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,
  search_criteria = NULL)

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 - for available parameters.

hyper_params

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

is_supervised

(Optional) If specified then override the default heuristic which decides if the given algorithm name and parameters specify a supervised or unsupervised algorithm.

do_hyper_params_check

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

search_criteria

(Optional) List of control parameters for smarter hyperparameter search. The default strategy 'Cartesian' covers the entire space of hyperparameter combinations. Specify the 'RandomDiscrete' strategy to get random search of all the combinations of your hyperparameters. RandomDiscrete should be usually combined with at least one early stopping criterion, max_models and/or max_runtime_secs, e.g. list(strategy = "RandomDiscrete", max_models = 42, max_runtime_secs = 28800) or list(strategy = "RandomDiscrete", stopping_metric = "AUTO", stopping_tolerance = 0.001, stopping_rounds = 10) or list(strategy = "RandomDiscrete", stopping_metric = "misclassification", stopping_tolerance = 0.00001, stopping_rounds = 5).

Details

Launch grid search with given algorithm and parameters.

Examples

Run this code


library(h2o)
library(jsonlite)
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)})

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