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Merge pull request #143 from ModelOriented/improve-tests
More compact tests
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,190 @@ | ||
| # Model with non-linearities and interactions | ||
| fit <- lm( | ||
| Sepal.Length ~ poly(Petal.Width, degree = 2L) * Species + Petal.Length, data = iris | ||
| ) | ||
| x <- c("Petal.Width", "Species", "Petal.Length") | ||
| preds <- unname(predict(fit, iris)) | ||
| J <- c(1L, 51L, 101L) | ||
|
|
||
| shap <- list( | ||
| kernelshap(fit, iris[x], bg_X = iris, verbose = FALSE), | ||
| permshap(fit, iris[x], bg_X = iris, verbose = FALSE) | ||
| ) | ||
|
|
||
| test_that("baseline equals average prediction on background data", { | ||
| for (s in shap) | ||
| expect_equal(s$baseline, mean(iris$Sepal.Length)) | ||
| }) | ||
|
|
||
| test_that("SHAP + baseline = prediction for exact mode", { | ||
| for (s in shap) | ||
| expect_equal(rowSums(s$S) + s$baseline, preds) | ||
| }) | ||
|
|
||
| test_that("auto-selection of background data works", { | ||
| # Here, the background data equals the full X | ||
| shap2 <- list( | ||
| kernelshap(fit, iris[x], verbose = FALSE), | ||
| permshap(fit, iris[x], verbose = FALSE) | ||
| ) | ||
|
|
||
| for (i in 1:2) { | ||
| expect_equal(shap$S, shap2$S) | ||
| } | ||
| }) | ||
|
|
||
| test_that("missing bg_X gives error if X is very small", { | ||
| for (algo in c(kernelshap, permshap)) | ||
| expect_error(algo(fit, iris[1:10, x], verbose = FALSE)) | ||
|
|
||
| }) | ||
|
|
||
| test_that("missing bg_X gives warning if X is quite small", { | ||
| for (algo in c(kernelshap, permshap)) | ||
| expect_warning(algo(fit, iris[1:30, x], verbose = FALSE)) | ||
| }) | ||
|
|
||
| test_that("selection of bg_X can be controlled via bg_n", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| s <- algo(fit, iris[x], verbose = FALSE, bg_n = 20L) | ||
| expect_equal(nrow(s$bg_X), 20L) | ||
| } | ||
| }) | ||
|
|
||
| test_that("using foreach (non-parallel) gives the same as normal mode", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| s <- algo(fit, iris[J, x], bg_X = iris, verbose = FALSE) | ||
| s2 <- suppressWarnings( | ||
| algo(fit, iris[J, x], bg_X = iris, verbose = FALSE, parallel = TRUE) | ||
| ) | ||
| expect_equal(s, s2) | ||
| } | ||
| }) | ||
|
|
||
| test_that("verbose is chatty", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| capture_output(expect_message(algo(fit, iris[J, x], bg_X = iris, verbose = TRUE))) | ||
| } | ||
| }) | ||
|
|
||
| test_that("large background data cause warning", { | ||
| # Takes a bit of time, thus only for one algo | ||
| large_bg <- iris[rep(1:150, 230), ] | ||
| expect_warning( | ||
| kernelshap(fit, iris[1L, x], bg_X = large_bg, verbose = FALSE) | ||
| ) | ||
| }) | ||
|
|
||
| test_that("Decomposing a single row works", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| s <- algo(fit, iris[1L, x], bg_X = iris, verbose = FALSE) | ||
| expect_equal(s$baseline, mean(iris$Sepal.Length)) | ||
| expect_equal(rowSums(s$S) + s$baseline, preds[1]) | ||
| } | ||
| }) | ||
|
|
||
| test_that("Background data can contain additional columns", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| s <- algo(fit, iris[1L, x], bg_X = cbind(d = 1, iris), verbose = FALSE) | ||
| expect_true(is.kernelshap(s)) | ||
| } | ||
| }) | ||
|
|
||
| test_that("Background data can contain only one single row", { | ||
| for (algo in c(kernelshap, permshap)) | ||
| expect_no_error(algo(fit, iris[1L, x], bg_X = iris[150L, ], verbose = FALSE)) | ||
| }) | ||
|
|
||
