H2o Package

: H2O H2O Package h2o_package h2o-r docs-website 7 rel-wolpert h2o

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"h2o"
April 14, 2018

R topics documented:
h2o-package . . . . . . .
aaa . . . . . . . . . . . .
apply . . . . . . . . . .
as.character.H2OFrame .
as.data.frame.H2OFrame
as.factor . . . . . . . . .
as.h2o . . . . . . . . . .
as.matrix.H2OFrame . .
as.numeric . . . . . . . .
as.vector.H2OFrame . .
australia . . . . . . . . .
colnames . . . . . . . .
dim.H2OFrame . . . . .
dimnames.H2OFrame . .
h2o.abs . . . . . . . . .
h2o.acos . . . . . . . . .
h2o.aggregated_frame . .
h2o.aggregator . . . . .
h2o.aic . . . . . . . . . .
h2o.all . . . . . . . . . .
h2o.anomaly . . . . . . .
h2o.any . . . . . . . . .
h2o.anyFactor . . . . . .
h2o.arrange . . . . . . .
h2o.ascharacter . . . . .
h2o.asfactor . . . . . . .
h2o.asnumeric . . . . . .
h2o.assign . . . . . . . .
h2o.as_date . . . . . . .
h2o.auc . . . . . . . . .
h2o.automl . . . . . . .
h2o.betweenss . . . . . .
h2o.biases . . . . . . . .
h2o.bottomN . . . . . .
h2o.cbind . . . . . . . .
h2o.ceiling . . . . . . .
h2o.centers . . . . . . .

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R topics documented:

2
h2o.centersSTD . . . . . . . . . . . . . .
h2o.centroid_stats . . . . . . . . . . . . .
h2o.clearLog . . . . . . . . . . . . . . .
h2o.clusterInfo . . . . . . . . . . . . . .
h2o.clusterIsUp . . . . . . . . . . . . . .
h2o.clusterStatus . . . . . . . . . . . . .
h2o.cluster_sizes . . . . . . . . . . . . .
h2o.coef . . . . . . . . . . . . . . . . . .
h2o.coef_norm . . . . . . . . . . . . . .
h2o.colnames . . . . . . . . . . . . . . .
h2o.columns_by_type . . . . . . . . . . .
h2o.computeGram . . . . . . . . . . . .
h2o.confusionMatrix . . . . . . . . . . .
h2o.connect . . . . . . . . . . . . . . . .
h2o.cor . . . . . . . . . . . . . . . . . .
h2o.cos . . . . . . . . . . . . . . . . . .
h2o.cosh . . . . . . . . . . . . . . . . . .
h2o.coxph . . . . . . . . . . . . . . . . .
h2o.createFrame . . . . . . . . . . . . . .
h2o.cross_validation_fold_assignment . .
h2o.cross_validation_holdout_predictions
h2o.cross_validation_models . . . . . . .
h2o.cross_validation_predictions . . . . .
h2o.cummax . . . . . . . . . . . . . . . .
h2o.cummin . . . . . . . . . . . . . . . .
h2o.cumprod . . . . . . . . . . . . . . .
h2o.cumsum . . . . . . . . . . . . . . . .
h2o.cut . . . . . . . . . . . . . . . . . .
h2o.day . . . . . . . . . . . . . . . . . .
h2o.dayOfWeek . . . . . . . . . . . . . .
h2o.dct . . . . . . . . . . . . . . . . . .
h2o.ddply . . . . . . . . . . . . . . . . .
h2o.decryptionSetup . . . . . . . . . . .
h2o.deepfeatures . . . . . . . . . . . . .
h2o.deeplearning . . . . . . . . . . . . .
h2o.deepwater . . . . . . . . . . . . . . .
h2o.deepwater.available . . . . . . . . . .
h2o.describe . . . . . . . . . . . . . . . .
h2o.difflag1 . . . . . . . . . . . . . . . .
h2o.dim . . . . . . . . . . . . . . . . . .
h2o.dimnames . . . . . . . . . . . . . . .
h2o.distance . . . . . . . . . . . . . . . .
h2o.downloadAllLogs . . . . . . . . . . .
h2o.downloadCSV . . . . . . . . . . . .
h2o.download_mojo . . . . . . . . . . .
h2o.download_pojo . . . . . . . . . . . .
h2o.entropy . . . . . . . . . . . . . . . .
h2o.exp . . . . . . . . . . . . . . . . . .
h2o.exportFile . . . . . . . . . . . . . . .
h2o.exportHDFS . . . . . . . . . . . . .
h2o.fillna . . . . . . . . . . . . . . . . .
h2o.filterNACols . . . . . . . . . . . . .

