Preprocess Date Features

Based on POSIXct/Date 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 or Date 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 package. If operation on only particular POSIXct/Date columns is requested, use the affect_columns parameter inherited from PipeOpTaskPreprocSimple.

For Date columns, the features "hour", "minute", "second", and "is_day" are skipped.

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())

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

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:

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.
quarter :: logical(1)
Should the quarter 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 (ISO 8601) 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.

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.

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))
#> $output
#> 
#> ── <TaskClassif> (150x14) ──────────────────────────────────────────────────────
#> • Target: Species
#> • Target classes: setosa (33%), versicolor (33%), virginica (33%)
#> • Properties: multiclass
#> • Features (13):
#>   • int (8): date.day_of_month, date.day_of_week, date.day_of_year, date.hour,
#>   date.month, date.quarter, date.week_of_year, date.year
#>   • dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#>   • lgl (1): date.is_day
#> 
pop$state
#> $dt_columns
#> [1] "date"
#> 
#> $affected_cols
#> [1] "Petal.Length" "Petal.Width"  "Sepal.Length" "Sepal.Width"  "date"        
#> 
#> $intasklayout
#> Key: <id>
#>              id    type
#>          <char>  <char>
#> 1: Petal.Length numeric
#> 2:  Petal.Width numeric
#> 3: Sepal.Length numeric
#> 4:  Sepal.Width numeric
#> 5:         date POSIXct
#> 
#> $outtasklayout
#> Key: <id>
#>                    id    type
#>                <char>  <char>
#>  1:      Petal.Length numeric
#>  2:       Petal.Width numeric
#>  3:      Sepal.Length numeric
#>  4:       Sepal.Width numeric
#>  5: date.day_of_month integer
#>  6:  date.day_of_week integer
#>  7:  date.day_of_year integer
#>  8:         date.hour integer
#>  9:       date.is_day logical
#> 10:        date.month integer
#> 11:      date.quarter integer
#> 12: date.week_of_year integer
#> 13:         date.year integer
#> 
#> $outtaskshell
#> Empty data.table (0 rows and 14 cols): Species,Petal.Length,Petal.Width,Sepal.Length,Sepal.Width,date.year...
#>