Skip to content

Commit fd08a30

Browse files
mllgmllg
committed
Merge remote-tracking branch 'origin/master' into survival
2 parents d78d59b + 09068c9 commit fd08a30

14 files changed

+117
-54
lines changed

‎LICENSE

Lines changed: 2 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -1,25 +1,2 @@
1-
BSD 2-Clause License
2-
3-
Copyright (c) 2013-2017, Bernd Bischl
4-
All rights reserved.
5-
6-
Redistribution and use in source and binary forms, with or without
7-
modification, are permitted provided that the following conditions are met:
8-
9-
* Redistributions of source code must retain the above copyright notice, this
10-
list of conditions and the following disclaimer.
11-
12-
* Redistributions in binary form must reproduce the above copyright notice,
13-
this list of conditions and the following disclaimer in the documentation
14-
and/or other materials provided with the distribution.
15-
16-
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
17-
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
18-
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
19-
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
20-
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
21-
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
22-
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
23-
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
24-
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
25-
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
1+
YEAR: 2013-2017
2+
COPYRIGHT HOLDER: Bernd Bischl

‎NEWS.md

Lines changed: 4 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,9 @@
11
# mlr 2.12:
22

3+
## general
4+
* relaxed the way wrappers can be nested -- the only explicitly forbidden
5+
combination is to wrap a tuning wrapper around another optimization wrapper
6+
37
## functions - general
48
* generatePartialDependenceData: added parameter "range" to allow to specify the
59
range of values for the partial dependencies

‎R/BaseWrapper.R

Lines changed: 2 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1,8 +1,7 @@
11
makeBaseWrapper = function(id, type, next.learner, package = character(0L), par.set = makeParamSet(),
22
par.vals = list(), learner.subclass, model.subclass) {
3-
4-
if (inherits(next.learner, "OptWrapper"))
5-
stop("Cannot wrap an optimization wrapper with something else!")
3+
if (inherits(next.learner, "OptWrapper") && is.element("TuneWrapper", learner.subclass))
4+
stop("Cannot wrap a tuning wrapper around another optimization wrapper!")
65
ns = intersect(names(par.set$pars), names(next.learner$par.set$pars))
76
if (length(ns) > 0L)
87
stopf("Hyperparameter names in wrapper clash with base learner names: %s", collapse(ns))

‎R/ClusterTask.R

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,7 @@ makeClusterTask = function(id = deparse(substitute(data)), data, weights = NULL,
66
assertChoice(fixup.data, choices = c("no", "quiet", "warn"))
77
assertFlag(check.data)
88

9-
task = makeUnsupervisedTask("cluster", data, weights, blocking)
9+
task = makeUnsupervisedTask("cluster", data, weights, blocking, fixup.data, check.data)
1010
task$task.desc = makeClusterTaskDesc(id, data, weights, blocking)
1111
addClasses(task, "ClusterTask")
1212
}

‎R/MultilabelTask.R

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -8,7 +8,7 @@ makeMultilabelTask = function(id = deparse(substitute(data)), data, target, weig
88
assertChoice(fixup.data, choices = c("no", "quiet", "warn"))
99
assertFlag(check.data)
1010

11-
task = makeSupervisedTask("multilabel", data, target, weights, blocking)
11+
task = makeSupervisedTask("multilabel", data, target, weights, blocking, fixup.data, check.data)
1212
# currently we dont do any fixup here
1313
if (check.data) {
1414
for (cn in target)

‎R/RLearner_classif_xgboost.R

Lines changed: 20 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -7,7 +7,7 @@ makeRLearner.classif.xgboost = function() {
77
# we pass all of what goes in 'params' directly to ... of xgboost
88
# makeUntypedLearnerParam(id = "params", default = list()),
99
makeDiscreteLearnerParam(id = "booster", default = "gbtree", values = c("gbtree", "gblinear", "dart")),
10-
makeIntegerLearnerParam(id = "silent", default = 0L, tunable = FALSE),
10+
makeUntypedLearnerParam(id = "watchlist", default = NULL, tunable = FALSE),
1111
makeNumericLearnerParam(id = "eta", default = 0.3, lower = 0, upper = 1),
1212
makeNumericLearnerParam(id = "gamma", default = 0, lower = 0),
1313
makeIntegerLearnerParam(id = "max_depth", default = 6L, lower = 1L),
@@ -16,7 +16,7 @@ makeRLearner.classif.xgboost = function() {
1616
makeNumericLearnerParam(id = "colsample_bytree", default = 1, lower = 0, upper = 1),
1717
makeNumericLearnerParam(id = "colsample_bylevel", default = 1, lower = 0, upper = 1),
1818
makeIntegerLearnerParam(id = "num_parallel_tree", default = 1L, lower = 1L),
19-
makeNumericLearnerParam(id = "lambda", default = 0, lower = 0),
19+
makeNumericLearnerParam(id = "lambda", default = 1, lower = 0),
2020
makeNumericLearnerParam(id = "lambda_bias", default = 0, lower = 0),
2121
makeNumericLearnerParam(id = "alpha", default = 0, lower = 0),
2222
makeUntypedLearnerParam(id = "objective", default = "binary:logistic", tunable = FALSE),
@@ -26,6 +26,7 @@ makeRLearner.classif.xgboost = function() {
2626
makeNumericLearnerParam(id = "missing", default = NULL, tunable = FALSE, when = "both",
2727
special.vals = list(NA, NA_real_, NULL)),
2828
makeIntegerVectorLearnerParam(id = "monotone_constraints", default = 0, lower = -1, upper = 1),
29+
makeNumericLearnerParam(id = "tweedie_variance_power", lower = 1, upper = 2, default = 1.5, requires = quote(objective == "reg:tweedie")),
2930
makeIntegerLearnerParam(id = "nthread", lower = 1L, tunable = FALSE),
3031
makeIntegerLearnerParam(id = "nrounds", default = 1L, lower = 1L),
3132
# 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() {
3839
makeDiscreteLearnerParam(id = "sample_type", default = "uniform", values = c("uniform", "weighted"), requires = quote(booster == "dart")),
3940
makeDiscreteLearnerParam(id = "normalize_type", default = "tree", values = c("tree", "forest"), requires = quote(booster == "dart")),
4041
makeNumericLearnerParam(id = "rate_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
41-
makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart"))
42+
makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
43+
# TODO: uncomment the following after the next CRAN update, and set max_depth's lower = 0L
44+
#makeLogicalLearnerParam(id = "one_drop", default = FALSE, requires = quote(booster == "dart")),
45+
#makeDiscreteLearnerParam(id = "tree_method", default = "exact", values = c("exact", "hist"), requires = quote(booster != "gblinear")),
46+
#makeDiscreteLearnerParam(id = "grow_policy", default = "depthwise", values = c("depthwise", "lossguide"), requires = quote(tree_method == "hist")),
47+
#makeIntegerLearnerParam(id = "max_leaves", default = 0L, lower = 0L, requires = quote(grow_policy == "lossguide")),
48+
#makeIntegerLearnerParam(id = "max_bin", default = 256L, lower = 2L, requires = quote(tree_method == "hist")),
49+
makeUntypedLearnerParam(id = "callbacks", default = list(), tunable = FALSE)
4250
),
4351
par.vals = list(nrounds = 1L, verbose = 0L),
4452
properties = c("twoclass", "multiclass", "numerics", "prob", "weights", "missings", "featimp"),
@@ -54,8 +62,6 @@ trainLearner.classif.xgboost = function(.learner, .task, .subset, .weights = NUL
5462

5563
td = getTaskDesc(.task)
5664
parlist = list(...)
57-
parlist$data = data.matrix(getTaskData(.task, .subset, target.extra = TRUE)$data)
58-
parlist$label = match(as.character(getTaskData(.task, .subset, target.extra = TRUE)$target), td$class.levels) - 1
5965
nc = length(td$class.levels)
6066

6167
if (is.null(parlist$objective))
@@ -68,10 +74,17 @@ trainLearner.classif.xgboost = function(.learner, .task, .subset, .weights = NUL
6874
if (parlist$objective %in% c("multi:softprob", "multi:softmax"))
6975
parlist$num_class = nc
7076