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R topics documented:
h2o.findSynonyms . . . . . . . . .
h2o.find_row_by_threshold . . . . .
h2o.find_threshold_by_max_metric
h2o.floor . . . . . . . . . . . . . . .
h2o.flow . . . . . . . . . . . . . . .
h2o.gainsLift . . . . . . . . . . . .
h2o.gbm . . . . . . . . . . . . . . .
h2o.getConnection . . . . . . . . .
h2o.getFrame . . . . . . . . . . . .
h2o.getFutureModel . . . . . . . . .
h2o.getGLMFullRegularizationPath
h2o.getGrid . . . . . . . . . . . . .
h2o.getId . . . . . . . . . . . . . .
h2o.getModel . . . . . . . . . . . .
h2o.getTimezone . . . . . . . . . .
h2o.getTypes . . . . . . . . . . . .
h2o.getVersion . . . . . . . . . . .
h2o.giniCoef . . . . . . . . . . . .
h2o.glm . . . . . . . . . . . . . . .
h2o.glrm . . . . . . . . . . . . . . .
h2o.grep . . . . . . . . . . . . . . .
h2o.grid . . . . . . . . . . . . . . .
h2o.group_by . . . . . . . . . . . .
h2o.gsub . . . . . . . . . . . . . . .
h2o.head . . . . . . . . . . . . . . .
h2o.hist . . . . . . . . . . . . . . .
h2o.hit_ratio_table . . . . . . . . .
h2o.hour . . . . . . . . . . . . . . .
h2o.ifelse . . . . . . . . . . . . . .
h2o.importFile . . . . . . . . . . .
h2o.import_sql_select . . . . . . . .
h2o.import_sql_table . . . . . . . .
h2o.impute . . . . . . . . . . . . .
h2o.init . . . . . . . . . . . . . . .
h2o.insertMissingValues . . . . . .
h2o.interaction . . . . . . . . . . .
h2o.isax . . . . . . . . . . . . . . .
h2o.ischaracter . . . . . . . . . . .
h2o.isfactor . . . . . . . . . . . . .
h2o.isnumeric . . . . . . . . . . . .
h2o.is_client . . . . . . . . . . . . .
h2o.kfold_column . . . . . . . . . .
h2o.killMinus3 . . . . . . . . . . .
h2o.kmeans . . . . . . . . . . . . .
h2o.kurtosis . . . . . . . . . . . . .
h2o.levels . . . . . . . . . . . . . .
h2o.listTimezones . . . . . . . . . .
h2o.list_all_extensions . . . . . . .
h2o.list_api_extensions . . . . . . .
h2o.list_core_extensions . . . . . .
h2o.loadModel . . . . . . . . . . .
h2o.log . . . . . . . . . . . . . . .

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R topics documented:

4
h2o.log10 . . . . . . . . . .
h2o.log1p . . . . . . . . . .
h2o.log2 . . . . . . . . . . .
h2o.logAndEcho . . . . . .
h2o.logloss . . . . . . . . .
h2o.ls . . . . . . . . . . . .
h2o.lstrip . . . . . . . . . .
h2o.mae . . . . . . . . . . .
h2o.makeGLMModel . . . .
h2o.make_metrics . . . . . .
h2o.match . . . . . . . . . .
h2o.max . . . . . . . . . . .
h2o.mean . . . . . . . . . .
h2o.mean_per_class_error .
h2o.mean_residual_deviance
h2o.median . . . . . . . . .
h2o.merge . . . . . . . . . .
h2o.metric . . . . . . . . . .
h2o.min . . . . . . . . . . .
h2o.mktime . . . . . . . . .
h2o.month . . . . . . . . . .
h2o.mse . . . . . . . . . . .
h2o.nacnt . . . . . . . . . .
h2o.naiveBayes . . . . . . .
h2o.names . . . . . . . . . .
h2o.na_omit . . . . . . . . .
h2o.nchar . . . . . . . . . .
h2o.ncol . . . . . . . . . . .
h2o.networkTest . . . . . . .
h2o.nlevels . . . . . . . . .
h2o.no_progress . . . . . . .
h2o.nrow . . . . . . . . . .
h2o.null_deviance . . . . . .
h2o.null_dof . . . . . . . . .
h2o.num_iterations . . . . .
h2o.num_valid_substrings .
h2o.openLog . . . . . . . .
h2o.parseRaw . . . . . . . .
h2o.parseSetup . . . . . . .
h2o.partialPlot . . . . . . . .
h2o.performance . . . . . .
h2o.pivot . . . . . . . . . .
h2o.prcomp . . . . . . . . .
h2o.predict_json . . . . . . .
h2o.print . . . . . . . . . . .
h2o.prod . . . . . . . . . . .
h2o.proj_archetypes . . . . .
h2o.quantile . . . . . . . . .
h2o.r2 . . . . . . . . . . . .
h2o.randomForest . . . . . .
h2o.range . . . . . . . . . .
h2o.rbind . . . . . . . . . .

