xgboost: expose watchlist and callbacks (#1859)

* xgboost: expose watchlist and callbacks; remove silent from params; set default lambda=1; add tweedie_variance_power param

* disable TODO linter

Author: khotilov

R/RLearner_classif_xgboost.R

@@ -7,7 +7,7 @@ makeRLearner.classif.xgboost = function() {
       # we pass all of what goes in 'params' directly to ... of xgboost
       # makeUntypedLearnerParam(id = "params", default = list()),
       makeDiscreteLearnerParam(id = "booster", default = "gbtree", values = c("gbtree", "gblinear", "dart")),
-      makeIntegerLearnerParam(id = "silent", default = 0L, tunable = FALSE),
+      makeUntypedLearnerParam(id = "watchlist", default = NULL, tunable = FALSE),
       makeNumericLearnerParam(id = "eta", default = 0.3, lower = 0, upper = 1),
       makeNumericLearnerParam(id = "gamma", default = 0, lower = 0),
       makeIntegerLearnerParam(id = "max_depth", default = 6L, lower = 1L),
@@ -16,7 +16,7 @@ makeRLearner.classif.xgboost = function() {
       makeNumericLearnerParam(id = "colsample_bytree", default = 1, lower = 0, upper = 1),
       makeNumericLearnerParam(id = "colsample_bylevel", default = 1, lower = 0, upper = 1),
       makeIntegerLearnerParam(id = "num_parallel_tree", default = 1L, lower = 1L),
-      makeNumericLearnerParam(id = "lambda", default = 0, lower = 0),
+      makeNumericLearnerParam(id = "lambda", default = 1, lower = 0),
       makeNumericLearnerParam(id = "lambda_bias", default = 0, lower = 0),
       makeNumericLearnerParam(id = "alpha", default = 0, lower = 0),
       makeUntypedLearnerParam(id = "objective", default = "binary:logistic", tunable = FALSE),
@@ -26,6 +26,7 @@ makeRLearner.classif.xgboost = function() {
       makeNumericLearnerParam(id = "missing", default = NULL, tunable = FALSE, when = "both",
         special.vals = list(NA, NA_real_, NULL)),
       makeIntegerVectorLearnerParam(id = "monotone_constraints", default = 0, lower = -1, upper = 1),
+      makeNumericLearnerParam(id = "tweedie_variance_power", lower = 1, upper = 2, default = 1.5, requires = quote(objective == "reg:tweedie")),
       makeIntegerLearnerParam(id = "nthread", lower = 1L, tunable = FALSE),
       makeIntegerLearnerParam(id = "nrounds", default = 1L, lower = 1L),
       # FIXME nrounds seems to have no default in xgboost(), if it has 1, par.vals is redundant
@@ -38,7 +39,14 @@ makeRLearner.classif.xgboost = function() {
       makeDiscreteLearnerParam(id = "sample_type", default = "uniform", values = c("uniform", "weighted"), requires = quote(booster == "dart")),
       makeDiscreteLearnerParam(id = "normalize_type", default = "tree", values = c("tree", "forest"), requires = quote(booster == "dart")),
       makeNumericLearnerParam(id = "rate_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
-      makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart"))
+      makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
+      # TODO: uncomment the following after the next CRAN update, and set max_depth's lower = 0L
+      #makeLogicalLearnerParam(id = "one_drop", default = FALSE, requires = quote(booster == "dart")),
+      #makeDiscreteLearnerParam(id = "tree_method", default = "exact", values = c("exact", "hist"), requires = quote(booster != "gblinear")),
+      #makeDiscreteLearnerParam(id = "grow_policy", default = "depthwise", values = c("depthwise", "lossguide"), requires = quote(tree_method == "hist")),
+      #makeIntegerLearnerParam(id = "max_leaves", default = 0L, lower = 0L, requires = quote(grow_policy == "lossguide")),
+      #makeIntegerLearnerParam(id = "max_bin", default = 256L, lower = 2L, requires = quote(tree_method == "hist")),
+      makeUntypedLearnerParam(id = "callbacks", default = list(), tunable = FALSE)
     ),
     par.vals = list(nrounds = 1L, verbose = 0L),
     properties = c("twoclass", "multiclass", "numerics", "prob", "weights", "missings", "featimp"),
@@ -54,8 +62,6 @@ trainLearner.classif.xgboost = function(.learner, .task, .subset, .weights = NUL
 
