4.1 The Building Blocks: PipeOps
The building blocks of mlr3pipelines are PipeOp-objects (PO).
They can be constructed directly using PipeOp<NAME>$new(), but the recommended way is to retrieve them from the mlr_pipeops dictionary:
library("mlr3pipelines")
as.data.table(mlr_pipeops)## key packages tags
## 1: boxcox bestNormalize data transform
## 2: branch meta
## 3: chunk meta
## 4: classbalancing imbalanced data,data transform
## 5: classifavg stats ensemble
## 6: classweights imbalanced data,data transform
## 7: colapply data transform
## 8: collapsefactors data transform
## 9: colroles data transform
## 10: copy meta
## 11: datefeatures data transform
## 12: encode stats encode,data transform
## 13: encodeimpact encode,data transform
## 14: encodelmer lme4,nloptr encode,data transform
## 15: featureunion ensemble
## 16: filter feature selection,data transform
## 17: fixfactors robustify,data transform
## 18: histbin graphics data transform
## 19: ica fastICA data transform
## 20: imputeconstant missings
## 21: imputehist graphics missings
## 22: imputelearner missings
## 23: imputemean missings
## 24: imputemedian stats missings
## 25: imputemode missings
## 26: imputeoor missings
## 27: imputesample missings
## 28: kernelpca kernlab data transform
## 29: learner learner
## 30: learner_cv learner,ensemble,data transform
## 31: missind missings,data transform
## 32: modelmatrix stats data transform
## 33: multiplicityexply multiplicity
## 34: multiplicityimply multiplicity
## 35: mutate data transform
## 36: nmf MASS,NMF data transform
## 37: nop meta
## 38: ovrsplit target transform,multiplicity
## 39: ovrunite multiplicity,ensemble
## 40: pca data transform
## 41: proxy meta
## 42: quantilebin stats data transform
## 43: randomprojection data transform
## 44: randomresponse abstract
## 45: regravg ensemble
## 46: removeconstants robustify,data transform
## 47: renamecolumns data transform
## 48: replicate multiplicity
## 49: scale data transform
## 50: scalemaxabs data transform
## 51: scalerange data transform
## 52: select feature selection,data transform
## 53: smote smotefamily imbalanced data,data transform
## 54: spatialsign data transform
## 55: subsample data transform
## 56: targetinvert abstract
## 57: targetmutate target transform
## 58: targettrafoscalerange target transform
## 59: textvectorizer quanteda,stopwords data transform
## 60: threshold target transform
## 61: tunethreshold bbotk target transform
## 62: unbranch meta
## 63: vtreat vtreat encode,missings,data transform
## 64: yeojohnson bestNormalize data transform
## key packages tags
## feature_types input.num output.num
## 1: numeric,integer 1 1
## 2: NA 1 NA
## 3: NA 1 NA
## 4: logical,integer,numeric,character,factor,ordered,... 1 1
## 5: NA NA 1
## 6: logical,integer,numeric,character,factor,ordered,... 1 1
## 7: logical,integer,numeric,character,factor,ordered,... 1 1
## 8: factor,ordered 1 1
## 9: logical,integer,numeric,character,factor,ordered,... 1 1
## 10: NA 1 NA
## 11: POSIXct 1 1
## 12: factor,ordered 1 1
## 13: factor,ordered 1 1
## 14: factor,ordered 1 1
## 15: NA NA 1
## 16: logical,integer,numeric,character,factor,ordered,... 1 1
## 17: factor,ordered 1 1
## 18: numeric,integer 1 1
## 19: numeric,integer 1 1
## 20: logical,integer,numeric,character,factor,ordered,... 1 1
## 21: integer,numeric 1 1
## 22: logical,factor,ordered 1 1
## 23: numeric,integer 1 1
## 24: numeric,integer 1 1
## 25: factor,integer,logical,numeric,ordered 1 1
## 26: character,factor,integer,numeric,ordered 1 1
## 27: factor,integer,logical,numeric,ordered 1 1
## 28: numeric,integer 1 1
## 29: NA 1 1
## 30: logical,integer,numeric,character,factor,ordered,... 1 1
## 31: logical,integer,numeric,character,factor,ordered,... 1 1
## 32: logical,integer,numeric,character,factor,ordered,... 1 1
## 33: NA 1 NA
## 34: NA NA 1
## 35: logical,integer,numeric,character,factor,ordered,... 1 1
## 36: numeric,integer 1 1
## 37: NA 1 1
## 38: NA 1 1
## 39: NA 1 1
## 40: numeric,integer 1 1
## 41: NA NA 1
## 42: numeric,integer 1 1
## 43: numeric,integer 1 1
## 44: NA 1 1
## 45: NA NA 1
## 46: logical,integer,numeric,character,factor,ordered,... 1 1
## 47: logical,integer,numeric,character,factor,ordered,... 1 1
## 48: NA 1 1
## 49: numeric,integer 1 1
## 50: numeric,integer 1 1
## 51: numeric,integer 1 1
## 52: logical,integer,numeric,character,factor,ordered,... 1 1
## 53: logical,integer,numeric,character,factor,ordered,... 1 1
## 54: numeric,integer 1 1
## 55: logical,integer,numeric,character,factor,ordered,... 1 1
## 56: NA 2 1
## 57: NA 1 2
## 58: NA 1 2
## 59: character 1 1
## 60: NA 1 1
## 61: NA 1 1
## 62: NA NA 1
## 63: logical,integer,numeric,character,factor,ordered,... 1 1
## 64: numeric,integer 1 1
## feature_types input.num output.num
## input.type.train input.type.predict output.type.train output.type.predict
## 1: Task Task Task Task
## 2: * * * *
## 3: Task Task Task Task
## 4: TaskClassif TaskClassif TaskClassif TaskClassif
## 5: NULL PredictionClassif NULL PredictionClassif
## 6: TaskClassif TaskClassif TaskClassif TaskClassif
## 7: Task Task Task Task
## 8: Task Task Task Task
## 9: Task Task Task Task
## 10: * * * *
## 11: Task Task Task Task
## 12: Task Task Task Task
## 13: Task Task Task Task
## 14: Task Task Task Task
## 15: Task Task Task Task
## 16: Task Task Task Task
## 17: Task Task Task Task
## 18: Task Task Task Task
## 19: Task Task Task Task
## 20: Task Task Task Task
## 21: Task Task Task Task
## 22: Task Task Task Task
## 23: Task Task Task Task
## 24: Task Task Task Task
## 25: Task Task Task Task
## 26: Task Task Task Task
## 27: Task Task Task Task
## 28: Task Task Task Task
## 29: TaskClassif TaskClassif NULL PredictionClassif
## 30: TaskClassif TaskClassif TaskClassif TaskClassif
## 31: Task Task Task Task
## 32: Task Task Task Task
## 33: [*] [*] * *
## 34: * * [*] [*]
## 35: Task Task Task Task
## 36: Task Task Task Task
## 37: * * * *
## 38: TaskClassif TaskClassif [TaskClassif] [TaskClassif]
## 39: [NULL] [PredictionClassif] NULL PredictionClassif
## 40: Task Task Task Task
## 41: * * * *
## 42: Task Task Task Task
## 43: Task Task Task Task
## 44: NULL Prediction NULL Prediction
## 45: NULL PredictionRegr NULL PredictionRegr
## 46: Task Task Task Task
## 47: Task Task Task Task
## 48: * * [*] [*]
## 49: Task Task Task Task
## 50: Task Task Task Task
## 51: Task Task Task Task
## 52: Task Task Task Task
## 53: Task Task Task Task
## 54: Task Task Task Task
## 55: Task Task Task Task
## 56: NULL,NULL function,Prediction NULL Prediction
## 57: Task Task NULL,Task function,Task
## 58: TaskRegr TaskRegr NULL,TaskRegr function,TaskRegr
## 59: Task Task Task Task
## 60: NULL PredictionClassif NULL PredictionClassif
## 61: Task Task NULL Prediction
## 62: * * * *
## 63: Task Task Task Task
## 64: Task Task Task Task
## input.type.train input.type.predict output.type.train output.type.predictSingle POs can be created using po(<name>):
pca = po("pca")or using syntactic sugar
pca = po("pca")Some POs require additional arguments for construction:
learner = po("learner")
# Error in as_learner(learner) : argument "learner" is missing, with no default argument "learner" is missing, with no defaultlearner = po("learner", lrn("classif.rpart"))or in short po("learner", lrn("classif.rpart")).
Hyperparameters of POs can be set through the param_vals argument.
Here we set the fraction of features for a filter:
filter = po("filter",
filter = mlr3filters::flt("variance"),
param_vals = list(filter.frac = 0.5))or in short notation:
po("filter", mlr3filters::flt("variance"), filter.frac = 0.5)The figure below shows an exemplary PipeOp.
It takes an input, transforms it during .$train and .$predict and returns data:
