85 lines
3.1 KiB
R
85 lines
3.1 KiB
R
context('Test generalized linear models')
|
|
|
|
n_threads <- 2
|
|
|
|
test_that("gblinear works", {
|
|
data(agaricus.train, package = 'xgboost')
|
|
data(agaricus.test, package = 'xgboost')
|
|
dtrain <- xgb.DMatrix(
|
|
agaricus.train$data, label = agaricus.train$label, nthread = n_threads
|
|
)
|
|
dtest <- xgb.DMatrix(
|
|
agaricus.test$data, label = agaricus.test$label, nthread = n_threads
|
|
)
|
|
|
|
param <- list(objective = "binary:logistic", eval_metric = "error", booster = "gblinear",
|
|
nthread = n_threads, eta = 0.8, alpha = 0.0001, lambda = 0.0001)
|
|
evals <- list(eval = dtest, train = dtrain)
|
|
|
|
n <- 5 # iterations
|
|
ERR_UL <- 0.005 # upper limit for the test set error
|
|
VERB <- 0 # chatterbox switch
|
|
|
|
param$updater <- 'shotgun'
|
|
bst <- xgb.train(param, dtrain, n, evals, verbose = VERB, feature_selector = 'shuffle')
|
|
ypred <- predict(bst, dtest)
|
|
expect_equal(length(getinfo(dtest, 'label')), 1611)
|
|
expect_lt(attributes(bst)$evaluation_log$eval_error[n], ERR_UL)
|
|
|
|
bst <- xgb.train(param, dtrain, n, evals, verbose = VERB, feature_selector = 'cyclic',
|
|
callbacks = list(xgb.cb.gblinear.history()))
|
|
expect_lt(attributes(bst)$evaluation_log$eval_error[n], ERR_UL)
|
|
h <- xgb.gblinear.history(bst)
|
|
expect_equal(dim(h), c(n, ncol(dtrain) + 1))
|
|
expect_is(h, "matrix")
|
|
|
|
param$updater <- 'coord_descent'
|
|
bst <- xgb.train(param, dtrain, n, evals, verbose = VERB, feature_selector = 'cyclic')
|
|
expect_lt(attributes(bst)$evaluation_log$eval_error[n], ERR_UL)
|
|
|
|
bst <- xgb.train(param, dtrain, n, evals, verbose = VERB, feature_selector = 'shuffle')
|
|
expect_lt(attributes(bst)$evaluation_log$eval_error[n], ERR_UL)
|
|
|
|
bst <- xgb.train(param, dtrain, 2, evals, verbose = VERB, feature_selector = 'greedy')
|
|
expect_lt(attributes(bst)$evaluation_log$eval_error[2], ERR_UL)
|
|
|
|
bst <- xgb.train(param, dtrain, n, evals, verbose = VERB, feature_selector = 'thrifty',
|
|
top_k = 50, callbacks = list(xgb.cb.gblinear.history(sparse = TRUE)))
|
|
expect_lt(attributes(bst)$evaluation_log$eval_error[n], ERR_UL)
|
|
h <- xgb.gblinear.history(bst)
|
|
expect_equal(dim(h), c(n, ncol(dtrain) + 1))
|
|
expect_s4_class(h, "dgCMatrix")
|
|
})
|
|
|
|
test_that("gblinear early stopping works", {
|
|
data(agaricus.train, package = 'xgboost')
|
|
data(agaricus.test, package = 'xgboost')
|
|
dtrain <- xgb.DMatrix(
|
|
agaricus.train$data, label = agaricus.train$label, nthread = n_threads
|
|
)
|
|
dtest <- xgb.DMatrix(
|
|
agaricus.test$data, label = agaricus.test$label, nthread = n_threads
|
|
)
|
|
|
|
param <- list(
|
|
objective = "binary:logistic", eval_metric = "error", booster = "gblinear",
|
|
nthread = n_threads, eta = 0.8, alpha = 0.0001, lambda = 0.0001,
|
|
updater = "coord_descent"
|
|
)
|
|
|
|
es_round <- 1
|
|
n <- 10
|
|
booster <- xgb.train(
|
|
param, dtrain, n, list(eval = dtest, train = dtrain), early_stopping_rounds = es_round
|
|
)
|
|
expect_equal(xgb.attr(booster, "best_iteration"), 4)
|
|
predt_es <- predict(booster, dtrain)
|
|
|
|
n <- xgb.attr(booster, "best_iteration") + es_round + 1
|
|
booster <- xgb.train(
|
|
param, dtrain, n, list(eval = dtest, train = dtrain), early_stopping_rounds = es_round
|
|
)
|
|
predt <- predict(booster, dtrain)
|
|
expect_equal(predt_es, predt)
|
|
})
|