$x, traindata$y, testdata$x,
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, type="d", printevery=10^10)



set.seed(42)
fit.bark <- bark(y ~ ., data=data.frame(traindata),

# train ata is not a dataframe
expect_error(bark(y ~ ., data=traindata,
testdata= data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification = FALSE,
common_lambdas = FALSE,
selection = FALSE,
printevery=10^10,
keeptrain = TRUE)
printevery=10^10))

# testdata is not a dataframe
expect_error(bark(y ~ ., data=data.frame(traindata),
testdata=testdata,
nburn=10, nkeep=100, keepevery=10,
classification = FALSE,
common_lambdas = FALSE,
selection = FALSE,
printevery=10^10))

set.seed(42)
n = 500
circle2 = data.frame(sim_circle(n, dim = 2))
train = sample(1:n, size = floor(n/2), rep=FALSE)

expect_error(bark(y ~ ., data=circle2, subset=train,
testdata = circle2[-train, ],
classification = 1,
nburn = 10,
nkeep = 10,
printevery = 10^10))

# dim of test & training disagree
expect_error(bark(y ~ x.1 + x.2., data=circle2, subset=train,
testdata = circle2[-train, "x.1"],
classification = TRUE,
nburn = 10,
nkeep = 10,
printevery = 10^10))

expect_error(bark(y ~ ., data=circle2, subset=train,
testdata = as.matrix(circle2[-train, ]),
classification = 1,
nburn = 10,
nkeep = 10,
printevery = 10^10))

expect_equal(mean((fit.bark.depc$yhat.test.mean-testdata$y)^2),
mean((fit.bark$yhat.test.mean-testdata$y)^2))

set.seed(42)
fit.bark.depc <- bark_mat(traindata$x, traindata$y, testdata$x,
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, type="e", printevery=500)
set.seed(42)
fit.bark <- bark(y ~ ., data=data.frame(traindata),
testdata =data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, selection = FALSE,
printevery = 500, verbose=TRUE)



expect_equal(mean((fit.bark.depc$yhat.test.mean-testdata$y)^2),
mean((fit.bark$yhat.test.mean-testdata$y)^2))


set.seed(42)
fit.bark.depc <- bark_mat(traindata$x, traindata$y, testdata$x,
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, type="sd", printevery=10^10)
set.seed(42)
fit.bark <- bark(y ~ ., data=data.frame(traindata),
testdata =data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, common_lambdas=FALSE,
printevery=10^10)



expect_equal(mean((fit.bark.depc$yhat.test.mean-testdata$y)^2),
mean((fit.bark$yhat.test.mean-testdata$y)^2))

set.seed(42)
n = 500
circle2 = data.frame(sim_circle(n, dim = 2))
train = sample(1:n, size = floor(n/2), rep=FALSE)
set.seed(42)
circle2.bark = bark(y ~ ., data=circle2, subset=train,
testdata = circle2[-train, ],
expect_no_error(bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 10,
nkeep = 10,
selection = TRUE,
common_lambdas = TRUE,
printevery = 10, verbose = TRUE)
set.seed(42)
circle2.bark.depr = bark_mat(y.train=circle2[train,"y"],
x.train=as.matrix(circle2[train, 1:2]),
x.test = as.matrix(circle2[-train, 1:2]),
classification = TRUE,
type="se",
nburn = 10,
nkeep = 10,
printevery = 10^10)
expect_equal((circle2.bark$yhat.test.mean > 0) != circle2[-train, "y"],
(circle2.bark.depr$yhat.test.mean > 0) != circle2[-train, "y"])


expect_error(bark(y ~ ., data=circle2, subset=train,
testdata = circle2[-train, ],
classification = 1,
nburn = 10,
nkeep = 10,
printevery = 10^10))

# dim of test & training disagree
expect_error(bark(y ~ x.1 + x.2., data=circle2, subset=train,
testdata = circle2[-train, "x.1"],
classification = TRUE,
nburn = 10,
nkeep = 10,
printevery = 10^10))

expect_error(bark(y ~ ., data=circle2, subset=train,
testdata = as.matrix(circle2[-train, ]),
classification = 1,
nburn = 10,
nkeep = 10,
printevery = 10^10))

expect_no_error(bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 10,
nkeep = 10,
printevery = 10^10))
expect_error(bark(y ~ ., data=circle2, subset=train,
classification = TRUE,
nburn = 10,
nkeep = 10,
fixed = list(alpha = 2.0),
printevery = 10^10))
})
printevery = 10^10))
expect_error(bark(y ~ ., data=circle2, subset=train,
classification = TRUE,
nburn = 10,
nkeep = 10,
fixed = list(alpha = 2.0),
printevery = 10^10))
})

