[R] additional and modified tests
This commit is contained in:
Vadim Khotilovich
parent
3b6b344561
commit
fd4300b95a
@ -9,69 +9,136 @@ test <- agaricus.test
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set.seed(1994)
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test_that("train and predict binary classification", {
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nround = 2
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nrounds = 2
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expect_output(
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nthread = 2, nround = nround, objective = "binary:logistic")
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bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = nrounds, objective = "binary:logistic")
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, "train-error")
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expect_equal(class(bst), "xgb.Booster")
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expect_true(!is.null(bst$evaluation_log))
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expect_equal(nrow(bst$evaluation_log), nround)
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expect_equal(bst$niter, nrounds)
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expect_false(is.null(bst$evaluation_log))
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expect_equal(nrow(bst$evaluation_log), nrounds)
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expect_lt(bst$evaluation_log[, min(train_error)], 0.03)
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expect_equal(bst$nboost, bst$ntree)
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pred <- predict(bst, test$data)
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expect_equal(length(pred), 1611)
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expect_length(pred, 1611)
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pred1 <- predict(bst, train$data, ntreelimit = 1)
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expect_length(pred1, 6513)
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err_pred1 <- sum((pred1 > 0.5) != train$label)/length(train$label)
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err_log <- bst$evaluation_log[1, train_error]
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expect_lt(abs(err_pred1 - err_log), 10e-6)
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})
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test_that("train and predict softprob", {
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lb <- as.numeric(iris$Species) - 1
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set.seed(11)
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expect_output(
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bst <- xgboost(data = as.matrix(iris[, -5]), label = as.numeric(iris$Species) - 1,
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max.depth = 3, eta = 0.5, nthread = 2, nround = 5,
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
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max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
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objective = "multi:softprob", num_class=3)
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, "train-merror")
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expect_true(!is.null(bst$evaluation_log))
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expect_false(is.null(bst$evaluation_log))
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expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
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expect_equal(bst$nboost * 3, bst$ntree)
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expect_equal(bst$niter * 3, xgb.ntree(bst))
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pred <- predict(bst, as.matrix(iris[, -5]))
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expect_equal(length(pred), nrow(iris) * 3)
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expect_length(pred, nrow(iris) * 3)
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# row sums add up to total probability of 1:
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expect_equal(rowSums(matrix(pred, ncol=3, byrow=TRUE)), rep(1, nrow(iris)), tolerance = 1e-7)
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# manually calculate error at the last iteration:
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mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE)
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expect_equal(as.numeric(t(mpred)), pred)
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pred_labels <- max.col(mpred) - 1
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err <- sum(pred_labels != lb)/length(lb)
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expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
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# manually calculate error at the 1st iteration:
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mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 1)
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pred_labels <- max.col(mpred) - 1
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err <- sum(pred_labels != lb)/length(lb)
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expect_equal(bst$evaluation_log[1, train_merror], err, tolerance = 5e-6)
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})
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test_that("train and predict softmax", {
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lb <- as.numeric(iris$Species) - 1
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set.seed(11)
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expect_output(
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bst <- xgboost(data = as.matrix(iris[, -5]), label = as.numeric(iris$Species) - 1,
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max.depth = 3, eta = 0.15, nthread = 2, nround = 25,
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
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max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
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objective = "multi:softmax", num_class=3)
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, "train-merror")
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expect_true(!is.null(bst$evaluation_log))
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expect_false(is.null(bst$evaluation_log))
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expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
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expect_equal(bst$nboost * 3, bst$ntree)
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expect_equal(bst$niter * 3, xgb.ntree(bst))
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pred <- predict(bst, as.matrix(iris[, -5]))
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expect_equal(length(pred), nrow(iris))
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expect_length(pred, nrow(iris))
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err <- sum(pred != lb)/length(lb)
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expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
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})
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test_that("early stopping xgb.train works", {
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test_that("train and predict RF", {
