wehub-resource-sync
100 lines
2.9 KiB
R
100 lines
2.9 KiB
R
.sigmoid <- function(x) {
|
|
1.0 / (1.0 + exp(-x))
|
|
}
|
|
.logit <- function(x) {
|
|
log(x / (1.0 - x))
|
|
}
|
|
|
|
test_that("lgb.plot.interpretation works as expected for binary classification", {
|
|
data(agaricus.train, package = "lightgbm")
|
|
train <- agaricus.train
|
|
dtrain <- lgb.Dataset(train$data, label = train$label)
|
|
set_field(
|
|
dataset = dtrain
|
|
, field_name = "init_score"
|
|
, data = rep(
|
|
.logit(mean(train$label))
|
|
, length(train$label)
|
|
)
|
|
)
|
|
data(agaricus.test, package = "lightgbm")
|
|
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
|
|
, verbosity = .LGB_VERBOSITY
|
|
, num_threads = .LGB_MAX_THREADS
|
|
)
|
|
model <- lgb.train(
|
|
params = params
|
|
, data = dtrain
|
|
, nrounds = 3L
|
|
)
|
|
num_trees <- 5L
|
|
tree_interpretation <- lgb.interpret(
|
|
model = model
|
|
, data = test$data
|
|
, idxset = seq_len(num_trees)
|
|
)
|
|
expect_true({
|
|
lgb.plot.interpretation(
|
|
tree_interpretation_dt = tree_interpretation[[1L]]
|
|
, top_n = 5L
|
|
)
|
|
TRUE
|
|
})
|
|
|
|
# should also work when you explicitly pass cex
|
|
plot_res <- lgb.plot.interpretation(
|
|
tree_interpretation_dt = tree_interpretation[[1L]]
|
|
, top_n = 5L
|
|
, cex = 0.95
|
|
)
|
|
expect_null(plot_res)
|
|
})
|
|
|
|
test_that("lgb.plot.interpretation works as expected for multiclass classification", {
|
|
data(iris)
|
|
|
|
# We must convert factors to numeric
|
|
# They must be starting from number 0 to use multiclass
|
|
# For instance: 0, 1, 2, 3, 4, 5...
|
|
iris$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 <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])
|
|
dtest <- lgb.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
|
|
, min_data = 1L
|
|
, num_threads = .LGB_MAX_THREADS
|
|
)
|
|
model <- lgb.train(
|
|
params = params
|
|
, data = dtrain
|
|
, nrounds = 3L
|
|
, verbose = .LGB_VERBOSITY
|
|
)
|
|
num_trees <- 5L
|
|
tree_interpretation <- lgb.interpret(
|
|
model = model
|
|
, data = test[, 1L:4L]
|
|
, idxset = seq_len(num_trees)
|
|
)
|
|
plot_res <- lgb.plot.interpretation(
|
|
tree_interpretation_dt = tree_interpretation[[1L]]
|
|
, top_n = 5L
|
|
)
|
|
expect_null(plot_res)
|
|
})
|