mlr_pipeops_datefeatures | R Documentation |
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.
R6Class
object inheriting from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
PipeOpDateFeatures$new(id = "datefeatures", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "datefeatures"
.
param_vals
:: named list
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise
be set during construction. Default list()
.
Input and output channels are inherited from PipeOpTaskPreprocSimple
.
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
).
The $state
is a named list
with the $state
elements inherited from
PipeOpTaskPreprocSimple
.
The parameters are the parameters inherited from PipeOpTaskPreprocSimple
, as well as:
keep_date_var
:: logical(1)
Should the POSIXct
columns be kept as features? Default FALSE.
cyclic
:: logical(1)
Should cyclic features be computed? See Internals. Default FALSE.
year
:: logical(1)
Should the year be extracted as a feature? Default TRUE.
month
:: logical(1)
Should the month be extracted as a feature? Default TRUE.
week_of_year
:: logical(1)
Should the week of the year be extracted as a feature? Default TRUE.
day_of_year
:: logical(1)
Should the day of the year be extracted as a feature? Default TRUE.
day_of_month
:: logical(1)
Should the day of the month be extracted as a feature? Default TRUE.
day_of_week
:: logical(1)
Should the day of the week be extracted as a feature? Default TRUE.
hour
:: logical(1)
Should the hour be extracted as a feature? Default TRUE.
minute
:: logical(1)
Should the minute be extracted as a feature? Default TRUE.
second
:: logical(1)
Should the second be extracted as a feature? Default TRUE.
is_day
:: logical(1)
Should a feature be extracted indicating whether it is day time (06:00am - 08:00pm)?
Default TRUE.
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.
Only methods inherited from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
Only fields inherited from PipeOpTaskPreproc
/PipeOp
.
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreprocSimple
,
PipeOpTaskPreproc
,
PipeOp
,
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_encodeimpact
,
mlr_pipeops_encodelmer
,
mlr_pipeops_encode
,
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_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_scalemaxabs
,
mlr_pipeops_scalerange
,
mlr_pipeops_scale
,
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_tunethreshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
,
mlr_pipeops
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
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