Fixes to stabilize CRAN version.

DESCRIPTION

@@ -1,43 +1,43 @@
 Package: Rforestry
 Type: Package
 Title: Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability
-Version: 0.9.0.3
-Author: Sören Künzel, 
-        Theo Saarinen, 
-        Simon Walter, 
-        Edward Liu, 
-        Allen Tang, 
-        Jasjeet Sekhon
+Version: 0.11.0.0
+Authors@R: c(
+    person("Sören", "Künzel", role = "aut"),
+    person("Theo", "Saarinen", role = c("aut","cre"), email = "theo_s@berkeley.edu"),
+    person("Simon", "Walter", role = "aut"),
+    person("Sam", "Antonyan", role = "aut"),
+    person("Edward", "Liu", role = "aut"),
+    person("Allen", "Tang", role = "aut"),
+    person("Jasjeet", "Sekhon", role = "aut")
+    )
 Maintainer: Theo Saarinen <theo_s@berkeley.edu>
 BugReports: https://github.com/forestry-labs/Rforestry/issues
 URL: https://github.com/forestry-labs/Rforestry
-Description: Provides fast implementations of Honest Random Forests, 
+Description: Provides fast implementations of Random Forests, 
     Gradient Boosting, and Linear Random Forests, with an emphasis on inference 
     and interpretability. Additionally contains methods for variable 
     importance, out-of-bag prediction, regression monotonicity, and
     several methods for missing data imputation.
 License: GPL (>=3) | file LICENSE
 Encoding: UTF-8
-LazyData: true
 Imports:
     Rcpp (>= 0.12.9),
     parallel,
     methods,
     visNetwork, 
-    glmnet, 
+    glmnet (>= 4.1), 
     grDevices,
     onehot
 LinkingTo: Rcpp,
   RcppArmadillo,
   RcppThread
-RoxygenNote: 7.1.1
-SystemRequirements: C++11
+RoxygenNote: 7.2.3
 Suggests:
     testthat,
     knitr,
     rmarkdown,
     mvtnorm
-VignetteBuilder: knitr
 Collate:
     'R_preprocessing.R'
     'RcppExports.R'

R/compute_rf_lp.R

@@ -34,7 +34,7 @@
 compute_lp <- function(object, feature.new, feature, p){
 
   # Checks and parsing:
-  if (class(object) != "forestry") {
+  if (!inherits(object, "forestry")) {
     stop("The object submitted is not a forestry random forest")
   }
   feature.new <- as.data.frame(feature.new)

R/plottree.R

@@ -28,15 +28,15 @@
 #'   replace = FALSE,
 #'   nodesizeStrictSpl = 10,
 #'   mtry = 4,
-#'   ntree = 1000,
+#'   ntree = 10,
 #'   minSplitGain = .004,
 #'   linear = TRUE,
 #'   overfitPenalty = 1.65,
 #'   linFeats = 1:2)
 #'
 #' plot(x = ridge_rf)
 #' plot(x = ridge_rf, tree.id = 2)
-#' plot(x = ridge_rf, tree.id = 1000)
+#' plot(x = ridge_rf, tree.id = 10)
 #'
 #' @export
 #' @import visNetwork

README.md

@@ -11,8 +11,8 @@ and Linear Random Forests, with an emphasis on inference and interpretability.
 
 ## How to install
 1. The GFortran compiler has to be up to date. GFortran Binaries can be found [here](https://gcc.gnu.org/wiki/GFortranBinaries).
-2. The [devtools](https://github.com/hadley/devtools) package has to be installed. You can install it using,  `install.packages("devtools")`.
-3. The package contains compiled code, and you must have a development environment to install the development version. You can use `devtools::has_devel()` to check whether you do. If no development environment exists, Windows users download and install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) and macOS users download and install [Xcode](https://itunes.apple.com/us/app/xcode/id497799835).
+2. The [devtools](https://github.com/r-lib/devtools) package has to be installed. You can install it using,  `install.packages("devtools")`.
+3. The package contains compiled code, and you must have a development environment to install the development version. You can use `devtools::has_devel()` to check whether you do. If no development environment exists, Windows users download and install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) and macOS users download and install [Xcode](https://apps.apple.com/us/app/xcode/id497799835).
 4. The latest development version can then be installed using
 `devtools::install_github("forestry-labs/Rforestry")`. For Windows users, you'll need to skip 64-bit compilation `devtools::install_github("forestry-labs/Rforestry", INSTALL_opts = c('--no-multiarch'))` due to an outstanding gcc issue.
 

src/RcppExports.cpp

@@ -2,10 +2,16 @@
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <RcppArmadillo.h>
+#include <RcppThread.h>
 #include <Rcpp.h>
 
 using namespace Rcpp;
 