   td = getTaskDesc(.task)
   parlist = list(...)
-  parlist$data = data.matrix(getTaskData(.task, .subset, target.extra = TRUE)$data)
-  parlist$label = match(as.character(getTaskData(.task, .subset, target.extra = TRUE)$target), td$class.levels) - 1
   nc = length(td$class.levels)
 
   if (is.null(parlist$objective))
@@ -68,10 +74,17 @@ trainLearner.classif.xgboost = function(.learner, .task, .subset, .weights = NUL
   if (parlist$objective %in% c("multi:softprob", "multi:softmax"))
     parlist$num_class = nc
 
+  task.data = getTaskData(.task, .subset, target.extra = TRUE)
+  label = match(as.character(task.data$target), td$class.levels) - 1
+  parlist$data = xgboost::xgb.DMatrix(data = data.matrix(task.data$data), label = label)
+
   if (!is.null(.weights))
-    parlist$data = xgboost::xgb.DMatrix(data = parlist$data, label = parlist$label, weight = .weights)
+    xgboost::setinfo(parlist$data, "weight", .weights)
+
+  if (is.null(parlist$watchlist))
+    parlist$watchlist = list(train = parlist$data)
 
-  do.call(xgboost::xgboost, parlist)
+  do.call(xgboost::xgb.train, parlist)
 }
 
 #' @export
@@ -131,5 +144,3 @@ getFeatureImportanceLearner.classif.xgboost = function(.learner, .model, ...) {
   fiv = imp$Gain
   setNames(fiv, imp$Feature)
 }
-
-

R/RLearner_regr_xgboost.R

@@ -7,7 +7,7 @@ makeRLearner.regr.xgboost = function() {
       # we pass all of what goes in 'params' directly to ... of xgboost
       #makeUntypedLearnerParam(id = "params", default = list()),
       makeDiscreteLearnerParam(id = "booster", default = "gbtree", values = c("gbtree", "gblinear", "dart")),
-      makeIntegerLearnerParam(id = "silent", default = 0L, tunable = FALSE),
+      makeUntypedLearnerParam(id = "watchlist", default = NULL, tunable = FALSE),
       makeNumericLearnerParam(id = "eta", default = 0.3, lower = 0, upper = 1),
       makeNumericLearnerParam(id = "gamma", default = 0, lower = 0),
       makeIntegerLearnerParam(id = "max_depth", default = 6L, lower = 1L),
@@ -16,16 +16,17 @@ makeRLearner.regr.xgboost = function() {
       makeNumericLearnerParam(id = "colsample_bytree", default = 1, lower = 0, upper = 1),
       makeNumericLearnerParam(id = "colsample_bylevel", default = 1, lower = 0, upper = 1),
       makeIntegerLearnerParam(id = "num_parallel_tree", default = 1L, lower = 1L),
-      makeNumericLearnerParam(id = "lambda", default = 0, lower = 0),
+      makeNumericLearnerParam(id = "lambda", default = 1, lower = 0),
       makeNumericLearnerParam(id = "lambda_bias", default = 0, lower = 0),
       makeNumericLearnerParam(id = "alpha", default = 0, lower = 0),
       makeUntypedLearnerParam(id = "objective", default = "reg:linear", tunable = FALSE),
       makeUntypedLearnerParam(id = "eval_metric", default = "rmse", tunable = FALSE),
       makeNumericLearnerParam(id = "base_score", default = 0.5, tunable = FALSE),
-
+      makeNumericLearnerParam(id = "max_delta_step", lower = 0, default = 0),
       makeNumericLearnerParam(id = "missing", default = NULL, tunable = FALSE, when = "both",
         special.vals = list(NA, NA_real_, NULL)),
       makeIntegerVectorLearnerParam(id = "monotone_constraints", default = 0, lower = -1, upper = 1),
+      makeNumericLearnerParam(id = "tweedie_variance_power", lower = 1, upper = 2, default = 1.5, requires = quote(objective == "reg:tweedie")),
       makeIntegerLearnerParam(id = "nthread", lower = 1L, tunable = FALSE),
       makeIntegerLearnerParam(id = "nrounds", default = 1L, lower = 1L),
       # FIXME nrounds seems to have no default in xgboost(), if it has 1, par.vals is redundant
@@ -35,9 +36,17 @@ makeRLearner.regr.xgboost = function() {
         requires = quote(verbose == 1L)),
       makeIntegerLearnerParam(id = "early_stopping_rounds", default = NULL, lower = 1L, special.vals = list(NULL), tunable = FALSE),
       makeLogicalLearnerParam(id = "maximize", default = NULL, special.vals = list(NULL), tunable = FALSE),
+      makeDiscreteLearnerParam(id = "sample_type", default = "uniform", values = c("uniform", "weighted"), requires = quote(booster == "dart")),
       makeDiscreteLearnerParam(id = "normalize_type", default = "tree", values = c("tree", "forest"), requires = quote(booster == "dart")),
       makeNumericLearnerParam(id = "rate_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
-      makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart"))
+      makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
+      # TODO: uncomment the following after the next CRAN update, and set max_depth's lower = 0L
+      #makeLogicalLearnerParam(id = "one_drop", default = FALSE, requires = quote(booster == "dart")),
+      #makeDiscreteLearnerParam(id = "tree_method", default = "exact", values = c("exact", "hist"), requires = quote(booster != "gblinear")),
+      #makeDiscreteLearnerParam(id = "grow_policy", default = "depthwise", values = c("depthwise", "lossguide"), requires = quote(tree_method == "hist")),
+      #makeIntegerLearnerParam(id = "max_leaves", default = 0L, lower = 0L, requires = quote(grow_policy == "lossguide")),
+      #makeIntegerLearnerParam(id = "max_bin", default = 256L, lower = 2L, requires = quote(tree_method == "hist")),
+      makeUntypedLearnerParam(id = "callbacks", default = list(), tunable = FALSE)
     ),
     par.vals = list(nrounds = 1L, verbose = 0L),
     properties = c("numerics", "weights", "featimp", "missings"),
@@ -52,16 +61,19 @@ makeRLearner.regr.xgboost = function() {
 trainLearner.regr.xgboost = function(.learner, .task, .subset, .weights = NULL,  ...) {
   parlist = list(...)
 
