cf4f019ed6
* Change DefaultEvalMetric of classification from error to logloss * Change default binary metric in plugin/example/custom_obj.cc * Set old error metric in python tests * Set old error metric in R tests * Fix missed eval metrics and typos in R tests * Fix setting eval_metric twice in R tests * Add warning for empty eval_metric for classification * Fix Dask tests Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
49 lines
2.0 KiB
R
49 lines
2.0 KiB
R
context('Test generalized linear models')
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require(xgboost)
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test_that("gblinear works", {
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data(agaricus.train, package = 'xgboost')
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data(agaricus.test, package = 'xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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param <- list(objective = "binary:logistic", eval_metric = "error", booster = "gblinear",
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nthread = 2, eta = 0.8, alpha = 0.0001, lambda = 0.0001)
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watchlist <- list(eval = dtest, train = dtrain)
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n <- 5 # iterations
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ERR_UL <- 0.005 # upper limit for the test set error
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VERB <- 0 # chatterbox switch
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param$updater <- 'shotgun'
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bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'shuffle')
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ypred <- predict(bst, dtest)
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expect_equal(length(getinfo(dtest, 'label')), 1611)
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expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
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bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'cyclic',
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callbacks = list(cb.gblinear.history()))
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expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
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h <- xgb.gblinear.history(bst)
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expect_equal(dim(h), c(n, ncol(dtrain) + 1))
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expect_is(h, "matrix")
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param$updater <- 'coord_descent'
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bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'cyclic')
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expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
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bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'shuffle')
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expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
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bst <- xgb.train(param, dtrain, 2, watchlist, verbose = VERB, feature_selector = 'greedy')
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expect_lt(bst$evaluation_log$eval_error[2], ERR_UL)
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bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'thrifty',
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top_k = 50, callbacks = list(cb.gblinear.history(sparse = TRUE)))
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expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
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h <- xgb.gblinear.history(bst)
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expect_equal(dim(h), c(n, ncol(dtrain) + 1))
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expect_s4_class(h, "dgCMatrix")
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})
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