Merge pull request #103 from tlverse/rm-lassi-artifacts

Remove lassi artifacts

.github/workflows/R-CMD-check.yml

@@ -32,7 +32,7 @@ jobs:
         uses: actions/checkout@v2
 
       - name: Setup R
-        uses: r-lib/actions/setup-r@master
+        uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
 

DESCRIPTION

@@ -68,5 +68,5 @@ LinkingTo:
   Rcpp,
   RcppEigen
 VignetteBuilder: knitr
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.0
 Roxygen: list(markdown = TRUE)

NAMESPACE

@@ -16,7 +16,6 @@ export(make_copy_map)
 export(make_design_matrix)
 export(make_reduced_basis_map)
 export(squash_hal_fit)
-import(Rcpp)
 importFrom(Matrix,tcrossprod)
 importFrom(Rcpp,sourceCpp)
 importFrom(assertthat,assert_that)
@@ -27,13 +26,9 @@ importFrom(data.table,setorder)
 importFrom(glmnet,cv.glmnet)
 importFrom(glmnet,glmnet)
 importFrom(methods,is)
-importFrom(methods,new)
 importFrom(origami,cross_validate)
-importFrom(origami,fold_index)
 importFrom(origami,folds2foldvec)
 importFrom(origami,make_folds)
-importFrom(origami,training)
-importFrom(origami,validation)
 importFrom(stats,aggregate)
 importFrom(stats,as.formula)
 importFrom(stats,coef)

R/RcppExports.R

@@ -51,16 +51,6 @@ apply_copy_map <- function(X, copy_map) {
     .Call('_hal9001_apply_copy_map', PACKAGE = 'hal9001', X, copy_map)
 }
 
-#' Prediction from a Lassi Model
-#'
-#' @param X A sparse matrix of HAL basis functions.
-#' @param beta A vector of coefficient values for the HAL basis functions.
-#' @param intercept A numeric value giving the intercept of the HAL model.
-#'
-lassi_predict <- function(X, beta, intercept) {
-    .Call('_hal9001_lassi_predict', PACKAGE = 'hal9001', X, beta, intercept)
-}
-
 #' Sort Basis Functions
 #'
 #' Build a sorted list of unique basis functions based on columns, where each

R/cv_lasso.R

@@ -1,55 +1,3 @@
-#' Single Lasso estimation for cross-validation with Origami
-#'
-#' Fits Lasso regression over a single fold of a cross-validated data set. This
-#' is meant to be called using \code{\link[origami]{cross_validate}}, which is
-#' done through \code{\link{cv_lasso}}. Note that this procedure is NOT meant
-#' to be invoked by itself. INTERNAL USE ONLY.
-#'
-#' @param fold A \code{fold} object produced by a call to \code{make_folds}
-#'  from the \pkg{origami}.
-#' @param data A \code{dgCMatrix} object containing the outcome values (Y) in
-#'  its first column and vectors corresponding to the basis functions of HAL in
-#'  all other columns. Consult the description of HAL regression for details.
-#' @param lambdas A \code{numeric} vector corresponding to a sequence of lambda
-#'  values obtained by fitting the Lasso on the full data.
-#' @param center binary. If \code{TRUE}, covariates are centered. This is much
-#'  slower, but matches the \code{glmnet} implementation. Default \code{FALSE}.
-#'
-#' @importFrom origami training validation fold_index
-lassi_origami <- function(fold, data, lambdas, center = FALSE) {
-  # make sure data is an (augmented) sparse matrix of basis functions
-  stopifnot(class(data) == "dgCMatrix")
-
-  # split data for V-fold cross-validation
-  train_data <- origami::training(data)
-  valid_data <- origami::validation(data)
-
-  # wrangle objects to clearer forms
-  train_x_basis <- train_data[, -1]
-  valid_x_basis <- valid_data[, -1]
-  train_y <- train_data[, 1]
-  valid_y <- valid_data[, 1]
-
-  # compute the predicted betas for the given training and validation sets
-  lassi_fit <- lassi(
-    x = train_x_basis, y = train_y, lambdas = lambdas,
-    center = center
-  )
-  pred_mat <- predict(lassi_fit, valid_x_basis)
-
-  # compute the MSE for the given training and validation sets
-  mses <- apply(pred_mat, 2, function(preds) {
-    mean((preds - valid_y)^2)
-  })
-
-  # the only output needed is the lambda-wise MSE over each fold
-  mses_out <- matrix(mses, nrow = 1)
-  out <- list(mses = mses_out)
-  return(out)
-}
-
-###############################################################################
-
 #' Cross-validated Lasso on Indicator Bases
 #'
 #' Fits Lasso regression using a customized procedure, with cross-validation