Package website: release | dev
Extending mlr3 to functional data.
Install the last release from CRAN:
install.packages("mlr3fda")Install the development version from GitHub:
# install.packages("pak")
pak::pak("mlr-org/mlr3fda")The goal of mlr3fda is to extend mlr3 to functional
data. This is achieved by adding support for functional feature
types and providing preprocessing PipeOps that operates on
functional columns. For representing functional data, the
tfd_reg and tfd_irreg datatypes from the tf package are used and are
available after loading mlr3fda:
library(mlr3fda)
mlr_reflections$task_feature_types[c("tfr", "tfi")]
#> tfr tfi
#> "tfd_reg" "tfd_irreg"These datatypes can be used to represent regular and irregular
functional data respectively. Currently, Learners that
directly operate on functional data are not available, so it is
necessary to first extract scalar features from the functional
columns.
Here we will start with the predefined dti (Diffusion
Tensor Imaging) task, see tsk("dti")$help() for more
details. Besides scalar columns, this task also contains two functional
columns cca and rcst.
task = tsk("dti")
task
#> <TaskRegr:dti> (340 x 4): Diffusion Tensor Imaging (DTI)
#> * Target: pasat
#> * Properties: groups
#> * Features (3):
#> - tfi (2): cca, rcst
#> - fct (1): sex
#> * Groups: subject_idTo train a model on this task we first need to extract scalar features from the functions. We illustrate this below by extracting the mean value.
po_fmean = po("fda.extract", features = "mean")
task_fmean = po_fmean$train(list(task))[[1L]]
task_fmean$head()
#> pasat sex cca_mean rcst_mean
#> 1: 31 female 0.4493332 0.4968519
#> 2: 31 female 0.4441292 0.4810724
#> 3: 29 female 0.4257795 0.5102722
#> 4: 34 female 0.4418538 0.5453188
#> 5: 37 female 0.4700994 0.5471177
#> 6: 40 female 0.4873356 0.4969408This can be combined with a Lerner into a
GraphLearner that first extracts features and then trains a
model.
# split data into train and test set
ids = partition(task, stratify = FALSE)
# define a Graph and convert it to a GraphLearner
graph = po("fda.extract", features = "mean", drop = TRUE) %>>%
po("learner", learner = lrn("regr.rpart"))
glrn = as_learner(graph)
# train the graph learner on the train set
glrn$train(task, row_ids = ids$train)
# make predictions on the test set
glrn$predict(task, row_ids = ids$test)
#> <PredictionRegr> for 111 observations:
#> row_ids truth response
#> 11 48 49.99174
#> 12 40 49.99174
#> 13 43 52.42105
#> ---
#> 324 57 52.42105
#> 325 57 41.30769
#> 326 60 49.99174| Key | Label | Packages | Tags |
|---|---|---|---|
| fda.cor | Cross-Correlation of Functional Data | tf | fda, data transform |
| fda.extract | Extracts Simple Features from Functional Columns | tf | fda, data transform |
| fda.flatten | Flattens Functional Columns | tf | fda, data transform |
| fda.fpca | Functional Principal Component Analysis | tf | fda, data transform |
| fda.interpol | Interpolate Functional Columns | tf | fda, data transform |
| fda.scalerange | Linearly Transform the Domain of Functional Data. | tf | fda, data transform |
| fda.smooth | Smoothing Functional Columns | tf, stats | fda, data transform |
mlr3fda is a free and open source software project that encourages participation and feedback. If you have any issues, questions, suggestions or feedback, please do not hesitate to open an “issue” about it on the GitHub page!
In case of problems / bugs, it is often helpful if you provide a “minimum working example” that showcases the behaviour (but don’t worry about this if the bug is obvious).
Please understand that the resources of the project are limited: response may sometimes be delayed by a few days, and some feature suggestions may be rejected if they are deemed too tangential to the vision behind the project.
The development of this R-package was supported by Roche Diagonstics R&D.