update R6 docs checks; reduce pip output

.travis.yml

@@ -23,8 +23,8 @@ r:
 before_install:
   - sudo apt-get -y install libx11-dev mesa-common-dev libglu1-mesa-dev
   - sudo apt-get -y install python3 python3-pip python3-setuptools
-  - sudo -H pip3 install --upgrade pip setuptools
-  - sudo -H pip3 install --ignore-installed numpy tensorflow keras
+  - sudo -H pip3 install --quiet --progress-bar off --upgrade pip setuptools
+  - sudo -H pip3 install --quiet --progress-bar off --ignore-installed numpy tensorflow keras
   #- Rscript -e 'update.packages(ask = FALSE)'
 
 r_packages:

DESCRIPTION

@@ -86,13 +86,9 @@ Suggests:
     caret,
     mgcv
 Remotes:
-    github::osofr/simcausal,
     github::osofr/condensier,
-    github::tlverse/delayed,
-    github::tlverse/origami,
     github::tlverse/hal9001,
     github::nhejazi/haldensify,
-    github::vdorie/dbarts,
     github::tpospisi/RFCDE/r
 License: GPL-3
 URL: https://tlverse.org/sl3
@@ -104,4 +100,4 @@ VignetteBuilder:
     knitr,
     R.rsp
 RoxygenNote: 7.0.2
-Roxygen: list(markdown = TRUE)
+Roxygen: list(markdown = TRUE, old_usage = TRUE, r6 = FALSE)

R/Lrnr_bilstm.R

@@ -76,13 +76,13 @@ Lrnr_bilstm <- R6Class(
 
       # Build the model
       model <- kerasR::Sequential()
-      #keras::bidirectional(
+      # keras::bidirectional(
       #  object = model, layer = kerasR::LSTM(args$units,
       #    return_sequences = TRUE
       #  ),
       #  input_shape = c(num_steps, num_features)
-      #)
-      model$add(kerasR::Bidirectional(kerasR::LSTM(args$units,return_sequences = TRUE)))
+      # )
+      model$add(kerasR::Bidirectional(kerasR::LSTM(args$units, return_sequences = TRUE)))
       model$add(kerasR::Dropout(rate = args$dropout))
       model$add(kerasR::Flatten())
       model$add(kerasR::Dense(args$dense))

R/Lrnr_lstm.R

@@ -85,7 +85,7 @@ Lrnr_lstm <- R6Class(
         epochs = args$epochs
       )
       fit_object <- model
-      
+
       return(fit_object)
     },
 

R/Lrnr_sl.R

@@ -83,11 +83,11 @@ Lrnr_sl <- R6Class(
       return(private$.fit_object$cv_meta_fit$fit_object)
     },
     cv_risk = function(loss_fun) {
-      
+
       # get risks for cv learners (nested cv)
       cv_stack_fit <- self$fit_object$cv_fit
       stack_risks <- cv_stack_fit$cv_risk(loss_fun)
-      
+
       coefs <- self$coefficients
       if (!is.null(coefs)) {
         ordered_coefs <- coefs[match(stack_risks$learner, names(coefs))]
@@ -96,7 +96,7 @@ Lrnr_sl <- R6Class(
         ordered_coefs <- rep(NA, length(stack_risks$learner))
       }
       set(stack_risks, , "coefficients", ordered_coefs)
-      
+
       # Make sure that coefficients is the second column, even if the metalearner
       # did not provide coefficients.
       data.table::setcolorder(stack_risks, c(names(stack_risks)[1], "coefficients"))

README.Rmd

@@ -26,8 +26,7 @@ knitr::opts_chunk$set(
 [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3558317.svg)](https://doi.org/10.5281/zenodo.3558317)
 
-> A modern implementation of the Super Learner algorithm for ensemble learning
-> and model stacking
+> A modern implementation of the Super Learner ensemble learning algorithm
 
