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