context("gpb.interpete") # Avoid that long tests get executed on CRAN if(Sys.getenv("GPBOOST_ALL_TESTS") == "GPBOOST_ALL_TESTS"){ .sigmoid <- function(x) { 1.0 / (1.0 + exp(-x)) } .logit <- function(x) { log(x / (1.0 - x)) } test_that("gpb.intereprete works as expected for binary classification", { data(agaricus.train, package = "gpboost") train <- agaricus.train dtrain <- gpb.Dataset(train$data, label = train$label) setinfo( dataset = dtrain , "init_score" , rep( .logit(mean(train$label)) , length(train$label) ) ) data(agaricus.test, package = "gpboost") test <- agaricus.test params <- list( objective = "binary" , learning_rate = 0.01 , num_leaves = 63L , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 ) model <- gpb.train( params = params , data = dtrain , nrounds = 3L , verbose = 0 ) num_trees <- 5L tree_interpretation <- gpb.interprete( model = model , data = test$data , idxset = seq_len(num_trees) ) expect_identical(class(tree_interpretation), "list") expect_true(length(tree_interpretation) == num_trees) expect_null(names(tree_interpretation)) expect_true(all( sapply( X = tree_interpretation , FUN = function(treeDT) { checks <- c( data.table::is.data.table(treeDT) , identical(names(treeDT), c("Feature", "Contribution")) , is.character(treeDT[, Feature]) , is.numeric(treeDT[, Contribution]) ) return(all(checks)) } ) )) }) test_that("gpb.intereprete works as expected for multiclass classification", { data(iris) # We must convert factors to numeric # They must be starting from number 0 to use multiclass # For instance: 0, 1, 2, 3, 4, 5... iris$Species <- as.numeric(as.factor(iris$Species)) - 1L # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2) train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ]) # The 10 last samples of each class are for validation test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) dtrain <- gpb.Dataset(data = train[, 1L:4L], label = train[, 5L]) dtest <- gpb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) params <- list( objective = "multiclass" , metric = "multi_logloss" , num_class = 3L , learning_rate = 0.00001 ) model <- gpb.train( params = params , data = dtrain , nrounds = 3L , min_data = 1L , verbose = 0 ) num_trees <- 5L tree_interpretation <- gpb.interprete( model = model , data = test[, 1L:4L] , idxset = seq_len(num_trees) ) expect_identical(class(tree_interpretation), "list") expect_true(length(tree_interpretation) == num_trees) expect_null(names(tree_interpretation)) expect_true(all( sapply( X = tree_interpretation , FUN = function(treeDT) { checks <- c( data.table::is.data.table(treeDT) , identical(names(treeDT), c("Feature", "Class 0", "Class 1", "Class 2")) , is.character(treeDT[, Feature]) , is.numeric(treeDT[, `Class 0`]) , is.numeric(treeDT[, `Class 1`]) , is.numeric(treeDT[, `Class 2`]) ) return(all(checks)) } ) )) }) }