Title: Preprocessing Operators and Pipelines for 'mlr3'
Version: 0.9.0
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.
License: LGPL-3
URL: https://mlr3pipelines.mlr-org.com, https://github.com/mlr-org/mlr3pipelines
BugReports: https://github.com/mlr-org/mlr3pipelines/issues
Depends: R (≥ 3.3.0)
Imports: backports, checkmate, data.table, digest, lgr, mlr3 (≥ 0.20.0), mlr3misc (≥ 0.17.0), paradox (≥ 1.0.0), R6, withr
Suggests: ggplot2, glmnet, igraph, knitr, lme4, mlbench, bbotk (≥ 0.3.0), mlr3filters (≥ 0.8.1), mlr3learners, mlr3measures, nloptr, quanteda, rmarkdown, rpart, stopwords, testthat, visNetwork, bestNormalize, fastICA, kernlab, smotefamily, evaluate, NMF, MASS, GenSA, methods, vtreat, future, htmlwidgets, ranger, themis
ByteCompile: true
Encoding: UTF-8
Config/testthat/edition: 3
Config/testthat/parallel: true
NeedsCompilation: no
RoxygenNote: 7.3.2
VignetteBuilder: knitr, rmarkdown
Collate: 'CnfAtom.R' 'CnfClause.R' 'CnfFormula.R' 'CnfFormula_simplify.R' 'CnfSymbol.R' 'CnfUniverse.R' 'Graph.R' 'GraphLearner.R' 'mlr_pipeops.R' 'multiplicity.R' 'utils.R' 'PipeOp.R' 'PipeOpEnsemble.R' 'LearnerAvg.R' 'NO_OP.R' 'PipeOpTaskPreproc.R' 'PipeOpADAS.R' 'PipeOpBLSmote.R' 'PipeOpBoxCox.R' 'PipeOpBranch.R' 'PipeOpChunk.R' 'PipeOpClassBalancing.R' 'PipeOpClassWeights.R' 'PipeOpClassifAvg.R' 'PipeOpColApply.R' 'PipeOpColRoles.R' 'PipeOpCollapseFactors.R' 'PipeOpCopy.R' 'PipeOpDateFeatures.R' 'PipeOpDecode.R' 'PipeOpEncode.R' 'PipeOpEncodeImpact.R' 'PipeOpEncodeLmer.R' 'PipeOpEncodePL.R' 'PipeOpFeatureUnion.R' 'PipeOpFilter.R' 'PipeOpFixFactors.R' 'PipeOpHistBin.R' 'PipeOpICA.R' 'PipeOpImpute.R' 'PipeOpImputeConstant.R' 'PipeOpImputeHist.R' 'PipeOpImputeLearner.R' 'PipeOpImputeMean.R' 'PipeOpImputeMedian.R' 'PipeOpImputeMode.R' 'PipeOpImputeOOR.R' 'PipeOpImputeSample.R' 'PipeOpKernelPCA.R' 'PipeOpLearner.R' 'PipeOpLearnerCV.R' 'PipeOpLearnerPICVPlus.R' 'PipeOpLearnerQuantiles.R' 'PipeOpMissingIndicators.R' 'PipeOpModelMatrix.R' 'PipeOpMultiplicity.R' 'PipeOpMutate.R' 'PipeOpNMF.R' 'PipeOpNOP.R' 'PipeOpNearmiss.R' 'PipeOpOVR.R' 'PipeOpPCA.R' 'PipeOpProxy.R' 'PipeOpQuantileBin.R' 'PipeOpRandomProjection.R' 'PipeOpRandomResponse.R' 'PipeOpRegrAvg.R' 'PipeOpRemoveConstants.R' 'PipeOpRenameColumns.R' 'PipeOpRowApply.R' 'PipeOpScale.R' 'PipeOpScaleMaxAbs.R' 'PipeOpScaleRange.R' 'PipeOpSelect.R' 'PipeOpSmote.R' 'PipeOpSmoteNC.R' 'PipeOpSpatialSign.R' 'PipeOpSubsample.R' 'PipeOpTextVectorizer.R' 'PipeOpThreshold.R' 'PipeOpTomek.R' 'PipeOpTrafo.R' 'PipeOpTuneThreshold.R' 'PipeOpUnbranch.R' 'PipeOpVtreat.R' 'PipeOpYeoJohnson.R' 'Selector.R' 'TaskRegr_boston_housing.R' 'assert_graph.R' 'bibentries.R' 'greplicate.R' 'gunion.R' 'mlr_graphs.R' 'operators.R' 'pipeline_bagging.R' 'pipeline_branch.R' 'pipeline_convert_types.R' 'pipeline_greplicate.R' 'pipeline_ovr.R' 'pipeline_robustify.R' 'pipeline_stacking.R' 'pipeline_targettrafo.R' 'po.R' 'ppl.R' 'preproc.R' 'reexports.R' 'typecheck.R' 'zzz.R'
Packaged: 2025-07-31 14:15:06 UTC; user
Author: Martin Binder [aut, cre], Florian Pfisterer ORCID iD [aut], Lennart Schneider ORCID iD [aut], Bernd Bischl ORCID iD [aut], Michel Lang ORCID iD [aut], Sebastian Fischer ORCID iD [aut], Susanne Dandl [aut], Keno Mersmann [ctb], Maximilian Mücke ORCID iD [ctb], Lona Koers [ctb]
Maintainer: Martin Binder <mlr.developer@mb706.com>
Repository: CRAN
Date/Publication: 2025-07-31 23:20:11 UTC

mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

Description

logo

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 mlr.developer@mb706.com

Authors:

Other contributors:

See Also

Useful links:

PipeOp Composition Operator

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

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

g1

(Graph | PipeOp | Learner | Filter | list | ...)
Graph / PipeOp / object-convertible-to-PipeOp to put in front of g2.

g2

(Graph | PipeOp | Learner | Filter | list | ...)
Graph / PipeOp / object-convertible-to-PipeOp to put after g1.

in_place

(logical(1))
Whether to try to avoid cloning g1. If g1 is not a Graph, then it is cloned regardless.

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.

Atoms for CNF Formulas

Description

CnfAtom objects represent a single statement that is used to build up CNF formulae. They are mostly intermediate, created using the %among% operator or CnfAtom() directly, and combined into CnfClause and CnfFormula objects. CnfClause and CnfFormula do not, however, contain CnfAtom objects directly,

CnfAtoms contain an indirect reference to a CnfSymbol by referencing its name and its CnfUniverse. They furthermore contain a set of values. An CnfAtom represents a statement asserting that the given symbol takes up one of the given values.

If the set of values is empty, the CnfAtom represents a contradiction (FALSE). If it is the full domain of the symbol, the CnfAtom represents a tautology (TRUE). These values can be converted to, and from, logical(1) values using as.logical() and as.CnfAtom().

CnfAtom objects can be negated using the ! operator, which will return the CnfAtom representing set membership in the complement of the symbol with respect to its domain. CnfAtoms can furthermore be combined using the | operator to form a CnfClause, and using the & operator to form a CnfFormula. This happens even if the resulting statement could be represented as a single CnfAtom.

This is part of the CNF representation tooling, which is currently considered experimental; it is for internal use.

Usage

CnfAtom(symbol, values)

e1 %among% e2

as.CnfAtom(x)

Arguments

symbol

(CnfSymbol)
The symbol to which the atom refers.

values

(character)
The values that the symbol can take.

e1

(CnfSymbol)
Left-hand side of the ⁠%among%⁠ operator. Passed as symbol to CnfAtom().

e2

(character)
Right-hand side of the ⁠%among%⁠ operator. Passed as values to CnfAtom().

x

(any)
The object to be coerced to a CnfAtom by as.CnfAtom. Only logical(1) and CnfAtom itself are currently supported.

Details

We would have preferred to overload the %in% operator, but this is currently not easily possible in R. We therefore created the ⁠%among%⁠ operator.

The internal representation of a CnfAtom may change in the future.

Value

A new CnfAtom object.

See Also

Other CNF representation objects: CnfClause(), CnfFormula(), CnfSymbol(), CnfUniverse()

Examples

u = CnfUniverse()
X = CnfSymbol(u, "X", c("a", "b", "c"))

CnfAtom(X, c("a", "b"))
X %among% "a"
X %among% character(0)
X %among% c("a", "b", "c")

as.logical(X %among% character(0))
as.CnfAtom(TRUE)

!(X %among% "a")

X %among% "a" | X %among% "b"  # creates a CnfClause

X %among% "a" & X %among% c("a", "b")  # creates a CnfFormula

Clauses in CNF Formulas

Description

A CnfClause is a disjunction of CnfAtom objects. It represents a statement that is true if at least one of the atoms is true. These are for example of the form 

  X %among% c("a", "b", "c") | Y %among% c("d", "e", "f") | ...

CnfClause objects can be constructed explicitly, using the CnfClause() constructor, or implicitly, by using the | operator on CnfAtoms or other CnfClause objects.

CnfClause objects which are not tautologies or contradictions are named lists; the value ranges of each symbol can be accessed using [[, and these clauses can be subset using [ to get clauses containing only the indicated symbols. However, to get a list of CnfAtom objects, use as.list(). Note that the simplified form of a clause containing a contradiction is the empty list.

Upon construction, the CnfClause is simplified by (1) removing contradictions, (2) unifying atoms that refer to the same symbol, and (3) evaluating to TRUE if any atom is TRUE. Note that the order of atoms in a clause is not preserved.

Using CnfClause() on lists that contain other CnfClause objects will create a clause that is the disjunction of all atoms in all clauses.

If a CnfClause contains no atoms, or only FALSE atoms, it evaluates to FALSE. If it contains at least one atom that is always true, the clause evaluates to TRUE. These values can be converted to, and from, logical(1) values using as.logical() and as.CnfClause().

CnfClause objects can be negated using the ! operator, and combined using the & operator. Both of these operations return a CnfFormula, even if the result could in principle be represented as a single CnfClause.

This is part of the CNF representation tooling, which is currently considered experimental; it is for internal use.

Usage

CnfClause(atoms)

as.CnfClause(x)

Arguments

atoms

(list of (CnfAtom | CnfClause))
A list of CnfAtom or other CnfClause objects. The clause represents the disjunction of these atoms.

x

(any)
The object to be coerced to a CnfClause by as.CnfClause. Only logical(1), CnfAtom, and CnfClause itself are currently supported.

Details

We are undecided whether it is a better idea to have as.list() return a named list or an unnamed one. Calling as.list() on a CnfClause with a tautology returns a tautology-atom, which does not have a name. We currently return a named list for other clauses, as this makes subsetting by name commute with as.list(). However, this behaviour may change in the future.

Value

A new CnfClause object.

See Also

Other CNF representation objects: CnfAtom(), CnfFormula(), CnfSymbol(), CnfUniverse()

Examples

u = CnfUniverse()
X = CnfSymbol(u, "X", c("a", "b", "c"))
Y = CnfSymbol(u, "Y", c("d", "e", "f"))

CnfClause(list(X %among% c("a", "b"), Y %among% c("d", "e")))
cls = X %among% c("a", "b") | Y %among% c("d", "e")
cls

as.list(cls)

as.CnfClause(X %among% c("a", "b"))

# The same symbols are unified
X %among% "a" | Y %among% "d" | X %among% "b"

# tautology evaluates to TRUE
X %among% "a" | X %among% "b" | X %among% "c"

# contradictions are removed
X %among% "a" | Y %among% character(0)

# create CnfFormula:
!(X %among% "a" | Y %among% "d")

# also a CnfFormula, even if it contains a single clause:
!CnfClause(list(X %among% "a"))
(X %among% c("a", "c") | Y %among% "d") &
  (X %among% c("a", "b") | Y %among% "d")

CNF Formulas

Description

A CnfFormula is a conjunction of CnfClause objects. It represents a statement that is true if all of the clauses are true. These are for example of the form 

  (X %among% "a" | Y %among% "d") & Z %among% "g"

CnfFormula objects can be constructed explicitly, using the CnfFormula() constructor, or implicitly, by using the & operator on CnfAtoms, CnfClauses, or other CnfFormula objects.

To get individual clauses from a formula, [[ should not be used; instead, use as.list(). Note that the simplified form of a formula containing a tautology is the empty list.

Upon construction, the CnfFormula is simplified by using various heuristics. This includes unit propagation, subsumption elimination, self/hidden subsumption elimination, hidden tautology elimination, and resolution subsumption elimination (see examples). Note that the order of clauses in a formula is not preserved.

Using CnfFormula() on lists that contain other CnfFormula objects will create a formula that is the conjunction of all clauses in all formulas. This may be somewhat more efficient than applying & many times in a row.

If a CnfFormula contains no clauses, or only TRUE clauses, it evaluates to TRUE. If it contains at least one clause that is, by itself, always false, the formula evaluates to FALSE. Not all contradictions between clauses are recognized, however. These values can be converted to, and from, logical(1) values using as.logical() and as.CnfFormula().

CnfFormula objects can be negated using the ! operator. Beware that this may lead to an exponential blow-up in the number of clauses.

This is part of the CNF representation tooling, which is currently considered experimental; it is for internal use.

Usage

CnfFormula(clauses)

as.CnfFormula(x)

Arguments

clauses

(list of CnfClause)
A list of CnfClause objects. The formula represents the conjunction of these clauses.

x

(any)
The object to be coerced to a CnfFormula by as.CnfFormula. Only logical(1), CnfAtom, CnfClause, and CnfFormula itself are currently supported.

Value

A new CnfFormula object.

See Also

Other CNF representation objects: CnfAtom(), CnfClause(), CnfSymbol(), CnfUniverse()

Examples

u = CnfUniverse()
X = CnfSymbol(u, "X", c("a", "b", "c"))
Y = CnfSymbol(u, "Y", c("d", "e", "f"))
Z = CnfSymbol(u, "Z", c("g", "h", "i"))

frm = (X %among% c("a", "b") | Y %among% c("d", "e")) &
  Z %among% c("g", "h")
frm

# retrieve individual clauses
as.list(frm)

# Negation of a formula
# Note the parentheses, otherwise `!` would be applied to the first clause only.
!((X %among% c("a", "b") | Y %among% c("d", "e")) &
   Z %among% c("g", "h"))

## unit propagation
# The second clause can not be satisfied when X is "b", so "b" can be
# removed from the possibilities in the first clause.
(X %among% c("a", "b") | Y %among% c("d", "e")) &
  X %among% c("a", "c")

## subsumption elimination
# The first clause is a subset of the second clause; whenever the
# first clause is satisfied, the second clause is satisfied as well, so the
# second clause can be removed.
(X %among% "a" | Y %among% c("d", "e")) &
  (X %among% c("a", "b") | Y %among% c("d", "e") | Z %among% "g")

## self subsumption elimination
# If the first clause is satisfied but X is not "a", then Y must be "e".
# The `Y %among% "d"` part of the first clause can therefore be removed.
(X %among% c("a", "b") | Y %among% "d") &
  (X %among% "a" | Y %among% "e")

## resolution subsumption elimination
# The first two statements can only be satisfied if Y is either "d" or "e",
# since when X is "a" then Y must be "e", and when X is "b" then Y must be "d".
# The third statement is therefore implied by the first two, and can be
# removed.
(X %among% "a" | Y %among% "d") &
  (X %among% "b" | Y %among% "e") &
  (Y %among% c("d", "e"))

## hidden tautology elimination / hidden subsumption elimination
# When considering the first two clauses only, adding another atom
# `Z %among% "i"` to the first clause would not change the formula, since
# whenever Z is "i", the second clause would need to be satisfied in a way
# that would also satisfy the first clause, making this atom redundant
# ("hidden literal addition"). Considering the pairs of clause 1 and 3, and
# clauses 1 and 4, one could likewise add `Z %among% "g"` and
#' `Z %among% "h"`, respectively. This would reveal the first clausee to be
# a "hidden" tautology: it is equivalent to a clause containing the
# atom `Z %among% c("g", "h", "i")` == TRUE.
# Alternatively, one could perform "hidden" resolution subsumption using
# clause 4 after having added the atom `Z %among% c("g", "i")` to the first
# clause by using clauses 2 and 3.
(X %among% c("a", "b") | Y %among% c("d", "e")) &
  (X %among% "a" | Z %among% c("g", "h")) &
  (X %among% "b" | Z %among% c("h", "i")) &
  (Y %among% c("d", "e") | Z %among% c("g", "i"))

## Simple contradictions are recognized:
(X %among% "a") & (X %among% "b")
# Tautologies are preserved
(X %among% c("a", "b", "c")) & (Y %among% c("d", "e", "f"))

# But not all contradictions are recognized.
# Builtin heuristic CnfFormula preprocessing is not a SAT solver.
contradiction = (X %among% "a" | Y %among% "d") &
  (X %among% "b" | Y %among% "e") &
  (X %among% "c" | Y %among% "f")
contradiction

# Negation of a contradiction results in a tautology, which is recognized
# and simplified to TRUE. However, note that this operation (1) generally has
# exponential complexity in the number of terms and (2) is currently also not
# particularly well optimized
!contradiction

Symbols for CNF Formulas

Description

Representation of Symbols used in CNF formulas. Symbols have a name and a domain (a set of possible values), and are stored in a CnfUniverse.

Once created, it is currently not intended to modify or delete symbols.

Symbols can be used in CNF formulas by creating CnfAtom objects, either by using the ⁠%among%⁠ operator or by using the CnfAtom() constructor explicitly.

This is part of the CNF representation tooling, which is currently considered experimental; it is for internal use.