-  parlist$label = getTaskData(.task, .subset, target.extra = TRUE)$target
-  parlist$data = data.matrix(getTaskData(.task, .subset, target.extra = TRUE)$data)
-
   if (is.null(parlist$objective))
     parlist$objective = "reg:linear"
 
+  task.data = getTaskData(.task, .subset, target.extra = TRUE)
+  parlist$data = xgboost::xgb.DMatrix(data = data.matrix(task.data$data), label = task.data$target)
+
   if (!is.null(.weights))
-    parlist$data = xgboost::xgb.DMatrix(data = parlist$data, label = parlist$label, weight = .weights)
+    xgboost::setinfo(parlist$data, "weight", .weights)
+
+  if (is.null(parlist$watchlist))
+    parlist$watchlist = list(train = parlist$data)
 
-  do.call(xgboost::xgboost, parlist)
+  do.call(xgboost::xgb.train, parlist)
 }
 
 #' @export

tests/testthat/helper_lint.R

@@ -266,7 +266,7 @@ if (isLintrVersionOk() && require("lintr", quietly = TRUE) && require("rex", qui
     seq = lintr::seq_linter,
     unneeded.concatenation = lintr::unneeded_concatenation_linter,
     trailing.whitespace = lintr::trailing_whitespace_linter,
-    todo.comment = lintr::todo_comment_linter(todo = "todo"), # is case-insensitive
+    #todo.comment = lintr::todo_comment_linter(todo = "todo"), # is case-insensitive
     spaces.inside = lintr::spaces_inside_linter,
     infix.spaces = infix.spaces.linter,
     object.naming = object.naming.linter)

tests/testthat/test_regr_xgboost.R

@@ -31,9 +31,9 @@ test_that("regr_xgboost", {
 })
 
 test_that("xgboost works with different 'missing' arg vals", {
-  lrn = makeLearner("classif.xgboost", missing = NA_real_)
-  lrn = makeLearner("classif.xgboost", missing = NA)
-  lrn = makeLearner("classif.xgboost", missing = NULL)
+  lrn = makeLearner("regr.xgboost", missing = NA_real_)
+  lrn = makeLearner("regr.xgboost", missing = NA)
+  lrn = makeLearner("regr.xgboost", missing = NULL)
 })