gcForest-package: gcForest-package

In gcForest: Deep Forest Model

Description

R application programming interface (API) for Deep Forest which based on Zhi-hua Zhou and Ji Feng. Deep Forest: Towards an Alternative to Deep Neural Networks. In IJCAI-2017. (https://arxiv.org/abs/1702.08835v2) or Zhi-hua Zhou and Ji Feng. Deep Forest. In IJCAI-2017.(<https://arxiv.org/abs/1702.08835>), and the Python application programming interface (API) (https://github.com/pylablanche/gcForest)

Author(s)

Xu Jing

See Also

[1] Zhi-hua Zhou and Ji Feng. Deep Forest: Towards an Alternative to Deep Neural Networks.In IJCAI-2017. (https://arxiv.org/abs/1702.08835v2)

[2] Zhi-hua Zhou and Ji Feng. Deep Forest. In IJCAI-2017.(https://arxiv.org/abs/1702.08835)

[3] https://github.com/pylablanche/gcForest

Examples

# ========= Model train=======

have_numpy <- reticulate::py_module_available("numpy")
have_sklearn <- reticulate::py_module_available("sklearn")

if(have_numpy && have_sklearn){
   library(gcForest)
   # req_py()

   sk <- NULL

   .onLoad <- function(libname, pkgname) {
        sk <<- reticulate::import("sklearn", delay_load = TRUE)
   }
   sk <<- reticulate::import("sklearn", delay_load = TRUE)
   train_test_split <- sk$model_selection$train_test_split

   data <- sk$datasets$load_iris
   iris <- data()
   X = iris$data
   y = iris$target
   data_split = train_test_split(X, y, test_size=0.33)

   X_tr <- data_split[[1]]
   X_te <- data_split[[2]]
   y_tr <- data_split[[3]]
   y_te <- data_split[[4]]

   gcforest_m <- gcforest(shape_1X=4L, window=2L, tolerance=0.0)
   gcforest_m$fit(X_tr, y_tr)
   gcf_model <- model_save(gcforest_m,'gcforest_model.model')

   gcf <- model_load('gcforest_model.model')
   gcf$predict(X_te)

   # learn more from gcForest package tutorial
   utils::vignette('gcForest-docs')
}else{
   print('You should have the Python testing environment!')
}

gcForest documentation built on May 2, 2019, 5:40 a.m.