| test_that("feature_names can drop columns from SHAP calculations", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| s <- algo(fit, iris[J, ], bg_X = iris, feature_names = x, verbose = FALSE) | ||
| expect_equal(colnames(s$S), x) | ||
| } | ||
| }) | ||
|
|
||
| test_that("feature_names can rearrange column names in result", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| s <- algo(fit, iris[J, ], bg_X = iris, feature_names = rev(x), verbose = FALSE) | ||
| expect_equal(colnames(s$S), rev(x)) | ||
| } | ||
| }) | ||
|
|
||
| test_that("feature_names must be in colnames(X) and colnames(bg_X)", { | ||
| for (algo in c(kernelshap, permshap)) { | ||
| expect_error(algo(fit, iris, bg_X = cbind(iris, a = 1), feature_names = "a")) | ||
| expect_error(algo(fit, cbind(iris, a = 1), bg_X = iris, feature_names = "a")) | ||
| } | ||
| }) | ||
|
|
||
| test_that("Matrix input is fine", { | ||
| X <- data.matrix(iris) | ||
| pred_fun <- function(m, X) { | ||
| data <- as.data.frame(X) |> | ||
| transform(Species = factor(Species, labels = levels(iris$Species))) | ||
| predict(m, data) | ||
| } | ||
|
|
||
| for (algo in c(kernelshap, permshap)) { | ||
| s <- algo(fit, X[J, x], pred_fun = pred_fun, bg_X = X, verbose = FALSE) | ||
|
|
||
| expect_equal(s$baseline, mean(iris$Sepal.Length)) # baseline is mean of bg | ||
| expect_equal(rowSums(s$S) + s$baseline, preds[J]) # sum shap = centered preds | ||
| expect_no_error( # additional cols in bg are ok | ||
| algo(fit, X[J, x], pred_fun = pred_fun, bg_X = cbind(d = 1, X), verbose = FALSE) | ||
| ) | ||
| expect_error( # feature_names are less flexible | ||
| algo(fit, X[J, ], pred_fun = pred_fun, bg_X = X, | ||
| verbose = FALSE, feature_names = "Sepal.Width") | ||
| ) | ||
| } | ||
| }) | ||
|
|
||
| test_that("Special case p = 1 works only for kernelshap()", { | ||
| capture_output( | ||
| expect_message( | ||
| s <- kernelshap(fit, X = iris[J, ], bg_X = iris, feature_names = "Petal.Width") | ||
| ) | ||
| ) | ||
| expect_equal(s$baseline, mean(iris$Sepal.Length)) | ||
| expect_equal(unname(rowSums(s$S)) + s$baseline, preds[J]) | ||
| expect_equal(s$SE[1L], 0) | ||
|
|
||
| expect_error( # Not implemented | ||
| permshap( | ||
| fit, iris[J, ], bg_X = iris, verbose = FALSE, feature_names = "Petal.Width" | ||
| ) | ||
| ) | ||
| }) | ||
|
|
||
| test_that("exact hybrid kernelshap() is similar to exact (non-hybrid)", { | ||
| s1 <- kernelshap( | ||
| fit, iris[J, x], bg_X = iris, exact = FALSE, hybrid_degree = 1L, verbose = FALSE | ||
| ) | ||
| expect_equal(s1$S, shap[[1L]]$S[J, ]) | ||
| }) | ||
|
|
||
| test_that("baseline equals average prediction on background data in sampling mode", { | ||
| s2 <- s_sampling <- kernelshap( | ||
| fit, iris[J, x], bg_X = iris, hybrid_degree = 0L, verbose = FALSE, exact = FALSE | ||
| ) | ||
| expect_equal(s2$baseline, mean(iris$Sepal.Length)) | ||
| }) | ||
|
|
||
| test_that("SHAP + baseline = prediction for sampling mode", { | ||
| s2 <- s_sampling <- kernelshap( | ||
| fit, iris[J, x], bg_X = iris, hybrid_degree = 0L, verbose = FALSE, exact = FALSE | ||
| ) | ||
| expect_equal(rowSums(s2$S) + s2$baseline, preds[J]) | ||
| }) | ||
|
|
||
| test_that("kernelshap works for large p (hybrid case)", { | ||
| set.seed(9L) | ||
| X <- data.frame(matrix(rnorm(20000L), ncol = 100L)) | ||
| y <- X[, 1L] * X[, 2L] * X[, 3L] | ||
| fit <- lm(y ~ X1:X2:X3 + ., data = cbind(y = y, X)) | ||
| s <- kernelshap(fit, X[1L, ], bg_X = X, verbose = FALSE) | ||
|
|
||
| expect_equal(s$baseline, mean(y)) | ||
| expect_equal(rowSums(s$S) + s$baseline, unname(predict(fit, X[1L, ]))) | ||
| }) | ||
|
|
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