Package 'mlr3pipelines'

Description

Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.

Author(s)

Maintainer: Martin Binder [email protected]

Authors:

• Florian Pfisterer [email protected] (ORCID)

• Lennart Schneider [email protected] (ORCID)

• Bernd Bischl [email protected] (ORCID)

• Michel Lang [email protected] (ORCID)

• Sebastian Fischer [email protected] (ORCID)

• Susanne Dandl [email protected]

Other contributors:

• Keno Mersmann [email protected] [contributor]

• Maximilian Mücke [email protected] (ORCID) [contributor]

• Lona Koers [email protected] [contributor]

See Also

Useful links:

• https://mlr3pipelines.mlr-org.com

• https://github.com/mlr-org/mlr3pipelines

• Report bugs at https://github.com/mlr-org/mlr3pipelines/issues

Description

These operators creates a connection that "pipes" data from the source g1 into the sink g2. Both source and sink can either be a Graph or a PipeOp (or an object that can be automatically converted into a Graph or PipeOp, see as_graph() and as_pipeop()).

⁠%>>%⁠ and ⁠%>>!%⁠ try to automatically match output channels of g1 to input channels of g2; this is only possible if either

• the number of output channels of g1 (as given by g1$output) is equal to the number of input channels of g2 (as given by g2$input), or

• g1 has only one output channel (i.e. g1$output has one line), or

• g2 has only one input channel, which is a vararg channel (i.e. g2$input has one line, with name entry "...").

Connections between channels are created in the order in which they occur in g1 and g2, respectively: g1's output channel 1 is connected to g2's input channel 1, channel 2 to 2 etc.

⁠%>>%⁠ always creates deep copies of its input arguments, so they cannot be modified by reference afterwards. To access individual PipeOps after composition, use the resulting Graph's ⁠$pipeops⁠ list. ⁠%>>!%⁠, on the other hand, tries to avoid cloning its first argument: If it is a Graph, then this Graph will be modified in-place.

When ⁠%>>!%⁠ fails, then it leaves g1 in an incompletely modified state. It is therefore usually recommended to use ⁠%>>%⁠, since the very marginal gain of performance from using ⁠%>>!%⁠ often does not outweigh the risk of either modifying objects by-reference that should not be modified or getting graphs that are in an incompletely modified state. However, when creating long Graphs, chaining with ⁠%>>!%⁠ instead of ⁠%>>%⁠ can give noticeable performance benefits because ⁠%>>%⁠ makes a number of clone()-calls that is quadratic in chain length, ⁠%>>!%⁠ only linear.

concat_graphs(g1, g2, in_place = FALSE) is equivalent to g1 %>>% g2. concat_graphs(g1, g2, in_place = TRUE) is equivalent to g1 %>>!% g2.

Both arguments of ⁠%>>%⁠ are automatically converted to Graphs using as_graph(); this means that objects on either side may be objects that can be automatically converted to PipeOps (such as Learners or Filters), or that can be converted to Graphs. This means, in particular, lists of Graphs, PipeOps or objects convertible to that, because as_graph() automatically applies gunion() to lists. See examples. If the first argument of ⁠%>>!%⁠ is not a Graph, then it is cloned just as when ⁠%>>%⁠ is used; ⁠%>>!%⁠ only avoids clone() if the first argument is a Graph.

Note that if g1 is NULL, g2 converted to a Graph will be returned. Analogously, if g2 is NULL, g1 converted to a Graph will be returned.

Usage

g1 %>>% g2

concat_graphs(g1, g2, in_place = FALSE)

g1 %>>!% g2

Arguments

Value

Graph: the constructed Graph.

See Also

Other Graph operators: as_graph(), as_pipeop(), assert_graph(), assert_pipeop(), chain_graphs(), greplicate(), gunion(), mlr_graphs_greplicate

Examples

o1 = PipeOpScale$new()
o2 = PipeOpPCA$new()
o3 = PipeOpFeatureUnion$new(2)

# The following two are equivalent:
pipe1 = o1 %>>% o2

pipe2 = Graph$new()$
  add_pipeop(o1)$
  add_pipeop(o2)$
  add_edge(o1$id, o2$id)

# Note automatical gunion() of lists.
# The following three are equivalent:
graph1 = list(o1, o2) %>>% o3

graph2 = gunion(list(o1, o2)) %>>% o3

graph3 = Graph$new()$
  add_pipeop(o1)$
  add_pipeop(o2)$
  add_pipeop(o3)$
  add_edge(o1$id, o3$id, dst_channel = 1)$
  add_edge(o2$id, o3$id, dst_channel = 2)

pipe1 %>>!% o3  # modify pipe1 in-place

pipe1  # contains o1, o2, and o3 now.

o1 %>>!% o2

o1  # not changed, becuase not a Graph.

Description

Add a class hierarchy to the class hierarchy cache. This is necessary whenever an S3 class's class hierarchy is important when inferring compatibility between types.

Usage

add_class_hierarchy_cache(hierarchy)

Arguments

Value

NULL

See Also

Other class hierarchy operations: register_autoconvert_function(), reset_autoconvert_register(), reset_class_hierarchy_cache()

Examples

# This lets mlr3pipelines handle "data.table" as "data.frame".
# This is an example and not necessary, because mlr3pipelines adds it by default.

add_class_hierarchy_cache(c("data.table", "data.frame"))

Description

The argument is turned into a Graph if possible. If clone is TRUE, a deep copy is made if the incoming object is a Graph to ensure the resulting object is a different reference from the incoming object.

as_graph() is an S3 method and can therefore be implemented by other packages that may add objects that can naturally be converted to Graphs.

By default, as_graph() tries to

• apply gunion() to x if it is a list, which recursively applies as_graph() to all list elements first

• create a Graph with only one element if x is a PipeOp or can be converted to one using as_pipeop().

Usage

as_graph(x, clone = FALSE)

Arguments

Value

Graph x or a deep clone of it.

See Also

Other Graph operators: %>>%(), as_pipeop(), assert_graph(), assert_pipeop(), chain_graphs(), greplicate(), gunion(), mlr_graphs_greplicate

Description

The argument is turned into a PipeOp if possible. If clone is TRUE, a deep copy is made if the incoming object is a PipeOp to ensure the resulting object is a different reference from the incoming object.

as_pipeop() is an S3 method and can therefore be implemented by other packages that may add objects that can naturally be converted to PipeOps. Objects that can be converted are for example Learner (using PipeOpLearner) or Filter (using PipeOpFilter).

Usage

as_pipeop(x, clone = FALSE)

Arguments

Value

PipeOp x or a deep clone of it.

See Also

Other Graph operators: %>>%(), as_graph(), assert_graph(), assert_pipeop(), chain_graphs(), greplicate(), gunion(), mlr_graphs_greplicate

Description

Convert an object to a Multiplicity.

Usage

as.Multiplicity(x)

Arguments

Value

Multiplicity

Description

Function that checks that a given object is a Graph and throws an error if not.

Usage

assert_graph(x)

Arguments

Value

Graph invisible(x)

See Also

Other Graph operators: %>>%(), as_graph(), as_pipeop(), assert_pipeop(), chain_graphs(), greplicate(), gunion(), mlr_graphs_greplicate

Description

Function that checks that a given object is a PipeOp and throws an error if not.

Usage

assert_pipeop(x)

Arguments

Value

PipeOp invisible(x)

See Also

Other Graph operators: %>>%(), as_graph(), as_pipeop(), assert_graph(), chain_graphs(), greplicate(), gunion(), mlr_graphs_greplicate

Description

Takes an arbitrary amount of Graphs or PipeOps (or objects that can be automatically converted into Graphs or PipeOps, see as_graph() and as_pipeop()) as inputs and joins them in a serial Graph, as if connecting them using %>>%.

Care is taken to avoid unnecessarily cloning of components. A call of chain_graphs(list(g1, g2, g3, g4, ...), in_place = FALSE) is equivalent to g1 %>>% g2 %>>!% g3 %>>!% g4 %>>!% .... A call of chain_graphs(list(g1, g2, g3, g4, ...), in_place = FALSE) is equivalent to g1 %>>!% g2 %>>!% g3 %>>!% g4 %>>!% ... (differing in the first operator being ⁠%>>!%⁠ as well).

Usage

chain_graphs(graphs, in_place = FALSE)

Arguments

Value

Graph the resulting Graph, or NULL if there are no non-null values in graphs.

See Also

Other Graph operators: %>>%(), as_graph(), as_pipeop(), assert_graph(), assert_pipeop(), greplicate(), gunion(), mlr_graphs_greplicate

Description

Remove all NO_OP elements from a list.

Usage

filter_noop(x)

Arguments

Value

list: The input list, with all NO_OP elements removed.

See Also

Other Path Branching: NO_OP, is_noop(), mlr_pipeops_branch, mlr_pipeops_unbranch

Description

A Graph is a representation of a machine learning pipeline graph. It can be trained, and subsequently used for prediction.

A Graph is most useful when used together with Learner objects encapsulated as PipeOpLearner. In this case, the Graph produces Prediction data during its ⁠$predict()⁠ phase and can be used as a Learner itself (using the GraphLearner wrapper). However, the Graph can also be used without Learner objects to simply perform preprocessing of data, and, in principle, does not even need to handle data at all but can be used for general processes with dependency structure (although the PipeOps for this would need to be written).

Format

R6Class.

Construction

Graph$new()

Internals

A Graph is made up of a list of PipeOps, and a data.table of edges. Both for training and prediction, the Graph performs topological sorting of the PipeOps and executes their respective ⁠$train()⁠ or ⁠$predict()⁠ functions in order, moving the PipeOp results along the edges as input to other PipeOps.

Fields

• pipeops :: named list of PipeOp
  Contains all PipeOps in the Graph, named by the PipeOp's ⁠$id⁠s.

• edges :: data.table with columns src_id (character), src_channel (character), dst_id (character), dst_channel (character)
  Table of connections between the PipeOps. A data.table. src_id and dst_id are ⁠$id⁠s of PipeOps that must be present in the ⁠$pipeops⁠ list. src_channel and dst_channel must respectively be ⁠$output⁠ and ⁠$input⁠ channel names of the respective PipeOps.

• is_trained :: logical(1)
  Is the Graph, i.e. are all of its PipeOps, trained, and can the Graph be used for prediction?

• lhs :: character
  Ids of the 'left-hand-side' PipeOps that have some unconnected input channels and therefore act as Graph input layer.

• rhs :: character
  Ids of the 'right-hand-side' PipeOps that have some unconnected output channels and therefore act as Graph output layer.

• input :: data.table with columns name (character), train (character), predict (character), op.id (character), channel.name (character)
  Input channels of the Graph. For each channel lists the name, input type during training, input type during prediction, PipeOp ⁠$id⁠ of the PipeOp the channel pertains to, and channel name as the PipeOp knows it.

• output :: data.table with columns name (character), train (character), predict (character), op.id (character), channel.name (character)
  Output channels of the Graph. For each channel lists the name, output type during training, output type during prediction, PipeOp ⁠$id⁠ of the PipeOp the channel pertains to, and channel name as the PipeOp knows it.

• packages :: character
  Set of all required packages for the various methods in the Graph, a set union of all required packages of all contained PipeOp objects.

• state :: named list
  Get / Set the ⁠$state⁠ of each of the members of PipeOp.

• param_set :: ParamSet
  Parameters and parameter constraints. Parameter values are in ⁠$param_set$values⁠. These are the union of ⁠$param_set⁠s of all PipeOps in the Graph. Parameter names as seen by the Graph have the naming scheme ⁠<PipeOp$id>.<PipeOp original parameter name>⁠. Changing ⁠$param_set$values⁠ also propagates the changes directly to the contained PipeOps and is an alternative to changing a PipeOps ⁠$param_set$values⁠ directly.

• hash :: character(1)
  Stores a checksum calculated on the Graph configuration, which includes all PipeOp hashes (and therefore their ⁠$param_set$values⁠) and a hash of ⁠$edges⁠.

• phash :: character(1)
  Stores a checksum calculated on the Graph configuration, which includes all PipeOp hashes except their ⁠$param_set$values⁠, and a hash of ⁠$edges⁠.

• keep_results :: logical(1)
  Whether to store intermediate results in the PipeOp's ⁠$.result⁠ slot, mostly for debugging purposes. Default FALSE.

• man :: character(1)
  Identifying string of the help page that shows with help().

Methods

• ids(sorted = FALSE)
  (logical(1)) -> character
  Get IDs of all PipeOps. This is in order that PipeOps were added if sorted is FALSE, and topologically sorted if sorted is TRUE.

• add_pipeop(op, clone = TRUE)
  (PipeOp | Learner | Filter | ..., logical(1)) -> self
  Mutates Graph by adding a PipeOp to the Graph. This does not add any edges, so the new PipeOp will not be connected within the Graph at first.
  Instead of supplying a PipeOp directly, an object that can naturally be converted to a PipeOp can also be supplied, e.g. a Learner or a Filter; see as_pipeop(). The argument given as op is cloned if clone is TRUE (default); to access a Graph's PipeOps by-reference, use ⁠$pipeops⁠.
  Note that ⁠$add_pipeop()⁠ is a relatively low-level operation, it is recommended to build graphs using %>>%.

• add_edge(src_id, dst_id, src_channel = NULL, dst_channel = NULL)
  (character(1), character(1), character(1) | numeric(1) | NULL, character(1) | numeric(1) | NULL) -> self
  Add an edge from PipeOp src_id, and its channel src_channel (identified by its name or number as listed in the PipeOp's ⁠$output⁠), to PipeOp dst_id's channel dst_channel (identified by its name or number as listed in the PipeOp's ⁠$input⁠). If source or destination PipeOp have only one input / output channel and src_channel / dst_channel are therefore unambiguous, they can be omitted (i.e. left as NULL).

• chain(gs, clone = TRUE)
  (list of Graphs, logical(1)) -> self
  Takes a list of Graphs or PipeOps (or objects that can be automatically converted into Graphs or PipeOps, see as_graph() and as_pipeop()) as inputs and joins them in a serial Graph coming after self, as if connecting them using %>>%.

• plot(html = FALSE, horizontal = FALSE)
  (logical(1), logical(1)) -> NULL
  Plot the Graph, using either the igraph package (for html = FALSE, default) or the visNetwork package for html = TRUE producing a htmlWidget. The htmlWidget can be rescaled using visOptions. For html = FALSE, the orientation of the plotted graph can be controlled through horizontal.

• print(dot = FALSE, dotname = "dot", fontsize = 24L)
  (logical(1), character(1), integer(1)) -> NULL
  Print a representation of the Graph on the console. If dot is FALSE, output is a table with one row for each contained PipeOp and columns ID (⁠$id⁠ of PipeOp), State (short representation of ⁠$state⁠ of PipeOp), sccssors (PipeOps that take their input directly from the PipeOp on this line), and prdcssors (the PipeOps that produce the data that is read as input by the PipeOp on this line). If dot is TRUE, print a DOT representation of the Graph on the console. The DOT output can be named via the argument dotname and the fontsize can also be specified.