 __Authors:__ [Jeremy Coyle](https://github.com/jeremyrcoyle), [Nima
 Hejazi](https://nimahejazi.org), [Ivana
@@ -138,9 +137,9 @@ head(preds)
 
 ## Learner Properties
 
-You can find the properties that sl3 learners support in the table below:
+Properties supported by `sl3` learners are presented in the following table:
 
-```{r warning=FALSE, echo = FALSE, message = FALSE}
+```{r sl3-learner-properties, warning=FALSE, echo = FALSE, message = FALSE}
 library(sl3)
 library(kableExtra)
 
@@ -164,25 +163,30 @@ get_learner_class_properties <- function(learner_class) {
 }
 learner_property_lst <- lapply(all_learners, get_learner_class_properties)
 
-# get a list where each element is a list of True/False's indicating whether a learner has a property or not
+# get a list where each element is a list of logicals indicating whether a
+# learner has a property or not
 get_learner_class_properties_ownership <- function(properties) {
   return(sl3_list_properties() %in% unlist(properties))
 }
-learner_property_ownership_mat <- lapply(learner_property_lst, get_learner_class_properties_ownership)
+learner_property_ownership <- lapply(learner_property_lst,
+                                     get_learner_class_properties_ownership)
 
 get_learner_name <- function(learner_class) {
   return(learner_class$classname)
 }
 learner_names <- lapply(all_learners, get_learner_name)
 
-# generate the data matrix, whose columns are properties and rows are learner names
-final_mat <- matrix(unlist(learner_property_ownership_mat), nrow = length(learner_property_ownership_mat), ncol = length(sl3_list_properties()), byrow = TRUE)
+# generate matrix whose columns are properties and rows are learner names
+final_mat <- matrix(unlist(learner_property_ownership),
+                    nrow = length(learner_property_ownership),
+                    ncol = length(sl3_list_properties()), byrow = TRUE)
 dimnames(final_mat) <- list(unlist(learner_names), sl3_list_properties())
 final_mat.df <- as.data.frame(final_mat)
 
 char_final_mat.df <- ifelse(final_mat.df == TRUE, "√", "x")
 kable(char_final_mat.df) %>%
-  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
+  kable_styling(bootstrap_options = c("striped", "hover", "condensed",
+                                      "responsive")) %>%
   scroll_box(width = "100%", height = "200px", fixed_thead = T)
 ```
 
@@ -201,23 +205,23 @@ submitting a pull request.
 
 After using the `sl3` R package, please cite the following:
 
-        @manual{coyle2019sl3,
+        @manual{coyle2020sl3,
           author = {Coyle, Jeremy R and Hejazi, Nima S and Malenica, Ivana and
             Sofrygin, Oleg},
           title = {{sl3}: Modern Pipelines for Machine Learning and {Super
             Learning}},
-          year = {2019},
+          year = {2020},
           howpublished = {\url{https://github.com/tlverse/sl3}},
-          note = {{R} package version 1.3.5},
-          url = {https://doi.org/10.5281/zenodo.3558317},
-          doi = {10.5281/zenodo.3558317}
+          note = {{R} package version 1.3.7},
+          url = {},
+          doi = {}
         }
 
 ---
 
 ## License
 
-&copy; 2017-2019 [Jeremy R. Coyle](https://github.com/jeremyrcoyle), [Nima S.
+&copy; 2017-2020 [Jeremy R. Coyle](https://github.com/jeremyrcoyle), [Nima S.
 Hejazi](https://nimahejazi.org), [Ivana
 Malenica](https://github.com/podTockom), [Oleg
 Sofrygin](https://github.com/osofr)

inst/CITATION

@@ -11,5 +11,5 @@ bibentry(bibtype = "Manual",
                     person("Oleg", "Sofrygin")),
          year = year,
          note = note,
-         doi = "10.5281/zenodo.3558317",
+         doi = "",
          url = "https://github.com/tlverse/sl3")