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[CORE] The update process for a tree model, and its application to fe…
…ature importance (#1670) * [CORE] allow updating trees in an existing model * [CORE] in refresh updater, allow keeping old leaf values and update stats only * [R-package] xgb.train mod to allow updating trees in an existing model * [R-package] added check for nrounds when is_update * [CORE] merge parameter declaration changes; unify their code style * [CORE] move the update-process trees initialization to Configure; rename default process_type to 'default'; fix the trees and trees_to_update sizes comparison check * [R-package] unit tests for the update process type * [DOC] documentation for process_type parameter; improved docs for updater, Gamma and Tweedie; added some parameter aliases; metrics indentation and some were non-documented * fix my sloppy merge conflict resolutions * [CORE] add a TreeProcessType enum * whitespace fix
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| Original file line number | Diff line number | Diff line change |
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| require(xgboost) | ||
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| context("update trees in an existing model") | ||
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| data(agaricus.train, package = 'xgboost') | ||
| data(agaricus.test, package = 'xgboost') | ||
| dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label) | ||
| dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label) | ||
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| test_that("updating the model works", { | ||
| watchlist = list(train = dtrain, test = dtest) | ||
| cb = list(cb.evaluation.log()) # to run silent, but store eval. log | ||
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| # no-subsampling | ||
| p1 <- list(objective = "binary:logistic", max_depth = 2, eta = 0.05, nthread = 2) | ||
| set.seed(11) | ||
| bst1 <- xgb.train(p1, dtrain, nrounds = 10, watchlist, verbose = 0, callbacks = cb) | ||
| tr1 <- xgb.model.dt.tree(model = bst1) | ||
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| # with subsampling | ||
| p2 <- modifyList(p1, list(subsample = 0.1)) | ||
| set.seed(11) | ||
| bst2 <- xgb.train(p2, dtrain, nrounds = 10, watchlist, verbose = 0, callbacks = cb) | ||
| tr2 <- xgb.model.dt.tree(model = bst2) | ||
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| # the same no-subsampling boosting with an extra 'refresh' updater: | ||
| p1r <- modifyList(p1, list(updater = 'grow_colmaker,prune,refresh', refresh_leaf = FALSE)) | ||
| set.seed(11) | ||
| bst1r <- xgb.train(p1r, dtrain, nrounds = 10, watchlist, verbose = 0, callbacks = cb) | ||
| tr1r <- xgb.model.dt.tree(model = bst1r) | ||
| # all should be the same when no subsampling | ||
| expect_equal(bst1$evaluation_log, bst1r$evaluation_log) | ||
| expect_equal(tr1, tr1r, tolerance = 0.00001, check.attributes = FALSE) | ||
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| # the same boosting with subsampling with an extra 'refresh' updater: | ||
| p2r <- modifyList(p2, list(updater = 'grow_colmaker,prune,refresh', refresh_leaf = FALSE)) | ||
| set.seed(11) | ||
| bst2r <- xgb.train(p2r, dtrain, nrounds = 10, watchlist, verbose = 0, callbacks = cb) | ||
| tr2r <- xgb.model.dt.tree(model = bst2r) | ||
| # should be the same evaluation but different gains and larger cover | ||
| expect_equal(bst2$evaluation_log, bst2r$evaluation_log) | ||
| expect_equal(tr2[Feature == 'Leaf']$Quality, tr2r[Feature == 'Leaf']$Quality) | ||
| expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2r[Feature != 'Leaf']$Quality)), 100) | ||
| 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: | ||
| p1u <- modifyList(p1, list(process_type = 'update', updater = 'refresh', refresh_leaf = TRUE)) | ||
| bst1u <- xgb.train(p1u, dtrain, nrounds = 10, watchlist, verbose = 0, callbacks = cb, xgb_model = bst1) | ||
| tr1u <- xgb.model.dt.tree(model = bst1u) | ||
| # all should be the same when no subsampling | ||
| expect_equal(bst1$evaluation_log, bst1u$evaluation_log) | ||
| 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: | ||
| p2u <- modifyList(p2, list(process_type = 'update', updater = 'refresh', refresh_leaf = FALSE)) | ||
| bst2u <- xgb.train(p2u, dtrain, nrounds = 10, watchlist, verbose = 0, callbacks = cb, xgb_model = bst2) | ||
| tr2u <- xgb.model.dt.tree(model = bst2u) | ||
| # should be the same evaluation but different gains and larger cover | ||
| expect_equal(bst2$evaluation_log, bst2u$evaluation_log) | ||
| expect_equal(tr2[Feature == 'Leaf']$Quality, tr2u[Feature == 'Leaf']$Quality) | ||
| expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2u[Feature != 'Leaf']$Quality)), 100) | ||
| expect_gt(sum(tr2u$Cover) / sum(tr2$Cover), 1.5) | ||
| # the results should be the same as for the model with an extra 'refresh' updater | ||
| expect_equal(bst2r$evaluation_log, bst2u$evaluation_log) | ||
| 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: | ||
| p1ut <- modifyList(p1, list(process_type = 'update', updater = 'refresh', refresh_leaf = FALSE)) | ||
| bst1ut <- xgb.train(p1ut, dtest, nrounds = 10, watchlist, verbose = 0, callbacks = cb, xgb_model = bst1) | ||
| tr1ut <- xgb.model.dt.tree(model = bst1ut) | ||
| # should be the same evaluations but different gains and smaller cover (test data is smaller) | ||
| expect_equal(bst1$evaluation_log, bst1ut$evaluation_log) | ||
| expect_equal(tr1[Feature == 'Leaf']$Quality, tr1ut[Feature == 'Leaf']$Quality) | ||
| expect_gt(sum(abs(tr1[Feature != 'Leaf']$Quality - tr1ut[Feature != 'Leaf']$Quality)), 100) | ||
| expect_lt(sum(tr1ut$Cover) / sum(tr1$Cover), 0.5) | ||
| }) |
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