Usage

CnfSymbol(universe, name, domain)

Arguments

universe

(CnfUniverse)
The universe in which the symbol is defined.

name

(character(1))
The name of the symbol.

domain

(character)
The domain, i.e. the set of possible values for the symbol. Must not be empty.

Value

A new CnfSymbol object.

See Also

Other CNF representation objects: CnfAtom(), CnfClause(), CnfFormula(), CnfUniverse()

Examples

u = CnfUniverse()
X = CnfSymbol(u, "X", c("a", "b", "c"))

# Use symbols to create CnfAtom objects
X %among% c("a", "b")
X %among% "a"
X %among% character(0)
X %among% c("a", "b", "c")

Symbol Table for CNF Formulas

Description

A symbol table for CNF formulas. The CnfUniverse is a by-reference object that stores the domain of each symbol. Symbols are created with CnfSymbol() and can be retrieved with $. Using [[ retrieves a given symbol's domain.

It is only possible to combine symbols from the same (identical) universe.

This is part of the CNF representation tooling, which is currently considered experimental; it is for internal use.

Usage

CnfUniverse()

Value

A new CnfUniverse object.

See Also

Other CNF representation objects: CnfAtom(), CnfClause(), CnfFormula(), CnfSymbol()

Examples

u = CnfUniverse()
X = CnfSymbol(u, "X", c("a", "b", "c"))
Y = CnfSymbol(u, "Y", c("d", "e", "f"))

u$X
u[["Y"]]

X %among% c("a", "c")
u$X %among% c("a", "c")
Y %among% c("d", "e", "f")
Y %among% character(0)

u$X %among% "a" | u$Y %among% "d"

Graph Base Class

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

Methods

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()

Multiplicity

Description

A Multiplicity class S3 object.

The function of multiplicities is to indicate that PipeOps should be executed multiple times with multiple values.

A Multiplicity is a container, like a list(), that contains multiple values. If the message that is passed along the edge of a Graph is a Multiplicity-object, then the PipeOp that receives this object will usually be called once for each contained value. The result of each of these calls is then, again, packed in a Multiplicity and sent along the outgoing edge(s) of that PipeOp. This means that a Multiplicity can cause multiple PipeOps in a row to be run multiple times, where the run for each element of the Multiplicity is independent from the others.

Most PipeOps only return a Multiplicity if their input was a Multiplicity (and after having run their code multiple times, once for each entry). However, there are a few special PipeOps that are "aware" of Multiplicity objects. These may either create a Multiplicity even though not having a Multiplicity input (e.g. PipeOpReplicate or PipeOpOVRSplit) – causing the subsequent PipeOps to be run multiple times – or collect a Multiplicity, being called only once even though their input is a Multiplicity (e.g. PipeOpOVRUnite or PipeOpFeatureUnion if constructed with the collect_multiplicity argument set to TRUE). The combination of these mechanisms makes it possible for parts of a Graph to be called variably many times if "sandwiched" between Multiplicity creating and collecting PipeOps.

Whether a PipeOp creates or collects a Multiplicity is indicated by the ⁠$input⁠ or ⁠$output⁠ slot (which indicate names and types of in/out channels). If the train and predict types of an input or output are surrounded by square brackets ("[", "⁠]⁠"), then this channel handles a Multiplicity explicitly. Depending on the function of the PipeOp, it will usually collect (input channel) or create (output channel) a Multiplicity. PipeOps without this indicator are Multiplicity agnostic and blindly execute their function multiple times when given a Multiplicity.

If a PipeOp is trained on a Multiplicity, the ⁠$state⁠ slot is set to a Multiplicity as well; this Multiplicity contains the "original" ⁠$state⁠ resulting from each individual call of the PipeOp with the input Multiplicity's content. If a PipeOp was trained with a Multiplicity, then the predict() argument must be a Multiplicity with the same number of elements.

Usage

Multiplicity(...)

Arguments

...

any
Can be anything.

Value

Multiplicity

See Also

Other Special Graph Messages: NO_OP

Other Experimental Features: mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_replicate

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

No-Op Sentinel Used for Alternative Branching

Description

Special data type for no-ops. Distinct from NULL for easier debugging and distinction from unintentional NULL returns.

Usage

NO_OP

Format

R6 object.

See Also

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

Other Special Graph Messages: Multiplicity()

PipeOp Base Class

Description

A PipeOp represents a transformation of a given "input" into a given "output", with two stages: "training" and "prediction". It can be understood as a generalized function that not only has multiple inputs, but also multiple outputs (as well as two stages). The "training" stage is used when training a machine learning pipeline or fitting a statistical model, and the "predicting" stage is then used for making predictions on new data.

To perform training, the ⁠$train()⁠ function is called which takes inputs and transforms them, while simultaneously storing information in its ⁠$state⁠ slot. For prediction, the ⁠$predict()⁠ function is called, where the ⁠$state⁠ information can be used to influence the transformation of the new data.

A PipeOp is usually used in a Graph object, a representation of a computational graph. It can have multiple input channels—think of these as multiple arguments to a function, for example when averaging different models—, and multiple output channels—a transformation may return different objects, for example different subsets of a Task. The purpose of the Graph is to connect different outputs of some PipeOps to inputs of other PipeOps.

Input and output channel information of a PipeOp is defined in the ⁠$input⁠ and ⁠$output⁠ slots; each channel has a name, a required type during training, and a required type during prediction. The ⁠$train()⁠ and ⁠$predict()⁠ functions are called with a list argument that has one entry for each declared channel (with one exception, see next paragraph). The list is automatically type-checked for each channel against ⁠$input⁠ and then passed on to the private$.train() or private$.predict() functions. There the data is processed and a result list is created. This list is again type-checked for declared output types of each channel. The length and types of the result list is as declared in ⁠$output⁠.

A special input channel name is "...", which creates a vararg channel that takes arbitrarily many arguments, all of the same type. If the ⁠$input⁠ table contains an "..."-entry, then the input given to ⁠$train()⁠ and ⁠$predict()⁠ may be longer than the number of declared input channels.

This class is an abstract base class that all PipeOps being used in a Graph should inherit from, and is not intended to be instantiated.

Format

Abstract R6Class.

Construction

PipeOp$new(id, param_set = ps(), param_vals = list(), input, output, packages = character(0), tags = character(0))

Internals

PipeOp is an abstract class with abstract functions private$.train() and private$.predict(). To create a functional PipeOp class, these two methods must be implemented. Each of these functions receives a named list according to the PipeOp's input channels, and must return a list (names are ignored) with values in the order of output channels in ⁠$output⁠. The private$.train() and private$.predict() function should not be called by the user; instead, a ⁠$train()⁠ and ⁠$predict()⁠ should be used. The most convenient usage is to add the PipeOp to a Graph (possibly as singleton in that Graph), and using the Graph's ⁠$train()⁠ / ⁠$predict()⁠ methods.

private$.train() and private$.predict() should treat their inputs as read-only. If they are R6 objects, they should be cloned before being manipulated in-place. Objects, or parts of objects, that are not changed, do not need to be cloned, and it is legal to return the same identical-by-reference objects to multiple outputs.

Fields

Programatic access to all available properties is possible via mlr_reflections$pipeops$properties.

Methods

The following public ⁠$train()⁠ and ⁠$predict()⁠ methods are the primary user-facing functions intended for direct use:

To implement a PipeOp the following abstract private functions should be overloaded in the inheriting PipeOp. Note that these should not be called by a user; instead the public ⁠$train()⁠ and ⁠$predict()⁠ method should be used.

Inheriting

To create your own PipeOp, you need to overload the private$.train() and private$.predict() functions. It is most likely also necessary to overload the ⁠$initialize()⁠ function to do additional initialization. The ⁠$initialize()⁠ method should have at least the arguments id and param_vals, which should be passed on to super$initialize() unchanged. id should have a useful default value, and param_vals should have the default value list(), meaning no initialization of hyperparameters.

If the ⁠$initialize()⁠ method has more arguments, then it is necessary to also overload the private$.additional_phash_input() function. This function should return either all objects, or a hash of all objects, that can change the function or behavior of the PipeOp and are independent of the class, the id, the ⁠$state⁠, and the ⁠$param_set$values⁠. The last point is particularly important: changing the ⁠$param_set$values⁠ should not change the return value of private$.additional_phash_input().

When you are implementing a PipeOp that operates a task (and is not a PipeOpTaskPreproc), you also need to handle the ⁠$internal_valid_task⁠ field of the input task, if there is one.

See Also

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

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

Other PipeOps: PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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

# example (bogus) PipeOp that returns the sum of two numbers during $train()
# as well as a letter of the alphabet corresponding to that sum during $predict().

PipeOpSumLetter = R6::R6Class("sumletter",
  inherit = PipeOp,  # inherit from PipeOp
  public = list(
    initialize = function(id = "posum", param_vals = list()) {
      super$initialize(id, param_vals = param_vals,
        # declare "input" and "output" during construction here
        # training takes two 'numeric' and returns a 'numeric';
        # prediction takes 'NULL' and returns a 'character'.
        input = data.table::data.table(name = c("input1", "input2"),
          train = "numeric", predict = "NULL"),
        output = data.table::data.table(name = "output",
          train = "numeric", predict = "character")
      )
    }
  ),
  private = list(
    # PipeOp deriving classes must implement .train and
    # .predict; each taking an input list and returning
    # a list as output.
    .train = function(input) {
      sum = input[[1]] + input[[2]]
      self$state = sum
      list(sum)
    },
    .predict = function(input) {
      list(letters[self$state])
    }
  )
)
posum = PipeOpSumLetter$new()

print(posum)

posum$train(list(1, 2))
# note the name 'output' is the name of the output channel specified
# in the $output data.table.

posum$predict(list(NULL, NULL))

Piecewise Linear Encoding Base Class

Description

Abstract base class for piecewise linear encoding.

Piecewise linear encoding works by splitting values of features into distinct bins, through an algorithm implemented in private$.get_bins(), and then creating new feature columns through a continuous alternative to one-hot encoding. Here, one new feature per bin is constructed, with values being either

PipeOps inheriting from this encode columns of type numeric and integer. Use the PipeOpTaskPreproc ⁠$affect_columns⁠ functionality to only encode a subset of columns, or only encode columns of a certain type, etc.

Format

Abstract R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncodePL$new(id = "encodepl", param_set = ps(), param_vals = list(), packages = character(0), task_type = "Task")

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected numeric and integer columns encoded using piecewise linear encoding.

State

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

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc.

Internals

PipeOpEncodePL is an abstract class inheriting from PipeOpTaskPreprocSimple that allows easier implementation of different binning algorithms for piecewise linear encoding. The respective binning algorithm should be implemented as private$.get_bins().

Fields

Only fields inherited from PipeOp.

Methods

Methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp as well as

References

Gorishniy Y, Rubachev I, Babenko A (2022). “On Embeddings for Numerical Features in Tabular Deep Learning.” In Advances in Neural Information Processing Systems, volume 35, 24991–25004. https://proceedings.neurips.cc/paper_files/paper/2022/hash/9e9f0ffc3d836836ca96cbf8fe14b105-Abstract-Conference.html.

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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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 Piecewise Linear Encoding PipeOps: mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree

Ensembling Base Class

Description

Parent class for PipeOps that aggregate predictions. Implements the private$.train() and private$.predict() methods necessary for a PipeOp and requires deriving classes to create the private$weighted_avg_predictions() function.

Format

Abstract R6Class inheriting from PipeOp.

Construction

Note: This object is typically constructed via a derived class, e.g. PipeOpClassifAvg or PipeOpRegrAvg. 

PipeOpEnsemble$new(innum = 0, collect_multiplicity = FALSE, id, param_set = ps(), param_vals = list(), packages = character(0), prediction_type = "Prediction")

Input and Output Channels

PipeOpEnsemble has multiple input channels depending on the innum construction argument, named "input1", "input2", ... if innum is nonzero; if innum is 0, there is only one vararg input channel named "...". All input channels take only NULL during training and take a Prediction during prediction.

PipeOpEnsemble has one output channel named "output", producing NULL during training and a Prediction during prediction.

The output during prediction is in some way a weighted averaged representation of the input.

State

The ⁠$state⁠ is left empty (list()).

Parameters

Internals

The commonality of ensemble methods using PipeOpEnsemble is that they take a NULL-input during training and save an empty ⁠$state⁠. They can be used following a set of PipeOpLearner PipeOps to perform (possibly weighted) prediction averaging. See e.g. PipeOpClassifAvg and PipeOpRegrAvg which both inherit from this class.

Should it be necessary to use the output of preceding Learners during the "training" phase, then PipeOpEnsemble should not be used. In fact, if training time behaviour of a Learner is important, then one should use a PipeOpLearnerCV instead of a PipeOpLearner, and the ensemble can be created with a Learner encapsulated by a PipeOpLearner. See LearnerClassifAvg and LearnerRegrAvg for examples.

Fields

Only fields inherited from PipeOp.

Methods

Methods inherited from PipeOp as well as:

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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(), mlr_pipeops_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Other Ensembles: mlr_learners_avg, mlr_pipeops_classifavg, mlr_pipeops_ovrunite, mlr_pipeops_regravg

Imputation Base Class

Description

Abstract base class for feature imputation.

Format

Abstract R6Class object inheriting from PipeOp.

Construction

PipeOpImpute$$new(id, param_set = ps(), param_vals = list(), whole_task_dependent = FALSE, empty_level_control = FALSE,
  packages = character(0), task_type = "Task")

Input and Output Channels

PipeOpImpute has one input channel named "input", taking a Task, or a subclass of Task if the task_type construction argument is given as such; both during training and prediction.

PipeOpImpute has one output channel named "output", producing a Task, or a subclass; the Task type is the same as for input; both during training and prediction.

The output Task is the modified input Task with features imputed according to the private$.impute() function.

State

The ⁠$state⁠ is a named list; besides members added by inheriting classes, the members are:

Parameters

Internals

PipeOpImpute is an abstract class inheriting from PipeOp that makes implementing imputer PipeOps simple.

Internally, the construction argument empty_level_control and the hyperparameter create_empty_level (should it exist) modify the private$.create_empty_level field. Behavior then depends on whether this field is set to TRUE or FALSE and works by controlling for which cases imputation is performed on factor or ordered columns. Its setting has no impact on columns of other types.
If private$.create_empty_level is set to TRUE, private$.impute() is called for all factor or ordered columns during training, regardless of whether they have any missing values. For this to lead to the creation of an empty level for columns with no missing values, inheriting PipeOps must implement private$.train_imputer() in such a way that it returns the name of the level to be created for the feature types factor and ordered.
If private$.create_empty_level is set to FALSE, private$.impute() is not called during prediction for factor or ordered columns which were not modified during training. This means that NAs will not be imputed for these columns.
See PipeOpImputeOOR, for a detailed explanation of why these controls are necessary.

Fields

Fields inherited from PipeOp.

Methods

Methods inherited from PipeOp, as well as:

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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 Imputation PipeOps: mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Target Transformation Base Class

Description

Base class for handling target transformation operations. Target transformations are different from feature transformation because they have to be "inverted" after prediction. The target is transformed during the training phase and information to invert this transformation is sent along to PipeOpTargetInvert which then inverts this transformation during the prediction phase. This inversion may need info about both the training and the prediction data.

Users can overload up to four ⁠private$⁠-functions: .get_state() (optional), .transform() (mandatory), .train_invert() (optional), and .invert() (mandatory).

Format

Abstract R6Class inheriting from PipeOp.

Construction

PipeOpTargetTrafo$new(id, param_set = ps(), param_vals = list(), packages = character(0), task_type_in = "Task", task_type_out = task_type_in, tags = NULL)

Input and Output Channels

PipeOpTargetTrafo has one input channels named "input" taking a Task (or whatever class was specified by the task_type during construction) both during training and prediction.

PipeOpTargetTrafo has two output channels named "fun" and "output". During training, "fun" returns NULL and during prediction, "fun" returns a function that can later be used to invert the transformation done during training according to the overloaded .train_invert() and .invert() functions. "output" returns the modified input Task (or task_type) according to the overloaded transform() function both during training and prediction.

State

The ⁠$state⁠ is a named list and should be returned explicitly by the user in the overloaded .get_state() function.

Internals

PipeOpTargetTrafo is an abstract class inheriting from PipeOp. It implements the private$.train() and private$.predict() functions. These functions perform checks and go on to call .get_state(), .transform(), .train_invert(). .invert() is packaged and sent along the "fun" output to be applied to a Prediction by PipeOpTargetInvert. A subclass of PipeOpTargetTrafo should implement these functions and be used in combination with PipeOpTargetInvert.

Fields

Fields inherited from PipeOp.

Methods

Methods inherited from PipeOp, as well as:

See Also

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

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

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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

Task Preprocessing Base Class

Description

Base class for handling most "preprocessing" operations. These are operations that have exactly one Task input and one Task output, and expect the column layout of these Tasks during input and output to be the same.

Prediction-behavior of preprocessing operations should always be independent for each row in the input-Task. This means that the prediction-operation of preprocessing-PipeOps should commute with rbind(): Running prediction on an n-row Task should result in the same result as rbind()-ing the prediction-result from n 1-row Tasks with the same content. In the large majority of cases, the number and order of rows should also not be changed during prediction.

Users must implement private$.train_task() and private$.predict_task(), which have a Task input and should return that Task. The Task should, if possible, be manipulated in-place, and should not be cloned.

Alternatively, the private$.train_dt() and private$.predict_dt() functions can be implemented, which operate on data.table objects instead. This should generally only be done if all data is in some way altered (e.g. PCA changing all columns to principal components) and not if only a few columns are added or removed (e.g. feature selection) because this should be done at the Task-level with private$.train_task(). The private$.select_cols() function can be overloaded for private$.train_dt() and private$.predict_dt() to operate only on subsets of the Task's data, e.g. only on numerical columns.