• set_names(old, new)
  (character, character) -> self
  Rename PipeOps: Change ID of each PipeOp as identified by old to the corresponding item in new. This should be used instead of changing a PipeOp's ⁠$id⁠ value directly!

• update_ids(prefix = "", postfix = "")
  (character, character) -> self
  Pre- or postfix PipeOp's existing ids. Both prefix and postfix default to "", i.e. no changes.

• train(input, single_input = TRUE)
  (any, logical(1)) -> named list
  Train Graph by traversing the Graphs' edges and calling all the PipeOp's ⁠$train⁠ methods in turn. Return a named list of outputs for each unconnected PipeOp out-channel, named according to the Graph's ⁠$output⁠ name column. During training, the ⁠$state⁠ member of each PipeOps will be set and the ⁠$is_trained⁠ slot of the Graph (and each individual PipeOp) will consequently be set to TRUE.
  If single_input is TRUE, the input value will be sent to each unconnected PipeOp's input channel (as listed in the Graph's ⁠$input⁠). Typically, input should be a Task, although this is dependent on the PipeOps in the Graph. If single_input is FALSE, then input should be a list with the same length as the Graph's ⁠$input⁠ table has rows; each list item will be sent to a corresponding input channel of the Graph. If input is a named list, names must correspond to input channel names (⁠$input$name⁠) and inputs will be sent to the channels by name; otherwise they will be sent to the channels in order in which they are listed in ⁠$input⁠.

• predict(input, single_input = TRUE)
  (any, logical(1)) -> list of any
  Predict with the Graph by calling all the PipeOp's ⁠$train⁠ methods. Input and output, as well as the function of the single_input argument, are analogous to ⁠$train()⁠.

• help(help_type)
  (character(1)) -> help file
  Displays the help file of the concrete PipeOp instance. help_type is one of "text", "html", "pdf" and behaves as the help_type argument of R's help().

See Also

Other mlr3pipelines backend related: PipeOp, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_graphs, mlr_pipeops, mlr_pipeops_updatetarget

Examples

library("mlr3")

g = Graph$new()$
  add_pipeop(PipeOpScale$new(id = "scale"))$
  add_pipeop(PipeOpPCA$new(id = "pca"))$
  add_edge("scale", "pca")
g$input
g$output

task = tsk("iris")
trained = g$train(task)
trained[[1]]$data()

task$filter(1:10)
predicted = g$predict(task)
predicted[[1]]$data()

Description

Create a new Graph containing n copies of the input Graph / PipeOp. To avoid ID collisions, PipeOp IDs are suffixed with ⁠_i⁠ where i ranges from 1 to n.

This function is deprecated and will be removed in the next version in favor of using pipeline_greplicate / ppl("greplicate").

Usage

greplicate(graph, n)

Arguments

Value

Graph containing n copies of input graph.

See Also

Other Graph operators: %>>%(), as_graph(), as_pipeop(), assert_graph(), assert_pipeop(), chain_graphs(), gunion(), mlr_graphs_greplicate

Description

Takes an arbitrary amount of Graphs or PipeOps (or objects that can be automatically converted into Graphs or PipeOps, see as_graph() and as_pipeop()) as inputs and joins them in a new Graph.

The PipeOps of the input Graphs are not joined with new edges across Graphs, so if length(graphs) > 1, the resulting Graph will be disconnected.

This operation always creates deep copies of its input arguments, so they cannot be modified by reference afterwards. To access individual PipeOps after composition, use the resulting Graph's ⁠$pipeops⁠ list.

Usage

gunion(graphs, in_place = FALSE)

Arguments

Value

Graph the resulting Graph.

See Also

Other Graph operators: %>>%(), as_graph(), as_pipeop(), assert_graph(), assert_pipeop(), chain_graphs(), greplicate(), mlr_graphs_greplicate

Description

Test whether a given object is a NO_OP.

Usage

is_noop(x)

Arguments

Value

logical(1): Whether x is a NO_OP.

See Also

Other Path Branching: NO_OP, filter_noop(), mlr_pipeops_branch, mlr_pipeops_unbranch

Description

Check if an object is a Multiplicity.

Usage

is.Multiplicity(x)

Arguments

Value

logical(1)

Description

A simple Dictionary storing objects of class Graph. The dictionary contains a collection of often-used graph structures, and it's aim is solely to make often-used functions more accessible. Each Graph has an associated help page, which can be accessed via ⁠?mlr_graphs_<key>⁠, i.e. ?mlr_graphs_bagging.

Format

R6Class object inheriting from mlr3misc::Dictionary.

Methods

Methods inherited from Dictionary, as well as:

• add(key, value)
  (character(1), function)
  Adds constructor value to the dictionary with key key, potentially overwriting a previously stored item.

S3 methods

• as.data.table(dict)
Dictionary -> data.table::data.table
  Returns a data.table with column key (character).

See Also

Other mlr3pipelines backend related: Graph, PipeOp, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_updatetarget

Other Dictionaries: mlr_pipeops

Examples

library(mlr3)
lrn = lrn("regr.rpart")
task = mlr_tasks$get("boston_housing")

# Robustify the learner for the task.
gr = pipeline_robustify(task, lrn) %>>% po("learner", lrn)
# or equivalently
gr = mlr_graphs$get("robustify", task = task, learner = lrn) %>>% po(lrn)
# or equivalently
gr = ppl("robustify", task, lrn) %>>% po("learner", lrn)

# all Graphs currently in the dictionary:
as.data.table(mlr_graphs)

Description

Creates a Graph that performs bagging for a supplied graph. This is done as follows:

• Subsample the data in each step using PipeOpSubsample, afterwards apply graph

• Replicate this step iterations times (in parallel via multiplicities)

• Average outputs of replicated graphs predictions using the averager (note that setting collect_multipliciy = TRUE is required)

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_bagging(
  graph,
  iterations = 10,
  frac = 0.7,
  averager = NULL,
  replace = FALSE
)

Arguments

Value

Graph

Examples

library(mlr3)
lrn_po = po("learner", lrn("regr.rpart"))
task = mlr_tasks$get("boston_housing")
gr = pipeline_bagging(lrn_po, 3, averager = po("regravg", collect_multiplicity = TRUE))
resample(task, GraphLearner$new(gr), rsmp("holdout"))$aggregate()

# The original bagging method uses boosting by sampling with replacement.
gr = ppl("bagging", lrn_po, frac = 1, replace = TRUE,
  averager = po("regravg", collect_multiplicity = TRUE))
resample(task, GraphLearner$new(gr), rsmp("holdout"))$aggregate()

Description

Create a multiplexed graph.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_branch(graphs, prefix_branchops = "", prefix_paths = FALSE)

Arguments

Value

Graph

Examples

library("mlr3")

po_pca = po("pca")
po_nop = po("nop")

branches = pipeline_branch(list(pca = po_pca, nothing = po_nop))
# gives the same as
branches = c("pca", "nothing")
po("branch", branches) %>>%
  gunion(list(po_pca, po_nop)) %>>%
  po("unbranch", branches)

pipeline_branch(list(pca = po_pca, nothing = po_nop),
  prefix_branchops = "br_", prefix_paths = "xy_")
# gives the same as
po("branch", branches, id = "br_branch") %>>%
  gunion(list(xy_pca = po_pca, xy_nothing = po_nop)) %>>%
  po("unbranch", branches, id = "br_unbranch")

Description

Converts all columns of type type_from to type_to, using the corresponding R function (e.g. as.numeric(), as.factor()). It is possible to further subset the columns that should be affected using the affect_columns argument. The resulting Graph contains a PipeOpColApply, followed, if appropriate, by a PipeOpFixFactors.