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set.seed(11)
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lb <- train$label
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# single iteration
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bst <- xgboost(data = train$data, label = lb, max_depth = 5,
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nthread = 2, nrounds = 1, objective = "binary:logistic",
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num_parallel_tree = 20, subsample = 0.6, colsample_bytree = 0.1)
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expect_equal(bst$niter, 1)
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expect_equal(xgb.ntree(bst), 20)
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pred <- predict(bst, train$data)
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pred_err <- sum((pred > 0.5) != lb)/length(lb)
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expect_lt(abs(bst$evaluation_log[1, train_error] - pred_err), 10e-6)
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expect_lt(pred_err, 0.03)
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pred <- predict(bst, train$data, ntreelimit = 20)
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pred_err_20 <- sum((pred > 0.5) != lb)/length(lb)
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expect_equal(pred_err_20, pred_err)
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pred <- predict(bst, train$data, ntreelimit = 1)
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pred_err_1 <- sum((pred > 0.5) != lb)/length(lb)
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expect_lt(pred_err, pred_err_1)
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expect_lt(pred_err, 0.08)
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})
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test_that("train and predict RF with softprob", {
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lb <- as.numeric(iris$Species) - 1
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nrounds <- 15
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set.seed(11)
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
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max_depth = 3, eta = 0.9, nthread = 2, nrounds = nrounds,
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objective = "multi:softprob", num_class=3,
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num_parallel_tree = 4, subsample = 0.5, colsample_bytree = 0.5)
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expect_equal(bst$niter, 15)
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expect_equal(xgb.ntree(bst), 15*3*4)
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# predict for all iterations:
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pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE)
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expect_equal(dim(pred), c(nrow(iris), 3))
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pred_labels <- max.col(pred) - 1
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err <- sum(pred_labels != lb)/length(lb)
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expect_equal(bst$evaluation_log[nrounds, train_merror], err, tolerance = 5e-6)
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# predict for 7 iterations and adjust for 4 parallel trees per iteration
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pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE, ntreelimit = 7 * 4)
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err <- sum((max.col(pred) - 1) != lb)/length(lb)
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expect_equal(bst$evaluation_log[7, train_merror], err, tolerance = 5e-6)
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})
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test_that("use of multiple eval metrics works", {
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expect_output(
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
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early.stop.round = 3, maximize = FALSE)
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, "Stopping. Best iteration")
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expect_true(!is.null(bst$best_iteration))
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expect_lt(bst$best_iteration, 19)
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expect_equal(bst$nboost, bst$ntree)
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expect_equal(bst$best_iteration, bst$best_ntreelimit)
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pred <- predict(bst, test$data)
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expect_equal(length(pred), 1611)
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bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
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eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
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, "train-error.*train-auc.*train-logloss")
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expect_false(is.null(bst$evaluation_log))
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expect_equal(dim(bst$evaluation_log), c(2, 4))
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expect_equal(colnames(bst$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
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})
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test_that("training continuation works", {
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dtrain <- xgb.DMatrix(train$data, label = train$label)
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watchlist = list(train=dtrain)
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param <- list(objective = "binary:logistic", max.depth = 2, eta = 1, nthread = 2)
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param <- list(objective = "binary:logistic", max_depth = 2, eta = 1, nthread = 2)
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# for the reference, use 4 iterations at once:
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set.seed(11)
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@ -82,7 +149,7 @@ test_that("training continuation works", {
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# continue for two more:
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, xgb_model = bst1)
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expect_equal(bst$raw, bst2$raw)
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expect_true(!is.null(bst2$evaluation_log))
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expect_false(is.null(bst2$evaluation_log))
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expect_equal(dim(bst2$evaluation_log), c(4, 2))
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expect_equal(bst2$evaluation_log, bst$evaluation_log)
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# test continuing from raw model data
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@ -98,62 +165,18 @@ test_that("training continuation works", {
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test_that("xgb.cv works", {
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cv <- xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
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eta = 1., nthread = 2, nround = 2, objective = "binary:logistic",
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set.seed(11)
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cv <- xgb.cv(data = train$data, label = train$label, max_depth = 2, nfold = 5,
