R: Ağaçlar h2o.randomForest() ve h2o.gbm()

Question

Rstudio, H2O'nun Flow veya h2o'nun RF ve GBM modellerinden yerel html sayfasındaki ağaçları görüntülemedeki benzer bir şekilde çizmek için verimli bir yol arıyorsunuz aşağıdaki linkte. Özellikle, aşağıdaki kod tarafından üretilen rf1 ve gbm2 nesneler için ağaçları nasıl çizersiniz, belki de h2o.download_pojo (rf1) veya h2o.download_pojo (gbm1) öğelerini ayrıştırarak?

# # The following two commands remove any previously installed H2O packages for R. 
# if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) } 
# if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") } 

# # Next, we download packages that H2O depends on. 
# pkgs <- c("methods","statmod","stats","graphics","RCurl","jsonlite","tools","utils") 
# for (pkg in pkgs) { 
# if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) } 
# } 
# 
# # Now we download, install h2o package 
# install.packages("h2o", type="source", repos=(c("http://h2o-release.s3.amazonaws.com/h2o/rel-turchin/3/R"))) 
library(h2o) 

h2o.init(nthreads = -1, max_mem_size = "2G") 
h2o.removeAll() ##clean slate - just in case the cluster was already running 

## Load data - available to download from link below 
## https://www.dropbox.com/s/gu8e2o0mzlozbu4/SampleData.csv?dl=0 
df <- h2o.importFile(path = normalizePath("../SampleData.csv")) 

splits <- h2o.splitFrame(df, c(0.4, 0.3), seed = 1234) 

train <- h2o.assign(splits[[1]], "train.hex") 
valid <- h2o.assign(splits[[2]], "valid.hex") 
test <- h2o.assign(splits[[2]], "test.hex") 

predictor_col_start_pos <- 2 
predictor_col_end_pos <- 169 
predicted_col_pos <- 1 

rf1 <- h2o.randomForest(training_frame = train, validation_frame = valid, 
         x = predictor_col_start_pos:predictor_col_end_pos, y = predicted_col_pos, 
         model_id = "rf_covType_v1", ntrees = 2000, stopping_rounds = 10, score_each_iteration = T, 
         seed = 2001) 

gbm1 <- h2o.gbm(training_frame = train, validation_frame = valid, x = predictor_col_start_pos:predictor_col_end_pos, 
      y = predicted_col_pos, model_id = "gbm_covType2", seed = 2002, ntrees = 20, 
      learn_rate = 0.2, max_depth = 10, stopping_rounds = 2, stopping_tolerance = 0.01, 
      score_each_iteration = T) 


## Next step would be to plot trees for fitted models rf1 and gbm2 
# print the model, POJO (Plain Old Java Object) to screen 
h2o.download_pojo(rf1) 
h2o.download_pojo(gbm1) 

Answer 1

Ben aradığınız çözüm olabilir düşünüyorum;

library(h2o) 
h2o.init() 
df = h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") 
model = h2o.gbm(model_id = "model", 
      training_frame = df, 
      x = c("Year", "Month", "DayofMonth", "DayOfWeek", "UniqueCarrier"), 
      y = "IsDepDelayed", 
      max_depth = 3, 
      ntrees = 5) 
h2o.download_mojo(model, getwd(), FALSE) 

Şimdi http://www.h2o.ai/download/ gelen son kararlı h2o salınımını indirip komut satırından PrintMojo aracını çalıştırın.

java -cp h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i model.zip -o model.gv 
dot -Tpng model.gv -o model.png 

açık model.png

diğer bilgiler: http://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/index.html