Nothing
## Combined Gradient Forest
data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
set.seed(201808)
f1c <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6], ntree=10)
set.seed(201808)
f2c <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6+6], ntree=10)
set.seed(201808)
f12 <- combinedGradientForest(west=f1c,east=f2c)
test_that("combinedGradientForest fits", {
expect_snapshot_value(f12, "serialize")
expect_snapshot_output(print(f12))
})
X <- Xsimulation
Y <- Ysimulation
XY <- data.frame(Y,X)
names(XY)[c(13, 1)] <- c("invalid pred", "invalid resp")
preds_inv <- colnames(XY)[13:22]
specs_inv <- colnames(XY)[1:12]
set.seed(201808)
f1c_inv <- gradientForest(XY, preds_inv, specs_inv[1:6], ntree=10, check.names=FALSE)
set.seed(201808)
f2c_inv <- gradientForest(XY, preds_inv, specs_inv[1:6+6], ntree=10, check.names=FALSE)
set.seed(201808)
gfc <- combinedGradientForest(west = f1c_inv, east = f2c_inv)
test_that("combinedGradientForest fits even with invalid col names", {
expect_snapshot_value(gfc, "serialize")
expect_snapshot_output(print(gfc))
})
test_that("invalid col names do not change data output", {
expect_true(all(predict(f12)[preds] == predict(gfc)[preds_inv]))
})
## the various plots call the following functions:
## importance
## cumimp.*
## density.*
## whiten
if (FALSE) {
root_dir <- rprojroot::find_root(rprojroot::has_file("DESCRIPTION"))
plot_dir <- file.path(root_dir, "tests", "testthat", "_plots")
png(file.path(plot_dir, "gfcombined_overall_valid.png"))
plot(f12, "O")
dev.off()
png(file.path(plot_dir, "gfcombined_ranges_valid.png"))
plot(f12, "Predictor.Ranges")
dev.off()
png(file.path(plot_dir, "gfcombined_density_valid.png"))
plot(f12, "Predictor.Density")
dev.off()
png(file.path(plot_dir, "gfcombined_cumulative_valid.png"))
plot(f12, "C")
dev.off()
png(file.path(plot_dir, "gfcombined_performance_valid.png"))
plot(f12, "Per")
dev.off()
png(file.path(plot_dir, "gfcombined_overall_invalid.png"))
plot(gfc, "O")
dev.off()
png(file.path(plot_dir, "gfcombined_ranges_invalid.png"))
plot(gfc, "Predictor.Ranges")
dev.off()
png(file.path(plot_dir, "gfcombined_density_invalid.png"))
plot(gfc, "Predictor.Density")
dev.off()
png(file.path(plot_dir, "gfcombined_cumulative_invalid.png"))
plot(gfc, "C")
dev.off()
png(file.path(plot_dir, "gfcombined_performance_invalid.png"))
plot(gfc, "Per")
dev.off()
}
data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
set.seed(201808)
f1c <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6], ntree=10)
set.seed(201808)
Xsimulation[,1] <- 0.5
Xsimulation[1,1] <- 0.51
f2c <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6+6], ntree=10)
set.seed(201808)
f12 <- combinedGradientForest(west=f1c,east=f2c)
test_that("combinedGradiientForest works with some unused predictors", {
expect_snapshot_value(f12, "serialize")
expect_snapshot_output(print(f12))
expect_snapshot_value(importance(f12), "serialize")
expect_snapshot_value(predict(f12), "serialize")
})
data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
Xsimulation[,1] <- 0.5
Xsimulation[1,1] <- 0.51
set.seed(201808)
f1c <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6], ntree=10)
set.seed(201808)
f2c <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6+6], ntree=10)
set.seed(201808)
f12 <- combinedGradientForest(west=f1c,east=f2c)
test_that("combinedGradientForest works with a universally unused predictors", {
expect_snapshot_value(f12, "serialize")
expect_snapshot_output(print(f12))
expect_snapshot_value(importance(f12), "serialize")
expect_snapshot_value(predict(f12), "serialize")
})
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