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151

R topics documented:
h2o.reconstruct . . . . .
h2o.relevel . . . . . . . .
h2o.removeAll . . . . .
h2o.removeVecs . . . . .
h2o.rep_len . . . . . . .
h2o.residual_deviance . .
h2o.residual_dof . . . .
h2o.rm . . . . . . . . . .
h2o.rmse . . . . . . . . .
h2o.rmsle . . . . . . . .
h2o.round . . . . . . . .
h2o.rstrip . . . . . . . .
h2o.runif . . . . . . . . .
h2o.saveModel . . . . .
h2o.saveModelDetails . .
h2o.saveMojo . . . . . .
h2o.scale . . . . . . . .
h2o.scoreHistory . . . .
h2o.sd . . . . . . . . . .
h2o.sdev . . . . . . . . .
h2o.setLevels . . . . . .
h2o.setTimezone . . . .
h2o.show_progress . . .
h2o.shutdown . . . . . .
h2o.signif . . . . . . . .
h2o.sin . . . . . . . . . .
h2o.skewness . . . . . .
h2o.splitFrame . . . . .
h2o.sqrt . . . . . . . . .
h2o.stackedEnsemble . .
h2o.startLogging . . . .
h2o.std_coef_plot . . . .
h2o.stopLogging . . . .
h2o.str . . . . . . . . . .
h2o.stringdist . . . . . .
h2o.strsplit . . . . . . .
h2o.sub . . . . . . . . .
h2o.substring . . . . . .
h2o.sum . . . . . . . . .
h2o.summary . . . . . .
h2o.svd . . . . . . . . .
h2o.table . . . . . . . . .
h2o.tabulate . . . . . . .
h2o.tan . . . . . . . . .
h2o.tanh . . . . . . . . .
h2o.target_encode_apply
h2o.target_encode_create
h2o.toFrame . . . . . . .
h2o.tokenize . . . . . . .
h2o.tolower . . . . . . .
h2o.topN . . . . . . . .
h2o.totss . . . . . . . . .

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R topics documented:

6
h2o.tot_withinss . . . . . . . . . . . . . .
h2o.toupper . . . . . . . . . . . . . . . .
h2o.transform . . . . . . . . . . . . . . .
h2o.trim . . . . . . . . . . . . . . . . . .
h2o.trunc . . . . . . . . . . . . . . . . .
h2o.unique . . . . . . . . . . . . . . . .
h2o.var . . . . . . . . . . . . . . . . . .
h2o.varimp . . . . . . . . . . . . . . . .
h2o.varimp_plot . . . . . . . . . . . . . .
h2o.week . . . . . . . . . . . . . . . . .
h2o.weights . . . . . . . . . . . . . . . .
h2o.which . . . . . . . . . . . . . . . . .
h2o.which_max . . . . . . . . . . . . . .
h2o.which_min . . . . . . . . . . . . . .
h2o.withinss . . . . . . . . . . . . . . . .
h2o.word2vec . . . . . . . . . . . . . . .
h2o.xgboost . . . . . . . . . . . . . . . .
h2o.xgboost.available . . . . . . . . . . .
h2o.year . . . . . . . . . . . . . . . . . .
H2OAutoML-class . . . . . . . . . . . .
H2OClusteringModel-class . . . . . . . .
H2OConnection-class . . . . . . . . . . .
H2OConnectionMutableState . . . . . . .
H2OCoxPHModel-class . . . . . . . . .
H2OCoxPHModelSummary-class . . . .
H2OFrame-class . . . . . . . . . . . . .
H2OFrame-Extract . . . . . . . . . . . .
H2OGrid-class . . . . . . . . . . . . . .
H2OModel-class . . . . . . . . . . . . .
H2OModelFuture-class . . . . . . . . . .
H2OModelMetrics-class . . . . . . . . .
housevotes . . . . . . . . . . . . . . . . .
iris . . . . . . . . . . . . . . . . . . . . .
is.character . . . . . . . . . . . . . . . .
is.factor . . . . . . . . . . . . . . . . . .
is.h2o . . . . . . . . . . . . . . . . . . .
is.numeric . . . . . . . . . . . . . . . . .
Logical-or . . . . . . . . . . . . . . . . .
ModelAccessors . . . . . . . . . . . . . .
names.H2OFrame . . . . . . . . . . . . .
Ops.H2OFrame . . . . . . . . . . . . . .
plot.H2OModel . . . . . . . . . . . . . .
plot.H2OTabulate . . . . . . . . . . . . .
predict.H2OAutoML . . . . . . . . . . .
predict.H2OModel . . . . . . . . . . . .
predict_leaf_node_assignment.H2OModel
print.H2OFrame . . . . . . . . . . . . . .
print.H2OTable . . . . . . . . . . . . . .
prostate . . . . . . . . . . . . . . . . . .
range.H2OFrame . . . . . . . . . . . . .
show,H2OCoxPHModelSummary-method
str.H2OFrame . . . . . . . . . . . . . . .

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7

summary,H2OCoxPHModel-method
summary,H2OGrid-method . . . . .
summary,H2OModel-method . . . .
use.package . . . . . . . . . . . . .
walking . . . . . . . . . . . . . . .
zzz . . . . . . . . . . . . . . . . . .
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h2o-package

H2O R Interface

Description
This is a package for running H2O via its REST API from within R. To communicate with a H2O
instance, the version of the R package must match the version of H2O. When connecting to a new
H2O cluster, it is necessary to re-run the initializer.
Details
Package:
Type:
Version:
Branch:
Date:
License:
Depends:

h2o
Package
3.18.0.7
rel-wolpert
Sat Apr 14 22:16:02 UTC 2018
Apache License (== 2.0)
R (>= 2.13.0), RCurl, jsonlite, statmod, tools, methods, utils