If the can_subset_cols argument of the constructor is TRUE (the default), then the hyperparameter affect_columns is added, which can limit the columns of the Task that is modified by the PipeOpTaskPreproc using a Selector function. Note this functionality is entirely independent of the private$.select_cols() functionality.

PipeOpTaskPreproc is useful for operations that behave differently during training and prediction. For operations that perform essentially the same operation and only need to perform extra work to build a ⁠$state⁠ during training, the PipeOpTaskPreprocSimple class can be used instead.

Format

Abstract R6Class inheriting from PipeOp.

Construction

PipeOpTaskPreproc$new(id, param_set = ps(), param_vals = list(), can_subset_cols = TRUE,
  packages = character(0), task_type = "Task", tags = NULL, feature_types = mlr_reflections$task_feature_types)

Input and Output Channels

PipeOpTaskPreproc has one input channel named "input", taking a Task, or a subclass of Task if the task_type construction argument is given as such; both during training and prediction.

PipeOpTaskPreproc has one output channel named "output", producing a Task, or a subclass; the Task type is the same as for input; both during training and prediction.

The output Task is the modified input Task according to the overloaded private$.train_task()/private$.predict_taks() or private$.train_dt()/private$.predict_dt() functions.

State

The ⁠$state⁠ is a named list; besides members added by inheriting classes, the members are:

Parameters

Internals

PipeOpTaskPreproc is an abstract class inheriting from PipeOp. It implements the private$.train() and ⁠$.predict()⁠ functions. These functions perform checks and go on to call private$.train_task() and private$.predict_task(). A subclass of PipeOpTaskPreproc may implement these functions, or implement private$.train_dt() and private$.predict_dt() instead. This works by having the default implementations of private$.train_task() and private$.predict_task() call private$.train_dt() and private$.predict_dt(), respectively.

The affect_columns functionality works by unsetting columns by removing their "col_role" before processing, and adding them afterwards by setting the col_role to "feature".

Fields

Fields inherited from PipeOp.

Methods

Methods inherited from PipeOp, as well as:

See Also

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

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

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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

Simple Task Preprocessing Base Class

Description

Base class for handling many "preprocessing" operations that perform essentially the same operation during training and prediction. Instead implementing a private$.train_task() and a private$.predict_task() operation, only a private$.get_state() and a private$.transform() operation needs to be defined, both of which take one argument: a Task.

Alternatively, analogously to the PipeOpTaskPreproc approach of offering private$.train_dt()/private$.predict_dt(), the private$.get_state_dt() and private$.transform_dt() functions may be implemented.

private$.get_state must not change its input value in-place and must return something that will be written into ⁠$state⁠ (which must not be NULL), private$.transform() should modify its argument in-place; it is called both during training and prediction.

This inherits from PipeOpTaskPreproc and behaves essentially the same.

Format

Abstract R6Class inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpTaskPreprocSimple$new(id, param_set = ps(), param_vals = list(), can_subset_cols = TRUE,
  packages = character(0), task_type = "Task", tags = NULL, feature_types = mlr_reflections$task_feature_types)

(Construction is identical to PipeOpTaskPreproc.)

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output during training and prediction is the Task, modified by private$.transform() or private$.transform_dt().

State

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

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc.

Internals

PipeOpTaskPreprocSimple is an abstract class inheriting from PipeOpTaskPreproc and implementing the private$.train_task() and private$.predict_task() functions. A subclass of PipeOpTaskPreprocSimple may implement the functions private$.get_state() and private$.transform(), or alternatively the functions private$.get_state_dt() and private$.transform_dt() (as well as private$.select_cols(), in the latter case). This works by having the default implementations of private$.get_state() and private$.transform() call private$.get_state_dt() and private$.transform_dt().

Fields

Fields inherited from PipeOp.

Methods

Methods inherited from PipeOpTaskPreproc, as well as:

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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 mlr3pipelines backend related: Graph, PipeOp, PipeOpTargetTrafo, PipeOpTaskPreproc, mlr_graphs, mlr_pipeops, mlr_pipeops_updatetarget

Selector Functions

Description

A Selector function is used by different PipeOps, most prominently PipeOpSelect and many PipeOps inheriting from PipeOpTaskPreproc, to determine a subset of Tasks to operate on.

Even though a Selector is a function that can be written itself, it is preferable to use the Selector constructors shown here. Each of these can be called with its arguments to create a Selector, which can then be given to the PipeOpSelect selector parameter, or many PipeOpTaskPreprocs' affect_columns parameter. See there for examples of this usage.

Usage

selector_all()

selector_none()

selector_type(types)

selector_grep(pattern, ignore.case = FALSE, perl = FALSE, fixed = FALSE)

selector_name(feature_names, assert_present = FALSE)

selector_invert(selector)

selector_intersect(selector_x, selector_y)

selector_union(selector_x, selector_y)

selector_setdiff(selector_x, selector_y)

selector_missing()

selector_cardinality_greater_than(min_cardinality)

Arguments

types

(character)
Type of feature to select

pattern

(character(1))
grep pattern

ignore.case

(logical(1))
ignore case

perl

(logical(1))
perl regex

fixed

(logical(1))
fixed pattern instead of regex

feature_names

(character)
Select features by exact name match.

assert_present

(logical(1))
Throw an error if feature_names are not all present in the task being operated on.

selector

(Selector)
Selector to invert.

selector_x

(Selector)
First Selector to query.

selector_y

(Selector)
Second Selector to query.

min_cardinality

(integer)
Minimum number of levels required to be selected.

Value

function: A Selector function that takes a Task and returns the feature names to be processed.

Functions

Details

A Selector is a function that has one input argument (commonly named task). The function is called with the Task that a PipeOp is operating on. The return value of the function must be a character vector that is a subset of the feature names present in the Task.

For example, a Selector that selects all columns is 

function(task) {
  task$feature_names
}

(this is the selector_all()-Selector.) A Selector that selects all columns that have names shorter than four letters would be: 

function(task) {
  task$feature_names[
    nchar(task$feature_names) < 4
  ]
}

A Selector that selects only the column "Sepal.Length" (as in the iris task), if present, is 

function(task) {
  intersect(task$feature_names, "Sepal.Length")
}

It is preferable to use the Selector construction functions like select_type, select_grep etc. if possible, instead of writing custom Selectors.

See Also

Other Selectors: mlr_pipeops_select

Examples

library("mlr3")

iris_task = tsk("iris")
bh_task = tsk("boston_housing")

sela = selector_all()
sela(iris_task)
sela(bh_task)

self = selector_type("factor")
self(iris_task)
self(bh_task)

selg = selector_grep("a.*i")
selg(iris_task)
selg(bh_task)

selgi = selector_invert(selg)
selgi(iris_task)
selgi(bh_task)

selgf = selector_union(selg, self)
selgf(iris_task)
selgf(bh_task)

Add a Class Hierarchy to the Cache

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

hierarchy

character the class hierarchy to add; should correspond to the class() of the lowest object in the hierarchy.

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"))

Convert an object to a Multiplicity

Description

Convert an object to a Multiplicity.

Usage

as.Multiplicity(x)

Arguments

x

(any)
Object to convert.

Value

Multiplicity

Conversion to mlr3pipelines Graph

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

Usage

as_graph(x, clone = FALSE)

Arguments

x

(any)
Object to convert.

clone

(logical(1))
Whether to return a (deep copied) clone if x is a Graph.

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

Conversion to mlr3pipelines PipeOp

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

x

(any)
Object to convert.

clone

(logical(1))
Whether to return a (deep copied) clone if x is a PipeOp.

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

Assertion for mlr3pipelines Graph

Description

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

Usage

assert_graph(x)

Arguments

x

(any)
Object to check.

Value

Graph invisible(x)

See Also

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

Assertion for mlr3pipelines PipeOp

Description

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

Usage

assert_pipeop(x)

Arguments

x

(any)
Object to check.

Value

PipeOp invisible(x)

See Also

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

Chain a Series of Graphs

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

graphs

list of (Graph | PipeOp | NULL | ...)
List of elements which are the Graphs to be joined. Elements must be convertible to Graph or PipeOp using as_graph() and as_pipeop(). NULL is the neutral element of %>>% and skipped.

in_place

(logical(1))
Whether to try to avoid cloning the first element of graphs, similar to the difference of %>>!% over %>>%. This can only be avoided if graphs[[1]] is already a Graph. Beware that, if chain_graphs() fails because of id collisions, then graphs[[1]] will possibly be in an incompletely modified state when in_place is TRUE.

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

Remove NO_OPs from a List

Description

Remove all NO_OP elements from a list.

Usage

filter_noop(x)

Arguments

x

list
List to filter.

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

Create Disjoint Graph Union of Copies of a Graph

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

graph

Graph
Graph to replicate.

n

integer(1) Number of copies to create.

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

Disjoint Union of Graphs

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

graphs

list of (Graph | PipeOp | NULL | ...)
List of elements which are the Graphs to be joined. Elements must be convertible to Graph or PipeOp using as_graph() and as_pipeop(). NULL values automatically get converted to PipeOpNOP with a random ID of the format ⁠nop_********⁠. The list can be named, in which case the IDs of the elements are prefixed with the names, separated by a dot (.).

in_place

(logical(1) | logical)
Whether to try to avoid cloning the first element of graphs, similar to the difference of %>>!% over %>>%. This can only be avoided if graphs[[1]] is already a Graph.
Unlike chain_graphs(), gunion() does all checks before mutating graphs[[1]], so it will not leave graphs[[1]] in an incompletely modified state when it fails.
in_place may also be of length graph, in which case it determines for each element of graphs whether it is cloned. This is for internal usage and is not recommended.

Value

Graph the resulting Graph.

See Also

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

Check if an object is a Multiplicity

Description

Check if an object is a Multiplicity.

Usage

is.Multiplicity(x)

Arguments

x

(any)
Object to check.

Value

logical(1)

Test for NO_OP

Description

Test whether a given object is a NO_OP.

Usage

is_noop(x)

Arguments

x

any
Object to test.

Value

logical(1): Whether x is a NO_OP.

See Also

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

Dictionary of (sub-)graphs

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:

S3 methods

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)

Create a bagging learner

Description

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

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

graph

PipeOp | Graph
A PipeOpLearner or Graph to create a robustifying pipeline for. Outputs from the replicated graphs are connected with the averager.

iterations

integer(1)
Number of bagging iterations. Defaults to 10.

frac

numeric(1)
Percentage of rows to keep during subsampling. See PipeOpSubsample for more information. Defaults to 0.7.

averager

PipeOp | Graph
A PipeOp or Graph that averages the predictions from the replicated and subsampled graph's. In the simplest case, po("classifavg") and po("regravg") can be used in order to perform simple averaging of classification and regression predictions respectively. If NULL (default), no averager is added to the end of the graph. Note that setting collect_multipliciy = TRUE during construction of the averager is required.

replace

logical(1)
Whether to sample with replacement. Default FALSE.

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()

Branch Between Alternative Paths

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

graphs

list of Graph
Multiple graphs, possibly named. They all must have exactly one output. If any of the arguments are named, then all must have unique names.

prefix_branchops

character(1)
Optional id prefix to prepend to PipeOpBranch and PipeOpUnbranch id. Their resulting IDs will be "[prefix_branchops]branch" and "[prefix_branchops]unbranch". Default is "".

prefix_paths

logical(1) | character(1)
Whether to add prefixes to graph IDs when performing gunion. Can be helpful to avoid ID clashes in resulting graph. Default FALSE. If this is TRUE, the prefixes are taken from the names of the input arguments if present or "poX" where X counts up. If this is a character(1), it is a prefix that is added to the PipeOp IDs additionally to the input argument list.

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")

Convert Column Types

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

type_from

character
Which column types to convert. May be any combination of "logical", "integer", "numeric", "factor", "ordered", "character", or "POSIXct".

type_to

character(1)
Which type to convert to. Must be a scalar value, exactly one of the types allowed in type_from.

affect_columns

function | Selector | NULL
Which columns to affect. This argument can further restrict the columns being converted, beyond the type_from argument. Must be a Selector-like function, which takes a Task as argument and returns a character of features to use.

id

character(1) | NULL
ID to give to the constructed PipeOps. Defaults to an ID built automatically from type_from and type_to. If a PipeOpFixFactors is appended, its ID will be paste0(id, "_ff").

fixfactors

logical(1) | NULL
Whether to append a PipeOpFixFactors. Defaults to TRUE if and only if type_to is "factor" or "ordered".

more_args

list
Additional arguments to give to the conversion function. This could e.g. be used to pass the timezone to as.POSIXct.

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()

Create Disjoint Graph Union of Copies of a Graph

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

graph

Graph
Graph to replicate.

n

integer(1) Number of copies to create.

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)

Create A Graph to Perform "One vs. Rest" classification.

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

graph

Graph
Graph being wrapped between PipeOpOVRSplit and PipeOpOVRUnite. The Graph should return NULL during training and a classification Prediction during prediction.

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)

Robustify a learner

Description

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

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

task

Task
A Task to create a robustifying pipeline for. Optional, if omitted, the "worst possible" Task is assumed and the full pipeline is created.

learner

Learner
A learner to create a robustifying pipeline for. Optional, if omitted, the "worst possible" Learner is assumed and a more conservative pipeline is built.

impute_missings

logical(1) | NULL
Should missing values be imputed? Defaults to NULL: imputes if the task has missing values (or factors that are not encoded to numerics) and the learner can not handle them.

factors_to_numeric

logical(1) | NULL
Should (ordered and unordered) factors be encoded? Defaults to NULL: encodes if the task has factors (or character columns that get converted to factor) and the learner can not handle factors.

max_cardinality

integer(1)
Maximum number of factor levels allowed. See above. Default: 1000.

ordered_action

character(1)
How to handle ordered columns: "factor" (default) or "factor!": convert to factor columns; "numeric" or "numeric!": convert to numeric columns; "integer" or "integer!": convert to integer columns; "ignore" or "ignore!": ignore. When task is given and has no ordered columns, or when learner is given and can handle ordered, then "factor", "numeric" and "integer" are treated like "ignore". This means it is necessary to add the exclamation point to override Task or Learner properties when given. "ignore" and "ignore!" therefore behave completely identically, "ignore!" is only present for consistency.
When ordered features are converted to factor, then they are treated like factor features further down in the pipeline, and are possibly eventually converted to numerics, but in a different way: factors get one-hot encoded, ordered_action = "numeric" converts ordered using as.numeric to their integer-valued rank.

character_action

character(1)
How to handle character columns: "factor" (default) or "factor!": convert to factor columns; "matrix" or "matrix!": Use PipeOpTextVectorizer. "ignore" or "ignore!": ignore. When task is given and has no character columns, or when learner is given and can handle character, then "factor" and "matrix" are treated like "ignore". This means it is necessary to add the exclamation point to override Task or Learner properties when given. "ignore" and "ignore!" therefore behave completely identically, "ignore!" is only present for consistency.
When character columns are converted to factor, then they are treated like factor further down in the pipeline, and are possibly eventually converted to numerics, using one-hot encoding.

POSIXct_action

character(1)
How to handle POSIXct columns: "numeric" (default) or "numeric!": convert to numeric columns; "datefeatures" or "datefeatures!": Use PipeOpDateFeatures. "ignore" or "ignore!": ignore. When task is given and has no POSIXct columns, or when learner is given and can handle POSIXct, then "numeric" and "datefeatures" are treated like "ignore". This means it is necessary to add the exclamation point to override Task or Learner properties when given. "ignore" and "ignore!" therefore behave completely identically, "ignore!" is only present for consistency.

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"))

Create A Graph to Perform Stacking.

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

base_learners

list of Learner
A list of base learners.

super_learner

Learner
The super learner that makes the final prediction based on the base learners.

method

character(1)
"cv" (default) for building a super learner using cross-validated predictions of the base learners or "insample" for building a super learner using the predictions of the base learners trained on all training data.

folds

integer(1)
Number of cross-validation folds. Only used for method = "cv". Default 3.

use_features

logical(1)
Whether the original features should also be passed to the super learner. Default TRUE.

Value

Graph

Examples


library(mlr3)
library(mlr3learners)

base_learners = list(
  lrn("classif.rpart", predict_type = "prob"),
  lrn("classif.nnet", 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"))

Transform and Re-Transform the Target Variable

Description

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

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

graph

PipeOpLearner | Graph
A PipeOpLearner or Graph to wrap between a transformation and re-transformation of the target variable.

trafo_pipeop

PipeOp
A PipeOp that is a subclass of PipeOpTargetTrafo. Default is PipeOpTargetMutate.

id_prefix

character(1)
Optional id prefix to prepend to PipeOpTargetInvert ID. The resulting ID will be "[id_prefix]targetinvert". Default is "".

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)

Optimized Weighted Average of Features for Classification and Regression

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:

Methods

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

Encapsulate a Graph as a Learner

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)

Fields

Fields inherited from Learner, as well as:

Methods

Methods inherited from Learner, as well as:

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.

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()

Dictionary of PipeOps

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:

Methods

Methods inherited from Dictionary, as well as:

S3 methods

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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")]

ADAS Balancing

Description

Generates a more balanced data set by creating synthetic instances of the minority classes 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())

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 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:

Internals

If a target level is unobserved during training, no synthetic data points will be generated for that class. No error is raised; the unobserved class is simply ignored.

