Merge pull request #7 from reservoirpy/prebuild_vignette

Prebuild vignette

.Rbuildignore

@@ -2,4 +2,4 @@
 ^\.Rproj\.user$
 ^\.github$
 ^LICENSE\.md$
-^cran-comments\.md$
+^cran-comments\.md$

DESCRIPTION

@@ -1,29 +1,27 @@
-Package: reservoir
+Package: reservoirnet
 Type: Package
-Title: Simple and flexible code for Reservoir Computing architectures like Echo State Networks
-Version: 0.1.2
-Date: 2022-05-09
+Title: Reservoir Computing and Echo State Networks
+Version: 0.2.0
+Date: 2023-03-13
 Authors@R: c(person("Thomas", "Ferte", role = c("aut", "cre", "trl"), email = "[email protected]"),
              person("Kalidou", "Ba", role = c("aut", "trl"), email = "[email protected]"),
              person("Nathan", "Trouvain", role = c("aut"), email = "[email protected]"),
              person("Rodolphe", "Thiebaut", role = c("aut"), email = "[email protected]"),
              person("Xavier", "Hinaut", role = c("aut"), email = "[email protected]"),
              person("Boris", "Hejblum", role = c("aut", "trl"), email = "[email protected]"))
-Maintainer: <[email protected]>
 SystemRequirements: Python (>= 3.7)
-Description: ReservoirR is a simple user-friendly library based on Python scientific
-             modules. It provides a flexible interface to implement efficient Reservoir 
-             Computing (RC) architectures with a particular focus on Echo State Networks (ESN). 
-             Advanced features of ReservoirR allow to improve computation time efficiency on 
-             a simple laptop compared to basic Python implementation, with datasets of any size.
-             Some of its features are: offline and online training, parallel implementation, 
+Description: A simple user-friendly library based on the python module reservoirPy.
+             It provides a flexible interface to implement efficient Reservoir
+             Computing (RC) architectures with a particular focus on Echo State Networks
+             (ESN). Some of its features are: offline and online training, parallel implementation, 
              sparse matrix computation, fast spectral initialization, advanced learning 
              rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create 
              complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, 
              and complex feedback loops. Moreover, graphical tools are included to easily 
-             explore hyperparameters with the help of the hyperopt library. Finally, it includes 
-             several tutorials exploring exotic architectures and examples of scientific papers 
-             reproduction.
+             explore hyperparameters. Finally, it includes several tutorials exploring
+             time series forecasting, classification and hyperparameter tuning. This package was
+             developed in the framework of the University of Bordeaux’s IdEx "Investments
+             for the Future" program / RRI PHDS.
 Config/reticulate:
   list(
     packages = list(
@@ -32,9 +30,9 @@ Config/reticulate:
   )
 License: GPL (>= 3)
 Repository: CRAN
-URL: 
+URL: https://github.com/reservoirpy
 Depends: R (>= 3.6)
-RoxygenNote: 7.2.2
+RoxygenNote: 7.2.3
 Encoding: UTF-8
 Imports:
     reticulate,

R/reservoir.R

@@ -34,7 +34,8 @@
 #' @param rc_connectivity float, default to 0.1. Connectivity of recurrent weight matrix, i.e. ratio of reservoir neurons connected to other reservoir neurons, including themselves. Must be between 0 and 1. 
 #' @param input_connectivity float, default to 0.1. Connectivity of input neurons, i.e. ratio of input neurons connected to reservoir neurons. Must be between 0 and 1.
 #' @param activation str 'tanh'. Reservoir units activation function. Should be a activationsfunc function name ('tanh', 'identity', 'sigmoid', 'relu', 'softmax', 'softplus').
-#'
+#' @param seed set random seed
+#' 
 #'@examples
 #' if(interactive()){
 #' readout <- createNode("Ridge")
@@ -58,6 +59,7 @@ createNode <- function(nodeType = c("Ridge"),
                        rc_connectivity = 0.1,
                        activation = "tanh",
                        dtype = "float64",
+                       seed = NULL,
                        ...) {
 