Unlike R's as.factor() function, ppl("convert_types") will convert ordered types into (unordered) factor vectors.

Usage

pipeline_convert_types(
  type_from,
  type_to,
  affect_columns = NULL,
  id = NULL,
  fixfactors = NULL,
  more_args = list()
)

Arguments

Value

Graph

Examples

library("mlr3")

data_chr = data.table::data.table(
  x = factor(letters[1:3]),
  y = letters[1:3],
  z = letters[1:3]
)
task_chr = TaskClassif$new("task_chr", data_chr, "x")
str(task_chr$data())

graph = ppl("convert_types", "character", "factor")
str(graph$train(task_chr)[[1]]$data())

graph_z = ppl("convert_types", "character", "factor",
  affect_columns = selector_name("z"))
graph_z$train(task_chr)[[1]]$data()

# `affect_columns` and `type_from` are both applied. The following
# looks for a 'numeric' column with name 'z', which is not present;
# the task is therefore unchanged.
graph_z = ppl("convert_types", "numeric", "factor",
  affect_columns = selector_name("z"))
graph_z$train(task_chr)[[1]]$data()

Description

Create a new Graph containing n copies of the input Graph / PipeOp. To avoid ID collisions, PipeOp IDs are suffixed with ⁠_i⁠ where i ranges from 1 to n.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_greplicate(graph, n)

Arguments

Value

Graph containing n copies of input graph.

See Also

Other Graph operators: %>>%(), as_graph(), as_pipeop(), assert_graph(), assert_pipeop(), chain_graphs(), greplicate(), gunion()

Examples

library("mlr3")

po_pca = po("pca")
pipeline_greplicate(po_pca, n = 2)

Description

Create a new Graph for a classification Task to perform "One vs. Rest" classification.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_ovr(graph)

Arguments

Value

Graph

Examples

library("mlr3")

task = tsk("wine")

learner = lrn("classif.rpart")
learner$predict_type = "prob"

# Simple OVR
g1 = pipeline_ovr(learner)
g1$train(task)
g1$predict(task)

# Bagged Learners
gr = po("replicate", reps = 3) %>>%
  po("subsample") %>>%
  learner %>>%
  po("classifavg", collect_multiplicity = TRUE)
g2 = pipeline_ovr(gr)
g2$train(task)
g2$predict(task)

# Bagging outside OVR
g3 = po("replicate", reps = 3) %>>%
  pipeline_ovr(po("subsample") %>>% learner) %>>%
  po("classifavg", collect_multiplicity = TRUE)
g3$train(task)
g3$predict(task)

Description

Creates a Graph that can be used to robustify any subsequent learner. Performs the following steps:

• Drops empty factor levels using PipeOpFixFactors

• Imputes numeric features using PipeOpImputeHist and PipeOpMissInd

• Imputes factor features using PipeOpImputeOOR

• Encodes factors using one-hot-encoding. Factors with a cardinality > max_cardinality are collapsed using PipeOpCollapseFactors

The graph is built conservatively, i.e. the function always tries to assure everything works. If a learner is provided, some steps can be left out, i.e. if the learner can deal with factor variables, no encoding is performed.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_robustify(
  task = NULL,
  learner = NULL,
  impute_missings = NULL,
  factors_to_numeric = NULL,
  max_cardinality = 1000,
  ordered_action = "factor",
  character_action = "factor",
  POSIXct_action = "numeric"
)

Arguments

Value

Graph

Examples

library(mlr3)
lrn = lrn("regr.rpart")
task = mlr_tasks$get("boston_housing")
gr = pipeline_robustify(task, lrn) %>>% po("learner", lrn)
resample(task, GraphLearner$new(gr), rsmp("holdout"))

Description

Create a new Graph for stacking. A stacked learner uses predictions of several base learners and fits a super learner using these predictions as features in order to predict the outcome.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_stacking(
  base_learners,
  super_learner,
  method = "cv",
  folds = 3,
  use_features = TRUE
)

Arguments

Value

Graph

Examples

library(mlr3)
library(mlr3learners)

base_learners = list(
  lrn("classif.rpart", predict_type = "prob"),
  lrn("classif.kknn", predict_type = "prob")
)
super_learner = lrn("classif.log_reg")

graph_stack = pipeline_stacking(base_learners, super_learner)
graph_learner = as_learner(graph_stack)
graph_learner$train(tsk("german_credit"))

Description

Wraps a Graph that transforms a target during training and inverts the transformation during prediction. This is done as follows:

• Specify a transformation and inversion function using any subclass of PipeOpTargetTrafo, defaults to PipeOpTargetMutate, afterwards apply graph.

• At the very end, during prediction the transformation is inverted using PipeOpTargetInvert.

• To set a transformation and inversion function for PipeOpTargetMutate see the parameters trafo and inverter of the param_set of the resulting Graph.

• Note that the input graph is not explicitly checked to actually return a Prediction during prediction.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_targettrafo(
  graph,
  trafo_pipeop = PipeOpTargetMutate$new(),
  id_prefix = ""
)

Arguments

Value

Graph

Examples

library("mlr3")

tt = pipeline_targettrafo(PipeOpLearner$new(LearnerRegrRpart$new()))
tt$param_set$values$targetmutate.trafo = function(x) log(x, base = 2)
tt$param_set$values$targetmutate.inverter = function(x) list(response = 2 ^ x$response)

# gives the same as
g = Graph$new()
g$add_pipeop(PipeOpTargetMutate$new(param_vals = list(
  trafo = function(x) log(x, base = 2),
  inverter = function(x) list(response = 2 ^ x$response))
  )
)
g$add_pipeop(LearnerRegrRpart$new())
g$add_pipeop(PipeOpTargetInvert$new())
g$add_edge(src_id = "targetmutate", dst_id = "targetinvert",
  src_channel = 1, dst_channel = 1)
g$add_edge(src_id = "targetmutate", dst_id = "regr.rpart",
  src_channel = 2, dst_channel = 1)
g$add_edge(src_id = "regr.rpart", dst_id = "targetinvert",
  src_channel = 1, dst_channel = 2)

Description

Computes a weighted average of inputs. Used in the context of computing weighted averages of predictions.

Predictions are averaged using weights (in order of appearance in the data) which are optimized using nonlinear optimization from the package nloptr for a measure provided in measure. (defaults to classif.ce for LearnerClassifAvg and regr.mse for LearnerRegrAvg). Learned weights can be obtained from ⁠$model⁠. This Learner implements and generalizes an approach proposed in LeDell (2015) that uses non-linear optimization in order to learn base-learner weights that optimize a given performance metric (e.g AUC). The approach is similar but not exactly the same as the one implemented as AUC in the SuperLearner R package (when metric is "classif.auc"). For a more detailed analysis and the general idea, the reader is referred to LeDell (2015).

Note, that weights always sum to 1 by division by sum(weights) before weighting incoming features.

Usage

mlr_learners_classif.avg

mlr_learners_regr.avg

Format

R6Class object inheriting from mlr3::LearnerClassif/mlr3::Learner.

Parameters

The parameters are the parameters inherited from LearnerClassif, as well as:

• measure :: Measure | character
Measure to optimize for. Will be converted to a Measure in case it is character. Initialized to "classif.ce", i.e. misclassification error for classification and "regr.mse", i.e. mean squared error for regression.