77+
task.data = getTaskData(.task, .subset, target.extra = TRUE)
78+
label = match(as.character(task.data$target), td$class.levels) - 1
79+
parlist$data = xgboost::xgb.DMatrix(data = data.matrix(task.data$data), label = label)
80+
7181
if (!is.null(.weights))
72-
parlist$data = xgboost::xgb.DMatrix(data = parlist$data, label = parlist$label, weight = .weights)
82+
xgboost::setinfo(parlist$data, "weight", .weights)
83+
84+
if (is.null(parlist$watchlist))
85+
parlist$watchlist = list(train = parlist$data)
7386

74-
do.call(xgboost::xgboost, parlist)
87+
do.call(xgboost::xgb.train, parlist)
7588
}
7689

7790
#' @export
@@ -131,5 +144,3 @@ getFeatureImportanceLearner.classif.xgboost = function(.learner, .model, ...) {
131144
fiv = imp$Gain
132145
setNames(fiv, imp$Feature)
133146
}
134-
135-

‎R/RLearner_regr_xgboost.R

Lines changed: 21 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -7,7 +7,7 @@ makeRLearner.regr.xgboost = function() {
77
# we pass all of what goes in 'params' directly to ... of xgboost
88
#makeUntypedLearnerParam(id = "params", default = list()),
99
makeDiscreteLearnerParam(id = "booster", default = "gbtree", values = c("gbtree", "gblinear", "dart")),
10-
makeIntegerLearnerParam(id = "silent", default = 0L, tunable = FALSE),
10+
makeUntypedLearnerParam(id = "watchlist", default = NULL, tunable = FALSE),
1111
makeNumericLearnerParam(id = "eta", default = 0.3, lower = 0, upper = 1),
1212
makeNumericLearnerParam(id = "gamma", default = 0, lower = 0),
1313
makeIntegerLearnerParam(id = "max_depth", default = 6L, lower = 1L),
@@ -16,16 +16,17 @@ makeRLearner.regr.xgboost = function() {
1616
makeNumericLearnerParam(id = "colsample_bytree", default = 1, lower = 0, upper = 1),
1717
makeNumericLearnerParam(id = "colsample_bylevel", default = 1, lower = 0, upper = 1),
1818
makeIntegerLearnerParam(id = "num_parallel_tree", default = 1L, lower = 1L),
19-
makeNumericLearnerParam(id = "lambda", default = 0, lower = 0),
19+
makeNumericLearnerParam(id = "lambda", default = 1, lower = 0),
2020
makeNumericLearnerParam(id = "lambda_bias", default = 0, lower = 0),
2121
makeNumericLearnerParam(id = "alpha", default = 0, lower = 0),
2222
makeUntypedLearnerParam(id = "objective", default = "reg:linear", tunable = FALSE),
2323
makeUntypedLearnerParam(id = "eval_metric", default = "rmse", tunable = FALSE),
2424
makeNumericLearnerParam(id = "base_score", default = 0.5, tunable = FALSE),
25-
25+
makeNumericLearnerParam(id = "max_delta_step", lower = 0, default = 0),
2626
makeNumericLearnerParam(id = "missing", default = NULL, tunable = FALSE, when = "both",
2727
special.vals = list(NA, NA_real_, NULL)),
2828
makeIntegerVectorLearnerParam(id = "monotone_constraints", default = 0, lower = -1, upper = 1),
29+
makeNumericLearnerParam(id = "tweedie_variance_power", lower = 1, upper = 2, default = 1.5, requires = quote(objective == "reg:tweedie")),
2930
makeIntegerLearnerParam(id = "nthread", lower = 1L, tunable = FALSE),
3031
makeIntegerLearnerParam(id = "nrounds", default = 1L, lower = 1L),
3132
# 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() {
3536
requires = quote(verbose == 1L)),
3637
makeIntegerLearnerParam(id = "early_stopping_rounds", default = NULL, lower = 1L, special.vals = list(NULL), tunable = FALSE),
3738
makeLogicalLearnerParam(id = "maximize", default = NULL, special.vals = list(NULL), tunable = FALSE),
39+
makeDiscreteLearnerParam(id = "sample_type", default = "uniform", values = c("uniform", "weighted"), requires = quote(booster == "dart")),
3840
makeDiscreteLearnerParam(id = "normalize_type", default = "tree", values = c("tree", "forest"), requires = quote(booster == "dart")),
3941
makeNumericLearnerParam(id = "rate_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
40-
makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart"))
42+
makeNumericLearnerParam(id = "skip_drop", default = 0, lower = 0, upper = 1, requires = quote(booster == "dart")),
43+
# TODO: uncomment the following after the next CRAN update, and set max_depth's lower = 0L
44+
#makeLogicalLearnerParam(id = "one_drop", default = FALSE, requires = quote(booster == "dart")),
45+
#makeDiscreteLearnerParam(id = "tree_method", default = "exact", values = c("exact", "hist"), requires = quote(booster != "gblinear")),
46+
#makeDiscreteLearnerParam(id = "grow_policy", default = "depthwise", values = c("depthwise", "lossguide"), requires = quote(tree_method == "hist")),
47+
#makeIntegerLearnerParam(id = "max_leaves", default = 0L, lower = 0L, requires = quote(grow_policy == "lossguide")),
48+
#makeIntegerLearnerParam(id = "max_bin", default = 256L, lower = 2L, requires = quote(tree_method == "hist")),
49+
makeUntypedLearnerParam(id = "callbacks", default = list(), tunable = FALSE)
4150
),
4251
par.vals = list(nrounds = 1L, verbose = 0L),
4352
properties = c("numerics", "weights", "featimp", "missings"),
@@ -52,16 +61,19 @@ makeRLearner.regr.xgboost = function() {
5261
trainLearner.regr.xgboost = function(.learner, .task, .subset, .weights = NULL, ...) {
5362
parlist = list(...)
5463

