Merge pull request #527 from bbayukari/master

docs: fix docs build failing

.readthedocs.yaml

@@ -10,6 +10,8 @@ build:
  - pip install --no-cache-dir pybind11[global]
  pre_build:
  - pip install --no-cache-dir -r docs/requirements.txt
+ apt_packages:
+ - graphviz
 
 python:
  install:

R-package/DESCRIPTION

@@ -2,7 +2,7 @@ Package: abess
 Type: Package
 Title: Fast Best Subset Selection
 Version: 0.4.8
-Date: 2023-02-19
+Date: 2023-09-19
 Authors@R: c(
  person(given = "Jin", family = "Zhu", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8550-5822")),
  person(given = "Zezhi", family = "Wang", email = "[email protected]", role = c("aut")),

R-package/R/abess.R

@@ -202,6 +202,7 @@ abess <- function(x, ...) UseMethod("abess")
 #' @examples
 #' \donttest{
 #' library(abess)
+#' Sys.setenv("OMP_THREAD_LIMIT" = 2)
 #' n <- 100
 #' p <- 20
 #' support.size <- 3

R-package/R/abesspca.R

@@ -97,6 +97,7 @@
 #' @examples
 #' \donttest{
 #' library(abess)
+#' Sys.setenv("OMP_THREAD_LIMIT" = 2)
 #'
 #' ## predictor matrix input:
 #' head(USArrests)

R-package/R/abessrpca.R

@@ -60,6 +60,7 @@
 #' @examples
 #' \donttest{
 #' library(abess)
+#' Sys.setenv("OMP_THREAD_LIMIT" = 2)
 #' n <- 30
 #' p <- 30
 #' true_S_size <- 60

R-package/tests/testthat.R

@@ -1,3 +1,5 @@
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
+
 library(testthat)
 library(abess)
 

R-package/vignettes/_v13-Use-abess-via-mlr3.Rmd

@@ -17,6 +17,10 @@ output:
 
 This guide is to show how to use `abess` via mlr3 (Machine Learning in R universe).
 
+```{r warning=FALSE}
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
+```
+
 First, install `mlr3` and `mlr3extralearners` (from github) if they are not in the environment.
 
 ```

R-package/vignettes/v01-abess-guide.Rmd

@@ -20,6 +20,7 @@ vignette: |
 
 ```{r, echo=FALSE, message=FALSE, warning=FALSE}
 knitr::opts_chunk$set(warning = FALSE, eval = TRUE, message = FALSE)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 

R-package/vignettes/v03-classification.Rmd

@@ -18,6 +18,7 @@ output:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 ## Titanic Dataset and Classification

R-package/vignettes/v04-PoissonGammaReg.Rmd

@@ -21,6 +21,7 @@ vignette: |
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 ## Poisson Regression

R-package/vignettes/v05-coxreg.Rmd

@@ -18,6 +18,7 @@ output:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 <!-- In this vignette, a lung cancer dataset is first displayed to the -->

R-package/vignettes/v06-MultiTaskLearning.Rmd

@@ -18,6 +18,7 @@ output:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 ## Brief Introduction

R-package/vignettes/v07-advancedFeatures.Rmd

@@ -18,6 +18,7 @@ output:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 ## Introduction

R-package/vignettes/v08-sPCA.Rmd

@@ -15,6 +15,10 @@ output:
  keep_md: yes
 ---
 
+```{r warning=FALSE}
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
+```
+
 ## Introduction
 Principal component analysis (PCA) is an important method in the field of data science, which can reduce the dimension of data and simplify our model. It actually solve an optimization problem like:
 

R-package/vignettes/v09-fasterSetting.Rmd

@@ -21,6 +21,7 @@ editor_options:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 ## Introduction

R-package/vignettes/v10-algorithm.Rmd

@@ -23,6 +23,7 @@ vignette: |
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, message = F, warning = F)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 # Introduction 

R-package/vignettes/v11-power-of-abess.Rmd

@@ -18,6 +18,7 @@ output:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 In this part, we are going to explore the power of the abess package using simulated data. 
 We compare the abess package with popular R libraries: `glmnet`, `ncvreg` for linear and logistic regressions in the following section.

R-package/vignettes/v12-Robust-Principal-Component-Analysis.Rmd

@@ -5,6 +5,7 @@ output: html_document
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = T)
+Sys.setenv("OMP_THREAD_LIMIT" = 2)
 ```
 
 This vignette introduces what is adaptive best subset selection robust principal component analysis and then we will show how it works using *abess* package on an artificial example.

docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py

@@ -53,7 +53,7 @@
 data = np.concatenate((data0,data1))
 train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.33, random_state=0)
 
-mean = FrechetMean(metric=sphere.metric)
+mean = FrechetMean(sphere)
 mean.fit(train_data)
 mean_estimate = mean.estimate_