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commenting out all tests that use xgboost
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@mayer79
mayer79 committed Oct 14, 2023
1 parent 8e165f3 commit ebdc9db
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4 changes: 2 additions & 2 deletions CRAN-SUBMISSION
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Version: 0.9.2
Date: 2023-10-14 16:08:56 UTC
SHA: e923bacb41fd1057fd831e39ef13fe899a935e63
Date: 2023-10-14 16:44:29 UTC
SHA: 1d239694c309c6b48168b2ca19b8fd5c8d6f1d2d
1 change: 1 addition & 0 deletions NEWS.md
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## Other changes

- Revised vignette on "mshapviz".
- Commenting out most unit tests as they would not pass timings measured on Debian.

# shapviz 0.9.1

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2 changes: 1 addition & 1 deletion cran-comments.md
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## Resubmission 7

No effect of setDTthreads(2). Removing now all tests with xgboost.
No effect of setDTthreads(2). Commenting out now all tests with xgboost.

## Resubmission 6

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240 changes: 120 additions & 120 deletions tests/testthat/test-plots-mshapviz.R
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dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1L]), label = iris[, 1L])
fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
x <- shapviz(fit, X_pred = dtrain, X = iris[, -1L])
x <- c(m1 = x, m2 = x)

test_that("plots work for basic example", {
expect_s3_class(sv_waterfall(x, 2), "patchwork")
suppressMessages(expect_s3_class(sv_waterfall(x, 2:3), "patchwork"))
expect_s3_class(sv_force(x, 2), "patchwork")
suppressMessages(expect_s3_class(sv_force(x, 2:3), "patchwork"))
expect_s3_class(sv_importance(x), "ggplot")
expect_s3_class(sv_importance(x, bar_type = "stack"), "ggplot")
expect_s3_class(sv_importance(x, bar_type = "facets"), "ggplot")
expect_s3_class(
sv_importance(x, show_numbers = TRUE, bar_type = "separate"), "patchwork"
)
expect_s3_class(sv_importance(x, kind = "beeswarm"), "patchwork")
expect_s3_class(sv_dependence(x, "Petal.Length"), "patchwork")
expect_s3_class(sv_dependence2D(x, x = "Petal.Length", y = "Species"), "patchwork")
})

test_that("using 'max_display' gives no error", {
expect_s3_class(sv_waterfall(x, 2, max_display = 2L), "patchwork")
suppressMessages(expect_s3_class(sv_waterfall(x, 2:10, max_display = 2L), "patchwork"))
expect_s3_class(sv_force(x, 2, max_display = 2L), "patchwork")
suppressMessages(expect_s3_class(sv_force(x, 2:10, max_display = 2L), "patchwork"))
expect_s3_class(sv_importance(x, max_display = 2L), "ggplot")
expect_s3_class(sv_importance(x, max_display = 2L, bar_type = "stack"), "ggplot")
expect_s3_class(sv_importance(x, max_display = 2L, bar_type = "facets"), "ggplot")
expect_s3_class(
sv_importance(x, max_display = 2L, show_numbers = TRUE, bar_type = "separate"), "patchwork"
)
})

# SHAP interactions
x_inter <- shapviz(fit, X_pred = dtrain, X = iris[, -1L], interactions = TRUE)
x_inter <- c(m1 = x_inter, m2 = x_inter)

test_that("dependence plots work for interactions = TRUE", {
expect_s3_class(
sv_dependence(x_inter, v = "Petal.Length", interactions = TRUE),
"patchwork"
)
expect_s3_class(
sv_dependence(x_inter, v = "Petal.Length", interactions = TRUE),
"patchwork"
)
expect_s3_class(
sv_dependence(x_inter, "Petal.Length", color_var = "Species", interactions = TRUE),
"patchwork"
)
expect_s3_class(
sv_dependence2D(x_inter, x = "Petal.Length", y = "Species", interactions = TRUE),
"patchwork"
)
})

test_that("main effect plots equal case color_var = v", {
expect_equal(
sv_dependence(x_inter, "Petal.Length", color_var = NULL, interactions = TRUE),
sv_dependence(
x_inter, "Petal.Length", color_var = "Petal.Length", interactions = TRUE
)
)
})

test_that("Interaction plots provide patchwork object", {
expect_s3_class(sv_interaction(x_inter), "patchwork")
})

