feat: add cross-correlation pipe op (#109)

* feat: draft for correlation pipe op

* docs: remove some old args

* chore: add grid as param

* docs: update description for cor

* feat: bring tfr to common domain

* perf: only reshape function when outside domain

* feat: transform to common domain via min max scaling

* feat: more changes for cor

* tests: simpliy tests for cross-correlation

* tests: restructure

* refactor: move helpers back since only used in extract

* refactor: reuse ncol

* fix: missing document

* docs: remove stats tag and rebuild readme

* tests: test for non matching domain

* refactor: revert var rename

* docs: update news

* tests: more tests

* chore: update .Rbuildignore

* tests: use expect_identical

* docs(cor): missing indent

* tests: adding withr for random seed usage

* tests: add error message for test

* tests: add message for warning

* feat: add mlr3pipelines to depends

* chore: remove unneeded mlr3 and mlr3pieplines import in tests

* fix: still import

* docs: some more description

* docs: update news

.Rbuildignore

@@ -1,13 +1,12 @@
 ^.*\.Rproj$
-^.Renviron$
-^\.pre-commit-config\.yaml$
-^CITATION.cff$
+^CITATION\.cff$
 ^CRAN-RELEASE$
 ^CRAN-SUBMISSION$
 ^LICENSE$
 ^Meta$
-^README.html$
 ^README\.Rmd$
+^README\.html$
+^\.Renviron$
 ^\.Rproj\.user$
 ^\.ccache$
 ^\.editorconfig$
@@ -16,6 +15,7 @@
 ^\.httr-oauth$
 ^\.ignore$
 ^\.lintr$
+^\.pre-commit-config\.yaml$
 ^\.vscode$
 ^_pkgdown\.yml$
 ^attic$

DESCRIPTION

@@ -24,26 +24,28 @@ URL: https://mlr3fda.mlr-org.com, https://github.com/mlr-org/mlr3fda
 BugReports: https://github.com/mlr-org/mlr3fda/issues
 Depends:
  mlr3 (>= 0.14.0),
+ mlr3pipelines (>= 0.5.2),
  R (>= 3.1.0)
 Imports:
  checkmate,
  data.table,
  lgr,
  mlr3misc (>= 0.14.0),
- mlr3pipelines (>= 0.5.2),
  paradox,
  R6,
  tf (>= 0.3.4)
 Suggests:
  rpart,
- testthat (>= 3.0.0)
+ testthat (>= 3.0.0),
+ withr
 Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE, r6 = TRUE)
 RoxygenNote: 7.3.1
 Collate:
  'zzz.R'
+ 'PipeOpFDACor.R'
  'PipeOpFDAExtract.R'
  'PipeOpFDAFlatten.R'
  'PipeOpFDAInterpol.R'

NAMESPACE

@@ -2,6 +2,7 @@
 
 S3method(hash_input,tfd_irreg)
 S3method(hash_input,tfd_reg)
+export(PipeOpFDACor)
 export(PipeOpFDAExtract)
 export(PipeOpFDAFlatten)
 export(PipeOpFDAInterpol)

NEWS.md

@@ -1,5 +1,8 @@
 # dev
 
+* New PipeOp `PipeOpFDACor`
+* mlr3fda now depends on mlr3pipelines instead of importing it
+
 # mlr3fda 0.1.1
 
 * Fix CRAN issues

R/PipeOpFDACor.R

@@ -0,0 +1,76 @@
+#' @title Cross-Correlation of Functional Data
+#' @name mlr_pipeops_fda.cor
+#'
+#' @description
+#' Calculates the cross-correlation between two functional vectors using [tf::tf_crosscor()].
+#' Note that it only operates on regular data and that the cross-correlation assumes that each column
+#' has the same domain.
+#'
+#' @section Parameters:
+#' The parameters are the parameters inherited from [`PipeOpTaskPreprocSimple`], as well as the following
+#' parameters:
+#' * `arg` :: `numeric()` \cr
+#' Grid to use for the cross-correlation.
+#'
+#' @export
+#' @examples
+#' set.seed(1234L)
+#' dt = data.table(y = 1:100, x1 = tf::tf_rgp(100L), x2 = tf::tf_rgp(100L))
+#' task = as_task_regr(dt, target = "y")
+#' po_cor = po("fda.cor")
+#' task_cor = po_cor$train(list(task))[[1L]]
+#' task_cor
+PipeOpFDACor = R6Class("PipeOpFDACor",
+ inherit = mlr3pipelines::PipeOpTaskPreprocSimple,
+ public = list(
+ #' @description Initializes a new instance of this Class.
+ #' @param id (`character(1)`)\cr
+ #' Identifier of resulting object, default `"fda.cor"`.
+ #' @param param_vals (named `list`)\cr
+ #' List of hyperparameter settings, overwriting the hyperparameter settings that would
+ #' otherwise be set during construction. Default `list()`.
+ initialize = function(id = "fda.cor", param_vals = list()) {
+ param_set = ps(
+ arg = p_uty(tags = c("train", "predict"), custom_check = check_numeric)
+ )
+
+ super$initialize(
+ id = id,
+ param_set = param_set,
+ param_vals = param_vals,
+ packages = c("mlr3fda", "mlr3pipelines", "tf"),
+ feature_types = "tfd_reg",
+ tags = "fda"
+ )
+ }
+ ),
+ private = list(
+ .transform_dt = function(dt, levels) {
+ pars = self$param_set$get_values()
+
+ k = ncol(dt)
+ if (k < 2L) {
+ warningf("task has less than 2 columns")
+ return(dt)
+ }
+
+ nms = names(dt)
+ res = list()
+ for (i in 2:k) {
+ for (j in 1:(i - 1L)) {
+ x = dt[[i]]
+ y = dt[[j]]
+ if (!all(tf::tf_domain(x) == tf::tf_domain(y))) {
+ stopf("Domain of %s and %s do not match", nms[[j]], nms[[i]])
+ }
+ nm = sprintf("%s_%s_cor", nms[[j]], nms[[i]])
+ res[[nm]] = invoke(tf::tf_crosscor, x = x, y = y, .args = pars)
+ }
+ }
+ setDT(res)
+ }
+ )
+)
+
+#' @include zzz.R
+register_po("fda.cor", PipeOpFDACor)

