R:从h2o.randomForest()和h2o.gbm()绘制树 [英] R: Plot trees from h2o.randomForest() and h2o.gbm()

问题描述

从h2o的RF和GBM模型中寻找类似于rlink，H2O的Flow或本地html页面中树的有效方法，类似于下面链接中的图像. 具体来说，如何通过解析h2o.download_pojo(rf1)或h2o.download_pojo(gbm1)为下面的代码生成的对象(拟合模型)rf1和gbm2绘制树?

Looking for an efficient way to plot trees in rstudio, H2O's Flow or in local html page from h2o's RF and GBM models similar to the one in the image in link below. Specifically, how do you plot trees for the objects, (fitted models) rf1 and gbm2 produced by code below perhaps by parsing h2o.download_pojo(rf1) or h2o.download_pojo(gbm1)?

# # 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)

推荐答案

我认为这可能是您正在寻找的解决方案；

I think it may be the solution you are looking for;

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)

现在从 http://www.h2o.ai/download/，然后从命令行运行PrintMojo工具.

Now download the latest stable h2o release from http://www.h2o.ai/download/ and run the PrintMojo tool from the command line.

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

open model.png

open model.png

更多信息: http://docs.h2o .ai/h2o/latest-stable/h2o-genmodel/javadoc/index.html

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