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[R-package] Speed-up lgb.importance() (#6364)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,158 @@ | ||
| NROUNDS <- 10L | ||
| MAX_DEPTH <- 3L | ||
| N <- nrow(iris) | ||
| X <- data.matrix(iris[2L:4L]) | ||
| FEAT <- colnames(X) | ||
| NCLASS <- nlevels(iris[, 5L]) | ||
|
|
||
| model_reg <- lgb.train( | ||
| params = list( | ||
| objective = "regression" | ||
| , num_threads = .LGB_MAX_THREADS | ||
| , max.depth = MAX_DEPTH | ||
| ) | ||
| , data = lgb.Dataset(X, label = iris[, 1L]) | ||
| , verbose = .LGB_VERBOSITY | ||
| , nrounds = NROUNDS | ||
| ) | ||
|
|
||
| model_binary <- lgb.train( | ||
| params = list( | ||
| objective = "binary" | ||
| , num_threads = .LGB_MAX_THREADS | ||
| , max.depth = MAX_DEPTH | ||
| ) | ||
| , data = lgb.Dataset(X, label = iris[, 5L] == "setosa") | ||
| , verbose = .LGB_VERBOSITY | ||
| , nrounds = NROUNDS | ||
| ) | ||
|
|
||
| model_multiclass <- lgb.train( | ||
| params = list( | ||
| objective = "multiclass" | ||
| , num_threads = .LGB_MAX_THREADS | ||
| , max.depth = MAX_DEPTH | ||
| , num_classes = NCLASS | ||
| ) | ||
| , data = lgb.Dataset(X, label = as.integer(iris[, 5L]) - 1L) | ||
| , verbose = .LGB_VERBOSITY | ||
| , nrounds = NROUNDS | ||
| ) | ||
|
|
||
| model_rank <- lgb.train( | ||
| params = list( | ||
| objective = "lambdarank" | ||
| , num_threads = .LGB_MAX_THREADS | ||
| , max.depth = MAX_DEPTH | ||
| , lambdarank_truncation_level = 3L | ||
| ) | ||
| , data = lgb.Dataset( | ||
| X | ||
| , label = as.integer(iris[, 1L] > 5.8) | ||
| , group = rep(10L, times = 15L) | ||
| ) | ||
| , verbose = .LGB_VERBOSITY | ||
| , nrounds = NROUNDS | ||
| ) | ||
|
|
||
| models <- list( | ||
| reg = model_reg | ||
| , bin = model_binary | ||
| , multi = model_multiclass | ||
| , rank = model_rank | ||
| ) | ||
|
|
||
| for (model_name in names(models)) { | ||
| model <- models[[model_name]] | ||
| expected_n_trees <- NROUNDS | ||
| if (model_name == "multi") { | ||
| expected_n_trees <- NROUNDS * NCLASS | ||
| } | ||
| df <- as.data.frame(lgb.model.dt.tree(model)) | ||
| df_list <- split(df, f = df$tree_index, drop = TRUE) | ||
|
|
||
| df_leaf <- df[!is.na(df$leaf_index), ] | ||
| df_internal <- df[is.na(df$leaf_index), ] | ||
|
|
||
| test_that("lgb.model.dt.tree() returns the right number of trees", { | ||
| expect_equal(length(unique(df$tree_index)), expected_n_trees) | ||
| }) | ||
|
|
||
| test_that("num_iteration can return less trees", { | ||
| expect_equal( | ||
| length(unique(lgb.model.dt.tree(model, num_iteration = 2L)$tree_index)) | ||
| , 2L * (if (model_name == "multi") NCLASS else 1L) | ||
| ) | ||
| }) | ||
|
|
||
| test_that("Tree index from lgb.model.dt.tree() is in 0:(NROUNS-1)", { | ||
| expect_equal(unique(df$tree_index), (0L:(expected_n_trees - 1L))) | ||
| }) | ||
|
|
||
| test_that("Depth calculated from lgb.model.dt.tree() respects max.depth", { | ||
| expect_true(max(df$depth) <= MAX_DEPTH) | ||
| }) | ||
|
|
||
| test_that("Each tree from lgb.model.dt.tree() has single root node", { | ||
| expect_equal( | ||
| unname(sapply(df_list, function(df) sum(df$depth == 0L))) | ||
| , rep(1L, expected_n_trees) | ||
| ) | ||
| }) | ||
|
|
||
| test_that("Each tree from lgb.model.dt.tree() has two depth 1 nodes", { | ||
| expect_equal( | ||
| unname(sapply(df_list, function(df) sum(df$depth == 1L))) | ||
| , rep(2L, expected_n_trees) | ||
| ) | ||
| }) | ||
|
|
||
| test_that("leaves from lgb.model.dt.tree() do not have split info", { | ||
| internal_node_cols <- c( | ||
| "split_index" | ||
| , "split_feature" | ||
| , "split_gain" | ||
| , "threshold" | ||
| , "decision_type" | ||
| , "default_left" | ||
| , "internal_value" | ||
| , "internal_count" | ||
| ) | ||
| expect_true(all(is.na(df_leaf[internal_node_cols]))) | ||
| }) | ||
|
|
||
| test_that("leaves from lgb.model.dt.tree() have valid leaf info", { | ||
| expect_true(all(df_leaf$leaf_index %in% 0L:(2.0^MAX_DEPTH - 1.0))) | ||
| expect_true(all(is.finite(df_leaf$leaf_value))) | ||
| expect_true(all(df_leaf$leaf_count > 0L & df_leaf$leaf_count <= N)) | ||
| }) | ||
|
|
||
| test_that("non-leaves from lgb.model.dt.tree() do not have leaf info", { | ||
| leaf_node_cols <- c( | ||
| "leaf_index", "leaf_parent", "leaf_value", "leaf_count" | ||
| ) | ||
| expect_true(all(is.na(df_internal[leaf_node_cols]))) | ||
| }) | ||
|
|
||
| test_that("non-leaves from lgb.model.dt.tree() have valid split info", { | ||
| expect_true( | ||
| all( | ||
| sapply( | ||
| split(df_internal, df_internal$tree_index), | ||
| function(x) all(x$split_index %in% 0L:(nrow(x) - 1L)) | ||
| ) | ||
| ) | ||
| ) | ||
|
|
||
| expect_true(all(df_internal$split_feature %in% FEAT)) | ||
|
|
||
| num_cols <- c("split_gain", "threshold", "internal_value") | ||
| expect_true(all(is.finite(unlist(df_internal[, num_cols])))) | ||
|
|
||
| # range of decision type? | ||
| expect_true(all(df_internal$default_left %in% c(TRUE, FALSE))) | ||
|
|
||
| counts <- df_internal$internal_count | ||
| expect_true(all(counts > 1L & counts <= N)) | ||
| }) | ||
| } |