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eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
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verbose=TRUE)
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expect_is(cv, 'xgb.cv.synchronous')
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expect_true(!is.null(cv$evaluation_log))
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expect_false(is.null(cv$evaluation_log))
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expect_lt(cv$evaluation_log[, min(test_error_mean)], 0.03)
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expect_lt(cv$evaluation_log[, min(test_error_std)], 0.004)
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expect_equal(cv$nboost, cv$ntree)
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expect_true(!is.null(cv$folds) && is.list(cv$folds))
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expect_equal(cv$niter, 2)
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expect_false(is.null(cv$folds) && is.list(cv$folds))
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expect_length(cv$folds, 5)
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expect_true(!is.null(cv$params) && is.list(cv$params))
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expect_true(!is.null(cv$callbacks))
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expect_true(!is.null(cv$call))
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})
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test_that("early stopping xgb.cv works", {
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expect_output(
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cv <- xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
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eta = 0.5, nthread = 2, nround = 20, objective = "binary:logistic",
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early.stop.round = 3, maximize = FALSE, verbose=TRUE)
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, "Stopping. Best iteration")
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expect_true(!is.null(cv$best_iteration))
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expect_lt(cv$best_iteration, 19)
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expect_equal(cv$nboost, cv$ntree)
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expect_equal(cv$best_iteration, cv$best_ntreelimit)
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})
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test_that("prediction in xgb.cv works", {
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nround = 2
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cv <- xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
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eta = 0.5, nthread = 2, nround = nround, objective = "binary:logistic",
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verbose=TRUE, prediction = T)
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expect_true(!is.null(cv$evaluation_log))
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expect_true(!is.null(cv$pred))
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expect_length(cv$pred, nrow(train$data))
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err_pred <- sum((cv$pred > 0.5) != train$label)/length(train$label)
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err_log <- cv$evaluation_log[nround, test_error_mean]
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expect_lt(abs(err_pred - err_log), 10e-6)
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})
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test_that("prediction in early-stopping xgb.cv works", {
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set.seed(123)
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# add some label noise
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lb <- train$label
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lb[sample(length(train$label), 2000)] <- 0
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expect_output(
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cv <- xgb.cv(data = train$data, label = lb, max.depth = 3, nfold = 5,
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eta = 1., nthread = 2, nround = 20, objective = "binary:logistic",
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early.stop.round = 3, maximize = FALSE, verbose=TRUE, predict=TRUE)
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, "Stopping. Best iteration")
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expect_true(!is.null(cv$best_iteration))
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expect_lt(cv$best_iteration, 19)
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expect_true(!is.null(cv$evaluation_log))
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expect_true(!is.null(cv$pred))
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expect_length(cv$pred, nrow(train$data))
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err_pred <- sum((cv$pred > 0.5) != lb)/length(lb)
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err_log <- cv$evaluation_log[cv$best_iteration, test_error_mean]
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expect_lt(abs(err_pred - err_log), 10e-6)
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expect_false(is.null(cv$params) && is.list(cv$params))
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expect_false(is.null(cv$callbacks))
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expect_false(is.null(cv$call))
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})
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@ -6,26 +6,42 @@ require(data.table)
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context("callbacks")
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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train <- agaricus.train
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test <- agaricus.test
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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watchlist = list(train=dtrain)
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param <- list(objective = "binary:logistic", max.depth = 2, nthread = 2)
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# add some label noise for early stopping tests
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add.noise <- function(label, frac) {
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inoise <- sample(length(label), length(label) * frac)
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label[inoise] <- !label[inoise]
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label
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}
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set.seed(11)
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ltrain <- add.noise(train$label, 0.2)
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ltest <- add.noise(test$label, 0.2)
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dtrain <- xgb.DMatrix(train$data, label = ltrain)
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dtest <- xgb.DMatrix(test$data, label = ltest)
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watchlist = list(train=dtrain, test=dtest)
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test_that("cb.print_evaluation works as expected", {
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err <- function(label, pr) sum((pr > 0.5) != label)/length(label)