This package allows the user to run basic H2O commands using R commands. In order to use it,
you must first have H2O running. To run H2O on your local machine, call h2o.init without any
arguments, and H2O will be automatically launched at localhost:54321, where the IP is "127.0.0.1"
and the port is 54321. If H2O is running on a cluster, you must provide the IP and port of the remote
machine as arguments to the h2o.init() call.
H2O supports a number of standard statistical models, such as GLM, K-means, and Random Forest.
For example, to run GLM, call h2o.glm with the H2O parsed data and parameters (response variable, error distribution, etc...) as arguments. (The operation will be done on the server associated
with the data object where H2O is running, not within the R environment).
Note that no actual data is stored in the R workspace; and no actual work is carried out by R. R only
saves the named objects, which uniquely identify the data set, model, etc on the server. When the
user makes a request, R queries the server via the REST API, which returns a JSON file with the
relevant information that R then displays in the console.
If you are using an older version of H2O, use the following porting guide to update your scripts:
Porting Scripts
Author(s)
Maintainer: The H2O.ai team 

8

apply

References
• H2O.ai Homepage
• H2O Documentation
• H2O on GitHub

aaa

Starting H2O For examples

Description
Starting H2O For examples
Examples
if(Sys.info()['sysname'] == "Darwin" && Sys.info()['release'] == '13.4.0'){
quit(save="no")
}else{
h2o.init(nthreads = 2)
}

apply

Apply on H2O Datasets

Description
Method for apply on H2OFrame objects.
Usage
apply(X, MARGIN, FUN, ...)
Arguments
X

an H2OFrame object on which apply will operate.

MARGIN

the vector on which the function will be applied over, either 1 for rows or 2 for
columns.

FUN

the function to be applied.

...

optional arguments to FUN.

Value
Produces a new H2OFrame of the output of the applied function. The output is stored in H2O so
that it can be used in subsequent H2O processes.
See Also
apply for the base generic

as.character.H2OFrame

9

Examples
h2o.init()
irisPath <- system.file("extdata", "iris.csv", package="h2o")
iris.hex <- h2o.importFile(path = irisPath, destination_frame = "iris.hex")
summary(apply(iris.hex, 2, sum))

as.character.H2OFrame Convert an H2OFrame to a String

Description
Convert an H2OFrame to a String
Usage
## S3 method for class 'H2OFrame'
as.character(x, ...)
Arguments
x

An H2OFrame object

...

Further arguments to be passed from or to other methods.

Examples
h2o.init()
pretrained.frame <- as.h2o(data.frame(
C1 = c("a", "b"), C2 = c(0, 1), C3 = c(1, 0), C4 = c(0.2, 0.8),
stringsAsFactors = FALSE))
pretrained.w2v <- h2o.word2vec(pre_trained = pretrained.frame, vec_size = 3)
words <- as.character(as.h2o(c("b", "a", "c", NA, "a")))
vecs <- h2o.transform(pretrained.w2v, words = words)

as.data.frame.H2OFrame
Converts parsed H2O data into an R data frame

Description
Downloads the H2O data and then scans it in to an R data frame.
Usage
## S3 method for class 'H2OFrame'
as.data.frame(x, ...)

10

as.factor

Arguments
x

An H2OFrame object.

...

Further arguments to be passed down from other methods.

Details
Method as.data.frame.H2OFrame will use fread if data.table package is installed in required
version.
See Also
use.package
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
as.data.frame(prostate.hex)

as.factor

Convert H2O Data to Factors

Description
Convert a column into a factor column.
Usage
as.factor(x)
Arguments
x

a column from an H2OFrame data set.

See Also
as.factor.
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
prostate.hex[,2] <- as.factor(prostate.hex[,2])
summary(prostate.hex)

as.h2o

11

as.h2o

Create H2OFrame

Description
Import R object to the H2O cloud.
Usage
as.h2o(x, destination_frame = "", ...)
## Default S3 method:
as.h2o(x, destination_frame = "", ...)
## S3 method for class 'H2OFrame'
as.h2o(x, destination_frame = "", ...)
## S3 method for class 'data.frame'
as.h2o(x, destination_frame = "", ...)
## S3 method for class 'Matrix'
as.h2o(x, destination_frame = "", ...)
Arguments
x
An R object.
destination_frame
A string with the desired name for the H2OFrame.
...

arguments passed to method arguments.

Details
Method as.h2o.data.frame will use fwrite if data.table package is installed in required version.
To speedup execution time for large sparse matrices, use h2o datatable. Make sure you have installed and imported data.table and slam packages. Turn on h2o datatable by options("h2o.use.data.table"=TRUE)
References
http://blog.h2o.ai/2016/04/fast-csv-writing-for-r/
See Also
use.package
Examples
h2o.init()
hi <- as.h2o(iris)
he <- as.h2o(euro)
hl <- as.h2o(letters)
hm <- as.h2o(state.x77)

12

as.matrix.H2OFrame
hh <- as.h2o(hi)
stopifnot(is.h2o(hi), dim(hi)==dim(iris),
is.h2o(he), dim(he)==c(length(euro),1L),
is.h2o(hl), dim(hl)==c(length(letters),1L),
is.h2o(hm), dim(hm)==dim(state.x77),
is.h2o(hh), dim(hh)==dim(hi))
if (requireNamespace("Matrix", quietly=TRUE)) {
data <- rep(0, 100)
data[(1:10)^2] <- 1:10 * pi
m <- matrix(data, ncol = 20, byrow = TRUE)
m <- Matrix::Matrix(m, sparse = TRUE)
hs <- as.h2o(m)
stopifnot(is.h2o(hs), dim(hs)==dim(m))
}

as.matrix.H2OFrame

Convert an H2OFrame to a matrix

Description
Convert an H2OFrame to a matrix

Usage
## S3 method for class 'H2OFrame'
as.matrix(x, ...)
Arguments
x

An H2OFrame object

...

Further arguments to be passed down from other methods.

Examples
h2o.init()
irisPath <- system.file("extdata", "iris.csv", package="h2o")
iris <- h2o.uploadFile(path = irisPath)
iris.hex <- as.h2o(iris)
describe <- h2o.describe(iris.hex)
mins = as.matrix(apply(iris.hex, 2, min))
print(mins)

as.numeric

13

as.numeric

Convert H2O Data to Numeric

Description
Converts an H2O column into a numeric value column.
Usage
as.numeric(x)
Arguments
x

a column from an H2OFrame data set.

...

Further arguments to be passed from or to other methods.

Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
prostate.hex[,2] <- as.factor (prostate.hex[,2])
prostate.hex[,2] <- as.numeric(prostate.hex[,2])

as.vector.H2OFrame

Convert an H2OFrame to a vector

Description
Convert an H2OFrame to a vector
Usage
## S3 method for class 'H2OFrame'
as.vector(x,mode)
Arguments
x

An H2OFrame object

mode

Mode to coerce vector to

14

colnames

Examples
h2o.init()
irisPath <- system.file("extdata", "iris.csv", package="h2o")
iris <- h2o.uploadFile(path = irisPath)
hex <- as.h2o(iris)
cor_R <- cor(as.matrix(iris[,1]))
cor_h2o <- cor(hex[,1])
iris_Rcor <- cor(iris[,1:4])
iris_H2Ocor <- as.data.frame(cor(hex[,1:4]))
h2o_vec <- as.vector(unlist(iris_H2Ocor))
r_vec <- as.vector(unlist(iris_Rcor))

australia

Australia Coastal Data

Description
Temperature, soil moisture, runoff, and other environmental measurements from the Australia coast.
The data is available from http://cs.colby.edu/courses/S11/cs251/labs/lab07/AustraliaSubset.
csv.
Format
A data frame with 251 rows and 8 columns

colnames

Returns the column names of an H2OFrame

Description
Returns the column names of an H2OFrame
Usage
colnames(x, do.NULL = TRUE, prefix = "col")
Arguments
x

An H2OFrame object.

do.NULL

logical. If FALSE and names are NULL, names are created.

prefix

for created names.

Examples
h2o.init()
iris.hex <- as.h2o(iris)
colnames(iris) # Returns "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"

dim.H2OFrame

dim.H2OFrame

15

Returns the Dimensions of an H2OFrame

Description
Returns the number of rows and columns for an H2OFrame object.
Usage
## S3 method for class 'H2OFrame'
dim(x)
Arguments
x

An H2OFrame object.

See Also
dim for the base R method.
Examples
h2o.init()
iris.hex <- as.h2o(iris)
dim(iris.hex)

dimnames.H2OFrame

Column names of an H2OFrame

Description
Set column names of an H2O Frame
Usage
## S3 method for class 'H2OFrame'
dimnames(x)
Arguments
x

An H2OFrame

16

h2o.abs

Examples
h2o.init()
n <- 2000
# Generate variables V1, ... V10
X <- matrix(rnorm(10*n), n, 10)
# y = +1 if sum_i x_{ij}^2 > chisq median on 10 df
y <- rep(-1, n)
y[apply(X*X, 1, sum) > qchisq(.5, 10)] <- 1
# Assign names to the columns of X:
dimnames(X)[[2]] <- c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10")

h2o.abs

Compute the absolute value of x

Description
Compute the absolute value of x
Usage
h2o.abs(x)
Arguments
x

An H2OFrame object.

See Also
abs for the base R implementation.
Examples
h2o.init()
url <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/smtrees.csv"
smtreesH2O <- h2o.importFile(url)
smtreesR <- read.csv("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/smtrees.csv")
fith2o <- h2o.gbm(x=c("girth", "height"), y="vol", ntrees=3, max_depth=1, distribution="gaussian",
min_rows=2, learn_rate=.1, training_frame=smtreesH2O)
pred <- as.data.frame(predict(fith2o, newdata=smtreesH2O))
diff <- pred-smtreesR[,4]
diff_abs <- abs(diff)
print(diff_abs)

h2o.acos

17

h2o.acos

Compute the arc cosine of x

Description
Compute the arc cosine of x
Usage
h2o.acos(x)
Arguments
x

An H2OFrame object.

See Also
acos for the base R implementation.
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.acos(prostate.hex[,2])

h2o.aggregated_frame

Retrieve an aggregated frame from an Aggregator model

Description
Retrieve an aggregated frame from the Aggregator model and use it to create a new frame.
Usage
h2o.aggregated_frame(model)
Arguments
model

an H2OClusteringModel corresponding from a h2o.aggregator call.

18

h2o.aggregator

Examples
library(h2o)
h2o.init()
df <- h2o.createFrame(rows=100, cols=5, categorical_fraction=0.6, integer_fraction=0,
binary_fraction=0, real_range=100, integer_range=100, missing_fraction=0)
target_num_exemplars=1000
rel_tol_num_exemplars=0.5
encoding="Eigen"
agg <- h2o.aggregator(training_frame=df,
target_num_exemplars=target_num_exemplars,
rel_tol_num_exemplars=rel_tol_num_exemplars,
categorical_encoding=encoding)
# Use the aggregated frame to create a new dataframe
new_df <- h2o.aggregated_frame(agg)

h2o.aggregator

Build an Aggregated Frame

Description
Builds an Aggregated Frame of an H2OFrame.
Usage
h2o.aggregator(training_frame, x, model_id = NULL, ignore_const_cols = TRUE,
target_num_exemplars = 5000, rel_tol_num_exemplars = 0.5,
transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"),
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
save_mapping_frame = FALSE)
Arguments
training_frame Id of the training data frame.
x

A vector containing the character names of the predictors in the model.

model_id
Destination id for this model; auto-generated if not specified.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
target_num_exemplars
Targeted number of exemplars Defaults to 5000.
rel_tol_num_exemplars
Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents) Defaults
to 0.5.
transform

Transformation of training data Must be one of: "NONE", "STANDARDIZE",
"NORMALIZE", "DEMEAN", "DESCALE". Defaults to NORMALIZE.
categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.

h2o.aic

19

save_mapping_frame
Logical. Whether to export the mapping of the aggregated frame Defaults to
FALSE.
Examples
library(h2o)
h2o.init()
df <- h2o.createFrame(rows=100, cols=5, categorical_fraction=0.6, integer_fraction=0,
binary_fraction=0, real_range=100, integer_range=100, missing_fraction=0)
target_num_exemplars=1000
rel_tol_num_exemplars=0.5
encoding="Eigen"
agg <- h2o.aggregator(training_frame=df,
target_num_exemplars=target_num_exemplars,
rel_tol_num_exemplars=rel_tol_num_exemplars,
categorical_encoding=encoding)

h2o.aic

Retrieve the Akaike information criterion (AIC) value

Description
Retrieves the AIC value. If "train", "valid", and "xval" parameters are FALSE (default), then the
training AIC value is returned. If more than one parameter is set to TRUE, then a named vector of
AICs are returned, where the names are "train", "valid" or "xval".
Usage
h2o.aic(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel or H2OModelMetrics.

train

Retrieve the training AIC

valid

Retrieve the validation AIC

xval

Retrieve the cross-validation AIC

Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
p.sid <- h2o.runif(prostate.hex)
prostate.train <- h2o.assign(prostate.hex[p.sid > .2,], "prostate.train")
prostate.glm <- h2o.glm(x=3:7, y=2, training_frame=prostate.train)
aic.basic <- h2o.aic(prostate.glm)
print(aic.basic)

20

h2o.anomaly

h2o.all

Given a set of logical vectors, are all of the values true?

Description
Given a set of logical vectors, are all of the values true?
Usage
h2o.all(x)
Arguments
x

An H2OFrame object.

See Also
all for the base R implementation.

h2o.anomaly

Anomaly Detection via H2O Deep Learning Model

Description
Detect anomalies in an H2O dataset using an H2O deep learning model with auto-encoding.
Usage
h2o.anomaly(object, data, per_feature = FALSE)
Arguments
object

An H2OAutoEncoderModel object that represents the model to be used for
anomaly detection.

data

An H2OFrame object.

per_feature

Whether to return the per-feature squared reconstruction error

Value
Returns an H2OFrame object containing the reconstruction MSE or the per-feature squared error.
See Also
h2o.deeplearning for making an H2OAutoEncoderModel.

h2o.any

21

Examples
library(h2o)
h2o.init()
prosPath = system.file("extdata", "prostate.csv", package = "h2o")
prostate.hex = h2o.importFile(path = prosPath)
prostate.dl = h2o.deeplearning(x = 3:9, training_frame = prostate.hex, autoencoder = TRUE,
hidden = c(10, 10), epochs = 5)
prostate.anon = h2o.anomaly(prostate.dl, prostate.hex)
head(prostate.anon)
prostate.anon.per.feature = h2o.anomaly(prostate.dl, prostate.hex, per_feature=TRUE)
head(prostate.anon.per.feature)

h2o.any

Given a set of logical vectors, is at least one of the values true?

Description
Given a set of logical vectors, is at least one of the values true?
Usage
h2o.any(x)
Arguments
x

An H2OFrame object.

See Also
all for the base R implementation.

h2o.anyFactor

Check H2OFrame columns for factors

Description
Determines if any column of an H2OFrame object contains categorical data.
Usage
h2o.anyFactor(x)
Arguments
x

An H2OFrame object.

Value
Returns a logical value indicating whether any of the columns in x are factors.

22

h2o.ascharacter

Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris_wheader.csv", package="h2o")
iris.hex <- h2o.importFile(path = irisPath)
h2o.anyFactor(iris.hex)

h2o.arrange

Sorts an H2O frame by columns

Description
Sorts H2OFrame by the columns specified. H2OFrame can contain String columns but should not
sort on any String columns. Otherwise, an error will be thrown. To sort column c1 in descending
order, do desc(c1). Returns a new H2OFrame, like dplyr::arrange.
Usage
h2o.arrange(x, ...)
Arguments
x

The H2OFrame input to be sorted.

...

The column names to sort by.

h2o.ascharacter

Convert H2O Data to Characters

Description
Convert H2O Data to Characters
Usage
h2o.ascharacter(x)
Arguments
x

An H2OFrame object.

See Also
as.character for the base R implementation.

h2o.asfactor

23

h2o.asfactor

Convert H2O Data to Factors

Description
Convert H2O Data to Factors

Usage
h2o.asfactor(x)
Arguments
x

An H2OFrame object.

See Also
as.factor for the base R implementation.

h2o.asnumeric

Convert H2O Data to Numerics

Description
Convert H2O Data to Numerics

Usage
h2o.asnumeric(x)
Arguments
x

An H2OFrame object.

See Also
as.numeric for the base R implementation.

24

h2o.as_date

h2o.assign

Rename an H2O object.

Description
Makes a copy of the data frame and gives it the desired the key.

Usage
h2o.assign(data, key)

Arguments
data

An H2OFrame object

key

The hex key to be associated with the H2O parsed data object

h2o.as_date

Convert between character representations and objects of Date class

Description
Functions to convert between character representations and objects of class "Date" representing
calendar dates.

Usage
h2o.as_date(x, format, ...)

Arguments
x

H2OFrame column of strings or factors to be converted

format

A character string indicating date pattern

...

Further arguments to be passed from or to other methods.

h2o.auc

h2o.auc

25

Retrieve the AUC

Description
Retrieves the AUC value from an H2OBinomialMetrics. If "train", "valid", and "xval" parameters
are FALSE (default), then the training AUC value is returned. If more than one parameter is set to
TRUE, then a named vector of AUCs are returned, where the names are "train", "valid" or "xval".
Usage
h2o.auc(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OBinomialMetrics object.

train

Retrieve the training AUC

valid

Retrieve the validation AUC

xval

Retrieve the cross-validation AUC

See Also
h2o.giniCoef for the Gini coefficient, h2o.mse for MSE, and h2o.metric for the various threshold metrics. See h2o.performance for creating H2OModelMetrics objects.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
perf <- h2o.performance(model, hex)
h2o.auc(perf)

h2o.automl

Automatic Machine Learning

Description
The Automatic Machine Learning (AutoML) function automates the supervised machine learning
model training process. The current version of AutoML trains and cross-validates a Random Forest, an Extremely-Randomized Forest, a random grid of Gradient Boosting Machines (GBMs), a
random grid of Deep Neural Nets, and then trains a Stacked Ensemble using all of the models.

26

h2o.automl

Usage
h2o.automl(x, y, training_frame, validation_frame = NULL,
leaderboard_frame = NULL, nfolds = 5, fold_column = NULL,
weights_column = NULL, max_runtime_secs = 3600, max_models = NULL,
stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE",
"RMSLE", "AUC", "lift_top_group", "misclassification",
"mean_per_class_error"), stopping_tolerance = NULL, stopping_rounds = 3,
seed = NULL, project_name = NULL, exclude_algos = NULL)
Arguments
x

A vector containing the names or indices of the predictor variables to use in
building the model. If x is missing, then all columns except y are used.
y
The name or index of the response variable in the model. For classification, the
y column must be a factor, otherwise regression will be performed. Indexes are
1-based in R.
training_frame Training frame (H2OFrame or ID).
validation_frame
Validation frame (H2OFrame or ID); Optional. This frame is used for early
stopping of individual models and early stopping of the grid searches (unless
max_models or max_runtimes_secs overrides metric-based early stopping).
leaderboard_frame
Leaderboard frame (H2OFrame or ID); Optional. If provided, the Leaderboard
will be scored using this data frame intead of using cross-validation metrics,
which is the default.
nfolds
Number of folds for k-fold cross-validation. Defaults to 5. Use 0 to disable
cross-validation; this will also disable Stacked Ensemble (thus decreasing the
overall model performance).
fold_column
Column with cross-validation fold index assignment per observation; used to
override the default, randomized, 5-fold cross-validation scheme for individual
models in the AutoML run.
weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed.
max_runtime_secs
Maximum allowed runtime in seconds for the entire model training process. Use
0 to disable. Defaults to 3600 secs (1 hour).
max_models
Maximum number of models to build in the AutoML process (does not include
Stacked Ensembles). Defaults to NULL.
stopping_metric
Metric to use for early stopping (AUTO is logloss for classification, deviance for
regression). Must be one of "AUTO", "deviance", "logloss", "MSE", "RMSE",
"MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error".
Defaults to AUTO.
stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much). This value defaults to 0.001 if the dataset is at
least 1 million rows; otherwise it defaults to a bigger value determined by the
size of the dataset and the non-NA-rate. In that case, the value is computed as
1/sqrt(nrows * non-NA-rate).