• optimizer :: Optimizer | character(1)
Optimizer used to find optimal thresholds. If character, converts to Optimizer via opt. Initialized to OptimizerNLoptr. Nloptr hyperparameters are initialized to xtol_rel = 1e-8, algorithm = "NLOPT_LN_COBYLA" and equal initial weights for each learner. For more fine-grained control, it is recommended to supply a instantiated Optimizer.

• log_level :: character(1) | integer(1)
  Set a temporary log-level for lgr::get_logger("bbotk"). Initialized to: "warn".

Methods

• ⁠LearnerClassifAvg$new(), id = "classif.avg")⁠
  (chr) -> self
  Constructor.

• ⁠LearnerRegrAvg$new(), id = "regr.avg")⁠
  (chr) -> self
  Constructor.

References

LeDell, Erin (2015). Scalable Ensemble Learning and Computationally Efficient Variance Estimation. Ph.D. thesis, UC Berkeley.

See Also

Other Learners: mlr_learners_graph

Other Ensembles: PipeOpEnsemble, mlr_pipeops_classifavg, mlr_pipeops_ovrunite, mlr_pipeops_regravg

Description

A Learner that encapsulates a Graph to be used in mlr3 resampling and benchmarks.

The Graph must return a single Prediction on its ⁠$predict()⁠ call. The result of the ⁠$train()⁠ call is discarded, only the internal state changes during training are used.

The predict_type of a GraphLearner can be obtained or set via it's predict_type active binding. Setting a new predict type will try to set the predict_type in all relevant PipeOp / Learner encapsulated within the Graph. Similarly, the predict_type of a Graph will always be the smallest denominator in the Graph.

A GraphLearner is always constructed in an untrained state. When the graph argument has a non-NULL ⁠$state⁠, it is ignored.

Format

R6Class object inheriting from mlr3::Learner.

Construction

GraphLearner$new(graph, id = NULL, param_vals = list(), task_type = NULL, predict_type = NULL)

• graph :: Graph | PipeOp
Graph to wrap. Can be a PipeOp, which is automatically converted to a Graph. This argument is usually cloned, unless clone_graph is FALSE; to access the Graph inside GraphLearner by-reference, use ⁠$graph⁠.
  

• id :: character(1) Identifier of the resulting Learner.

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings . Default list().

• task_type :: character(1)
  What task_type the GraphLearner should have; usually automatically inferred for Graphs that are simple enough.

• predict_type :: character(1)
  What predict_type the GraphLearner should have; usually automatically inferred for Graphs that are simple enough.

• clone_graph :: logical(1)
  Whether to clone graph upon construction. Unintentionally changing graph by reference can lead to unexpected behaviour, so TRUE (default) is recommended. In particular, note that the ⁠$state⁠ of ⁠$graph⁠ is set to NULL by reference on construction of GraphLearner, during ⁠$train()⁠, and during ⁠$predict()⁠ when clone_graph is FALSE.

Fields

Fields inherited from Learner, as well as:

• graph :: Graph
Graph that is being wrapped. This field contains the prototype of the Graph that is being trained, but does not contain the model. Use graph_model to access the trained Graph after ⁠$train()⁠. Read-only.

• graph_model :: Learner
Graph that is being wrapped. This Graph contains a trained state after ⁠$train()⁠. Read-only.

• pipeops :: named list of PipeOp
  Contains all PipeOps in the underlying Graph, named by the PipeOp's ⁠$id⁠s. Shortcut for ⁠$graph_model$pipeops⁠. See Graph for details.

• edges :: data.table with columns src_id (character), src_channel (character), dst_id (character), dst_channel (character)
  Table of connections between the PipeOps in the underlying Graph. Shortcut for ⁠$graph$edges⁠. See Graph for details.

• param_set :: ParamSet
  Parameters of the underlying Graph. Shortcut for ⁠$graph$param_set⁠. See Graph for details.

• pipeops_param_set :: named list()
  Named list containing the ParamSets of all PipeOps in the Graph. See there for details.

• pipeops_param_set_values :: named list()
  Named list containing the set parameter values of all PipeOps in the Graph. See there for details.

• internal_tuned_values :: named list() or NULL
  The internal tuned parameter values collected from all PipeOps. NULL is returned if the learner is not trained or none of the wrapped learners supports internal tuning.

• internal_valid_scores :: named list() or NULL
  The internal validation scores as retrieved from the PipeOps. The names are prefixed with the respective IDs of the PipeOps. NULL is returned if the learner is not trained or none of the wrapped learners supports internal validation.

• validate :: numeric(1), "predefined", "test" or NULL
  How to construct the validation data. This also has to be configured for the individual PipeOps such as PipeOpLearner, see set_validate.GraphLearner. For more details on the possible values, see mlr3::Learner.

• marshaled :: logical(1)
  Whether the learner is marshaled.

• impute_selected_features :: logical(1)
  Whether to heuristically determine ⁠$selected_features()⁠ as all ⁠$selected_features()⁠ of all "base learner" Learners, even if they do not have the "selected_features" property / do not implement ⁠$selected_features()⁠. If impute_selected_features is TRUE and the base learners do not implement ⁠$selected_features()⁠, the GraphLearner's ⁠$selected_features()⁠ method will return all features seen by the base learners. This is useful in cases where feature selection is performed inside the Graph: The ⁠$selected_features()⁠ will then be the set of features that were selected by the Graph. If impute_selected_features is FALSE, the ⁠$selected_features()⁠ method will throw an error if ⁠$selected_features()⁠ is not implemented by the base learners.
  This is a heuristic and may report more features than actually used by the base learners, in cases where the base learners do not implement ⁠$selected_features()⁠. The default is FALSE.

Methods

Methods inherited from Learner, as well as:

• ids(sorted = FALSE)
  (logical(1)) -> character
  Get IDs of all PipeOps. This is in order that PipeOps were added if sorted is FALSE, and topologically sorted if sorted is TRUE.

• plot(html = FALSE, horizontal = FALSE)
  (logical(1), logical(1)) -> NULL
  Plot the Graph, using either the igraph package (for html = FALSE, default) or the visNetwork package for html = TRUE producing a htmlWidget. The htmlWidget can be rescaled using visOptions. For html = FALSE, the orientation of the plotted graph can be controlled through horizontal.

• marshal
  (any) -> self
  Marshal the model.

• unmarshal
  (any) -> self
  Unmarshal the model.

• base_learner(recursive = Inf, return_po = FALSE, return_all = FALSE, resolve_branching = TRUE)
  (numeric(1), logical(1), logical(1), character(1)) -> Learner | PipeOp | list of Learner | list of PipeOp
  Return the base learner of the GraphLearner. If recursive is 0, the GraphLearner itself is returned. Otherwise, the Graph is traversed backwards to find the first PipeOp containing a ⁠$learner_model⁠ field. If recursive is 1, that ⁠$learner_model⁠ (or containing PipeOp, if return_po is TRUE) is returned. If recursive is greater than 1, the discovered base learner's base_learner() method is called with recursive - 1. recursive must be set to 1 if return_po is TRUE, and must be set to at most 1 if return_all is TRUE.
  If return_po is TRUE, the container-PipeOp is returned instead of the Learner. This will typically be a PipeOpLearner or a PipeOpLearnerCV.
  If return_all is TRUE, a list of Learners or PipeOps is returned. If return_po is FALSE, this list may contain Multiplicity objects, which are not unwrapped. If return_all is FALSE and there are multiple possible base learners, an error is thrown. This may also happen if only a single PipeOpLearner is present that was trained with a Multiplicity.
  If resolve_branching is TRUE, and when a PipeOpUnbranch is encountered, the corresponding PipeOpBranch is searched, and its hyperparameter configuration is used to select the base learner. There may be multiple corresponding PipeOpBranchs, which are all considered. If resolve_branching is FALSE, PipeOpUnbranch is treated as any other PipeOp with multiple inputs; all possible branch paths are considered equally.