   stopifnot(!is.null(nodeType))
@@ -71,25 +73,54 @@ createNode <- function(nodeType = c("Ridge"),
   else if(nodeType=="Reservoir"){
     if(!is.null(units))
       #units <- noquote(paste0(as.integer(units),'L'))
-      node <- reservoirpy$nodes$Reservoir(units = as.integer(units),
-                                          lr = lr,
-                                          sr = sr,
-                                          name = name,
-                                          input_bias = inputBias,
-                                          input_scaling = input_scaling,
-                                          rc_connectivity = rc_connectivity,
-                                          input_connectivity = input_connectivity,
-                                          activation = activation)
+
+      if(is.null(seed)){
+        node <- reservoirpy$nodes$Reservoir(units = as.integer(units),
+                                            lr = lr,
+                                            sr = sr,
+                                            name = name,
+                                            input_bias = inputBias,
+                                            input_scaling = input_scaling,
+                                            rc_connectivity = rc_connectivity,
+                                            input_connectivity = input_connectivity,
+                                            activation = activation)
+      } else {
+        node <- reservoirpy$nodes$Reservoir(units = as.integer(units),
+                                            lr = lr,
+                                            sr = sr,
+                                            name = name,
+                                            input_bias = inputBias,
+                                            input_scaling = input_scaling,
+                                            rc_connectivity = rc_connectivity,
+                                            input_connectivity = input_connectivity,
+                                            activation = activation,
+                                            seed = as.integer(seed))
+      }
     else
-      node <- reservoirpy$nodes$Reservoir(units = units,
-                                          lr = lr,
-                                          sr = sr,
-                                          name = name,
-                                          input_bias = inputBias,
-                                          input_scaling = input_scaling,
-                                          rc_connectivity = rc_connectivity,
-                                          input_connectivity = input_connectivity,
-                                          activation = activation)
+      if(is.null(seed)){
+        node <- reservoirpy$nodes$Reservoir(units = units,
+                                            lr = lr,
+                                            sr = sr,
+                                            name = name,
+                                            input_bias = inputBias,
+                                            input_scaling = input_scaling,
+                                            rc_connectivity = rc_connectivity,
+                                            input_connectivity = input_connectivity,
+                                            activation = activation)  
+      } else {
+        node <- reservoirpy$nodes$Reservoir(units = units,
+                                            lr = lr,
+                                            sr = sr,
+                                            name = name,
+                                            input_bias = inputBias,
+                                            input_scaling = input_scaling,
+                                            rc_connectivity = rc_connectivity,
+                                            input_connectivity = input_connectivity,
+                                            activation = activation,
+                                            seed = as.integer(seed))
+      }
+
+
   }
   else if(nodeType=="Input"){
     node <- reservoirpy$nodes$Input(input_dim = inputDim,
@@ -311,13 +342,14 @@ generate_data <- function(dataset = c("japanese_vowels","mackey_glass","both"),
 #' 
 #' @examples
 #' if(interactive()){
-#'   source <- reservoir::createNode("Input")
-#'   reservoir <- reservoir::createNode("Reservoir", units = 500, lr=0.1, sr=0.9)
+#'   source <- reservoirnet::createNode("Input")
+#'   reservoir <- reservoirnet::createNode("Reservoir", units = 500, lr=0.1, sr=0.9)
 #'   source %>>% reservoir
 #' 
-#'   readout <- reservoir::createNode("Ridge")
+#'   readout <- reservoirnet::createNode("Ridge")
 #'   list(source %>>% reservoir, source) %>>% readout
 #' }
 `%>>%` <- function(node1, node2){
-  rp$rshift$operatorRShift(node1, node2)
+  reservoirnet::link(node1, node2)
+  # rp$rshift$operatorRShift(node1, node2)
 }

R/zzz.R

@@ -1,13 +1,13 @@
 err_reservoirpy <- function(e) {
   message("Error importing reservoirpy")
   message("Check if reservoirpy is installed")
-  message("Install by reservoir::install_reservoirpy() and reload library")
+  message("Install by reservoirnet::install_reservoirpy() and reload library")
 }
 
 err_np <- function(e) {
   message("Error importing numpy")
   message("Check if numpy is installed")
-  message("Install by reservoir::install_numpy() and reload library")
+  message("Install by reservoirnet::install_numpy() and reload library")
 }
 
 err_rshift <- function(e) {

cran-comments.md

@@ -6,7 +6,24 @@ Environment :
 - github action : ubuntu v22.04
 - github action : windows server 2022 v10.0
 - github action : macos v12.6.3
+- check_rhub : Windows Server 2022, R-devel, 64 bit
+- check_win_devel : Windows Server 2022 x64 (build 20348)
 