R/PipeOpFDAExtract.R

@@ -35,8 +35,6 @@
 #'
 #' @export
 #' @examples
-#' library(mlr3pipelines)
-#'
 #' task = tsk("fuel")
 #' po_fmean = po("fda.extract", features = "mean")
 #' task_fmean = po_fmean$train(list(task))[[1L]]

R/PipeOpFDAFlatten.R

@@ -18,8 +18,6 @@
 #'
 #' @export
 #' @examples
-#' library(mlr3pipelines)
-#'
 #' task = tsk("fuel")
 #' pop = po("fda.flatten")
 #' task_flat = pop$train(list(task))

R/PipeOpFDAInterpol.R

@@ -41,8 +41,6 @@
 #'
 #' @export
 #' @examples
-#' library(mlr3pipelines)
-#'
 #' task = tsk("fuel")
 #' pop = po("fda.interpol")
 #' task_interpol = pop$train(list(task))[[1L]]

R/PipeOpFDASmooth.R

@@ -26,8 +26,6 @@
 #'
 #' @export
 #' @examples
-#' library(mlr3pipelines)
-#'
 #' task = tsk("fuel")
 #' po_smooth = po("fda.smooth", method = "rollmean", args = list(k = 5))
 #' task_smooth = po_smooth$train(list(task))[[1L]]

R/PipeOpFPCA.R

@@ -24,8 +24,6 @@
 #'
 #' @export
 #' @examples
-#' library(mlr3pipelines)
-#'
 #' task = tsk("fuel")
 #' po_fpca = po("fda.fpca", n_components = 3L)
 #' task_fpca = po_fpca$train(list(task))[[1L]]

README.Rmd

@@ -74,8 +74,6 @@ To train a model on this task we first need to extract scalar features from the
 We illustrate this below by extracting the mean value.
 
 ```{r fda.extract, fig.width = 5, fig.height = 3}
-library(mlr3pipelines)
-
 po_fmean = po("fda.extract", features = "mean")
 
 task_fmean = po_fmean$train(list(task))[[1L]]

README.md

@@ -76,8 +76,6 @@ from the functions. We illustrate this below by extracting the mean
 value.
 
 ``` r
-library(mlr3pipelines)
-
 po_fmean = po("fda.extract", features = "mean")
 
 task_fmean = po_fmean$train(list(task))[[1L]]
@@ -122,13 +120,14 @@ glrn$predict(task, row_ids = ids$test)
 
 ## Implemented PipeOps
 
-| Key | Label | Packages | Tags |
-|:-------------------------------------------------------------------------------|:-------------------------------------------------|:---------------------------------------------------|:--------------------|
-| [fda.extract](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.extract) | Extracts Simple Features from Functional Columns | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
-| [fda.flatten](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.flatten) | Flattens Functional Columns | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
-| [fda.fpca](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.fpca) | Functional Principal Component Analysis | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
-| [fda.interpol](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.interpol) | Interpolate Functional Columns | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
-| [fda.smooth](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.smooth) | Smoothing Functional Columns | [tf](https://cran.r-project.org/package=tf), stats | fda, data transform |
+| Key | Label | Packages | Tags |
+|:---|:---|:---|:---|
+| [fda.cor](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.cor) | Cross-Correlation of Functional Data | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
+| [fda.extract](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.extract) | Extracts Simple Features from Functional Columns | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
+| [fda.flatten](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.flatten) | Flattens Functional Columns | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
+| [fda.fpca](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.fpca) | Functional Principal Component Analysis | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
+| [fda.interpol](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.interpol) | Interpolate Functional Columns | [tf](https://cran.r-project.org/package=tf) | fda, data transform |
+| [fda.smooth](https://mlr3fda.mlr-org.com/reference/mlr_pipeops_fda.smooth) | Smoothing Functional Columns | [tf](https://cran.r-project.org/package=tf), stats | fda, data transform |
 
 ## Bugs, Questions, Feedback