The following standard extractors as defined by the Learner class are available. Note that these typically only extract information from the ⁠$base_learner()⁠. This works well for simple Graphs that do not modify features too much, but may give unexpected results for Graphs that add new features or move information between features.

As an example, consider a feature A with missing values, and a feature B that is used for imputation, using a po("imputelearner"). In a case where the following Learner performs embedded feature selection and only selects feature A, the selected_features() method could return only feature A, and ⁠$importance()⁠ may even report 0 for feature B. This would not be entirely accurate when considering the entire GraphLearner, as feature B is used for imputation and would therefore have an impact on predictions. The following should therefore only be used if the Graph is known to not have an impact on the relevant properties.

• importance()
  () -> numeric
  The ⁠$importance()⁠ returned by the base learner, if it has the ⁠"importance⁠ property. Throws an error otherwise.

• selected_features()
  () -> character
  The ⁠$selected_features()⁠ returned by the base learner, if it has the ⁠"selected_features⁠ property. If the base learner does not have the "selected_features" property and impute_selected_features is TRUE, all features seen by the base learners are returned. Throws an error otherwise.

• oob_error()
  () -> numeric(1)
  The ⁠$oob_error()⁠ returned by the base learner, if it has the ⁠"oob_error⁠ property. Throws an error otherwise.

• loglik()
  () -> numeric(1)
  The ⁠$loglik()⁠ returned by the base learner, if it has the ⁠"loglik⁠ property. Throws an error otherwise.

Internals

as_graph() is called on the graph argument, so it can technically also be a list of things, which is automatically converted to a Graph via gunion(); however, this will usually not result in a valid Graph that can work as a Learner. graph can furthermore be a Learner, which is then automatically wrapped in a Graph, which is then again wrapped in a GraphLearner object; this usually only adds overhead and is not recommended.

See Also

Other Learners: mlr_learners_avg

Examples

library("mlr3")

graph = po("pca") %>>% lrn("classif.rpart")

lr = GraphLearner$new(graph)
lr = as_learner(graph)  # equivalent

lr$train(tsk("iris"))

lr$graph$state  # untrained version!
# The following is therefore NULL:
lr$graph$pipeops$classif.rpart$learner_model$model

# To access the trained model from the PipeOpLearner's Learner, use:
lr$graph_model$pipeops$classif.rpart$learner_model$model

# Feature importance (of principal components):
lr$graph_model$pipeops$classif.rpart$learner_model$importance()

Description

A simple Dictionary storing objects of class PipeOp. Each PipeOp has an associated help page, see mlr_pipeops_[id].

Format

R6Class object inheriting from mlr3misc::Dictionary.

Fields

Fields inherited from Dictionary, as well as:

• metainf :: environment
  Environment that stores the metainf argument of the ⁠$add()⁠ method. Only for internal use.

Methods

Methods inherited from Dictionary, as well as:

• add(key, value, metainf = NULL)
  (character(1), R6ClassGenerator, NULL | list)
  Adds constructor value to the dictionary with key key, potentially overwriting a previously stored item. If metainf is not NULL (the default), it must be a list of arguments that will be given to the value constructor (i.e. value$new()) when it needs to be constructed for as.data.table PipeOp listing.

S3 methods

• as.data.table(dict)
Dictionary -> data.table::data.table
  Returns a data.table with columns key (character), packages (character), input.num (integer), output.num (integer), input.type.train (character), input.type.predict (character), output.type.train (character), output.type.predict (character).

See Also

Other mlr3pipelines backend related: Graph, PipeOp, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_graphs, mlr_pipeops_updatetarget

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Other Dictionaries: mlr_graphs

Examples

library("mlr3")

mlr_pipeops$get("learner", lrn("classif.rpart"))

# equivalent:
po("learner", learner = lrn("classif.rpart"))

# all PipeOps currently in the dictionary:
as.data.table(mlr_pipeops)[, c("key", "input.num", "output.num", "packages")]

Description

Generates a more balanced data set by creating synthetic instances of the minority class using the ADASYN algorithm.

The algorithm generates for each minority instance new data points based on its K nearest neighbors and the difficulty of learning for that data point. It can only be applied to tasks with numeric features that have no missing values.

See smotefamily::ADAS for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpADAS$new(id = "adas", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "smote".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output during training is the input Task with added synthetic rows for the minority class. The output during prediction is the unchanged input.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• K :: numeric(1)
  The number of nearest neighbors used for sampling new values. Default is 5. See ADAS().

Fields

Only fields inherited from PipeOpTaskPreproc/PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

He H, Bai Y, Garcia, A. E, Li S (2008). “ADASYN: Adaptive synthetic sampling approach for imbalanced learning.” In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322-1328. doi:10.1109/IJCNN.2008.4633969.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

# Create example task
data = data.frame(
  target = factor(sample(c("c1", "c2"), size = 300, replace = TRUE, prob = c(0.1, 0.9))),
  x1 = rnorm(300),
  x2 = rnorm(300)
)
task = TaskClassif$new(id = "example", backend = data, target = "target")
task$head()
table(task$data(cols = "target"))

# Generate synthetic data for minority class
pop = po("adas")
adas_result = pop$train(list(task))[[1]]$data()
nrow(adas_result)
table(adas_result$target)

Description

Adds new data points by generating synthetic instances for the minority class using the Borderline-SMOTE algorithm. This can only be applied to classification tasks with numeric features that have no missing values. See smotefamily::BLSMOTE for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpBLSmote$new(id = "blsmote", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "smote".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output during training is the input Task with added synthetic rows for the minority class. The output during prediction is the unchanged input.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• K :: numeric(1)
  The number of nearest neighbors used for sampling from the minority class. Default is 5. See BLSMOTE().

• C :: numeric(1)
  The number of nearest neighbors used for classifying sample points as SAFE/DANGER/NOISE. Default is 5. See BLSMOTE().

• dup_size :: numeric
  Desired times of synthetic minority instances over the original number of majority instances. 0 leads to balancing minority and majority class. Default is 0. See BLSMOTE().

• method :: character(1)
  The type of Borderline-SMOTE algorithm to use. Default is "type1". See BLSMOTE().

• quiet :: logical(1)
  Whether to suppress printing status during training. Initialized to TRUE.

Fields

Only fields inherited from PipeOpTaskPreproc/PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

Han, Hui, Wang, Wen-Yuan, Mao, Bing-Huan (2005). “Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning.” In Huang, De-Shuang, Zhang, Xiao-Ping, Huang, Guang-Bin (eds.), Advances in Intelligent Computing, 878–887. ISBN 978-3-540-31902-3, doi:10.1007/11538059_91.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

# Create example task
data = smotefamily::sample_generator(500, 0.8)
data$result = factor(data$result)
task = TaskClassif$new(id = "example", backend = data, target = "result")
task$head()
table(task$data(cols = "result"))

# Generate synthetic data for minority class
pop = po("blsmote")
bls_result = pop$train(list(task))[[1]]$data()
nrow(bls_result)
table(bls_result$result)

Description

Conducts a Box-Cox transformation on numeric features. The lambda parameter of the transformation is estimated during training and used for both training and prediction transformation. See bestNormalize::boxcox() for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpBoxCox$new(id = "boxcox", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "boxcox".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected numeric features replaced by their transformed versions.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as a list of class boxcox for each column, which is transformed.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• standardize :: logical(1)
  Whether to center and scale the transformed values to attempt a standard normal distribution. For details see boxcox().