-0 errors | 0 warnings | 0 note
+* checking CRAN incoming feasibility ... NOTE
+Maintainer: 'Thomas Ferte <[email protected]>'
+
+New submission
+
+Possibly misspelled words in DESCRIPTION:
+  ESN (16:15)
+  IdEx (23:75)
+  PHDS (24:44)
+  RRI (24:40)
+  reservoirPy (13:72)
+
+* checking for detritus in the temp directory ... NOTE
+Found the following files/directories:
+  'lastMiKTeXException'
+
+We believe the note is ok. The words are well spelled. The detritus temp directory does not seem to be a problem (https://stackoverflow.com/questions/62456137/r-cran-check-detritus-in-temp-directory).
 
-* This is a new release.

tests/testthat.R

@@ -1,4 +1,4 @@
 library(testthat)
-library(reservoir)
+library(reservoirnet)
 
-test_check("reservoir")
+test_check("reservoirnet")

tests/testthat/tests.R

@@ -1,45 +1,45 @@
-test_that("reservoir", {
+test_that("reservoirnet", {
   testthat::skip_on_cran()
 
   print(reticulate::py_config())
 
   timesteps <- 2500
-  X <- reservoir::generate_data(dataset = "mackey_glass",n_timesteps = timesteps)$mackey_glass
+  X <- reservoirnet::generate_data(dataset = "mackey_glass",n_timesteps = timesteps)$mackey_glass
 
   X <- 2 * (X - min(X)) / (max(X) - min(X)) - 1
-  source <- reservoir::createNode("Input")
-  readout <- reservoir::createNode("Ridge")
-  reservoir <- reservoir::createNode("Reservoir", units = 100,  lr=0.2, sr=0.8)
+  source <- reservoirnet::createNode("Input")
+  readout <- reservoirnet::createNode("Ridge")
+  reservoir <- reservoirnet::createNode("Reservoir", units = 100,  lr=0.2, sr=0.8)
 
-  model <- reservoir::link(reservoir, readout)
+  model <- reservoirnet::link(reservoir, readout)
 
   Xtrain <- as.matrix(X[1:2001])
   Ytrain <- as.matrix(X[10:2010])
 
-  model <- reservoir::reservoirR_fit(model, X=Xtrain, Y=Ytrain)
+  model <- reservoirnet::reservoirR_fit(model, X=Xtrain, Y=Ytrain)
 
   # Classification 
-  japanese_vowels <- reservoir::generate_data(
+  japanese_vowels <- reservoirnet::generate_data(
     dataset = "japanese_vowels",
     repeat_targets=TRUE)$japanese_vowels
 
-  source <- reservoir::createNode("Input")
-  readout <- reservoir::createNode("Ridge",ridge=1e-6)
-  reservoir <- reservoir::createNode("Reservoir", 
+  source <- reservoirnet::createNode("Input")
+  readout <- reservoirnet::createNode("Ridge",ridge=1e-6)
+  reservoir <- reservoirnet::createNode("Reservoir", 
                                      units = 500,  
                                      lr=0.1, sr=0.9)
 
 
   # Example: [source >> reservoir, source] >> readout
   model <- list(source %>>% reservoir, source) %>>% readout
 
-  model_fit <- reservoir::reservoirR_fit(node = model,
+  model_fit <- reservoirnet::reservoirR_fit(node = model,
                               X = japanese_vowels$X_train, 
                               Y = japanese_vowels$Y_train, 
                               stateful=FALSE,
                               warmup = 2)
 
-  Y_pred <- reservoir::predict_seq(node = model_fit$fit, X = japanese_vowels$X_test,stateful = FALSE)
+  Y_pred <- reservoirnet::predict_seq(node = model_fit$fit, X = japanese_vowels$X_test,stateful = FALSE)
   # formal test
   testthat::expect(class(model)[1] == "reservoirpy.model.Model",
                    failure_message = "Output of fit function is not a reservoirpy.model.Model object")