h2o (version 3.2.0.3)

h2o.grid: H2O Grid Support

Description

Provides a set of functions to launch a grid search and get its results.

Usage

h2o.grid(algorithm, grid_id, ..., hyper_params = list(),
  is_supervised = NULL, do_hyper_params_check = FALSE,
  conn = h2o.getConnection())

Arguments

algorithm

name of algorithm to use in grid search (gbm, randomForest, kmeans, glm, deeplearning, naivebayes, pca)

grid_id

optional id for resulting grid search, if it is not specified then it is autogenerated

...

arguments describing parameters to use with algorithm (i.e., x, y, training_frame). Look at the specific algorithm - h2o.gbm, h2o.glm, h2o.kmeans, h2o.deepLearning

hyper_params

list of hyper parameters (i.e., list(ntrees=c(1,2), max_depth=c(5,7)))

is_supervised

if specified then override default heuristing which decide if given algorithm name and parameters specify super/unsupervised algorithm.

do_hyper_params_check

perform client check for specified hyper parameters. It can be time expensive for large hyper space

conn

connection to H2O cluster

Details

Launch grid search with given algorithm and parameters.

Examples

library(h2o)
library(jsonlite)
localH2O <- h2o.init()
iris.hex <- as.h2o(iris)
grid <- h2o.grid("gbm", x = c(1:4), y = 5, training_frame = iris.hex,
                 hyper_params = list(ntrees = c(1,2,3)))
# Get grid summary
summary(grid)
# Fetch grid models
model_ids <- grid@model_ids
models <- lapply(model_ids, function(id) { h2o.getModel(id)})