# Non-standard name
ir <- iris
ir["strange name"] <- ir$Sepal.Width * ir$Petal.Length
dtrain <- xgboost::xgb.DMatrix(data.matrix(ir[, -1L]), label = ir[, 1L])
fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
x <- shapviz(fit, X_pred = dtrain, X = ir[, -1L])
x <- c(m1 = x, m2 = x)

test_that("plots work for non-syntactic column names", {
expect_s3_class(sv_waterfall(x, 2), "patchwork")
expect_s3_class(sv_force(x, 2), "patchwork")
expect_s3_class(sv_importance(x), "ggplot")
expect_s3_class(
sv_importance(x, bar_type = "separate", show_numbers = TRUE), "patchwork"
)
expect_s3_class(sv_importance(x, max_display = 2, kind = "beeswarm"), "patchwork")
expect_s3_class(sv_importance(x, kind = "beeswarm"), "patchwork")
expect_s3_class(sv_dependence(x, "strange name"), "patchwork")
expect_s3_class(
sv_dependence(x, "Petal.Length", color_var = "strange name"), "patchwork"
)
expect_s3_class(
sv_dependence2D(x, x = "Petal.Length", y = "strange name"), "patchwork"
)
})

test_that("sv_importance() and sv_interaction() and kind = 'no' gives matrix", {
X_pred <- data.matrix(iris[, -1L])
dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1L])
fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
x <- shapviz(fit, X_pred = X_pred, interactions = TRUE)
x <- c(m1 = x, m2 = x)

imp <- sv_importance(x, kind = "no")
expect_true(is.matrix(imp) && all(dim(imp) == c(4L, length(x))))

inter <- sv_interaction(x, kind = "no")
expect_true(is.list(inter) && all(dim(inter[[1L]]) == rep(ncol(X_pred), 2L)))
})

test_that("sv_dependence() does not work with multiple v", {
X_pred <- data.matrix(iris[, -1L])
dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1L])
fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
x <- c(m1 = shapviz(fit, X_pred = X_pred), m2 = shapviz(fit, X_pred = X_pred))
expect_error(sv_dependence(x, v = c("Species", "Sepal.Width")))