Fields

Only fields inherited from 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, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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)

BLSMOTE Balancing

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())

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 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:

Internals

If a target level is unobserved during training, no synthetic data points will be generated for that class. No error is raised; the unobserved class is simply ignored.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

Han H, Wang W, Mao B (2005). “Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning.” In Huang D, Zhang X, Huang G (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, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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)

Box-Cox Transformation of Numeric Features

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())

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:

Internals

Uses the bestNormalize::boxcox function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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

Path Branching

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())

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

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, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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"))

Chunk Input into Multiple Outputs

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())

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

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, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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))

Class Balancing

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())

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:

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". No upsampling is performed for classes that were not observed during training (i.e. empty factor levels in the target column).

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 PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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())

Majority Vote Prediction

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.

Format

R6Class inheriting from PipeOpEnsemble/PipeOp.

Construction

PipeOpClassifAvg$new(innum = 0, collect_multiplicity = FALSE, id = "classifavg", param_vals = 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 PipeOp.

Methods

Only methods inherited from PipeOpEnsemble/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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"))

Class Weights for Sample Weighting

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())

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:

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 PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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
}

Apply a Function to each Column of a Task

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())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

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 PipeOpTaskPreproc.

Parameters

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

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 PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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()

Collapse Factors

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 strictly above no_collapse_above_prevalence or absolute count strictly above no_collapse_above_absolute 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. In case both no_collapse_above_prevalence and no_collapse_above_absolute are given, the less strict threshold of the two will be used, i.e. if no_collapse_above_prevalence is 1 and no_collapse_above_absolute is 10 for a task with 100 samples, levels that are seen more than 10 times will not be collapsed.

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())

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:

Parameters

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

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".

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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")
op = PipeOpCollapseFactors$new()

# Create example training task
df = data.frame(
  target = runif(100),
  fct = factor(rep(LETTERS[1:6], times = c(25, 30, 5, 15, 5, 20))),
  ord = factor(rep(1:6, times = c(20, 25, 30, 5, 5, 15)), ordered = TRUE)
)
task = TaskRegr$new(df, target = "target", id = "example_train")

# Training
train_task_collapsed = op$train(list(task))[[1]]
train_task_collapsed$levels(c("fct", "ord"))

# Create example prediction task
df_pred = data.frame(
  target = runif(7),
  fct = factor(LETTERS[1:7]),
  ord = factor(1:7, ordered = TRUE)
)
pred_task = TaskRegr$new(df_pred, target = "target", id = "example_pred")

# Prediction
pred_task_collapsed = op$predict(list(pred_task))[[1]]
pred_task_collapsed$levels(c("fct", "ord"))

Change Column Roles of a Task

Description

Changes the column roles of the input Task according to new_role or its inverse new_role_direct.

Setting a new target variable or changing the role of an existing target variable is not supported.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpColRoles$new(id = "colroles", param_vals = 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 or its inverse new_role_direct.

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:

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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("penguins")
pop = po("colroles", param_vals = list(
  new_role = list(body_mass = c("order", "feature"))
))

train_out1 = pop$train(list(task))[[1L]]
train_out1$col_roles

pop$param_set$set_values(
 new_role = NULL,
 new_role_direct = list(order = character(), group = "island")
)

train_out2 = pop$train(list(train_out1))
train_out2$col_roles

Copy Input Multiple Times

Description

Copies its input outnum times. This PipeOp usually not needed, because copying happens automatically when one PipeOp is followed by multiple different PipeOps. However, when constructing big Graphs using the %>>%-operator, PipeOpCopy can be helpful to specify which PipeOp gets connected to which.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpCopy$new(outnum, id = "copy", param_vals = list())

Input and Output Channels

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

PipeOpCopy has multiple output channels depending on the outnum construction argument, named "output1", "output2", ... All output channels produce the object given as input ("*").

State

The ⁠$state⁠ is left empty (list()).

Parameters

PipeOpCopy has no parameters.

Internals

Note that copies are not clones, but only reference copies. This affects R6-objects: If R6 objects are copied using PipeOpCopy, they must be cloned beforehand.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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 Placeholder Pipeops: mlr_pipeops_nop

Examples

# The following copies the output of 'scale' automatically to both
# 'pca' and 'nop'
po("scale") %>>%
  gunion(list(
    po("pca"),
    po("nop")
  ))

# The following would not work: the '%>>%'-operator does not know
# which output to connect to which input
# > gunion(list(
# >   po("scale"),
# >   po("select")
# > )) %>>%
# >   gunion(list(
# >     po("pca"),
# >     po("nop"),
# >     po("imputemean")
# >   ))
# Instead, the 'copy' operator makes clear which output gets copied.
gunion(list(
  po("scale") %>>% po("copy", outnum = 2),
  po("select")
)) %>>%
  gunion(list(
    po("pca"),
    po("nop"),
    po("imputemean")
  ))

Preprocess Date Features

Description

Based on POSIXct columns of the data, a set of date related features is computed and added to the feature set of the output task. If no POSIXct column is found, the original task is returned unaltered. This functionality is based on the add_datepart() and add_cyclic_datepart() functions from the fastai library. If operation on only particular POSIXct columns is requested, use the affect_columns parameter inherited from PipeOpTaskPreprocSimple.

If cyclic = TRUE, cyclic features are computed for the features "month", "week_of_year", "day_of_year", "day_of_month", "day_of_week", "hour", "minute" and "second". This means that for each feature x, two additional features are computed, namely the sine and cosine transformation of 2 * pi * x / max_x (here max_x is the largest possible value the feature could take on + 1, assuming the lowest possible value is given by 0, e.g., for hours from 0 to 23, this is 24). This is useful to respect the cyclical nature of features such as seconds, i.e., second 21 and second 22 are one second apart, but so are second 60 and second 1 of the next minute.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpDateFeatures$new(id = "datefeatures", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with date-related features computed and added to the feature set of the output task and the POSIXct columns of the data removed from the feature set (depending on the value of keep_date_var).

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:

Internals

The cyclic feature transformation always assumes that values range from 0, so some values (e.g. day of the month) are shifted before sine/cosine transform.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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")
dat = iris
set.seed(1)
dat$date = sample(seq(as.POSIXct("2020-02-01"), to = as.POSIXct("2020-02-29"), by = "hour"),
  size = 150L)
task = TaskClassif$new("iris_date", backend = dat, target = "Species")
pop = po("datefeatures", param_vals = list(cyclic = FALSE, minute = FALSE, second = FALSE))
pop$train(list(task))
pop$state

Reverse Factor Encoding

Description

Reverses one-hot or treatment encoding of columns. It collapses multiple numeric or integer columns into one factor column based on a pre-specified grouping pattern of column names.

May be applied to multiple groups of columns, grouped by matching a common naming pattern. The grouping pattern is extracted to form the name of the newly derived factor column, and levels are constructed from the previous column names, with parts matching the grouping pattern removed (see examples). The level per row of the new factor column is generally determined as the name of the column with the maximum value in the group.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncode$new(id = "decode", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with encoding columns collapsed into new decoded columns.

State

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

Parameters

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

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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")

# Reverse one-hot encoding
df = data.frame(
  target = runif(4),
  x.1 = rep(c(1, 0), 2),
  x.2 = rep(c(0, 1), 2),
  y.1 = rep(c(1, 0), 2),
  y.2 = rep(c(0, 1), 2),
  a = runif(4)
)
task_one_hot = TaskRegr$new(id = "example", backend = df, target = "target")

pop = po("decode")

train_out = pop$train(list(task_one_hot))[[1]]
# x.1 and x.2 are collapsed into x, same for y; a is ignored.
train_out$data()

# Reverse treatment encoding from PipeOpEncode
df = data.frame(
  target = runif(6),
  fct = factor(rep(c("a", "b", "c"), 2))
)
task = TaskRegr$new(id = "example", backend = df, target = "target")

po_enc = po("encode", method = "treatment")
task_encoded = po_enc$train(list(task))[[1]]
task_encoded$data()

po_dec = po("decode", treatment_encoding = TRUE)
task_decoded = pop$train(list(task))[[1]]
# x.1 and x.2 are collapsed into x. All rows where all values
# are smaller or equal to 0, the level is set to the reference level.
task_decoded$data()

# Different group_pattern
df = data.frame(
  target = runif(4),
  x_1 = rep(c(1, 0), 2),
  x_2 = rep(c(0, 1), 2),
  y_1 = rep(c(2, 0), 2),
  y_2 = rep(c(0, 1), 2)
)
task = TaskRegr$new(id = "example", backend = df, target = "target")

# Grouped by first underscore
pop = po("decode", group_pattern = "^([^_]+)\\_")
train_out = pop$train(list(task))[[1]]
# x_1 and x_2 are collapsed into x, same for y
train_out$data()

# Empty string to collapse all matches into one factor column.
pop$param_set$set_values(group_pattern = "")
train_out = pop$train(list(task))[[1]]
# All columns are combined into a single column.
# The level for each row is determined by the column with the largest value in that row.
# By default, ties are resolved randomly.
train_out$data()

Factor Encoding

Description

Encodes columns of type factor and ordered.

Possible encodings are "one-hot" encoding, as well as encoding according to stats::contr.helmert(), stats::contr.poly(), stats::contr.sum() and stats::contr.treatment(). Newly created columns are named via pattern ⁠[column-name].[x]⁠ where x is the respective factor level for "one-hot" and "treatment" encoding, and an integer sequence otherwise.

Use the PipeOpTaskPreproc ⁠$affect_columns⁠ functionality to only encode a subset of columns, or only encode columns of a certain type.

character-type features can be encoded by converting them factor features first, using ppl("convert_types", "character", "factor").

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncode$new(id = "encode", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected factor and ordered columns encoded according to the method parameter.

State

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

Parameters

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

Internals

Uses the stats::contrasts functions. This is relatively inefficient for features with a large number of levels.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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")

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

poe = po("encode")

# poe is initialized with encoding: "one-hot"
poe$train(list(task))[[1]]$data()

# other kinds of encoding:
poe$param_set$values$method = "treatment"
poe$train(list(task))[[1]]$data()

poe$param_set$values$method = "helmert"
poe$train(list(task))[[1]]$data()

poe$param_set$values$method = "poly"
poe$train(list(task))[[1]]$data()

poe$param_set$values$method = "sum"
poe$train(list(task))[[1]]$data()

# converting character-columns
data_chr = data.table::data.table(x = factor(letters[1:3]), y = letters[1:3])
task_chr = TaskClassif$new("task_chr", data_chr, "x")

goe = ppl("convert_types", "character", "factor") %>>% po("encode")

goe$train(task_chr)[[1]]$data()

Conditional Target Value Impact Encoding

Description

Encodes columns of type factor, character and ordered.

Impact coding for classification Tasks converts factor levels of each (factorial) column to the difference between each target level's conditional log-likelihood given this level, and the target level's global log-likelihood.

Impact coding for regression Tasks converts factor levels of each (factorial) column to the difference between the target's conditional mean given this level, and the target's global mean.

Treats new levels during prediction like missing values.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncodeImpact$new(id = "encodeimpact", param_vals = list())

Input and Output Channels

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

The output is the input Task with all affected factor, character or ordered parameters encoded.

State

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

Parameters

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

Internals

Uses Laplace smoothing, mostly to avoid infinite values for classification Task.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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")
poe = po("encodeimpact")

task = TaskClassif$new("task",
  data.table::data.table(
    x = factor(c("a", "a", "a", "b", "b")),
    y = factor(c("a", "a", "b", "b", "b"))),
  "x")

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

poe$state

Impact Encoding with Random Intercept Models

Description

Encodes columns of type factor, character and ordered.

PipeOpEncodeLmer converts factor levels of each factorial column to the estimated coefficients of a simple random intercept model. Models are fitted with the glmer function of the lme4 package and are of the type target ~ 1 + (1 | factor). If the task is a regression task, the numeric target variable is used as dependent variable and the factor is used for grouping. If the task is a classification task, the target variable is used as dependent variable and the factor is used for grouping. If the target variable is multiclass, for each level of the multiclass target variable, binary "one vs. rest" models are fitted.

For training, multiple models can be estimated in a cross-validation scheme to ensure that the same factor level does not always result in identical values in the converted numerical feature. For prediction, a global model (which was fitted on all observations during training) is used for each factor. New factor levels are converted to the value of the intercept coefficient of the global model for prediction. NAs are ignored by the CPO.

Use the PipeOpTaskPreproc ⁠$affect_columns⁠ functionality to only encode a subset of columns, or only encode columns of a certain type.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncodeLmer$new(id = "encodelmer", param_vals = list())

Input and Output Channels

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

The output is the input Task with all affected factor, character or ordered parameters encoded according to the method parameter.

State

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

Parameters

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

Internals

Uses the lme4::glmer. This is relatively inefficient for features with a large number of levels.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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")
poe = po("encodelmer")

task = TaskClassif$new("task",
  data.table::data.table(
    x = factor(c("a", "a", "a", "b", "b")),
    y = factor(c("a", "a", "b", "b", "b"))),
  "x")

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

poe$state

Piecewise Linear Encoding using Quantiles

Description

Encodes numeric and integer feature columns using piecewise lienar encoding. For details, see documentation of PipeOpEncodePL or Gorishniy et al. (2022).

Bins are constructed by taking the quantiles of the respective feature column as bin boundaries. The first and last boundaries are set to the minimum and maximum value of the feature, respectively. The number of bins can be controlled with the numsplits hyperparameter. Affected feature columns may contain NAs. These are ignored when calculating quantiles.

Format

R6Class object inheriting from PipeOpEncodePL/PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncodePLQuantiles$new(id = "encodeplquantiles", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected numeric and integer columns encoded using piecewise linear encoding with bins being derived from the quantiles of the respective original feature column.

State

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

Parameters

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

Internals

This overloads the private$.get_bins() method of PipeOpEncodePL and uses the stats::quantile function to derive the bins used for piecewise linear encoding.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpEncodePL/PipeOpTaskPreproc/PipeOp.

References

Gorishniy Y, Rubachev I, Babenko A (2022). “On Embeddings for Numerical Features in Tabular Deep Learning.” In Advances in Neural Information Processing Systems, volume 35, 24991–25004. https://proceedings.neurips.cc/paper_files/paper/2022/hash/9e9f0ffc3d836836ca96cbf8fe14b105-Abstract-Conference.html.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodepltree, 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 Piecewise Linear Encoding PipeOps: PipeOpEncodePL, mlr_pipeops_encodepltree

Examples

library(mlr3)

task = tsk("iris")$select(c("Petal.Width", "Petal.Length"))
pop = po("encodeplquantiles")

train_out = pop$train(list(task))[[1L]]
# Calculated bin boundaries per feature
pop$state$bins
# Each feature was split into two encoded features using piecewise linear encoding
train_out$head()

# Prediction works the same as training, using the bins learned during training
predict_out = pop$predict(list(task))[[1L]]
predict_out$head()

# Binning into three bins per feature
# Using the nearest even order statistic for caluclating quantiles
pop$param_set$set_values(numsplits = 4, type = 3)

train_out = pop$train(list(task))[[1L]]
# Calculated bin boundaries per feature
pop$state$bins
# Each feature was split into three encoded features using
# piecewise linear encoding
train_out$head()

Piecewise Linear Encoding using Decision Trees

Description

Encodes numeric and integer feature columns using piecewise lienar encoding. For details, see documentation of PipeOpEncodePL or Gorishniy et al. (2022).

Bins are constructed by trainig one decision tree Learner per feature column, taking the target column into account, and using decision boundaries as bin boundaries.

Format

R6Class object inheriting from PipeOpEncodePL/PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpEncodePLTree$new(task_type, id = "encodepltree", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc. Instead of a Task, a TaskClassif or TaskRegr is used as input and output during training and prediction, depending on the task_type construction argument.

The output is the input Task with all affected numeric and integer columns encoded using piecewise linear encoding with bins being derived from a decision tree Learner trained on the respective feature column.

State

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

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as the parameters of the Learner used for obtaining the bins for piecewise linear encoding.

Internals

This overloads the private$.get_bins() method of PipeOpEncodePL. To derive the bins for each feature, the Task is split into smaller Tasks with only the target and respective feature as columns. On these Tasks either a LearnerClassifRpart or LearnerRegrRpart gets trained and the respective splits extracted as bin boundaries used for piecewise linear encodings.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpEncodePL/PipeOpTaskPreproc/PipeOp.

References

Gorishniy Y, Rubachev I, Babenko A (2022). “On Embeddings for Numerical Features in Tabular Deep Learning.” In Advances in Neural Information Processing Systems, volume 35, 24991–25004. https://proceedings.neurips.cc/paper_files/paper/2022/hash/9e9f0ffc3d836836ca96cbf8fe14b105-Abstract-Conference.html.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, 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 Piecewise Linear Encoding PipeOps: PipeOpEncodePL, mlr_pipeops_encodeplquantiles

Examples


library(mlr3)

# For classification task
task = tsk("iris")$select(c("Petal.Width", "Petal.Length"))
pop = po("encodepltree", task_type = "TaskClassif")
train_out = pop$train(list(task))[[1L]]

# Calculated bin boundaries per feature
pop$state$bins
# Each feature was split into three encoded features using piecewise linear encoding
train_out$head()

# Prediction works the same as training, using the bins learned during training
predict_out = pop$predict(list(task))[[1L]]
predict_out$head()

# Controlling behavior of the tree learner, here: setting minimum number of
# observations per node for a split to be attempted
pop$param_set$set_values(minsplit = 5)

train_out = pop$train(list(task))[[1L]]
# feature "hp" now gets split into five encoded features instead of three
pop$state$bins
train_out$head()

# For regression task
task = tsk("mtcars")$select(c("cyl", "hp"))
pop = po("encodepltree", task_type = "TaskRegr")
train_out = pop$train(list(task))[[1L]]

# Calculated bin boundaries per feature
pop$state$bins
# First feature was split into three encoded features,
# second into two, using piecewise linear encoding
train_out$head()

Aggregate Features from Multiple Inputs

Description

Aggregates features from all input tasks by cbind()ing them together into a single Task.