# github issue #3
test_that("test bark with p=1", {
Expand All @@ -166,12 +90,12 @@ test_that("test bark with p=1", {
keepevery = 10,
printevery = 10^10))
expect_no_error(bark_mat(y.train = df$y, x.train = as.matrix(df$x),
x.test = as.matrix(df$x),
classification = FALSE,
nburn = 10,
nkeep = 1000,
keepevery = 10,
printevery = 10^10))
x.test = as.matrix(df$x),
classification = FALSE,
nburn = 10,
nkeep = 1000,
keepevery = 10,
printevery = 10^10))

expect_no_error(bark(y ~ ., data=df,
classification = FALSE,
Expand All @@ -182,4 +106,5 @@ test_that("test bark with p=1", {
common_lambdas = FALSE,
printevery = 10^10))

})
})

92 changes: 92 additions & 0 deletions tests/testthat/test-depr_bark.r
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Expand Up @@ -73,3 +73,95 @@ expect_error(bark_mat(x.train=traindata, y.train = traindata$y,
classification=FALSE, type="sd", printevery=10))
})

test_that("new bark", {
skip_on_cran()
#regression
traindata <- sim_Friedman2(200, sd=125)
testdata <- sim_Friedman2(1000, sd=0)


set.seed(42)
fit.bark.depc <- bark_mat(traindata$x, traindata$y, testdata$x,
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, type="d", printevery=10^10)



set.seed(42)
fit.bark <- bark(y ~ ., data=data.frame(traindata),
testdata= data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification = FALSE,
common_lambdas = FALSE,
selection = FALSE,
printevery=10^10,
keeptrain = TRUE)



expect_equal(mean((fit.bark.depc$yhat.test.mean-testdata$y)^2),
mean((fit.bark$yhat.test.mean-testdata$y)^2))

set.seed(42)
fit.bark.depc <- bark_mat(traindata$x, traindata$y, testdata$x,
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, type="e", printevery=500)
set.seed(42)
fit.bark <- bark(y ~ ., data=data.frame(traindata),
testdata =data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, selection = FALSE,
printevery = 500, verbose=TRUE)



expect_equal(mean((fit.bark.depc$yhat.test.mean-testdata$y)^2),
mean((fit.bark$yhat.test.mean-testdata$y)^2))


set.seed(42)
fit.bark.depc <- bark_mat(traindata$x, traindata$y, testdata$x,
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, type="sd", printevery=10^10)
set.seed(42)
fit.bark <- bark(y ~ ., data=data.frame(traindata),
testdata =data.frame(testdata),
nburn=10, nkeep=100, keepevery=10,
classification=FALSE, common_lambdas=FALSE,
printevery=10^10)



expect_equal(mean((fit.bark.depc$yhat.test.mean-testdata$y)^2),
mean((fit.bark$yhat.test.mean-testdata$y)^2))

# classification
set.seed(42)
n = 500
circle2 = data.frame(sim_circle(n, dim = 2))
train = sample(1:n, size = floor(n/2), rep=FALSE)
set.seed(42)
circle2.bark = bark(y ~ ., data=circle2, subset=train,
testdata = circle2[-train, ],
classification = TRUE,
nburn = 10,
nkeep = 10,
selection = TRUE,
common_lambdas = TRUE,
printevery = 100, verbose = TRUE)
set.seed(42)
circle2.bark.depr = bark_mat(y.train=circle2[train,"y"],
x.train=as.matrix(circle2[train, 1:2]),
x.test = as.matrix(circle2[-train, 1:2]),
classification = TRUE,
type="se",
nburn = 10,
nkeep = 10,
printevery = 100)
# skip_on_cran()
expect_equal((circle2.bark$yhat.test.mean > 0) != circle2[-train, "y"],
(circle2.bark.depr$yhat.test.mean > 0) != circle2[-train, "y"])

})


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