2b5b96d760
* [R] xgb.save must work when handle in nil but raw exists * [R] print.xgb.Booster should still print other info when handle is nil * [R] rename internal function xgb.Booster to xgb.Booster.handle to make its intent clear * [R] rename xgb.Booster.check to xgb.Booster.complete and make it visible; more docs * [R] storing evaluation_log should depend only on watchlist, not on verbose * [R] reduce the excessive chattiness of unit tests * [R] only disable some tests in windows when it's not 64-bit * [R] clean-up xgb.DMatrix * [R] test xgb.DMatrix loading from libsvm text file * [R] store feature_names in xgb.Booster, use them from utility functions * [R] remove non-functional co-occurence computation from xgb.importance * [R] verbose=0 is enough without a callback * [R] added forgotten xgb.Booster.complete.Rd; cran check fixes * [R] update installation instructions
76 lines
4.0 KiB
R
76 lines
4.0 KiB
R
require(xgboost)
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context("update trees in an existing model")
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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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test_that("updating the model works", {
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watchlist = list(train = dtrain, test = dtest)
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# no-subsampling
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p1 <- list(objective = "binary:logistic", max_depth = 2, eta = 0.05, nthread = 2)
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set.seed(11)
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bst1 <- xgb.train(p1, dtrain, nrounds = 10, watchlist, verbose = 0)
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tr1 <- xgb.model.dt.tree(model = bst1)
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# with subsampling
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p2 <- modifyList(p1, list(subsample = 0.1))
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set.seed(11)
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bst2 <- xgb.train(p2, dtrain, nrounds = 10, watchlist, verbose = 0)
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tr2 <- xgb.model.dt.tree(model = bst2)
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# the same no-subsampling boosting with an extra 'refresh' updater:
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p1r <- modifyList(p1, list(updater = 'grow_colmaker,prune,refresh', refresh_leaf = FALSE))
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set.seed(11)
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bst1r <- xgb.train(p1r, dtrain, nrounds = 10, watchlist, verbose = 0)
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tr1r <- xgb.model.dt.tree(model = bst1r)
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# all should be the same when no subsampling
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expect_equal(bst1$evaluation_log, bst1r$evaluation_log)
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expect_equal(tr1, tr1r, tolerance = 0.00001, check.attributes = FALSE)
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# the same boosting with subsampling with an extra 'refresh' updater:
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p2r <- modifyList(p2, list(updater = 'grow_colmaker,prune,refresh', refresh_leaf = FALSE))
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set.seed(11)
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bst2r <- xgb.train(p2r, dtrain, nrounds = 10, watchlist, verbose = 0)
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tr2r <- xgb.model.dt.tree(model = bst2r)
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# should be the same evaluation but different gains and larger cover
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expect_equal(bst2$evaluation_log, bst2r$evaluation_log)
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expect_equal(tr2[Feature == 'Leaf']$Quality, tr2r[Feature == 'Leaf']$Quality)
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expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2r[Feature != 'Leaf']$Quality)), 100)
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expect_gt(sum(tr2r$Cover) / sum(tr2$Cover), 1.5)
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# process type 'update' for no-subsampling model, refreshing the tree stats AND leaves from training data:
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p1u <- modifyList(p1, list(process_type = 'update', updater = 'refresh', refresh_leaf = TRUE))
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bst1u <- xgb.train(p1u, dtrain, nrounds = 10, watchlist, verbose = 0, xgb_model = bst1)
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tr1u <- xgb.model.dt.tree(model = bst1u)
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# all should be the same when no subsampling
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expect_equal(bst1$evaluation_log, bst1u$evaluation_log)
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expect_equal(tr1, tr1u, tolerance = 0.00001, check.attributes = FALSE)
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# process type 'update' for model with subsampling, refreshing only the tree stats from training data:
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p2u <- modifyList(p2, list(process_type = 'update', updater = 'refresh', refresh_leaf = FALSE))
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bst2u <- xgb.train(p2u, dtrain, nrounds = 10, watchlist, verbose = 0, xgb_model = bst2)
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tr2u <- xgb.model.dt.tree(model = bst2u)
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# should be the same evaluation but different gains and larger cover
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expect_equal(bst2$evaluation_log, bst2u$evaluation_log)
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expect_equal(tr2[Feature == 'Leaf']$Quality, tr2u[Feature == 'Leaf']$Quality)
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expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2u[Feature != 'Leaf']$Quality)), 100)
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expect_gt(sum(tr2u$Cover) / sum(tr2$Cover), 1.5)
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# the results should be the same as for the model with an extra 'refresh' updater
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expect_equal(bst2r$evaluation_log, bst2u$evaluation_log)
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expect_equal(tr2r, tr2u, tolerance = 0.00001, check.attributes = FALSE)
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# process type 'update' for no-subsampling model, refreshing only the tree stats from TEST data:
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p1ut <- modifyList(p1, list(process_type = 'update', updater = 'refresh', refresh_leaf = FALSE))
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bst1ut <- xgb.train(p1ut, dtest, nrounds = 10, watchlist, verbose = 0, xgb_model = bst1)
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tr1ut <- xgb.model.dt.tree(model = bst1ut)
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# should be the same evaluations but different gains and smaller cover (test data is smaller)
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expect_equal(bst1$evaluation_log, bst1ut$evaluation_log)
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expect_equal(tr1[Feature == 'Leaf']$Quality, tr1ut[Feature == 'Leaf']$Quality)
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expect_gt(sum(abs(tr1[Feature != 'Leaf']$Quality - tr1ut[Feature != 'Leaf']$Quality)), 100)
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expect_lt(sum(tr1ut$Cover) / sum(tr1$Cover), 0.5)
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})
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