+#ifdef RCPP_USE_GLOBAL_ROSTREAM
+Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
+Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
+#endif
+
 // rcpp_cppDataFrameInterface
 SEXP rcpp_cppDataFrameInterface(Rcpp::List x, Rcpp::NumericVector y, Rcpp::NumericVector catCols, Rcpp::NumericVector linCols, int numRows, int numColumns, Rcpp::NumericVector featureWeights, Rcpp::NumericVector featureWeightsVariables, Rcpp::NumericVector deepFeatureWeights, Rcpp::NumericVector deepFeatureWeightsVariables, Rcpp::NumericVector observationWeights, Rcpp::NumericVector monotonicConstraints);
 RcppExport SEXP _Rforestry_rcpp_cppDataFrameInterface(SEXP xSEXP, SEXP ySEXP, SEXP catColsSEXP, SEXP linColsSEXP, SEXP numRowsSEXP, SEXP numColumnsSEXP, SEXP featureWeightsSEXP, SEXP featureWeightsVariablesSEXP, SEXP deepFeatureWeightsSEXP, SEXP deepFeatureWeightsVariablesSEXP, SEXP observationWeightsSEXP, SEXP monotonicConstraintsSEXP) {

src/forestry.cpp

@@ -165,7 +165,6 @@ void forestry::addTrees(size_t ntree) {
   const unsigned int see = this->getSeed();
 
   size_t splitSampleSize = (size_t) (getSplitRatio() * getSampleSize());
-  float percent_complete = 0.0;
 
   // Rcpp::Rcout << "Training Progress: " << std::endl;
   // R_FlushConsole();
@@ -913,7 +912,7 @@ std::vector<std::vector<double>>* forestry::neighborhoodImpute(
           // is inefficient because we have to iterate over the
           // vector about 1.5 times.
           double runningMax = -std::numeric_limits<double>::infinity();
-          size_t maxPosition;
+          size_t maxPosition=0;
           for(size_t l = 0; l < categoryContribution.size(); l++) {
             if(categoryContribution[l] > runningMax) {
               runningMax = categoryContribution[l];

src/forestryTree.cpp

@@ -347,7 +347,7 @@ void splitDataIntoTwoParts(
     double rightMean = trainingData->partitionMean(rightPartitionIndex);
 
     for (const auto& index : naIndices) {
-      if (abs(trainingData->getOutcomePoint(index) - leftMean) < abs(trainingData->getOutcomePoint(index) - rightMean)) {
+      if (fabs(trainingData->getOutcomePoint(index) - leftMean) < fabs(trainingData->getOutcomePoint(index) - rightMean)) {
         leftPartitionIndex->push_back(index);
         naLeftCount++;
       } else {

src/treeSplitting.cpp

@@ -1451,7 +1451,7 @@ void findBestSplitImpute(
       // If closer to left partitionmean, add to left sum, leftcount ++
       // This is okay to do after we check monotonicity, this shouldn't change
       // the ordering of the partition means as we allocate the NA examples greedily
-      if (abs(currOutcome - leftPartitionMean) < abs(currOutcome - rightPartitionMean)) {
+      if (fabs(currOutcome - leftPartitionMean) < fabs(currOutcome - rightPartitionMean)) {
         LeftPartitionNaSum += currOutcome;
         leftPartitionNaCount++;
       }
@@ -1638,7 +1638,7 @@ void findBestSplitImputeCategorical(
       double currOutcome = std::get<1>(pair);
 
       // If closer to left partitionmean, add to left sum, leftcount ++
-      if (abs(currOutcome - leftPartitionMean) < abs(currOutcome - rightPartitionMean)) {
+      if (fabs(currOutcome - leftPartitionMean) < fabs(currOutcome - rightPartitionMean)) {
         LeftPartitionNaSum += currOutcome;
         leftPartitionNaCount++;
       }

tests/testthat/test-OOBpredictions.R

@@ -25,11 +25,11 @@ test_that("Tests if OOB predictions are working correctly (normal setting)", {
 
   skip_if_not_mac()
 
-  expect_equal(all.equal(getOOBpreds(forest)[1:10], c(5.092647817, 4.664031165,
-                                                      4.650426049, 4.870883947,
-                                                      5.084049999, 5.344246144,
-                                                      5.069991851, 5.060238528,
-                                                      4.766551234, 4.790776227)), TRUE)
+  expect_equal(all.equal(getOOBpreds(forest)[1:10], c(5.09195014288, 4.66466649643,
+                                                      4.65042604918, 4.87281687100,
+                                                      5.08349279822, 5.34483093904,
+                                                      5.06971226922, 5.06069487707,
+                                                      4.76761805874, 4.79213639568)), TRUE)
 })
 