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param <- list(objective = "binary:logistic", max_depth = 2, nthread = 2)
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test_that("cb.print.evaluation works as expected", {
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bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
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bst_evaluation_err <- NULL
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begin_iteration <- 1
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end_iteration <- 7
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f0 <- cb.print_evaluation(period=0)
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f1 <- cb.print_evaluation(period=1)
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f5 <- cb.print_evaluation(period=5)
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f0 <- cb.print.evaluation(period=0)
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f1 <- cb.print.evaluation(period=1)
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f5 <- cb.print.evaluation(period=5)
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expect_true(!is.null(attr(f1, 'call')))
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expect_equal(attr(f1, 'name'), 'cb.print_evaluation')
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expect_false(is.null(attr(f1, 'call')))
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expect_equal(attr(f1, 'name'), 'cb.print.evaluation')
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iteration <- 1
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expect_silent(f0())
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@ -45,16 +61,16 @@ test_that("cb.print_evaluation works as expected", {
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expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\\+0.100000\ttest-auc:0.800000\\+0.200000")
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})
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test_that("cb.log_evaluation works as expected", {
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test_that("cb.evaluation.log works as expected", {
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bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
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bst_evaluation_err <- NULL
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evaluation_log <- list()
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f <- cb.log_evaluation()
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f <- cb.evaluation.log()
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expect_true(!is.null(attr(f, 'call')))
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expect_equal(attr(f, 'name'), 'cb.log_evaluation')
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expect_false(is.null(attr(f, 'call')))
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expect_equal(attr(f, 'name'), 'cb.evaluation.log')
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iteration <- 1
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expect_silent(f())
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@ -70,7 +86,7 @@ test_that("cb.log_evaluation works as expected", {
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bst_evaluation_err <- c('train-auc'=0.1, 'test-auc'=0.2)
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evaluation_log <- list()
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f <- cb.log_evaluation()
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f <- cb.evaluation.log()
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iteration <- 1
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expect_silent(f())
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@ -88,20 +104,23 @@ test_that("cb.log_evaluation works as expected", {
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test_auc_mean=c(0.8,0.8), test_auc_std=c(0.2,0.2)))
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})
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test_that("cb.reset_parameters works as expected", {
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param <- list(objective = "binary:logistic", max_depth = 4, nthread = 2)
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test_that("cb.reset.parameters works as expected", {
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# fixed eta
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set.seed(111)
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bst0 <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 0.9)
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expect_true(!is.null(bst0$evaluation_log))
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expect_true(!is.null(bst0$evaluation_log$train_error))
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expect_false(is.null(bst0$evaluation_log))
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expect_false(is.null(bst0$evaluation_log$train_error))
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# same eta but re-set as a vector parameter in the callback
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set.seed(111)
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my_par <- list(eta = c(0.9, 0.9))
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bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset_parameters(my_par)))
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expect_true(!is.null(bst1$evaluation_log$train_error))
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst1$evaluation_log$train_error))
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expect_equal(bst0$evaluation_log$train_error,
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bst1$evaluation_log$train_error)
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@ -109,8 +128,8 @@ test_that("cb.reset_parameters works as expected", {
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set.seed(111)
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my_par <- list(eta = function(itr, itr_end) 0.9)
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset_parameters(my_par)))
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expect_true(!is.null(bst2$evaluation_log$train_error))
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst2$evaluation_log$train_error))
|
||||
expect_equal(bst0$evaluation_log$train_error,
|
||||
bst2$evaluation_log$train_error)
|
||||
|
||||
@ -118,44 +137,43 @@ test_that("cb.reset_parameters works as expected", {
|
||||
set.seed(111)
|
||||
my_par <- list(eta = c(0.6, 0.5))
|
||||
bst3 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
|
||||
callbacks = list(cb.reset_parameters(my_par)))
|
||||
expect_true(!is.null(bst3$evaluation_log$train_error))
|
||||
expect_true(!all(bst0$evaluation_log$train_error == bst3$evaluation_log$train_error))
|
||||
callbacks = list(cb.reset.parameters(my_par)))
|
||||
expect_false(is.null(bst3$evaluation_log$train_error))
|
||||
expect_false(all(bst0$evaluation_log$train_error == bst3$evaluation_log$train_error))
|
||||
|
||||
# resetting multiple parameters at the same time runs with no error
|
||||
my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), alpha = c(0.01, 0.02))
|
||||
my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), max_depth = c(4, 8))
|
||||
expect_error(
|
||||
bst4 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
|
||||
callbacks = list(cb.reset_parameters(my_par)))
|
||||
, NA)
|
||||
callbacks = list(cb.reset.parameters(my_par)))
|
||||
, NA) # NA = no error
|
||||
|
||||
# expect no learning with 0 learning rate
|
||||
my_par <- list(eta = c(0., 0.))
|
||||
bstX <- xgb.train(param, dtrain, nrounds = 2, watchlist,
|
||||
callbacks = list(cb.reset_parameters(my_par)))
|
||||
expect_true(!is.null(bstX$evaluation_log$train_error))
|
||||
callbacks = list(cb.reset.parameters(my_par)))
|
||||
expect_false(is.null(bstX$evaluation_log$train_error))
|
||||
er <- unique(bstX$evaluation_log$train_error)
|
||||
expect_length(er, 1)
|
||||
expect_gt(er, 0.4)
|
||||
})
|
||||
|
||||
# Note: early stopping is tested in test_basic
|
||||
|
||||
test_that("cb.save_model works as expected", {
|
||||
test_that("cb.save.model works as expected", {
|
||||
files <- c('xgboost_01.model', 'xgboost_02.model', 'xgboost.model')
|
||||
for (f in files) if (file.exists(f)) file.remove(f)
|
||||
|
||||
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
|
||||
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1,
|
||||
save_period = 1, save_name = "xgboost_%02d.model")
|
||||
expect_true(file.exists('xgboost_01.model'))
|
||||
expect_true(file.exists('xgboost_02.model'))
|
||||
b1 <- xgb.load('xgboost_01.model')
|
||||
expect_length(grep('^booster', xgb.dump(b1)), 1)
|
||||
expect_equal(xgb.ntree(b1), 1)
|
||||
b2 <- xgb.load('xgboost_02.model')
|
||||
expect_equal(xgb.ntree(b2), 2)
|
||||
expect_equal(bst$raw, b2$raw)
|
||||
|
||||
# save_period = 0 saves the last iteration's model
|
||||
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, save_period = 0)
|
||||
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, save_period = 0)
|
||||
expect_true(file.exists('xgboost.model'))
|
||||
b2 <- xgb.load('xgboost.model')
|
||||
expect_equal(bst$raw, b2$raw)
|
||||
@ -165,10 +183,99 @@ test_that("cb.save_model works as expected", {
|
||||
|
||||
test_that("can store evaluation_log without printing", {
|
||||
expect_silent(
|
||||
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1,
|
||||
verbose = 0, callbacks = list(cb.log_evaluation()))
|
||||
bst <- xgb.train(param, dtrain, nrounds = 10, watchlist, eta = 1,
|
||||
verbose = 0, callbacks = list(cb.evaluation.log()))
|
||||
)
|
||||
expect_true(!is.null(bst$evaluation_log))
|
||||
expect_true(!is.null(bst$evaluation_log$train_error))
|
||||
expect_lt(bst$evaluation_log[2, train_error], 0.03)
|
||||
expect_false(is.null(bst$evaluation_log))
|
||||
expect_false(is.null(bst$evaluation_log$train_error))
|
||||
expect_lt(bst$evaluation_log[, min(train_error)], 0.2)
|
||||
})
|
||||
|
||||
test_that("early stopping xgb.train works", {
|
||||
set.seed(11)
|
||||
expect_output(
|
||||
bst <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
|
||||
early_stopping_rounds = 3, maximize = FALSE)
|
||||
, "Stopping. Best iteration")
|
||||
expect_false(is.null(bst$best_iteration))
|
||||
expect_lt(bst$best_iteration, 19)
|
||||
expect_equal(bst$best_iteration, bst$best_ntreelimit)
|
||||
|
||||
pred <- predict(bst, dtest)
|
||||
expect_equal(length(pred), 1611)
|
||||
err_pred <- err(ltest, pred)
|
||||
err_log <- bst$evaluation_log[bst$best_iteration, test_error]
|
||||