55-
parlist$label = getTaskData(.task, .subset, target.extra = TRUE)$target
56-
parlist$data = data.matrix(getTaskData(.task, .subset, target.extra = TRUE)$data)
57-
5864
if (is.null(parlist$objective))
5965
parlist$objective = "reg:linear"
6066

67+
task.data = getTaskData(.task, .subset, target.extra = TRUE)
68+
parlist$data = xgboost::xgb.DMatrix(data = data.matrix(task.data$data), label = task.data$target)
69+
6170
if (!is.null(.weights))
62-
parlist$data = xgboost::xgb.DMatrix(data = parlist$data, label = parlist$label, weight = .weights)
71+
xgboost::setinfo(parlist$data, "weight", .weights)
72+
73+
if (is.null(parlist$watchlist))
74+
parlist$watchlist = list(train = parlist$data)
6375

64-
do.call(xgboost::xgboost, parlist)
76+
do.call(xgboost::xgb.train, parlist)
6577
}
6678

6779
#' @export

‎R/SupervisedTask.R

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
makeSupervisedTask = function(type, data, target, weights = NULL, blocking = NULL, fixup.data = "warn", check.data = TRUE) {
1+
makeSupervisedTask = function(type, data, target, weights, blocking, fixup.data, check.data) {
22
task = makeTask(type = type, data = data, weights = weights, blocking = blocking, fixup.data = fixup.data, check.data = check.data)
33

44
if (check.data) {

‎R/UnsupervisedTask.R

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
makeUnsupervisedTask = function(type, data, weights = NULL, blocking = NULL, fixup.data = "warn", check.data = TRUE) {
1+
makeUnsupervisedTask = function(type, data, weights, blocking, fixup.data, check.data) {
22
task = makeTask(type, data, weights, blocking, fixup.data = fixup.data, check.data = check.data)
33
# we can't use getTaskData to access the tasks's data here because we then
44
# want to access the description object which is not existing yet

‎tests/testthat/helper_lint.R

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -266,7 +266,7 @@ if (isLintrVersionOk() && require("lintr", quietly = TRUE) && require("rex", qui
266266
seq = lintr::seq_linter,
267267
unneeded.concatenation = lintr::unneeded_concatenation_linter,
268268
trailing.whitespace = lintr::trailing_whitespace_linter,
269-
todo.comment = lintr::todo_comment_linter(todo = "todo"), # is case-insensitive
269+
#todo.comment = lintr::todo_comment_linter(todo = "todo"), # is case-insensitive
270270
spaces.inside = lintr::spaces_inside_linter,
271271
infix.spaces = infix.spaces.linter,
272272
object.naming = object.naming.linter)

0 commit comments

Comments
 (0)