 

tests/testthat/test-compute_rf_lp.R

@@ -33,14 +33,14 @@ test_that("Tests that compute the lp distances works correctly", {
   skip_if_not_mac()
 
   expect_equal(distances_1,
-               c(0.74127647652339, 0.56269154186560, 0.66700207007833, 0.48143305071905,
-                 0.42691537245113, 0.79361471149614, 0.69064814060102, 0.60005881782247,
-                 0.77731344373143, 0.53970499669885, 0.67328392159715),
-               tolerance = 1e-12)
+               c(0.741505444777, 0.557691541866, 0.667464191290, 0.481433050719,
+                 0.428715372451, 0.793614711496, 0.690142079995, 0.598410669674,
+                 0.777646777065, 0.542104996699, 0.671783921597),
+               tolerance = 1e-6)
   expect_equal(distances_2,
-               c(2.3726809930918, 2.4972611231916, 2.7047479310938, 1.9000801210562,
-                 1.6384876050554, 2.4063455932161, 2.1012051982558, 2.4272638737974,
-                 3.0785442045313, 2.4121460046764, 2.2978840528426),
-               tolerance = 1e-12)
+               c(2.37298192278, 2.48866693581, 2.70150942321, 1.90008012106,
+                 1.63721780222, 2.40468205396, 2.09935249333, 2.42295512410,
+                 3.07584694340, 2.41614965345, 2.29615354894),
+               tolerance = 1e-6)
 })
 

tests/testthat/test-forestry.R

@@ -31,13 +31,13 @@ test_that("Tests that random forest is working correctly", {
   skip_if_not_mac()
 
   mean((y_pred - y) ^ 2)
-  expect_equal(mean((y_pred - y) ^ 2), 0.064760523023031, tolerance = 1e-12)
+  expect_equal(mean((y_pred - y) ^ 2), 0.0646744712442, tolerance = 1e-6)
 
   # Test factors with missing obs and unused levels are correctly handled
   x$Species[1:70] <- NA
   forest <- forestry(
     x,
     y, seed = 2)
   y_pred <- predict(forest, x, seed = 2)
-  expect_equal(mean((y_pred - y) ^ 2), 0.10277113497796, tolerance = 1e-12)
+  expect_equal(mean((y_pred - y) ^ 2), 0.102692470551, tolerance = 1e-6)
 })

tests/testthat/test-impute_features.R

@@ -20,7 +20,7 @@ skip_if_not_mac()
 forest <- forestry(x_with_miss, y, ntree = 500, seed = 2, nthread = 1)
 imputed_x <- impute_features(forest, x_with_miss, seed = 2)
 expect_equal(sum(imputed_x$Species != x$Species), 2)
-expect_equal(mean(abs(x$Sepal.Width - imputed_x$Sepal.Width)), 0.074894503323687369734, tolerance = 1e-6)
+expect_equal(mean(abs(x$Sepal.Width - imputed_x$Sepal.Width)), 0.0748406666537, tolerance = 1e-6)
 
 # Testing mean imputation fallback:
 set.seed(1)

tests/testthat/test-max-depth.R

@@ -28,5 +28,5 @@ test_that("Tests that maxDepth parameter is working correctly", {
   skip_if_not_mac()
 
   # Mean Square Error
-  expect_equal(sum((y_pred - y) ^ 2), 11.076804560351238393, tolerance = 1e-12)
+  expect_equal(sum((y_pred - y) ^ 2), 11.075656291850588531, tolerance = 1e-6)
 })

tests/testthat/test-multilayerForestry.R

@@ -33,5 +33,5 @@ test_that("Tests that multilayerForestry is working correctly", {
 
   # Multilayer forestry is non deterministic, this needs to be fixed, but for
   # now test that it at least runs without crashing
-  expect_equal(sum((y_pred - y) ^ 2), 13.849777575910220, tolerance = 1e-8)
+  expect_equal(sum((y_pred - y) ^ 2), 13.841433707739128067, tolerance = 1e-8)
 })

tests/testthat/test-observationWeights.R

@@ -31,7 +31,7 @@ test_that("Tests that observationWeights for the bootstrap is working correctly"
   skip_if_not_mac()
 
   # Check the predictions from a weighted forest
-  expect_equal(sum((y_pred - y) ^ 2), 8.658285584297157, tolerance = 1e-8)
+  expect_equal(sum((y_pred - y) ^ 2), 8.6556977338637377528, tolerance = 1e-8)
 
   forest <- make_savable(forest)