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delta parameter for Huber distribution\n+      # makeDiscreteLearnerParam(id = \"risk\", values = c(\"inbag\", \"oobag\", \"none\")), we don't need this in FDboost\n+      makeNumericLearnerParam(id = \"df\", default = 4, lower = 0.5),  # effective degrees of freedom, depend on the regularization parameter of the penality matrix and number of splines, must be the same for all base learners(covariates)\n+      # makeDiscreteLearnerParam(id = \"baselearner\", values = c(\"bbs\", \"bols\")),  # we don't use \"btree\" in FDboost\n+      makeIntegerLearnerParam(id = \"bsignal.knots\", default = 10L, lower = 1L),  # determine the number of splines\n+      makeIntegerLearnerParam(id = \"bsignal.degree\", default = 3L, lower = 1L),  # degree of the b spline\n+      makeIntegerLearnerParam(id = \"bsignal.differences\", default = 1L, lower = 1L),  # degree of the penalty\n+      makeLogicalLearnerParam(id = \"bsignal.check.ident\", default = FALSE, tunable = FALSE)  # identifiability check by testing matrix degeneracy\n+      ),\n+    properties = c(\"numerics\"),\n+    name = \"FLAM regression\",\n+    short.name = \"FDboost\"\n+  )\n+}\n+\n+#' @export\n+trainLearner.fdaregr.FDboost = function(.learner, .task, .subset, .weights = NULL, mstop = 100L, \n+  bsignal.knots = 10L, df = 4L, bsignal.check.ident = FALSE, bsignal.degree = 3L, bsignal.differences = 1L, \n+  nu = 0.1, family = \"Gaussian\", custom.family.definition = NULL, nuirange = c(0,100), d = NULL, ...) {\n+  family = switch(family,\n+    Gaussian = mboost::Gaussian(),\n+    Laplace = mboost::Laplace(),\n+    Huber = mboost::Huber(d),\n+    Poisson = mboost::Poisson(),\n+    GammaReg = mboost::GammaReg(nuirange = nuirange),\n+    NBinomial = mboost::NBinomial(nuirange = nuirange),\n+    Hurdle = mboost::Hurdle(nuirange = nuirange),\n+    custom.family = custom.family.definition\n+  )\n+  ctrl = learnerArgsToControl(mboost::boost_control, mstop, nu)\n+  d = getTaskData(.task, subset = .subset)\n+  tn = getTaskTargetNames(.task)\n+  tdesc = getTaskDescription(.task)\n+  fdf = tdesc$fd.features\n+\n+  # later on, the grid elements in mat.list should have suffix \".grid\"\n+  fdg = setNames(tdesc$fd.grids, stri_paste(names(tdesc$fd.grids), \".grid\"))\n+  fdns = names(fdf)\n+  # setup mat.list: for each func covar we add its data matrix and its grid. and once the target col\n+  # also setup charvec of formula terms for func covars\n+  mat.list = namedList(fdns)\n+  formula.terms = c()\n+  for (fdn in fdns) {\n+    gn = stri_paste(fdn, \".grid\")\n+    mat.list[[fdn]] = as.matrix(d[, tdesc$fd.features[[fdn]], drop = FALSE])\n+    formula.terms[fdn] = sprintf(\"bsignal(%s, %s, knots = %i, df = %f, degree = %i, differences = %i, check.ident = %s)\",\n+      fdn, gn, bsignal.knots, df, bsignal.degree, bsignal.differences, bsignal.check.ident)\n+  }\n+  mat.list = c(mat.list, fdg)\n+  mat.list[[tn]] = d[, tn]\n+  form = as.formula(sprintf(\"%s ~ %s\", tn, collapse(formula.terms, \"+\")))\n+  FDboost::FDboost(formula = form, timeformula = ~bols(1), data = mat.list, \n+    control = ctrl, family = family)\n+}\n+\n+reformat2mat.list = function(data, tdesc) {\n+  tn = tdesc$target\n+  fdns = names(tdesc$fd.features)\n+  mat.list = namedList(fdns)\n+  for (fdn in fdns) {\n+    mat.list[[fdn]] = as.matrix(subset(data, select = tdesc$fd.features[[fdn]]))\n+    mat.list[[stri_paste(fdn, \".index\")]] = tdesc$fd.grids[[fdn]]\n+  }\n+  return(mat.list)\n+}\n+\n+#' @export\n+predictLearner.fdaregr.FDboost = function(.learner, .model, .newdata, ...) {\n+  mextra_para = list(...)","path":"R/RLearner_FDAregr_FDboost.R","position":81,"original_position":81,"commit_id":"526e026837fc1cb77856c28cdddb920f0d1324ff","original_commit_id":"526e026837fc1cb77856c28cdddb920f0d1324ff","user":{"login":"lbeggel","id":17455074,"avatar_url":"https://avatars2.githubusercontent.com/u/17455074?v=3","gravatar_id":"","url":"https://api.github.com/users/lbeggel","html_url":"https://github.com/lbeggel","followers_url":"https://api.github.com/users/lbeggel/followers","following_url":"https://api.github.com/users/lbeggel/following{/other_user}","gists_url":"https://api.github.com/users/lbeggel/gists{/gist_id}","starred_url":"https://api.github.com/users/lbeggel/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lbeggel/subscriptions","organizations_url":"https://api.github.com/users/lbeggel/orgs","repos_url":"https://api.github.com/users/lbeggel/repos","events_url":"https://api.github.com/users/lbeggel/events{/privacy}","received_events_url":"https://api.github.com/users/lbeggel/received_events","type":"User","site_admin":false},"body":"DONE. removed","created_at":"2017-03-09T16:07:51Z","updated_at":"2017-03-09T16:07:51Z","html_url":"https://github.com/mlr-org/mlr/pull/1596#discussion_r105201246","pull_request_url":"https://api.github.com/repos/mlr-org/mlr/pulls/1596","_links":{"self":{"href":"https://api.github.com/repos/mlr-org/mlr/pulls/comments/105201246"},"html":{"href":"https://github.com/mlr-org/mlr/pull/1596#discussion_r105201246"},"pull_request":{"href":"https://api.github.com/repos/mlr-org/mlr/pulls/1596"}}},"pull_request":{"url":"https://api.github.com/repos/mlr-org/mlr/pulls/1596","id":109317405,"html_url":"https://github.com/mlr-org/mlr/pull/1596","diff_url":"https://github.com/mlr-org/mlr/pull/1596.diff","patch_url":"https://github.com/mlr-org/mlr/pull/1596.patch","issue_url":"https://api.github.com/repos/mlr-org/mlr/issues/1596","number":1596,"state":"open","locked":false,"title":"Fda pull1 task","user":{"login":"smilesun","id":570694,"avatar_url":"https://avatars3.githubusercontent.com/u/570694?v=3","gravatar_id":"","url":"https://api.github.com/users/smilesun","html_url":"https://github.com/smilesun","followers_url":"https://api.github.com/users/smilesun/followers","following_url":"https://api.github.com/users/smilesun/following{/other_user}","gists_url":"https://api.github.com/users/smilesun/gists{/gist_id}","starred_url":"https://api.github.com/users/smilesun/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/smilesun/subscriptions","organizations_url":"https://api.github.com/users/smilesun/orgs","repos_url":"https://api.github.com/users/smilesun/repos","events_url":"https://api.github.com/users/smilesun/events{/privacy}","received_events_url":"https://api.github.com/users/smilesun/received_events","type":"User","site_admin":false},"body":"Laura, Florian and I finished this first pull request which only contain the minimal version of FDA since we want the review to go smoothly to merge to master. After this is done, we will add more functionalities to this. 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