• eps :: numeric(1)
  Tolerance parameter to identify if lambda parameter is equal to zero. For details see boxcox().

• lower :: numeric(1)
  Lower value for estimation of lambda parameter. For details see boxcox().

• upper :: numeric(1)
  Upper value for estimation of lambda parameter. For details see boxcox().

Internals

Uses the bestNormalize::boxcox function.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

task = tsk("iris")
pop = po("boxcox")

task$data()
pop$train(list(task))[[1]]$data()

pop$state

Description

Perform alternative path branching: PipeOpBranch has multiple output channels that connect to different paths in a Graph. At any time, only one of these paths will be taken for execution. At the end of the different paths, the PipeOpUnbranch PipeOp must be used to indicate the end of alternative paths.

Not to be confused with PipeOpCopy, the naming scheme is a bit unfortunate.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpBranch$new(options, id = "branch", param_vals = list())

• options :: numeric(1) | character
  If options is an integer number, it determines the number of output channels / options that are created, named output1...⁠output<n>⁠. The ⁠$selection⁠ parameter will then be an integer. If options is a character, it determines the names of channels directly. The ⁠$selection⁠ parameter will then be factorial.

• id :: character(1)
  Identifier of resulting object, default "branch".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

PipeOpBranch has one input channel named "input", taking any input ("*") both during training and prediction.

PipeOpBranch has multiple output channels depending on the options construction argument, named "output1", "output2", ... if options is numeric, and named after each options value if options is a character. All output channels produce the object given as input ("*") or NO_OP, both during training and prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

• selection :: numeric(1) | character(1)
  Selection of branching path to take. Is a ParamInt if the options parameter during construction was a numeric(1), and ranges from 1 to options. Is a ParamFct if the options parameter was a character and its possible values are the options values. Initialized to either 1 (if the options construction argument is numeric(1)) or the first element of options (if it is character).

Internals

Alternative path branching is handled by the PipeOp backend. To indicate that a path should not be taken, PipeOpBranch returns the NO_OP object on its output channel. The PipeOp handles each NO_OP input by automatically returning a NO_OP output without calling private$.train() or private$.predict(), until PipeOpUnbranch is reached. PipeOpUnbranch will then take multiple inputs, all except one of which must be a NO_OP, and forward the only non-NO_OP object on its output.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Other Path Branching: NO_OP, filter_noop(), is_noop(), mlr_pipeops_unbranch

Examples

library("mlr3")

pca = po("pca")
nop = po("nop")
choices = c("pca", "nothing")
gr = po("branch", choices) %>>%
  gunion(list(pca, nop)) %>>%
  po("unbranch", choices)

gr$param_set$values$branch.selection = "pca"
gr$train(tsk("iris"))

gr$param_set$values$branch.selection = "nothing"
gr$train(tsk("iris"))

Description

Chunks its input into outnum chunks. Creates outnum Tasks during training, and simply passes on the input during outnum times during prediction.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpChunk$new(outnum, id = "chunk", param_vals = list())

• outnum :: numeric(1)
  Number of output channels, and therefore number of chunks created.

• id :: character(1)
  Identifier of resulting object, default "chunk".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output

PipeOpChunk has one input channel named "input", taking a Task both during training and prediction.

PipeOpChunk has multiple output channels depending on the options construction argument, named "output1", "output2", ... All output channels produce (respectively disjoint, random) subsets of the input Task during training, and pass on the original Task during prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

• shuffle :: logical(1)
  Should the data be shuffled before chunking? Initialized to TRUE.

Internals

Uses the mlr3misc::chunk_vector() function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

task = tsk("wine")
opc = mlr_pipeops$get("chunk", 2)

# watch the row number: 89 during training (task is chunked)...
opc$train(list(task))

# ... 178 during predict (task is copied)
opc$predict(list(task))

Description

Both undersamples a Task to keep only a fraction of the rows of the majority class, as well as oversamples (repeats data points) rows of the minority class.

Sampling happens only during training phase. Class-balancing a Task by sampling may be beneficial for classification with imbalanced training data.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpClassBalancing$new(id = "classbalancing", param_vals = list())

• id :: character(1) Identifier of the resulting object, default "classbalancing"

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc. Instead of a Task, a TaskClassif is used as input and output during training and prediction.

The output during training is the input Task with added or removed rows to balance target classes. The output during prediction is the unchanged input.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc; however, the affect_columns parameter is not present. Further parameters are:

• ratio :: numeric(1)
  Ratio of number of rows of classes to keep, relative to the ⁠$reference⁠ value. Initialized to 1.

• reference :: numeric(1)
  What the ⁠$ratio⁠ value is measured against. Can be "all" (mean instance count of all classes), "major" (instance count of class with most instances), "minor" (instance count of class with fewest instances), "nonmajor" (average instance count of all classes except the major one), "nonminor" (average instance count of all classes except the minor one), and "one" (⁠$ratio⁠ determines the number of instances to have, per class). Initialized to "all".

• adjust :: numeric(1)
  Which classes to up / downsample. Can be "all" (up and downsample all to match required instance count), "major", "minor", "nonmajor", "nonminor" (see respective values for ⁠$reference⁠), "upsample" (only upsample), and "downsample". Initialized to "all".

• shuffle :: logical(1)
  Whether to shuffle the rows of the resulting task. In case the data is upsampled and shuffle = FALSE, the resulting task will have the original rows (which were not removed in downsampling) in the original order, followed by all newly added rows ordered by target class. Initialized to TRUE.

Internals

Up / downsampling happens as follows: At first, a "target class count" is calculated, by taking the mean class count of all classes indicated by the reference parameter (e.g. if reference is "nonmajor": the mean class count of all classes that are not the "major" class, i.e. the class with the most samples) and multiplying this with the value of the ratio parameter. If reference is "one", then the "target class count" is just the value of ratio (i.e. 1 * ratio).

Then for each class that is referenced by the adjust parameter (e.g. if adjust is "nonminor": each class that is not the class with the fewest samples), PipeOpClassBalancing either throws out samples (downsampling), or adds additional rows that are equal to randomly chosen samples (upsampling), until the number of samples for these classes equals the "target class count".

Uses task$filter() to remove rows. When identical rows are added during upsampling, then the task$row_roles$use can not be used to duplicate rows because of [inaudible]; instead the task$rbind() function is used, and a new data.table is attached that contains all rows that are being duplicated exactly as many times as they are being added.

Fields

Only fields inherited from PipeOpTaskPreproc/PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

task = tsk("spam")
opb = po("classbalancing")

# target class counts
table(task$truth())

# double the instances in the minority class (spam)
opb$param_set$values = list(ratio = 2, reference = "minor",
  adjust = "minor", shuffle = FALSE)
result = opb$train(list(task))[[1L]]
table(result$truth())

# up or downsample all classes until exactly 20 per class remain
opb$param_set$values = list(ratio = 20, reference = "one",
  adjust = "all", shuffle = FALSE)
result = opb$train(list(task))[[1]]
table(result$truth())

Description

Perform (weighted) majority vote prediction from classification Predictions by connecting PipeOpClassifAvg to multiple PipeOpLearner outputs.

Always returns a "prob" prediction, regardless of the incoming Learner's ⁠$predict_type⁠. The label of the class with the highest predicted probability is selected as the "response" prediction. If the Learner's ⁠$predict_type⁠ is set to "prob", the prediction obtained is also a "prob" type prediction with the probability predicted to be a weighted average of incoming predictions.