expect_error(sv_dependence2D(x, x = c("Species", "Sepal.Width"), y = "Petal.Width"))
expect_error(sv_dependence2D(x, x = "Petal.Width", y = c("Species", "Sepal.Width")))
})
# dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1L]), label = iris[, 1L])
# fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
# x <- shapviz(fit, X_pred = dtrain, X = iris[, -1L])
# x <- c(m1 = x, m2 = x)
#
# test_that("plots work for basic example", {
# expect_s3_class(sv_waterfall(x, 2), "patchwork")
# suppressMessages(expect_s3_class(sv_waterfall(x, 2:3), "patchwork"))
# expect_s3_class(sv_force(x, 2), "patchwork")
# suppressMessages(expect_s3_class(sv_force(x, 2:3), "patchwork"))
# expect_s3_class(sv_importance(x), "ggplot")
# expect_s3_class(sv_importance(x, bar_type = "stack"), "ggplot")
# expect_s3_class(sv_importance(x, bar_type = "facets"), "ggplot")
# expect_s3_class(
# sv_importance(x, show_numbers = TRUE, bar_type = "separate"), "patchwork"
# )
# expect_s3_class(sv_importance(x, kind = "beeswarm"), "patchwork")
# expect_s3_class(sv_dependence(x, "Petal.Length"), "patchwork")
# expect_s3_class(sv_dependence2D(x, x = "Petal.Length", y = "Species"), "patchwork")
# })
#
# test_that("using 'max_display' gives no error", {
# expect_s3_class(sv_waterfall(x, 2, max_display = 2L), "patchwork")
# suppressMessages(expect_s3_class(sv_waterfall(x, 2:10, max_display = 2L), "patchwork"))
# expect_s3_class(sv_force(x, 2, max_display = 2L), "patchwork")
# suppressMessages(expect_s3_class(sv_force(x, 2:10, max_display = 2L), "patchwork"))
# expect_s3_class(sv_importance(x, max_display = 2L), "ggplot")
# expect_s3_class(sv_importance(x, max_display = 2L, bar_type = "stack"), "ggplot")
# expect_s3_class(sv_importance(x, max_display = 2L, bar_type = "facets"), "ggplot")
# expect_s3_class(
# sv_importance(x, max_display = 2L, show_numbers = TRUE, bar_type = "separate"), "patchwork"
# )
# })
#
# # SHAP interactions
# x_inter <- shapviz(fit, X_pred = dtrain, X = iris[, -1L], interactions = TRUE)
# x_inter <- c(m1 = x_inter, m2 = x_inter)
#
# test_that("dependence plots work for interactions = TRUE", {
# expect_s3_class(
# sv_dependence(x_inter, v = "Petal.Length", interactions = TRUE),
# "patchwork"
# )
# expect_s3_class(
# sv_dependence(x_inter, v = "Petal.Length", interactions = TRUE),
# "patchwork"
# )
# expect_s3_class(
# sv_dependence(x_inter, "Petal.Length", color_var = "Species", interactions = TRUE),
# "patchwork"
# )
# expect_s3_class(
# sv_dependence2D(x_inter, x = "Petal.Length", y = "Species", interactions = TRUE),
# "patchwork"
# )
# })
#
# test_that("main effect plots equal case color_var = v", {
# expect_equal(
# sv_dependence(x_inter, "Petal.Length", color_var = NULL, interactions = TRUE),
# sv_dependence(
# x_inter, "Petal.Length", color_var = "Petal.Length", interactions = TRUE
# )
# )
# })
#
# test_that("Interaction plots provide patchwork object", {
# expect_s3_class(sv_interaction(x_inter), "patchwork")
# })
#
# # Non-standard name
# ir <- iris
# ir["strange name"] <- ir$Sepal.Width * ir$Petal.Length
# dtrain <- xgboost::xgb.DMatrix(data.matrix(ir[, -1L]), label = ir[, 1L])
# fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
# x <- shapviz(fit, X_pred = dtrain, X = ir[, -1L])
# x <- c(m1 = x, m2 = x)
#
# test_that("plots work for non-syntactic column names", {
# expect_s3_class(sv_waterfall(x, 2), "patchwork")
# expect_s3_class(sv_force(x, 2), "patchwork")
# expect_s3_class(sv_importance(x), "ggplot")
# expect_s3_class(
# sv_importance(x, bar_type = "separate", show_numbers = TRUE), "patchwork"
# )
# expect_s3_class(sv_importance(x, max_display = 2, kind = "beeswarm"), "patchwork")
# expect_s3_class(sv_importance(x, kind = "beeswarm"), "patchwork")
# expect_s3_class(sv_dependence(x, "strange name"), "patchwork")
# expect_s3_class(
# sv_dependence(x, "Petal.Length", color_var = "strange name"), "patchwork"
# )
# expect_s3_class(
# sv_dependence2D(x, x = "Petal.Length", y = "strange name"), "patchwork"
# )
# })
#
# test_that("sv_importance() and sv_interaction() and kind = 'no' gives matrix", {
# X_pred <- data.matrix(iris[, -1L])
# dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1L])
# fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
# x <- shapviz(fit, X_pred = X_pred, interactions = TRUE)
# x <- c(m1 = x, m2 = x)
#
# imp <- sv_importance(x, kind = "no")
# expect_true(is.matrix(imp) && all(dim(imp) == c(4L, length(x))))
#
# inter <- sv_interaction(x, kind = "no")
# expect_true(is.list(inter) && all(dim(inter[[1L]]) == rep(ncol(X_pred), 2L)))
# })
#
# test_that("sv_dependence() does not work with multiple v", {
# X_pred <- data.matrix(iris[, -1L])
# dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1L])
# fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 1L)
# x <- c(m1 = shapviz(fit, X_pred = X_pred), m2 = shapviz(fit, X_pred = X_pred))
# expect_error(sv_dependence(x, v = c("Species", "Sepal.Width")))
#
# expect_error(sv_dependence2D(x, x = c("Species", "Sepal.Width"), y = "Petal.Width"))
# expect_error(sv_dependence2D(x, x = "Petal.Width", y = c("Species", "Sepal.Width")))
# })
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