DataBackend primary keys and Task targets have to be equal across all Tasks. Only the target column(s) of the first Task are kept.

If assert_targets_equal is TRUE then target column names are compared and an error is thrown if they differ across inputs.

If input tasks share some feature names but these features are not identical an error is thrown. This check is performed by first comparing the features names and if duplicates are found, also the values of these possibly duplicated features. True duplicated features are only added a single time to the output task.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpFeatureUnion$new(innum = 0, collect_multiplicity = FALSE, id = "featureunion", param_vals = list(),
  assert_targets_equal = TRUE)

Input and Output Channels

PipeOpFeatureUnion has multiple input channels depending on the innum construction argument, named "input1", "input2", ... if innum is nonzero; if innum is 0, there is only one vararg input channel named "...". All input channels take a Task both during training and prediction.

PipeOpFeatureUnion has one output channel named "output", producing a Task both during training and prediction.

The output is a Task constructed by cbind()ing all features from all input Tasks, both during training and prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

PipeOpFeatureUnion has no Parameters.

Internals

PipeOpFeatureUnion uses the Task ⁠$cbind()⁠ method to bind the input values beyond the first input to the first Task. This means if the Tasks are database-backed, all of them except the first will be fetched into R memory for this. This behaviour may change in the future.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_classifavg, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Examples

library("mlr3")

task1 = tsk("iris")
gr = gunion(list(
  po("nop"),
  po("pca")
)) %>>% po("featureunion")

gr$train(task1)

task2 = tsk("iris")
task3 = tsk("iris")
po = po("featureunion", innum = c("a", "b"))

po$train(list(task2, task3))

Feature Filtering

Description

Feature filtering using a mlr3filters::Filter object, see the mlr3filters package.

If a Filter can only operate on a subset of columns based on column type, then only these features are considered and filtered. nfeat and frac will count for the features of the type that the Filter can operate on; this means e.g. that setting nfeat to 0 will only remove features of the type that the Filter can work with.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpFilter$new(filter, id = filter$id, param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with features removed that were filtered out.

State

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

Parameters

The parameters are the parameters inherited from the PipeOpTaskPreproc, as well as the parameters of the Filter used by this object. Besides, parameters introduced are:

Note that at least one of filter.nfeat, filter.frac, filter.cutoff, and filter.permuted must be given.

Internals

This does not use the ⁠$.select_cols⁠ feature of PipeOpTaskPreproc to select only features compatible with the Filter; instead the whole Task is used by private$.get_state() and subset internally.

Fields

Fields inherited from PipeOp, as well as:

Methods

Methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

References

Wu Y, Boos DD, Stefanski LA (2007). “Controlling Variable Selection by the Addition of Pseudovariables.” Journal of the American Statistical Association, 102(477), 235–243. doi:10.1198/016214506000000843.

Thomas J, Hepp T, Mayr A, Bischl B (2017). “Probing for Sparse and Fast Variable Selection with Model-Based Boosting.” Computational and Mathematical Methods in Medicine, 2017, 1–8. doi:10.1155/2017/1421409.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, 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")
library("mlr3filters")


# setup PipeOpFilter to keep the 5 most important
# features of the spam task w.r.t. their AUC
task = tsk("spam")
filter = flt("auc")
po = po("filter", filter = filter)
po$param_set
po$param_set$values$filter.nfeat = 5

# filter the task
filtered_task = po$train(list(task))[[1]]

# filtered task + extracted AUC scores
filtered_task$feature_names
head(po$state$scores, 10)

# feature selection embedded in a 3-fold cross validation
# keep 30% of features based on their AUC score
task = tsk("spam")
gr = po("filter", filter = flt("auc"), filter.frac = 0.5) %>>%
  po("learner", lrn("classif.rpart"))
learner = GraphLearner$new(gr)
rr = resample(task, learner, rsmp("holdout"), store_models = TRUE)
rr$learners[[1]]$model$auc$scores

Fix Factor Levels

Description

Fixes factors of type factor, ordered: Makes sure the factor levels during prediction are the same as during training; possibly dropping empty training factor levels before.

Note this may introduce missing values during prediction if unseen factor levels are found.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpFixFactors$new(id = "fixfactors", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected factor and ordered feature levels fixed.

State

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

Parameters

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

Internals

Changes factor levels of columns and attaches them with a new data.table backend and the virtual cbind() backend.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, 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")

Split Numeric Features into Equally Spaced Bins

Description

Splits numeric features into equally spaced bins. See graphics::hist() for details. Values that fall out of the training data range during prediction are binned with the lowest / highest bin respectively.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpHistBin$new(id = "histbin", param_vals = 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 binned versions.

State

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

Parameters

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

Internals

Uses the graphics::hist function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, 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("histbin")

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

pop$state

Independent Component Analysis

Description

Extracts statistically independent components from data. Only affects numerical features. See fastICA::fastICA for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpICA$new(id = "ica", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected numeric parameters replaced by independent components.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as the elements of the function fastICA::fastICA(), with the exception of the ⁠$X⁠ and ⁠$S⁠ slots. These are in particular:

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as the following parameters based on fastICA():

Internals

Uses the fastICA() function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, 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("ica")

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

pop$state

Impute Features by a Constant

Description

Impute features by a constant value.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeConstant$new(id = "imputeconstant", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected features missing values imputed by the value of the constant parameter.

State

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

The ⁠$state$model⁠ contains the value of the constant parameter that is used for imputation.

Parameters

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

Internals

The constructor is called with empty_level_control set to "always", to allow the creation of a new empty level for factor and ordered (but not character) features during training, if constant is not an already existing level and check_levels is set to FALSE. This has no impact if check_levels is TRUE, since in that case an error would be raised before imputation.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, 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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Examples

library("mlr3")

task = tsk("pima")
task$missings()

# impute missing values of the numeric feature "glucose" by the constant value -999
po = po("imputeconstant", param_vals = list(
  constant = -999, affect_columns = selector_name("glucose"))
)
new_task = po$train(list(task = task))[[1]]
new_task$missings()
new_task$data(cols = "glucose")[[1]]

Impute Numerical Features by Histogram

Description

Impute numerical features by histogram.

During training, a histogram is fitted on each column using R's hist() function. The fitted histogram is then sampled from for imputation. Sampling happens in a two-step process: First, a bin is sampled from the histogram, then a value is sampled uniformly from the bin. This is an approximation to sampling from the empirical training data distribution (i.e. sampling from training data with replacement), but is much more memory efficient for large datasets, since the ⁠$state⁠ does not need to save the training data.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeHist$new(id = "imputehist", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected numeric features missing values imputed by (column-wise) histogram; see Description for details.

State

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

The ⁠$state$model⁠ is a named list of lists containing elements ⁠$counts⁠ and ⁠$breaks⁠.

Parameters

The parameters are the parameters inherited from PipeOpImpute.

Internals

Uses the graphics::hist() function. Features that are entirely NA are imputed as 0.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, 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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputeconstant, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Examples

library("mlr3")

task = tsk("pima")
task$missings()

po = po("imputehist")
new_task = po$train(list(task = task))[[1]]
new_task$missings()

po$state$model

Impute Features by Fitting a Learner

Description

Impute features by fitting a Learner for each feature. Uses the features indicated by the context_columns parameter as features to train the imputation Learner. Note this parameter is part of the PipeOpImpute base class and explained there.

Additionally, only features supported by the learner can be imputed; i.e. learners of type regr can only impute features of type integer and numeric, while classif can impute features of type factor, ordered and logical.

The Learner used for imputation is trained on all context_columns; if these contain missing values, the Learner typically either needs to be able to handle missing values itself, or needs to do its own imputation (see examples).

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeLearner$new(learner, id = NULL, param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with missing values from all affected features imputed by the trained model.

State

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

The ⁠$state$models⁠ is a named list of models created by the Learner's ⁠$.train()⁠ function for each column. If a column consists of missing values only during training, the model is 0 or the levels of the feature; these are used for sampling during prediction.

This state is given the class "pipeop_impute_learner_state".

Parameters

The parameters are the parameters inherited from PipeOpImpute, in addition to the parameters of the Learner used for imputation.

Internals

Uses the ⁠$train⁠ and ⁠$predict⁠ functions of the provided learner. Features that are entirely NA are imputed as 0 or randomly sampled from available (factor / logical) levels.

The Learner does not necessarily need to handle missing values in cases where context_columns is chosen well (or there is only one column with missing values present).

Fields

Fields inherited from PipeOpTaskPreproc/PipeOp, as well as:

Methods

Only methods inherited from PipeOpImpute/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, 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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Examples


library("mlr3")

task = tsk("pima")
task$missings()

po = po("imputelearner", lrn("regr.rpart"))
new_task = po$train(list(task = task))[[1]]
new_task$missings()

# '$state' of the "regr.rpart" Learner, trained to predict the 'mass' column:
po$state$model$mass

library("mlr3learners")
# To use the "regr.lm" Learner, prefix it with its own imputation method!
# The "imputehist" PipeOp is used to train "regr.lm"; predictions of this
# trained Learner are then used to impute the missing values in the Task.
po = po("imputelearner",
  po("imputehist") %>>% lrn("regr.lm")
)

new_task = po$train(list(task = task))[[1]]
new_task$missings()

Impute Numerical Features by their Mean

Description

Impute numerical features by their mean.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeMean$new(id = "imputemean", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected numeric features missing values imputed by (column-wise) mean.

State

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

The ⁠$state$model⁠ is a named list of numeric(1) indicating the mean of the respective feature.

Parameters

The parameters are the parameters inherited from PipeOpImpute.

Internals

Uses the mean() function. Features that are entirely NA are imputed as 0.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Examples

library("mlr3")

task = tsk("pima")
task$missings()

po = po("imputemean")
new_task = po$train(list(task = task))[[1]]
new_task$missings()

po$state$model

Impute Numerical Features by their Median

Description

Impute numerical features by their median.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeMedian$new(id = "imputemedian", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected numeric features missing values imputed by (column-wise) median.

State

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

The ⁠$state$model⁠ is a named list of numeric(1) indicating the median of the respective feature.

Parameters

The parameters are the parameters inherited from PipeOpImpute.

Internals

Uses the stats::median() function. Features that are entirely NA are imputed as 0.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Examples

library("mlr3")

task = tsk("pima")
task$missings()

po = po("imputemedian")
new_task = po$train(list(task = task))[[1]]
new_task$missings()

po$state$model

Impute Features by their Mode

Description

Impute features by their mode. Supports factors as well as logical and numerical features. If multiple modes are present then imputed values are sampled randomly from them.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeMode$new(id = "imputemode", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected features missing values imputed by (column-wise) mode.

State

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

The ⁠$state$model⁠ is a named list of a vector of length one of the type of the feature, indicating the mode of the respective feature.

Parameters

The parameters are the parameters inherited from PipeOpImpute.

Internals

Features that are entirely NA are imputed as the following: For factor or ordered, random levels are sampled uniformly at random. For logicals, TRUE or FALSE are sampled uniformly at random. Numerics and integers are imputed as 0.

Note that every random imputation is drawn independently, so different values may be imputed if multiple values are missing.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputeoor, mlr_pipeops_imputesample

Examples

library("mlr3")

task = tsk("pima")
task$missings()

po = po("imputemode")
new_task = po$train(list(task = task))[[1]]
new_task$missings()

po$state$model

Out of Range Imputation

Description

Impute factorial features by adding a new level ".MISSING".

Impute numerical features by constant values shifted below the minimum or above the maximum by using min(x) - offset - multiplier * diff(range(x)) or max(x) + offset + multiplier * diff(range(x)).

This type of imputation is especially sensible in the context of tree-based methods, see also Ding & Simonoff (2010).

Learners expect input Tasks to have the same factor (or ordered) levels during training as well as prediction. This PipeOp modifies the levels of factor and ordered features, and since it may occur that a factor or ordered feature contains missing values only during prediction, but not during training, the output Task could also have different levels during the two stages.

To avoid problems with the Learners' expectation, controlling the PipeOps' handling of this edge-case is necessary. For this, use the create_empty_level hyperparameter inherited from PipeOpImpute.
If create_empty_level is set to TRUE, then an unseen level ".MISSING" is added to the feature during training and missing values are imputed as ".MISSING" during prediction. However, empty factor levels during training can be a problem for many Learners.
If create_empty_level is set to FALSE, then no empty level is introduced during training, but columns that have missing values only during prediction will not be imputed. This is why it may still be necessary to use po("imputesample", affect_columns = selector_type(types = c("factor", "ordered"))) (or another imputation method) after this imputation method. Note that setting create_empty_level to FALSE is the same as setting it to TRUE and using PipeOpFixFactors after this PipeOp.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeOOR$new(id = "imputeoor", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected features having missing values imputed as described above.

State

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

The ⁠$state$model⁠ contains either ".MISSING" used for character and factor (also ordered) features or numeric(1) indicating the constant value used for imputation of integer and numeric features.

Parameters

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

Internals

Adds an explicit new level() to factor and ordered features, but not to character features. For integer and numeric features uses the min, max, diff and range functions. integer and numeric features that are entirely NA are imputed as 0. factor and ordered features that are entirely NA are imputed as ".MISSING".

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

References

Ding Y, Simonoff JS (2010). “An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data.” Journal of Machine Learning Research, 11(6), 131-170. https://jmlr.org/papers/v11/ding10a.html.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputesample

Examples

library("mlr3")
set.seed(2409)
data = tsk("pima")$data()
data$y = factor(c(NA, sample(letters, size = 766, replace = TRUE), NA))
data$z = ordered(c(NA, sample(1:10, size = 767, replace = TRUE)))
task = TaskClassif$new("task", backend = data, target = "diabetes")
task$missings()
po = po("imputeoor")
new_task = po$train(list(task = task))[[1]]
new_task$missings()
new_task$data()

# recommended use when missing values are expected during prediction on
# factor columns that had no missing values during training
gr = po("imputeoor", create_empty_level = FALSE) %>>%
  po("imputesample", affect_columns = selector_type(types = c("factor", "ordered")))
t1 = as_task_classif(data.frame(l = as.ordered(letters[1:3]), t = letters[1:3]), target = "t")
t2 = as_task_classif(data.frame(l = as.ordered(c("a", NA, NA)), t = letters[1:3]), target = "t")
gr$train(t1)[[1]]$data()

# missing values during prediction are sampled randomly
gr$predict(t2)[[1]]$data()

Impute Features by Sampling

Description

Impute features by sampling from non-missing training data.

Format

R6Class object inheriting from PipeOpImpute/PipeOp.

Construction

PipeOpImputeSample$new(id = "imputesample", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected numeric features missing values imputed by values sampled (column-wise) from training data.

State

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

The ⁠$state$model⁠ is a named list of training data with missings removed.

Parameters

The parameters are the parameters inherited from PipeOpImpute.

Internals

Uses the sample() function. Features that are entirely NA are imputed as the following: For factor or ordered, random levels are sampled uniformly at random. For logicals, TRUE or FALSE are sampled uniformly at random. Numerics and integers are imputed as 0.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpImpute/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor

Examples

library("mlr3")

task = tsk("pima")
task$missings()

po = po("imputesample")
new_task = po$train(list(task = task))[[1]]
new_task$missings()

Kernelized Principal Component Analysis

Description

Extracts kernel principal components from data. Only affects numerical features. See kernlab::kpca for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpKernelPCA$new(id = "kernelpca", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected numeric parameters replaced by their principal components.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as the returned S4 object of the function kernlab::kpca().

The ⁠@rotated⁠ slot of the "kpca" object is overwritten with an empty matrix for memory efficiency.

The slots of the S4 object can be accessed by accessor function. See kernlab::kpca.

Parameters

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

Internals

Uses the kpca() function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("kernelpca", features = 3)  # only keep top 3 components

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

Wrap a Learner into a PipeOp

Description

Wraps an mlr3::Learner into a PipeOp.

Inherits the ⁠$param_set⁠ (and therefore ⁠$param_set$values⁠) from the Learner it is constructed from.

Using PipeOpLearner, it is possible to embed mlr3::Learners into Graphs, which themselves can be turned into Learners using GraphLearner. This way, preprocessing and ensemble methods can be included into a machine learning pipeline which then can be handled as singular object for resampling, benchmarking and tuning.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpLearner$new(learner, id = NULL, param_vals = list())

Input and Output Channels

PipeOpLearner has one input channel named "input", taking a Task specific to the Learner type given to learner during construction; both during training and prediction.

PipeOpLearner has one output channel named "output", producing NULL during training and a Prediction subclass during prediction; this subclass is specific to the Learner type given to learner during construction.

The output during prediction is the Prediction on the prediction input data, produced by the Learner trained on the training input data.

State

The ⁠$state⁠ is set to the ⁠$state⁠ slot of the Learner object. It is a named list with members:

Parameters

The parameters are exactly the parameters of the Learner wrapped by this object.

Internals

The ⁠$state⁠ is currently not updated by prediction, so the ⁠$state$predict_log⁠ and ⁠$state$predict_time⁠ will always be NULL.

Fields

Fields inherited from PipeOp, as well as:

Methods

Methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Meta PipeOps: mlr_pipeops_learner_cv, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles

Examples


library("mlr3")

task = tsk("iris")
learner = lrn("classif.rpart", cp = 0.1)
lrn_po = mlr_pipeops$get("learner", learner)

lrn_po$train(list(task))
lrn_po$predict(list(task))

Wrap a Learner into a PipeOp with Cross-validated Predictions as Features

Description

Wraps an mlr3::Learner into a PipeOp.

Returns cross-validated predictions during training as a Task and stores a model of the Learner trained on the whole data in ⁠$state⁠. This is used to create a similar Task during prediction.

The Task gets features depending on the capsuled Learner's ⁠$predict_type⁠. If the Learner's ⁠$predict.type⁠ is "response", a feature ⁠<ID>.response⁠ is created, for ⁠$predict.type⁠ "prob" the ⁠<ID>.prob.<CLASS>⁠ features are created, and for ⁠$predict.type⁠ "se" the new columns are ⁠<ID>.response⁠ and ⁠<ID>.se⁠. ⁠<ID>⁠ denotes the ⁠$id⁠ of the PipeOpLearnerCV object.

Inherits the ⁠$param_set⁠ (and therefore ⁠$param_set$values⁠) from the Learner it is constructed from.

PipeOpLearnerCV can be used to create "stacking" or "super learning" Graphs that use the output of one Learner as feature for another Learner. Because the PipeOpLearnerCV erases the original input features, it is often useful to use PipeOpFeatureUnion to bind the prediction Task to the original input Task.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpLearnerCV$new(learner, id = NULL, param_vals = list())

Input and Output Channels

PipeOpLearnerCV has one input channel named "input", taking a Task specific to the Learner type given to learner during construction; both during training and prediction.

PipeOpLearnerCV has one output channel named "output", producing a Task specific to the Learner type given to learner during construction; both during training and prediction.

The output is a task with the same target as the input task, with features replaced by predictions made by the Learner. During training, this prediction is the out-of-sample prediction made by resample, during prediction, this is the ordinary prediction made on the data by a Learner trained on the training phase data.

State

The ⁠$state⁠ is set to the ⁠$state⁠ slot of the Learner object, together with the ⁠$state⁠ elements inherited from the PipeOpTaskPreproc. It is a named list with the inherited members, as well as:

This state is given the class "pipeop_learner_cv_state".

Parameters

The parameters are the parameters inherited from the PipeOpTaskPreproc, as well as the parameters of the Learner wrapped by this object. Besides that, parameters introduced are:

Internals

The ⁠$state⁠ is currently not updated by prediction, so the ⁠$state$predict_log⁠ and ⁠$state$predict_time⁠ will always be NULL.

Fields

Fields inherited from PipeOp, as well as:

Methods

Methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other Meta PipeOps: mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles

Examples


library("mlr3")

task = tsk("iris")
learner = lrn("classif.rpart")

lrncv_po = po("learner_cv", learner)
lrncv_po$learner$predict_type = "response"

nop = mlr_pipeops$get("nop")

graph = gunion(list(
  lrncv_po,
  nop
)) %>>% po("featureunion")

graph$train(task)

graph$pipeops$classif.rpart$learner$predict_type = "prob"

graph$train(task)

Wrap a Learner into a PipeOp with Cross-validation Plus Confidence Intervals as Predictions

Description

Wraps an mlr3::Learner into a PipeOp.

Inherits the ⁠$param_set⁠ (and therefore ⁠$param_set$values⁠) from the Learner it is constructed from.

Using PipeOpLearnerPICVPlus, it is possible to embed a mlr3::Learner into a Graph. PipeOpLearnerPICVPlus can then be used to perform cross validation plus (or jackknife plus). During training, PipeOpLearnerPICVPlus performs cross validation on the training data. During prediction, the models from the training stage are used to construct predictive confidence intervals for the prediction data based on out-of-fold residuals and out-of-fold predictions.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpLearnerPICVPlus$new(learner, id = NULL, param_vals = list())

Input and Output Channels

PipeOpLearnerPICVPlus has one input channel named "input", taking a Task specific to the Learner type given to learner during construction; both during training and prediction.

PipeOpLearnerPICVPlus has one output channel named "output", producing NULL during training and a PredictionRegr during prediction.

The output during prediction is a PredictionRegr with predict_type quantiles on the prediction input data. The alpha and 1 - alpha quantiles are the quantiles of the prediction interval produced by the cross validation plus method. The response is the median of the prediction of all cross validation models on the prediction data.

State

The ⁠$state⁠ is a named list with members:

This state is given the class "pipeop_learner_cv_state".

Parameters

The parameters of the Learner wrapped by this object, as well as:

Internals

The ⁠$state⁠ is updated during training.

Fields

Fields inherited from PipeOp, as well as:

Methods

Methods inherited from PipeOp.

References

Barber RF, Candes EJ, Ramdasa A, Tibshirani RJ (2021). “Predictive inference with the jackknife+.” Annals of Statistics, 49, 486–507. doi:10.1214/20-AOS1965.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Meta PipeOps: mlr_pipeops_learner, mlr_pipeops_learner_cv, mlr_pipeops_learner_quantiles

Examples


library("mlr3")

task = tsk("mtcars")
learner = lrn("regr.rpart")
lrncvplus_po = mlr_pipeops$get("learner_pi_cvplus", learner)

lrncvplus_po$train(list(task))
lrncvplus_po$predict(list(task))

Wrap a Learner into a PipeOp to to predict multiple Quantiles

Description

Wraps a LearnerRegr into a PipeOp to predict multiple quantiles.

PipeOpLearnerQuantiles only supports LearnerRegrs that have quantiles as a possible pedict_type.

It produces quantile-based predictions for multiple quantiles in one PredictionRegr. This is especially helpful if the LearnerRegr can only predict one quantile (like for example LearnerRegrGBM in mlr3extralearners)

Inherits the ⁠$param_set⁠ (and therefore ⁠$param_set$values⁠) from the Learner it is constructed from.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpLearnerQuantiles$new(learner, id = NULL, param_vals = list())

Input and Output Channels

PipeOpLearnerQuantiles has one input channel named "input", taking a TaskRegr specific to the Learner type given to learner during construction; both during training and prediction.

PipeOpLearnerQuantiles has one output channel named "output", producing NULL during training and a PredictionRegr object during prediction.

The output during prediction is a PredictionRegr on the prediction input data that aggregates all results produced by the Learner for each quantile in quantiles. trained on the training input data.

State

The ⁠$state⁠ is set during training. It is a named list with the member:

Parameters

The parameters are exactly the parameters of the Learner wrapped by this object.

Internals

The ⁠$state⁠ is updated during training.

Fields

Fields inherited from PipeOp, as well as:

Methods

Methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Meta PipeOps: mlr_pipeops_learner, mlr_pipeops_learner_cv, mlr_pipeops_learner_pi_cvplus

Examples

library("mlr3")

task = tsk("boston_housing")
learner = lrn("regr.debug")
po = mlr_pipeops$get("learner_quantiles", learner)

po$train(list(task))
po$predict(list(task))

Add Missing Indicator Columns

Description

Add missing indicator columns ("dummy columns") to the Task. Drops original features; should probably be used in combination with PipeOpFeatureUnion and imputation PipeOps (see examples).

Note the affect_columns is initialized with selector_invert(selector_type(c("factor", "ordered", "character"))), since missing values in factorial columns are often indicated by out-of-range imputation (PipeOpImputeOOR).

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpMissInd$new(id = "missind", param_vals = list())

State

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

Parameters

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

Internals

This PipeOp should cover most cases where "dummy columns" or "missing indicators" are desired. Some edge cases:

Fields

Fields inherited from PipeOp.

Methods

Methods inherited from PipeOpTaskPreprocSimple(PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("pima")$select(c("insulin", "triceps"))
sum(complete.cases(task$data()))
task$missings()
tail(task$data())

po = po("missind")
new_task = po$train(list(task))[[1]]

tail(new_task$data())

# proper imputation + missing indicators

impgraph = list(
  po("imputesample"),
  po("missind")
) %>>% po("featureunion")

tail(impgraph$train(task)[[1]]$data())

Transform Columns by Constructing a Model Matrix

Description

Transforms columns using a given formula using the stats::model.matrix() function.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpModelMatrix$new(id = "modelmatrix", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with transformed columns according to the used formula.

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:

Internals

Uses the model.matrix() function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("modelmatrix", formula = ~ .  ^ 2)

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

pop$param_set$values$formula = ~ 0 + . ^ 2

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

Explicate a Multiplicity

Description

Explicate a Multiplicity by turning the input Multiplicity into multiple outputs.

This PipeOp has multiple output channels; the members of the input Multiplicity are forwarded each along a single edge. Therefore, only multiplicities with exactly as many members as outnum are accepted.

Note that Multiplicity is currently an experimental features and the implementation or UI may change.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpMultiplicityExply$new(outnum , id = "multiplicityexply", param_vals = list())

Input and Output Channels

PipeOpMultiplicityExply has a single input channel named "input", collecting a Multiplicity of type any ("[*]") both during training and prediction.

PipeOpMultiplicityExply has multiple output channels depending on the outnum construction argument, named "output1", "output2" returning the elements of the unclassed input Multiplicity.

State

The ⁠$state⁠ is left empty (list()).

Parameters

PipeOpMultiplicityExply has no Parameters.

Internals

outnum should match the number of elements of the unclassed input Multiplicity.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Other Experimental Features: Multiplicity(), mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_replicate

Examples

library("mlr3")
task1 = tsk("iris")
task2 = tsk("mtcars")
po = po("multiplicityexply", outnum = 2)
po$train(list(Multiplicity(task1, task2)))
po$predict(list(Multiplicity(task1, task2)))

Implicate a Multiplicity

Description

Implicate a Multiplicity by returning the input(s) converted to a Multiplicity.

This PipeOp has multiple input channels; all inputs are collected into a Multiplicity and then are forwarded along a single edge, causing the following PipeOps to be called multiple times, once for each Multiplicity member.

Note that Multiplicity is currently an experimental features and the implementation or UI may change.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpMultiplicityImply$new(innum = 0, id = "multiplicityimply", param_vals = list())

Input and Output Channels

PipeOpMultiplicityImply has multiple input channels depending on the innum construction argument, named "input1", "input2", ... if innum is nonzero; if innum is 0, there is only one vararg input channel named "...". All input channels take any input ("*") both during training and prediction.

PipeOpMultiplicityImply has one output channel named "output", emitting a Multiplicity of type any ("[*]"), i.e., returning the input(s) converted to a Multiplicity both during training and prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

PipeOpMultiplicityImply has no Parameters.

Internals

If innum is not numeric, e.g., a character, the output Multiplicity will be named based on the input channel names

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Other Experimental Features: Multiplicity(), mlr_pipeops_multiplicityexply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_replicate

Examples

library("mlr3")
task1 = tsk("iris")
task2 = tsk("mtcars")
po = po("multiplicityimply")
po$train(list(task1, task2))
po$predict(list(task1, task2))

Add Features According to Expressions

Description

Adds features according to expressions given as formulas that may depend on values of other features. This can add new features, or can change existing features.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpMutate$new(id = "mutate", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with added and/or mutated features according to the mutation parameter.

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:

Internals

A formula created using the ~ operator always contains a reference to the environment in which the formula is created. This makes it possible to use variables in the ~-expressions that both reference either column names or variable names.

Note that the formulas in mutation are evaluated sequentially. This allows for using variables that were constructed during evaluation of a previous formula. However, if existing features are changed, precedence is given to the original ones before the newly constructed ones.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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")

constant = 1
pom = po("mutate")
pom$param_set$values$mutation = list(
  Sepal.Length_plus_constant = ~ Sepal.Length + constant,
  Sepal.Area = ~ Sepal.Width * Sepal.Length,
  Petal.Area = ~ Petal.Width * Petal.Length,
  Sepal.Area_plus_Petal.Area = ~ Sepal.Area + Petal.Area
)

pom$train(list(tsk("iris")))[[1]]$data()

Nearmiss Down-Sampling

Description

Generates a more balanced data set by down-sampling the instances of non-minority classes using the NEARMISS algorithm.

The algorithm down-samples by selecting instances from the non-minority classes that have the smallest mean distance to their k nearest neighbors of different classes. For this only numeric and integer features are taken into account. These must have no missing values.

This can only be applied to classification tasks. Multiclass classification is supported.

See themis::nearmiss for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpNearmiss$new(id = "nearmiss", param_vals = 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 the rows removed from the non-minority 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, as well as

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

Zhang, J., Mani, I. (2003). “KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction.” In Proceedings of Workshop on Learning from Imbalanced Datasets (ICML).

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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
task = tsk("wine")
task$head()
table(task$data(cols = "type"))

# Down-sample and balance data
pop = po("nearmiss")
nearmiss_result = pop$train(list(task))[[1]]$data()
nrow(nearmiss_result)
table(nearmiss_result$type)

Non-negative Matrix Factorization

Description

Extracts non-negative components from data by performing non-negative matrix factorization. Only affects non-negative numerical features. See nmf() for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpNMF$new(id = "nmf", param_vals = 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 non-negative components.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as the elements of the object returned by nmf().

Parameters

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

Internals

Uses the nmf() function as well as basis(), coef() and ginv().

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("nmf")

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

pop$state

Simply Push Input Forward

Description

Simply pushes the input forward. Can be useful during Graph construction using the %>>%-operator to specify which PipeOp gets connected to which.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpNOP$new(id = "nop", param_vals = list())

Input and Output Channels

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

PipeOpNOP has one output channel named "output", producing the object given as input ("*") without changes.

State

The ⁠$state⁠ is left empty (list()).

Parameters

PipeOpNOP has no parameters.

Internals

PipeOpNOP is a useful "default" stand-in for a PipeOp/Graph that does nothing.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Placeholder Pipeops: mlr_pipeops_copy

Examples

library("mlr3")

nop = po("nop")

nop$train(list(1))

# use `gunion` and `%>>%` to create a "bypass"
# next to "pca"
gr = gunion(list(
  po("pca"),
  nop
)) %>>% po("featureunion")

gr$train(tsk("iris"))[[1]]$data()

Split a Classification Task into Binary Classification Tasks

Description

Splits a classification Task into several binary classification Tasks to perform "One vs. Rest" classification. This works in combination with PipeOpOVRUnite.

For each target level a new binary classification Task is constructed with the respective target level being the positive class and all other target levels being the new negative class "rest".

This PipeOp creates a Multiplicity, which means that subsequent PipeOps are executed multiple times, once for each created binary Task, until a PipeOpOVRUnite is reached.

Note that Multiplicity is currently an experimental features and the implementation or UI may change.

Format

R6Class inheriting from PipeOp.

Construction

PipeOpOVRSplit$new(id = "ovrsplit", param_vals = list())

Input and Output Channels

PipeOpOVRSplit has one input channel named "input" taking a TaskClassif both during training and prediction.

PipeOpOVRSplit has one output channel named "output" returning a Multiplicity of TaskClassifs both during training and prediction, i.e., the newly constructed binary classification Tasks.

State

The ⁠$state⁠ contains the original target levels of the TaskClassif supplied during training.

Parameters

PipeOpOVRSplit has no parameters.

Internals

The original target levels stored in the ⁠$state⁠ are also used during prediction when creating the new binary classification Tasks.

The names of the element of the output Multiplicity are given by the levels of the target.

If a target level "rest" is present in the input TaskClassif, the negative class will be labeled as ⁠"rest." (using as many ⁠"."' postfixes needed to yield a valid label).

Should be used in combination with PipeOpOVRUnite.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate

Other Experimental Features: Multiplicity(), mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrunite, mlr_pipeops_replicate

Examples


library(mlr3)
task = tsk("iris")
po = po("ovrsplit")
po$train(list(task))
po$predict(list(task))

Unite Binary Classification Tasks

Description

Perform "One vs. Rest" classification by (weighted) majority vote prediction from classification Predictions. This works in combination with PipeOpOVRSplit.

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

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.

Missing values during prediction are treated as each class label being equally likely.

This PipeOp uses a Multiplicity input, which is created by PipeOpOVRSplit and causes PipeOps on the way to this PipeOp to be called once for each individual binary Task.

Note that Multiplicity is currently an experimental features and the implementation or UI may change.

Format

R6Class inheriting from PipeOpEnsemble/PipeOp.

Construction

PipeOpOVRUnite$new(id = "ovrunite", param_vals = 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 and PipeOpEnsemble's collect parameter is initialized with TRUE to allow for collecting a Multiplicity input.

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$.predict() method.

Should be used in combination with PipeOpOVRSplit.

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, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Ensembles: PipeOpEnsemble, mlr_learners_avg, mlr_pipeops_classifavg, mlr_pipeops_regravg

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

Other Experimental Features: Multiplicity(), mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_replicate

Examples


library(mlr3)
task = tsk("iris")
gr = po("ovrsplit") %>>% lrn("classif.rpart") %>>% po("ovrunite")
gr$train(task)
gr$predict(task)
gr$pipeops$classif.rpart$learner$predict_type = "prob"
gr$predict(task)

Principle Component Analysis

Description

Extracts principle components from data. Only affects numerical features. See stats::prcomp() for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpPCA$new(id = "pca", param_vals = 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 principal components.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as the elements of the class stats::prcomp, with the exception of the ⁠$x⁠ slot. These are in particular:

Parameters

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

Internals

Uses the prcomp() function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("pca")

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

pop$state

Wrap another PipeOp or Graph as a Hyperparameter

Description

Wraps another PipeOp or Graph as determined by the content hyperparameter. Input is routed through the content and the contents' output is returned. The content hyperparameter can be changed during tuning, this is useful as an alternative to PipeOpBranch.

Format

Abstract R6Class inheriting from PipeOp.

Construction

PipeOpProxy$new(innum = 0, outnum = 1, id = "proxy", param_vals = list())

Input and Output Channels

PipeOpProxy has multiple input channels depending on the innum construction argument, named "input1", "input2", ... if innum is nonzero; if innum is 0, there is only one vararg input channel named "...".

PipeOpProxy has multiple output channels depending on the outnum construction argument, named "output1", "output2", ... The output is determined by the output of the content operation (a PipeOp or Graph).

State

The ⁠$state⁠ is the trained content PipeOp or Graph.

Parameters

Internals

The content will internally be coerced to a graph via as_graph() prior to train and predict.

The default value for content is PipeOpFeatureUnion,

Fields

Fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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")

set.seed(1234)
task = tsk("iris")

# use a proxy for preprocessing and a proxy for learning, i.e.,
# no preprocessing and classif.rpart
g = po("proxy", id = "preproc", param_vals = list(content = po("nop"))) %>>%
  po("proxy", id = "learner", param_vals = list(content = lrn("classif.rpart")))
rr_rpart = resample(task, learner = GraphLearner$new(g), resampling = rsmp("cv", folds = 3))
rr_rpart$aggregate(msr("classif.ce"))

# use pca for preprocessing and classif.rpart as the learner
g$param_set$values$preproc.content = po("pca")
g$param_set$values$learner.content = lrn("classif.rpart")
rr_pca_rpart = resample(task, learner = GraphLearner$new(g), resampling = rsmp("cv", folds = 3))
rr_pca_rpart$aggregate(msr("classif.ce"))

Split Numeric Features into Quantile Bins

Description

Splits numeric features into quantile bins.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpQuantileBin$new(id = "quantilebin", param_vals = 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 binned versions.

State

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

Parameters

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

Internals

Uses the stats::quantile function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("quantilebin")

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

pop$state

Project Numeric Features onto a Randomly Sampled Subspace

Description

Projects numeric features onto a randomly sampled subspace. All numeric features (or the ones selected by affect_columns) are replaced by numeric features PR1, PR2, ... PRn

Samples with features that contain missing values result in all PR1..PRn being NA for that sample, so it is advised to do imputation before random projections if missing values can be expected.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpRandomProjection$new(id = "randomprojection", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with affected numeric features projected onto a random subspace.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as an element ⁠$projection⁠, a matrix.

Parameters

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

Internals

If there are n (affected) numeric features in the input Task, then ⁠$state$projection⁠ is a rank x m matrix. The output is calculated as input %*% state$projection.

The random projection matrix is obtained through Gram-Schmidt orthogonalization from a matrix with values standard normally distributed, which gives a distribution that is rotation invariant, as per Eaton: Multivariate Statistics, A Vector Space Approach, Pg. 234.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("randomprojection", rank = 2)

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

pop$state

Generate a Randomized Response Prediction

Description

Takes in a Prediction of predict_type "prob" (for PredictionClassif) or "se" (for PredictionRegr) and generates a randomized "response" prediction.

For "prob", the responses are sampled according to the probabilities of the input PredictionClassif. For "se", responses are randomly drawn according to the rdistfun parameter (default is rnorm) by using the original responses of the input PredictionRegr as the mean and the original standard errors of the input PredictionRegr as the standard deviation (sampling is done observation-wise).

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpRandomResponse$new(id = "randomresponse", param_vals = list(), packages = character(0))

Input and Output Channels

PipeOpRandomResponse has one input channel named "input", taking NULL during training and a Prediction during prediction.

PipeOpRandomResponse has one output channel named "output", producing NULL during training and a Prediction with random responses during prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

Internals

If the predict_type of the input Prediction does not match "prob" or "se", the input Prediction will be returned unaltered.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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)
library(mlr3learners)

task1 = tsk("iris")
g1 = LearnerClassifRpart$new() %>>% PipeOpRandomResponse$new()
g1$train(task1)
g1$pipeops$classif.rpart$learner$predict_type = "prob"
set.seed(2409)
g1$predict(task1)

task2 = tsk("mtcars")
g2 = LearnerRegrLM$new() %>>% PipeOpRandomResponse$new()
g2$train(task2)
g2$pipeops$regr.lm$learner$predict_type = "se"
set.seed(2906)
g2$predict(task2)

Weighted Prediction Averaging

Description

Perform (weighted) prediction averaging from regression Predictions by connecting PipeOpRegrAvg to multiple PipeOpLearner outputs.

The resulting "response" prediction is a weighted average of the incoming "response" predictions. "se" prediction is currently not aggregated but discarded if present.

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.

Format

R6Class inheriting from PipeOpEnsemble/PipeOp.

Construction

PipeOpRegrAvg$new(innum = 0, collect_multiplicity = FALSE, id = "regravg", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpEnsemble. Instead of a Prediction, a PredictionRegr 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 PipeOp.

Methods

Only methods inherited from PipeOpEnsemble/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_replicate

Other Ensembles: PipeOpEnsemble, mlr_learners_avg, mlr_pipeops_classifavg, mlr_pipeops_ovrunite

Examples


library("mlr3")

# Simple Bagging
gr = ppl("greplicate",
  po("subsample") %>>%
  po("learner", lrn("classif.rpart")),
  n = 5
) %>>%
  po("classifavg")

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

Remove Constant Features

Description

Remove constant features from a mlr3::Task. For each feature, calculates the ratio of features which differ from their mode value. All features with a ratio below a settable threshold are removed from the task. Missing values can be ignored or treated as a regular value distinct from non-missing values.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpRemoveConstants$new(id = "removeconstants")

State

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

Parameters

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

Fields

Fields inherited from PipeOp.

Methods

Methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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")
data = data.table::data.table(y = runif(10), a = 1:10, b = rep(1, 10), c = rep(1:2, each = 5))

task = TaskRegr$new("example", data, target = "y")

po = po("removeconstants")

po$train(list(task = task))[[1]]$data()

po$state

Rename Columns

Description

Renames the columns of a Task both during training and prediction. Uses the ⁠$rename()⁠ mutator of the Task.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpRenameColumns$new(id = "renamecolumns", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with the old column names changed to the new ones.

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:

Internals

Uses the ⁠$rename()⁠ mutator of the Task to set the new column names.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("renamecolumns", param_vals = list(renaming = c("Petal.Length" = "PL")))
pop$train(list(task))

Replicate the Input as a Multiplicity

Description

Replicate the input as a Multiplicity, causing subsequent PipeOps to be executed multiple reps times.

Note that Multiplicity is currently an experimental features and the implementation or UI may change.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpReplicate$new(id = "replicate", param_vals = list())

Input and Output Channels

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

PipeOpReplicate has one output channel named "output" returning the replicated input as a Multiplicity of type any ("[*]") both during training and prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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_classifavg, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg

Other Experimental Features: Multiplicity(), mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite

Examples

library("mlr3")
task = tsk("iris")
po = po("replicate", param_vals = list(reps = 3))
po$train(list(task))
po$predict(list(task))

Apply a Function to each Row of a Task

Description

Applies a function to each row of a task. Use the affect_columns parameter inherited from PipeOpTaskPreprocSimple to limit the columns this function should be applied to.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

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

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with the original affected columns replaced by the columns created by applying applicator to each row.

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:

Internals

Calls apply on the data, using the value of applicator as FUN.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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")
pora = po("rowapply", applicator = scale)
pora$train(list(task))[[1]]  # rows are standardized

Center and Scale Numeric Features

Description

Centers all numeric features to mean = 0 (if center parameter is TRUE) and scales them by dividing them by their root-mean-square (if scale parameter is TRUE).

The root-mean-square here is defined as sqrt(sum(x^2)/(length(x)-1)). If the center parameter is TRUE, this corresponds to the sd().

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpScale$new(id = "scale", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected numeric parameters centered and/or scaled.

State

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

Parameters

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

Internals

Imitates the scale() function for robust = FALSE and alternatively subtracts the median and divides by mad for robust = TRUE.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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")
pos = po("scale")

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

one_line_of_iris = task$filter(13)

one_line_of_iris$data()

pos$predict(list(one_line_of_iris))[[1]]$data()

Scale Numeric Features with Respect to their Maximum Absolute Value

Description

Scales the numeric data columns so their maximum absolute value is maxabs, if possible. NA, Inf are ignored, and features that are constant 0 are not scaled.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpScaleMaxAbs$new(id = "scalemaxabs", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with scaled numeric features.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as the maximum absolute values of each numeric feature.

Parameters

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

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("scalemaxabs")

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

pop$state

Linearly Transform Numeric Features to Match Given Boundaries

Description

Linearly transforms numeric data columns so they are between lower and upper. The formula for this is x' = offset + x * scale, where scale is (upper - lower) / (max(x) - min(x)) and offset is -min(x) * scale + lower. The same transformation is applied during training and prediction.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpScaleRange$new(id = "scalerange", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with scaled numeric features.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as the two transformation parameters scale and offset for each numeric feature.

Parameters

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

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("scalerange", param_vals = list(lower = -1, upper = 1))

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

pop$state

Remove Features Depending on a Selector

Description

Removes features from Task depending on a Selector function: The selector parameter gives the features to keep. See Selector for selectors that are provided and how to write custom Selectors.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSelect$new(id = "select", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with features removed that were not selected by the Selector/function in selector.

State

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

Parameters

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

Internals

Uses task$select().

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 Selectors: Selector

Examples

library("mlr3")

task = tsk("boston_housing")
pos = po("select")

pos$param_set$values$selector = selector_all()
pos$train(list(task))[[1]]$feature_names

pos$param_set$values$selector = selector_type("factor")
pos$train(list(task))[[1]]$feature_names

pos$param_set$values$selector = selector_invert(selector_type("factor"))
pos$train(list(task))[[1]]$feature_names

pos$param_set$values$selector = selector_grep("^r")
pos$train(list(task))[[1]]$feature_names

SMOTE Balancing

Description

Generates a more balanced data set by creating synthetic instances of the minority class using the SMOTE algorithm. The algorithm samples for each minority instance a new data point based on the K nearest neighbors of that data point. It can only be applied to tasks with purely numeric features. See smotefamily::SMOTE for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSmote$new(id = "smote", param_vals = 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 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:

Internals

If a target level is unobserved during training, no synthetic data points will be generated for that class. No error is raised; the unobserved class is simply ignored.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002). “SMOTE: Synthetic Minority Over-sampling Technique.” Journal of Artificial Intelligence Research, 16, 321–357. doi:10.1613/jair.953.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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(1000, ratio = 0.80)
data$result = factor(data$result)
task = TaskClassif$new(id = "example", backend = data, target = "result")
task$data()
table(task$data()$result)

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

SMOTENC Balancing

Description

Generates a more balanced data set by creating synthetic instances of the minority class for nominal and continuous data using the SMOTENC algorithm.

The algorithm generates for each minority instance a new data point based on the k nearest neighbors of that data point. It treats integer features as numeric. To not change feature types, the numeric, synthetic data generated for these features are rounded back to integer. Because of this, data generated through usage of this PipeOp is not exactly equal to data generated by calling themis::smotenc directly on the same data set.

It can only be applied to classification tasks with factor (or ordered) features and at least one numeric (or integer) feature that have no missing values.

See themis::smotenc for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSmoteNC$new(id = "smotenc", param_vals = 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 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:

Internals

If a target level is unobserved during training, no synthetic data points will be generated for that class. No error is raised; the unobserved class is simply ignored.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002). “SMOTE: Synthetic Minority Over-sampling Technique.” Journal of Artificial Intelligence Research, 16, 321–357. doi:10.1613/jair.953.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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 = 200, replace = TRUE, prob = c(0.1, 0.9))),
  feature = rnorm(200)
)
task = TaskClassif$new(id = "example", backend = data, target = "target")
task$head()
table(task$data(cols = "target"))

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

Normalize Data Row-wise

Description

Normalizes the data row-wise. This is a natural generalization of the "sign" function to higher dimensions.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSpatialSign$new(id = "spatialsign", param_vals = 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 normalized versions.

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:

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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")

task$data()

pop = po("spatialsign")

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

Subsampling

Description

Subsamples a Task to use a fraction of the rows.

Sampling happens only during training phase. Subsampling a Task may be beneficial for training time at possibly (depending on original Task size) negligible cost of predictive performance.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpSubsample$new(id = "subsample", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output during training is the input Task with added or removed rows according to the sampling. 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:

Internals

Uses task$filter() to remove rows. If replace is TRUE and identical rows are added, then the task$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 PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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")

# Subsample with stratification
pop = po("subsample", frac = 0.7, stratify = TRUE, use_groups = FALSE)
pop$train(list(tsk("iris")))

# Subsample, respecting grouping
df = data.frame(
  target = runif(3000),
  x1 = runif(3000),
  x2 = runif(3000),
  grp = sample(paste0("g", 1:100), 3000, replace = TRUE)
)
task = TaskRegr$new(id = "example", backend = df, target = "target")
task$set_col_roles("grp", "group")

pop = po("subsample", frac = 0.7, use_groups = TRUE)
pop$train(list(task))

Invert Target Transformations

Description

Inverts target-transformations done during training based on a supplied inversion function. Typically should be used in combination with a subclass of PipeOpTargetTrafo.

During prediction phase the function supplied through "fun" is called with a list containing the "prediction" as a single element, and should return a list with a single element (a Prediction) that is returned by PipeOpTargetInvert.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpTargetInvert$new(id = "targetinvert", param_vals = list())

Input and Output Channels

PipeOpTargetInvert has two input channels named "fun" and "prediction". During training, both take NULL as input. During prediction, "fun" takes a function and "prediction" takes a Prediction.

PipeOpTargetInvert has one output channel named "output" and returns NULL during training and a Prediction during prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

PipeOpTargetInvert has no parameters.

Internals

Should be used in combination with a subclass of PipeOpTargetTrafo.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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

Transform a Target by a Function

Description

Changes the target of a Task according to a function given as hyperparameter. An inverter-function that undoes the transformation during prediction must also be given.

Format

R6Class object inheriting from PipeOpTargetTrafo/PipeOp

Construction

PipeOpTargetMutate$new(id = "targetmutate", param_vals = list(), new_task_type = NULL)

Input and Output Channels

Input and output channels are inherited from PipeOpTargetTrafo.

State

The ⁠$state⁠ is left empty (list()).

Parameters

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

Internals

Overloads PipeOpTargetTrafo's .transform() and .invert() functions. Should be used in combination with PipeOpTargetInvert.

Fields

Fields inherited from PipeOp, as well as:

Methods

Only methods inherited from PipeOpTargetTrafo/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("boston_housing")
po = PipeOpTargetMutate$new("logtrafo", param_vals = list(
  trafo = function(x) log(x, base = 2),
  inverter = function(x) list(response = 2 ^ x$response))
)
# Note that this example is ill-equipped to work with
# `predict_type == "se"` predictions.

po$train(list(task))
po$predict(list(task))

g = Graph$new()
g$add_pipeop(po)
g$add_pipeop(LearnerRegrRpart$new())
g$add_pipeop(PipeOpTargetInvert$new())
g$add_edge(src_id = "logtrafo", dst_id = "targetinvert",
  src_channel = 1, dst_channel = 1)
g$add_edge(src_id = "logtrafo", 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)

g$train(task)
g$predict(task)

#syntactic sugar using ppl():
tt = ppl("targettrafo", graph = 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)

Linearly Transform a Numeric Target to Match Given Boundaries

Description

Linearly transforms a numeric target of a TaskRegr so it is between lower and upper. The formula for this is x' = offset + x * scale, where scale is (upper - lower) / (max(x) - min(x)) and offset is -min(x) * scale + lower. The same transformation is applied during training and prediction.

Format

R6Class object inheriting from PipeOpTargetTrafo/PipeOp

Construction

PipeOpTargetTrafoScaleRange$new(id = "targettrafoscalerange", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTargetTrafo.

State

The ⁠$state⁠ is a named list containing the slots ⁠$offset⁠ and ⁠$scale⁠.

Parameters

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

Internals

Overloads PipeOpTargetTrafo's .get_state(), .transform(), and .invert(). Should be used in combination with PipeOpTargetInvert.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTargetTrafo/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_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("boston_housing")
po = PipeOpTargetTrafoScaleRange$new()

po$train(list(task))
po$predict(list(task))

#syntactic sugar for a graph using ppl():
ttscalerange = ppl("targettrafo", trafo_pipeop = PipeOpTargetTrafoScaleRange$new(),
  graph = PipeOpLearner$new(LearnerRegrRpart$new()))
ttscalerange$train(task)
ttscalerange$predict(task)
ttscalerange$state$regr.rpart

Bag-of-word Representation of Character Features

Description

Computes a bag-of-word representation from a (set of) columns. Columns of type character are split up into words. Uses the quanteda::dfm() and quanteda::dfm_trim() functions. TF-IDF computation works similarly to quanteda::dfm_tfidf() but has been adjusted for train/test data split using quanteda::docfreq() and quanteda::dfm_weight().

In short:

Parameters specify arguments to quanteda's dfm, dfm_trim, docfreq and dfm_weight. What belongs to what can be obtained from each parameter's tags where tokenizer are arguments passed on to quanteda::dfm(). Defaults to a bag-of-words representation with token counts as matrix entries.

In order to perform the default dfm_tfidf weighting, set the scheme_df parameter to "inverse". The scheme_df parameter is initialized to "unary", which disables document frequency weighting.

The PipeOp works as follows:

  1. Words are tokenized using quanteda::tokens.

  2. Ngrams are computed using quanteda::tokens_ngrams.

  3. A document-frequency matrix is computed using quanteda::dfm.

  4. The document-frequency matrix is trimmed using quanteda::dfm_trim during train-time.

  5. The document-frequency matrix is re-weighted (similar to quanteda::dfm_tfidf) if scheme_df is not set to "unary".

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpTextVectorizer$new(id = "textvectorizer", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected features converted to a bag-of-words representation.

State

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

Parameters

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

Internals

See Description. Internally uses the quanteda package. Calls quanteda::tokens, quanteda::tokens_ngrams and quanteda::dfm. During training, quanteda::dfm_trim is also called. Tokens not seen during training are dropped during prediction.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples


library("mlr3")
library("data.table")
# create some text data
dt = data.table(
  txt = replicate(150, paste0(sample(letters, 3), collapse = " "))
)
task = tsk("iris")$cbind(dt)

pos = po("textvectorizer", param_vals = list(stopwords_language = "en"))

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

one_line_of_iris = task$filter(13)

one_line_of_iris$data()

pos$predict(list(one_line_of_iris))[[1]]$data()

Change the Threshold of a Classification Prediction

Description

Change the threshold of a Prediction during the predict step. The incoming Learner's ⁠$predict_type⁠ needs to be "prob". Internally calls PredictionClassif$set_threshold.

Format

R6Class inheriting from PipeOp.

Construction

PipeOpThreshold$new(id = "threshold", param_vals = list())

Input and Output Channels

During training, the input and output are NULL. A PredictionClassif is required as input and returned as output during prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

Fields

Fields inherited from PipeOp, as well as:

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples


library("mlr3")
t = tsk("german_credit")
gr = po(lrn("classif.rpart", predict_type = "prob")) %>>%
  po("threshold", param_vals = list(thresholds = 0.9))
gr$train(t)
gr$predict(t)

Tomek Down-Sampling

Description

Generates a cleaner data set by removing all majority-minority Tomek links.

The algorithm down-samples the data by removing all pairs of observations that form a Tomek link, i.e. a pair of observations that are nearest neighbors and belong to different classes. For this only numeric and integer features are taken into account. These must have no missing values.

This can only be applied to classification tasks. Multiclass classification is supported.

See themis::tomek for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpTomek$new(id = "tomek", param_vals = 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 removed rows for pairs of observations that form a Tomek link. 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.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

References

Tomek I (1976). “Two Modifications of CNN.” IEEE Transactions on Systems, Man and Cybernetics, 6(11), 769–772. doi:10.1109/TSMC.1976.4309452.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples


library("mlr3")

# Create example task
task = tsk("iris")
task$head()
table(task$data(cols = "Species"))

# Down-sample data
pop = po("tomek")
tomek_result = pop$train(list(task))[[1]]$data()
nrow(tomek_result)
table(tomek_result$Species)

Tune the Threshold of a Classification Prediction

Description

Tunes optimal probability thresholds over different PredictionClassifs.

mlr3::Learner predict_type: "prob" is required. Thresholds for each learner are optimized using the Optimizer supplied via the param_set. Defaults to GenSA. Returns a single PredictionClassif.

This PipeOp should be used in conjunction with PipeOpLearnerCV in order to optimize thresholds of cross-validated predictions. In order to optimize thresholds without cross-validation, use PipeOpLearnerCV in conjunction with ResamplingInsample.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpTuneThreshold$new(id = "tunethreshold", param_vals = list())

Input and Output Channels

Input and output channels are inherited from PipeOp.

State

The ⁠$state⁠ is a named list with elements

Parameters

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

Internals

Uses the optimizer provided as a param_val in order to find an optimal threshold. See the optimizer parameter for more info.

Fields

Fields inherited from PipeOp, as well as:

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples


library("mlr3")

task = tsk("iris")
pop = po("learner_cv", lrn("classif.rpart", predict_type = "prob")) %>>%
  po("tunethreshold")

task$data()
pop$train(task)

pop$state

Unbranch Different Paths

Description

Used to bring together different paths created by PipeOpBranch.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpUnbranch$new(options, id = "unbranch", param_vals = list())

Input and Output

PipeOpUnbranch has multiple input channels depending on the options construction argument, named "input1", "input2", ... if options is a nonzero integer and named after each options value if options is a character; if options is 0, there is only one vararg input channel named "...". All input channels take any argument ("*") both during training and prediction.

PipeOpUnbranch has one output channel named "output", producing the only NO_OP object received as input ("*"), both during training and prediction.

State

The ⁠$state⁠ is left empty (list()).

Parameters

PipeOpUnbranch has no parameters.

Internals

See PipeOpBranch Internals on how alternative path branching works.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

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

Examples

# See PipeOpBranch for a complete branching example
pou = po("unbranch")

pou$train(list(NO_OP, NO_OP, "hello", NO_OP, NO_OP))

Transform a Target without an Explicit Inversion

Description

EXPERIMENTAL, API SUBJECT TO CHANGE

Handles target transformation operations that do not need explicit inversion. In case the new target is required during predict, creates a vector of NA. Works similar to PipeOpTargetTrafo and PipeOpTargetMutate, but forgoes the inversion step. In case target after the trafo is a factor, levels are saved to ⁠$state⁠.

During prediction: Sets all target values to NA before calling the trafo again. In case target after the trafo is a factor, levels saved in the state are set during prediction.

As a special case when trafo is identity and new_target_name matches an existing column name of the data of the input Task, this column is set as the new target. Depending on drop_original_target the original target is then either dropped or added to the features.

Format

Abstract R6Class inheriting from PipeOp.

Construction

PipeOpUpdateTarget$new(id, param_set = ps(),
  param_vals = list(), packages = character(0))

Parameters

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

State

The ⁠$state⁠ is a list of class levels for each target after trafo. list() if none of the targets have levels.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOp.

See Also

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

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_vtreat, mlr_pipeops_yeojohnson

Examples


## Not run: 
# Create a binary class task from iris
library(mlr3)
trafo_fun = function(x) {factor(ifelse(x$Species == "setosa", "setosa", "other"))}
po = PipeOpUpdateTarget$new(param_vals = list(trafo = trafo_fun, new_target_name = "setosa"))
po$train(list(tsk("iris")))
po$predict(list(tsk("iris")))

## End(Not run)

Interface to the vtreat Package

Description

Provides an interface to the vtreat package.

PipeOpVtreat naturally works for classification tasks and regression tasks. Internally, PipeOpVtreat follows the fit/prepare interface of vtreat, i.e., first creating a data treatment transform object via vtreat::NumericOutcomeTreatment(), vtreat::BinomialOutcomeTreatment(), or vtreat::MultinomialOutcomeTreatment(), followed by calling vtreat::fit_prepare() on the training data and vtreat::prepare() during predicton.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpVtreat$new(id = "vtreat", param_vals = list())

Input and Output Channels

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

The output is the input Task with all affected features "prepared" by vtreat. If vtreat found "no usable vars", the input Task is returned unaltered.

State

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

Parameters

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

For more information, see vtreat::regression_parameters(), vtreat::classification_parameters(), or vtreat::multinomial_parameters().

Internals

Follows vtreat's fit/prepare interface. See vtreat::NumericOutcomeTreatment(), vtreat::BinomialOutcomeTreatment(), vtreat::MultinomialOutcomeTreatment(), vtreat::fit_prepare() and vtreat::prepare().

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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_yeojohnson

Examples


library("mlr3")

set.seed(2020)

make_data <- function(nrows) {
    d <- data.frame(x = 5 * rnorm(nrows))
    d["y"] = sin(d[["x"]]) + 0.01 * d[["x"]] + 0.1 * rnorm(nrows)
    d[4:10, "x"] = NA  # introduce NAs
    d["xc"] = paste0("level_", 5 * round(d$y / 5, 1))
    d["x2"] = rnorm(nrows)
    d[d["xc"] == "level_-1", "xc"] = NA  # introduce a NA level
    return(d)
}

task = TaskRegr$new("vtreat_regr", backend = make_data(100), target = "y")

pop = PipeOpVtreat$new()
pop$train(list(task))

Yeo-Johnson Transformation of Numeric Features

Description

Conducts a Yeo-Johnson transformation on numeric features. It therefore estimates the optimal value of lambda for the transformation. See bestNormalize::yeojohnson() for details.

Format

R6Class object inheriting from PipeOpTaskPreproc/PipeOp.

Construction

PipeOpYeoJohnson$new(id = "yeojohnson", param_vals = 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 yeojohnson for each column, which is transformed.

Parameters

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

Internals

Uses the bestNormalize::yeojohnson function.

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreproc/PipeOp.

See Also

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

Other PipeOps: PipeOp, PipeOpEncodePL, 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_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, 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

Examples


library("mlr3")

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

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

pop$state

Housing Data for 506 Census Tracts of Boston

Description

Housing Data for 506 Census Tracts of Boston

Format

R6Class object inheriting from TaskRegr.

The BostonHousing2 dataset containing the corrected data from III AMF (1979). “The Hedonic Price Approach to Measuring Demand for Neighborhood Characteristics.” In The Economics of Neighborhood, 191–217. Elsevier. doi:10.1016/B978-0-12-636250-3.50015-5. as provided by the mlbench package. See data description there.

Shorthand PipeOp Constructor

Description

Create

The object is initialized with given parameters and param_vals.

po() taks a single obj (PipeOp id, Learner, ...) and converts it to a PipeOp. pos() (with plural-s) takes either a character-vector, or a list of objects, and creates a list of PipeOps.

Usage

po(.obj, ...)

pos(.objs, ...)

Arguments

.obj

⁠[any]⁠
The object from which to construct a PipeOp. If this is a character(1), it is looked up in the mlr_pipeops dictionary. Otherwise, it is converted to a PipeOp.

...

any
Additional parameters to give to constructed object. This may be an argument of the constructor of the PipeOp, in which case it is given to this constructor; or it may be a parameter value, in which case it is given to the param_vals argument of the constructor.

.objs

character | list
Either a character of PipeOps to look up in mlr_pipeops, or a list of other objects to be converted to a PipeOp. If this is a named list, then the names are used as ⁠$id⁠ slot for the resulting PipeOps.

Value

A PipeOp (for po()), or a list of PipeOps (for pos()).

Examples


library("mlr3")

po("learner", lrn("classif.rpart"), cp = 0.3)

po(lrn("classif.rpart"), cp = 0.3)

# is equivalent with:
mlr_pipeops$get("learner", lrn("classif.rpart"),
  param_vals = list(cp = 0.3))

mlr3pipelines::pos(c("pca", original = "nop"))

Shorthand Graph Constructor

Description

Creates a Graph from mlr_graphs from given ID

ppl() taks a character(1) and returns a Graph. ppls() takes a character vector of any list and returns a list of possibly muliple Graphs.

Usage

ppl(.key, ...)

ppls(.keys, ...)

Arguments

.key

⁠[character(1)]⁠
The key of the Graph in mlr_graphs.

...

any
Additional parameters to give to constructed object. This may be an argument of the constructor of the underlying function.

.keys

⁠[character]⁠
The key of possibly multiple Graphs in mlr_graphs. If this is named, a named list is returned, but unlike pos() it will not set any ⁠$id⁠ slots.

Value

Graph (for ppl()) or list of Graphs (for ppls()).

Examples


library("mlr3")

gr = ppl("bagging", graph = po(lrn("regr.rpart")),
  averager = po("regravg", collect_multiplicity = TRUE))

Simple Pre-processing

Description

Function that offers a simple and direct way to train or predict PipeOps and Graphs on Tasks, data.frames or data.tables.

Training happens if predict is set to FALSE and no state is passed to this function. Prediction happens if predict is set to TRUE and if the passed Graph or PipeOp is either trained or a state is explicitly passed to this function.

The passed PipeOp or Graph gets modified by-reference.

Usage

preproc(indata, processor, state = NULL, predict = !is.null(state))

Arguments

indata

(Task | data.frame | data.table )
Data to be pre-processed.

processor

(Graph | PipeOp)
Graph or PipeOp accepting a Task that has one output channel.
Whenever indata is passed a data.frame or data.table, the output channel must return a Task to be converted back into a data.frame or data.table. Additionally, processors which only work on sub-classes of TaskSupervised will not accept data.frame or data.table, as it would be unclear which column was the target.
Be aware that the processor gets modified by-reference both during training, and if a state is passed to this function. This especially means that the state of a trained processor will get overwritten when state is passed.
You may want to use dictionary sugar functions to select a processor and to set its hyperparameters, e.g. po() or ppl().

state

(named list | NULL)
Optional state to be used for prediction, if the processor is untrained or if the current state of the processor should be overwritten. Must be a complete and correct state for the respective processor. Default NULL (do not overwrite processor's state).

predict

(logical(1))
Whether to predict (TRUE) or train (FALSE). By default, this is FALSE if state is NULL (state's default), and TRUE otherwise.

Value

any | data.frame | data.table: If indata is a Task, whatever is returned by the processor's single output channel is returned. If indata is a data.frame or data.table, an object of the same class is returned, or if the processor's output channel does not return a Task, an error is thrown.

Internals

If processor is a PipeOp, the S3 method preproc.PipeOp gets called first, converting the PipeOp into a Graph and wrapping the state appropriately, before calling the S3 method preproc.Graph with the modified objects.

If indata is a data.frame or data.table, a TaskUnsupervised is constructed internally. This implies that processors which only work on sub-classes of TaskSupervised will not work with these input types for indata.

Examples


library("mlr3")
task = tsk("iris")
pop = po("pca")

# Training
preproc(task, pop)
# Note that the PipeOp gets trained through this
pop$is_trained

# Predicting a trained PipeOp (trained through previous call to preproc)
preproc(task, pop, predict = TRUE)

# Predicting using a given state
# We use the state of the PipeOp from the last example and then reset it
state = pop$state
pop$state = NULL
preproc(task, pop, state)

# Note that the PipeOp's state may get overwritten inadvertently during
# training or if a state is given
pop$state$sdev
preproc(tsk("wine"), pop)
pop$state$sdev

# Piping multiple preproc() calls, using dictionary sugar to set parameters
# tsk("penguins") |>
#   preproc(po("imputemode", affect_columns = selector_name("sex"))) |>
#   preproc(po("imputemean"))

# Use preproc with a Graph
gr = po("pca", rank. = 4) %>>% po("learner", learner = lrn("classif.rpart"))
preproc(tsk("sonar"), gr)  # returns NULL because of the learner
preproc(tsk("sonar"), gr, predict = TRUE)

# Training with a data.table input
# Note that `$data()` drops the information that "Species" is the target.
# It gets handled like an ordinary feature here.
dt = tsk("iris")$data()
preproc(dt, pop)

# Predicting with a data.table input
preproc(dt, pop)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

data.table

as.data.table

Add Autoconvert Function to Conversion Register

Description

Add functions that perform conversion to a desired class.

Whenever a Graph or a PipeOp is called with an object that does not conform to its declared input type, the "autoconvert register" is queried for functions that may turn the object into a desired type.

Conversion functions should try to avoid cloning.

Usage

register_autoconvert_function(cls, fun, packages = character(0))

Arguments

cls

character(1) The class that fun converts to.

fun

function The conversion function. Must take one argument and return an object of class cls, or possibly a sub-class as recognized by are_types_compatible().

packages

character The packages required to be loaded for fun to operate.

Value

NULL.

See Also

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

Examples

# This lets mlr3pipelines automatically try to convert a string into
# a `PipeOp` by querying the [`mlr_pipeops`] [`Dictionary`][mlr3misc::Dictionary].
# This is an example and not necessary, because mlr3pipelines adds it by default.
register_autoconvert_function("PipeOp", function(x) as_pipeop(x), packages = "mlr3pipelines")

Reset Autoconvert Register

Description

Reset autoconvert register to factory default, thereby undoing any calls to register_autoconvert_function() by the user.

Usage

reset_autoconvert_register()

Value

NULL

See Also

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

Reset the Class Hierarchy Cache

Description

Reset the class hierarchy cache to factory default, thereby undoing any calls to add_class_hierarchy_cache() by the user.

Usage

reset_class_hierarchy_cache()

Value

NULL

See Also

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

Configure Validation for a GraphLearner

Description

Configure validation for a graph learner.

In a GraphLearner, validation can be configured on two levels:

  1. On the GraphLearner level, which specifies how the validation set is constructed before entering the graph.

  2. On the level of the individual PipeOps (such as PipeOpLearner), which specifies which pipeops actually make use of the validation data (set its ⁠$validate⁠ field to "predefined") or not (set it to NULL). This can be specified via the argument ids.

Usage

## S3 method for class 'GraphLearner'
set_validate(
  learner,
  validate,
  ids = NULL,
  args_all = list(),
  args = list(),
  ...
)

Arguments

learner

(GraphLearner)
The graph learner to configure.

validate

(numeric(1), "predefined", "test", or NULL)
How to set the ⁠$validate⁠ field of the learner. If set to NULL all validation is disabled, both on the graph learner level, but also for all pipeops.

ids

(NULL or character())
For which pipeops to enable validation. This parameter is ignored when validate is set to NULL. By default, validation is enabled for the final PipeOp in the Graph.

args_all

(list())
Rarely needed. A named list of parameter values that are passed to all subsequet set_validate() calls on the individual PipeOps.

args

(named list())
Rarely needed. A named list of lists, specifying additional argments to be passed to set_validate() when calling it on the individual PipeOps.

...

(any)
Currently unused.

Examples

library(mlr3)

glrn = as_learner(po("pca") %>>% lrn("classif.debug"))
set_validate(glrn, 0.3)
glrn$validate
glrn$graph$pipeops$classif.debug$learner$validate

set_validate(glrn, NULL)
glrn$validate
glrn$graph$pipeops$classif.debug$learner$validate

set_validate(glrn, 0.2, ids = "classif.debug")
glrn$validate
glrn$graph$pipeops$classif.debug$learner$validate