All incoming Learner's ⁠$predict_type⁠ must agree.

Weights can be set as a parameter; if none are provided, defaults to equal weights for each prediction. Defaults to equal weights for each model.

If '

Format

R6Class inheriting from PipeOpEnsemble/PipeOp.

Construction

PipeOpClassifAvg$new(innum = 0, collect_multiplicity = FALSE, id = "classifavg", param_vals = list())

• innum :: numeric(1)
  Determines the number of input channels. If innum is 0 (default), a vararg input channel is created that can take an arbitrary number of inputs.

• collect_multiplicity :: logical(1)
  If TRUE, the input is a Multiplicity collecting channel. This means, a Multiplicity input, instead of multiple normal inputs, is accepted and the members are aggregated. This requires innum to be 0. Default is FALSE.

• id :: character(1) Identifier of the resulting object, default "classifavg".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpEnsemble. Instead of a Prediction, a PredictionClassif is used as input and output during prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

The parameters are the parameters inherited from the PipeOpEnsemble.

Internals

Inherits from PipeOpEnsemble by implementing the private$weighted_avg_predictions() method.

Fields

Only fields inherited from PipeOpEnsemble/PipeOp.

Methods

Only methods inherited from PipeOpEnsemble/PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Other Multiplicity PipeOps: Multiplicity(), PipeOpEnsemble, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Other Ensembles: PipeOpEnsemble, mlr_learners_avg, mlr_pipeops_ovrunite, mlr_pipeops_regravg

Examples

library("mlr3")

# Simple Bagging
gr = ppl("greplicate",
  po("subsample") %>>%
  po("learner", lrn("classif.rpart")),
  n = 3
) %>>%
  po("classifavg")

resample(tsk("iris"), GraphLearner$new(gr), rsmp("holdout"))

Description

Adds a class weight column to the Task that different Learners may be able to use for sample weighting. Sample weights are added to each sample according to the target class.

Only binary classification tasks are supported.

Caution: when constructed naively without parameter, the weights are all set to 1. The minor_weight parameter must be adjusted for this PipeOp to be useful.

Note this only sets the "weights_learner" column. It therefore influences the behaviour of subsequent Learners, but does not influence resampling or evaluation metric weights.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpClassWeights$new(id = "classweights", param_vals = list())

• id :: character(1) Identifier of the resulting object, default "classweights"

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc. Instead of a Task, a TaskClassif is used as input and output during training and prediction.

The output during training is the input Task with added weights column according to target class. The output during prediction is the unchanged input.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc; however, the affect_columns parameter is not present. Further parameters are:

• minor_weight :: numeric(1)
  Weight given to samples of the minor class. Major class samples have weight 1. Initialized to 1.

Internals

Introduces, or overwrites, the "weights" column in the Task. However, the Learner method needs to respect weights for this to have an effect.

The newly introduced column is named .WEIGHTS; there will be a naming conflict if this column already exists and is not a weight column itself.

Fields

Only fields inherited from PipeOpTaskPreproc/PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

task = tsk("spam")
opb = po("classweights")

# task weights
if ("weights_learner" %in% names(task)) {
  task$weights_learner  # recent mlr3-versions
} else {
  task$weights  # old mlr3-versions
}

# double the instances in the minority class (spam)
opb$param_set$values$minor_weight = 2
result = opb$train(list(task))[[1L]]
if ("weights_learner" %in% names(result)) {
  result$weights_learner  # recent mlr3-versions
} else {
  result$weights  # old mlr3-versions
}

Description

Applies a function to each column of a task. Use the affect_columns parameter inherited from PipeOpTaskPreprocSimple to limit the columns this function should be applied to. This can be used for simple parameter transformations or type conversions (e.g. as.numeric).

The same function is applied during training and prediction. One important relationship for machine learning preprocessing is that during the prediction phase, the preprocessing on each data row should be independent of other rows. Therefore, the applicator function should always return a vector / list where each result component only depends on the corresponding input component and not on other components. As a rule of thumb, if the function f generates output different from Vectorize(f), it is not a function that should be used for applicator.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpColApply$new(id = "colapply", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "colapply".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreprocSimple.

The output is the input Task with features changed according to the applicator parameter.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreprocSimple.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreprocSimple, as well as:

• applicator :: function
  Function to apply to each column of the task. The return value should be a vector of the same length as the input, i.e., the function vectorizes over the input. A typical example would be as.numeric.
  The return value can also be a matrix, data.frame, or data.table. In this case, the length of the input must match the number of returned rows. The names of the resulting features of the output Task is based on the (column) name(s) of the return value of the applicator function, prefixed with the original feature name separated by a dot (.). Use Vectorize to create a vectorizing function from any function that ordinarily only takes one element input.
  

Internals

Calls map on the data, using the value of applicator as f. and coerces the output via as.data.table.

Fields

Only fields inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_collapsefactors, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

task = tsk("iris")
poca = po("colapply", applicator = as.character)
poca$train(list(task))[[1]]  # types are converted

# function that does not vectorize
f1 = function(x) {
  # we could use `ifelse` here, but that is not the point
  if (x > 1) {
    "a"
  } else {
    "b"
  }
}
poca$param_set$values$applicator = Vectorize(f1)
poca$train(list(task))[[1]]$data()

# only affect Petal.* columns
poca$param_set$values$affect_columns = selector_grep("^Petal")
poca$train(list(task))[[1]]$data()

# function returning multiple columns
f2 = function(x) {
  cbind(floor = floor(x), ceiling = ceiling(x))
}
poca$param_set$values$applicator = f2
poca$param_set$values$affect_columns = selector_all()
poca$train(list(task))[[1]]$data()

Description

Collapses factors of type factor, ordered: Collapses the rarest factors in the training samples, until target_level_count levels remain. Levels that have prevalence above no_collapse_above_prevalence are retained, however. For factor variables, these are collapsed to the next larger level, for ordered variables, rare variables are collapsed to the neighbouring class, whichever has fewer samples.

Levels not seen during training are not touched during prediction; Therefore it is useful to combine this with the PipeOpFixFactors.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpCollapseFactors$new(id = "collapsefactors", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "collapsefactors".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with rare affected factor and ordered feature levels collapsed.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as:

• collapse_map :: named list of named list of character
  List of factor level maps. For each factor, collapse_map contains a named list that indicates what levels of the input task get mapped to what levels of the output task. If collapse_map has an entry feat_1 with an entry a = c("x", "y"), it means that levels "x" and "y" get collapsed to level "a" in feature "feat_1".

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• no_collapse_above_prevalence :: numeric(1)
  Fraction of samples below which factor levels get collapsed. Default is 1, which causes all levels to be collapsed until target_level_count remain.

• target_level_count :: integer(1)
  Number of levels to retain. Default is 2.

Internals

Makes use of the fact that levels(fact_var) = list(target1 = c("source1", "source2"), target2 = "source2") causes renaming of level "source1" and "source2" both to "target1", and also "source2" to "target2".

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")

Description

Changes the column roles of the input Task according to new_role.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpColRoles$new(id = "colroles", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "colroles".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with transformed column roles according to new_role.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• new_role :: list
  Named list of new column roles. The names must match the column names of the input task that will later be trained/predicted on. Each entry of the list must contain a character vector with possible values of mlr_reflections$task_col_roles. If the value is given as character(), the column will be dropped from the input task. Changing the role of a column results in this column loosing its previous role(s). Setting a new target variable or changing the role of an existing target variable is not supported.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_collapsefactors, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson