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_id
To 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 0.4969408
This 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)
# 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 Bugs, Questions, Feedback
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.
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