expect_equal(err_log, err_pred, tolerance = 5e-6)
|
||||
})
|
||||
|
||||
test_that("early stopping using a specific metric works", {
|
||||
set.seed(11)
|
||||
expect_output(
|
||||
bst <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.6,
|
||||
eval_metric="logloss", eval_metric="auc",
|
||||
callbacks = list(cb.early.stop(stopping_rounds = 3, maximize = FALSE,
|
||||
metric_name = 'test_logloss')))
|
||||
, "Stopping. Best iteration")
|
||||
expect_false(is.null(bst$best_iteration))
|
||||
expect_lt(bst$best_iteration, 19)
|
||||
expect_equal(bst$best_iteration, bst$best_ntreelimit)
|
||||
|
||||
pred <- predict(bst, dtest, ntreelimit = bst$best_ntreelimit)
|
||||
expect_equal(length(pred), 1611)
|
||||
logloss_pred <- sum(-ltest * log(pred) - (1 - ltest) * log(1 - pred)) / length(ltest)
|
||||
logloss_log <- bst$evaluation_log[bst$best_iteration, test_logloss]
|
||||
expect_equal(logloss_log, logloss_pred, tolerance = 5e-6)
|
||||
})
|
||||
|
||||
test_that("early stopping xgb.cv works", {
|
||||
set.seed(11)
|
||||
expect_output(
|
||||
cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.3, nrounds = 20,
|
||||
early_stopping_rounds = 3, maximize = FALSE)
|
||||
, "Stopping. Best iteration")
|
||||
expect_false(is.null(cv$best_iteration))
|
||||
expect_lt(cv$best_iteration, 19)
|
||||
expect_equal(cv$best_iteration, cv$best_ntreelimit)
|
||||
# the best error is min error:
|
||||
expect_true(cv$evaluation_log[, test_error_mean[cv$best_iteration] == min(test_error_mean)])
|
||||
})
|
||||
|
||||
test_that("prediction in xgb.cv works", {
|
||||
set.seed(11)
|
||||
nrounds = 4
|
||||
cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE)
|
||||
expect_false(is.null(cv$evaluation_log))
|
||||
expect_false(is.null(cv$pred))
|
||||
expect_length(cv$pred, nrow(train$data))
|
||||
err_pred <- mean( sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))) )
|
||||
err_log <- cv$evaluation_log[nrounds, test_error_mean]
|
||||
expect_equal(err_pred, err_log, tolerance = 1e-6)
|
||||
|
||||
# save CV models
|
||||
set.seed(11)
|
||||
cvx <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE,
|
||||
callbacks = list(cb.cv.predict(save_models = TRUE)))
|
||||
expect_equal(cv$evaluation_log, cvx$evaluation_log)
|
||||
expect_length(cvx$models, 5)
|
||||
expect_true(all(sapply(cvx$models, class) == 'xgb.Booster'))
|
||||
})
|
||||
|
||||
test_that("prediction in early-stopping xgb.cv works", {
|
||||
set.seed(1)
|
||||
expect_output(
|
||||
cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.1, nrounds = 20,
|
||||
early_stopping_rounds = 5, maximize = FALSE, prediction=TRUE)
|
||||
, "Stopping. Best iteration")
|
||||
|
||||
expect_false(is.null(cv$best_iteration))
|
||||
expect_lt(cv$best_iteration, 19)
|
||||
expect_false(is.null(cv$evaluation_log))
|
||||
expect_false(is.null(cv$pred))
|
||||
expect_length(cv$pred, nrow(train$data))
|
||||
|
||||
err_pred <- mean( sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))) )
|
||||
err_log <- cv$evaluation_log[cv$best_iteration, test_error_mean]
|
||||
expect_equal(err_pred, err_log, tolerance = 1e-6)
|
||||
err_log_last <- cv$evaluation_log[cv$niter, test_error_mean]
|
||||
expect_gt(abs(err_pred - err_log_last), 1e-4)
|
||||
})
|
||||
|
||||
@ -24,22 +24,22 @@ evalerror <- function(preds, dtrain) {
|
||||
return(list(metric = "error", value = err))
|
||||
}
|
||||
|
||||
param <- list(max.depth=2, eta=1, nthread = 2,
|
||||
param <- list(max_depth=2, eta=1, nthread = 2,
|
||||
objective=logregobj, eval_metric=evalerror)
|
||||
num_round <- 2
|
||||
|
||||
test_that("custom objective works", {
|
||||
bst <- xgb.train(param, dtrain, num_round, watchlist)
|
||||
expect_equal(class(bst), "xgb.Booster")
|
||||
expect_equal(length(bst$raw), 1064)
|
||||
expect_true(!is.null(bst$evaluation_log))
|
||||
expect_true(!is.null(bst$evaluation_log$eval_error))
|
||||
expect_equal(length(bst$raw), 1094)
|
||||
expect_false(is.null(bst$evaluation_log))
|
||||
expect_false(is.null(bst$evaluation_log$eval_error))
|
||||
expect_lt(bst$evaluation_log[num_round, eval_error], 0.03)
|
||||
})
|
||||
|
||||
test_that("custom objective in CV works", {
|
||||
cv <- xgb.cv(param, dtrain, num_round, nfold=10, verbose=FALSE)
|
||||
expect_true(!is.null(cv$evaluation_log))
|
||||
expect_false(is.null(cv$evaluation_log))
|
||||
expect_equal(dim(cv$evaluation_log), c(2, 5))
|
||||
expect_lt(cv$evaluation_log[num_round, test_error_mean], 0.03)
|
||||
})
|
||||
@ -58,5 +58,5 @@ test_that("custom objective using DMatrix attr works", {
|
||||
param$objective = logregobjattr
|
||||
bst <- xgb.train(param, dtrain, num_round, watchlist)
|
||||
expect_equal(class(bst), "xgb.Booster")
|
||||
expect_equal(length(bst$raw), 1064)
|
||||
expect_equal(length(bst$raw), 1094)
|
||||
})
|
||||
|
||||
@ -14,18 +14,18 @@ df[,ID := NULL]
|
||||
sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
|
||||
label <- df[, ifelse(Improved == "Marked", 1, 0)]
|
||||
|
||||
bst.Tree <- xgboost(data = sparse_matrix, label = label, max.depth = 9,
|
||||
eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gbtree")
|
||||
bst.Tree <- xgboost(data = sparse_matrix, label = label, max_depth = 9,
|
||||
eta = 1, nthread = 2, nrounds = 10, objective = "binary:logistic", booster = "gbtree")
|
||||
|
||||
bst.GLM <- xgboost(data = sparse_matrix, label = label,
|
||||
eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gblinear")
|
||||
eta = 1, nthread = 2, nrounds = 10, objective = "binary:logistic", booster = "gblinear")
|
||||
|
||||
feature.names <- colnames(sparse_matrix)
|
||||
|
||||
test_that("xgb.dump works", {
|
||||
expect_length(xgb.dump(bst.Tree), 172)
|
||||
expect_length(xgb.dump(bst.GLM), 14)
|
||||
expect_true(xgb.dump(bst.Tree, 'xgb.model.dump', with.stats = T))
|
||||
expect_true(xgb.dump(bst.Tree, 'xgb.model.dump', with_stats = T))
|
||||
expect_true(file.exists('xgb.model.dump'))
|
||||
expect_gt(file.size('xgb.model.dump'), 8000)
|
||||
})
|
||||
@ -35,12 +35,15 @@ test_that("xgb-attribute functionality", {
|
||||
list.val <- list(my_attr=val, a=123, b='ok')
|
||||
list.ch <- list.val[order(names(list.val))]
|
||||
list.ch <- lapply(list.ch, as.character)
|
||||
# note: iter is 0-index in xgb attributes
|
||||
list.default <- list(niter = "9")
|
||||
list.ch <- c(list.ch, list.default)
|
||||
# proper input:
|
||||
expect_error(xgb.attr(bst.Tree, NULL))
|
||||
expect_error(xgb.attr(val, val))
|
||||
# set & get:
|
||||
expect_null(xgb.attr(bst.Tree, "asdf"))
|
||||
expect_null(xgb.attributes(bst.Tree)) # initially, expect no attributes
|
||||
expect_equal(xgb.attributes(bst.Tree), list.default)
|
||||
xgb.attr(bst.Tree, "my_attr") <- val
|
||||
expect_equal(xgb.attr(bst.Tree, "my_attr"), val)
|
||||
xgb.attributes(bst.Tree) <- list.val
|
||||
@ -53,8 +56,10 @@ test_that("xgb-attribute functionality", {
|
||||
# deletion:
|
||||
xgb.attr(bst, "my_attr") <- NULL
|
||||
expect_null(xgb.attr(bst, "my_attr"))
|
||||
expect_equal(xgb.attributes(bst), list.ch[c("a", "b")])
|
||||
expect_equal(xgb.attributes(bst), list.ch[c("a", "b", "niter")])
|
||||
xgb.attributes(bst) <- list(a=NULL, b=NULL)
|
||||
expect_equal(xgb.attributes(bst), list.default)
|
||||
xgb.attributes(bst) <- list(niter=NULL)
|
||||
expect_null(xgb.attributes(bst))
|
||||
})
|
||||
|
||||
@ -88,10 +93,27 @@ test_that("xgb.plot.tree works with and without feature names", {
|
||||
})
|
||||
|
||||
test_that("xgb.plot.multi.trees works with and without feature names", {
|
||||
xgb.plot.multi.trees(model = bst.Tree, feature_names = feature.names, features.keep = 3)
|
||||
xgb.plot.multi.trees(model = bst.Tree, features.keep = 3)
|
||||
xgb.plot.multi.trees(model = bst.Tree, feature_names = feature.names, features_keep = 3)
|
||||
xgb.plot.multi.trees(model = bst.Tree, features_keep = 3)
|
||||
})
|
||||
|
||||
test_that("xgb.plot.deepness works", {
|
||||
xgb.plot.deepness(model = bst.Tree)
|
||||
})
|
||||
|
||||
test_that("check.deprecation works", {
|
||||
ttt <- function(a = NNULL, DUMMY=NULL, ...) {
|
||||
check.deprecation(...)
|
||||
as.list((environment()))
|
||||
}
|
||||
res <- ttt(a = 1, DUMMY = 2, z = 3)
|
||||
expect_equal(res, list(a = 1, DUMMY = 2))
|
||||
expect_warning(
|
||||
res <- ttt(a = 1, dummy = 22, z = 3)
|
||||
, "\'dummy\' is deprecated")
|
||||
expect_equal(res, list(a = 1, DUMMY = 22))
|
||||
expect_warning(
|
||||
res <- ttt(a = 1, dumm = 22, z = 3)
|
||||
, "\'dumm\' was partially matched to \'dummy\'")
|
||||
expect_equal(res, list(a = 1, DUMMY = 22))
|
||||
})
|
||||
|
||||
@ -11,13 +11,13 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
|
||||
bst <- xgboost(data = dtrain,
|
||||
max_depth = 2,
|
||||
eta = 1,
|
||||
nround = 10,
|
||||
nrounds = 10,
|
||||
nthread = 1,
|
||||
verbose = 0,
|
||||
objective = "binary:logistic")
|
||||
|
||||
test_that("call is exposed to R", {
|
||||
expect_true(!is.null(bst$call))
|
||||
expect_false(is.null(bst$call))
|
||||
expect_is(bst$call, "call")
|
||||
})
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user