h2o.betweenss

27

stopping_rounds
Integer. Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k
(stopping_rounds) scoring events. Defaults to 3 and must be an non-zero integer.
Use 0 to disable early stopping.
seed

Integer. Set a seed for reproducibility. AutoML can only guarantee reproducibility if max_models or early stopping is used because max_runtime_secs is resource limited, meaning that if the resources are not the same between runs,
AutoML may be able to train more models on one run vs another.

project_name

Character string to identify an AutoML project. Defaults to NULL, which means
a project name will be auto-generated based on the training frame ID.

exclude_algos

Vector of character strings naming the algorithms to skip during the modelbuilding phase. An example use is exclude_algos = c("GLM", "DeepLearning",
"DRF"), and the full list of options is: "GLM", "GBM", "DRF" (Random Forest
and Extremely-Randomized Trees), "DeepLearning" and "StackedEnsemble".
Defaults to NULL, which means that all appropriate H2O algorithms will be
used, if the search stopping criteria allow. Optional.

Details
AutoML finds the best model, given a training frame and response, and returns an H2OAutoML
object, which contains a leaderboard of all the models that were trained in the process, ranked by a
default model performance metric.
Value
An H2OAutoML object.
Examples
library(h2o)
h2o.init()
votes_path <- system.file("extdata", "housevotes.csv", package = "h2o")
votes_hf <- h2o.uploadFile(path = votes_path, header = TRUE)
aml <- h2o.automl(y = "Class", training_frame = votes_hf, max_runtime_secs = 30)

h2o.betweenss

Get the between cluster sum of squares

Description
Get the between cluster sum of squares. If "train", "valid", and "xval" parameters are FALSE
(default), then the training betweenss value is returned. If more than one parameter is set to TRUE,
then a named vector of betweenss’ are returned, where the names are "train", "valid" or "xval".
Usage
h2o.betweenss(object, train = FALSE, valid = FALSE, xval = FALSE)

28

h2o.bottomN

Arguments
object

An H2OClusteringModel object.

train

Retrieve the training between cluster sum of squares

valid

Retrieve the validation between cluster sum of squares

xval

Retrieve the cross-validation between cluster sum of squares

h2o.biases

Return the respective bias vector

Description
Return the respective bias vector
Usage
h2o.biases(object, vector_id = 1)
Arguments
object

An H2OModel or H2OModelMetrics

vector_id

An integer, ranging from 1 to number of layers + 1, that specifies the bias vector
to return.

h2o.bottomN

H2O bottomN

Description
bottomN function will will grab the bottom N percent of values of a column and return it in a
H2OFrame. Extract the top N percent of values of a column and return it in a H2OFrame.
Usage
h2o.bottomN(x, column, nPercent)
Arguments
x

an H2OFrame

column

is a column name or column index to grab the top N percent value from

nPercent

is a bottom percentage value to grab

Value
An H2OFrame with 2 columns. The first column is the original row indices, second column contains
the bottomN values

h2o.cbind

h2o.cbind

29

Combine H2O Datasets by Columns

Description
Takes a sequence of H2O data sets and combines them by column
Usage
h2o.cbind(...)
Arguments
...

A sequence of H2OFrame arguments. All datasets must exist on the same H2O
instance (IP and port) and contain the same number of rows.

Value
An H2OFrame object containing the combined . . . arguments column-wise.
See Also
cbind for the base R method.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
prostate.cbind <- h2o.cbind(prostate.hex, prostate.hex)
head(prostate.cbind)

h2o.ceiling

Take a single numeric argument and return a numeric vector with the
smallest integers

Description
ceiling takes a single numeric argument x and returns a numeric vector containing the smallest
integers not less than the corresponding elements of x.
Usage
h2o.ceiling(x)
Arguments
x

An H2OFrame object.

30

h2o.centroid_stats

See Also
ceiling for the base R implementation.
h2o.centers

Retrieve the Model Centers

Description
Retrieve the Model Centers
Usage
h2o.centers(object)
Arguments
object

An H2OClusteringModel object.

h2o.centersSTD

Retrieve the Model Centers STD

Description
Retrieve the Model Centers STD
Usage
h2o.centersSTD(object)
Arguments
object

An H2OClusteringModel object.

h2o.centroid_stats

Retrieve centroid statistics

Description
Retrieve the centroid statistics. If "train", "valid", and "xval" parameters are FALSE (default), then
the training centroid stats value is returned. If more than one parameter is set to TRUE, then a
named list of centroid stats data frames are returned, where the names are "train", "valid" or "xval".
Usage
h2o.centroid_stats(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object
train
valid
xval

An H2OClusteringModel object.
Retrieve the training centroid statistics
Retrieve the validation centroid statistics
Retrieve the cross-validation centroid statistics

h2o.clearLog

h2o.clearLog

31

Delete All H2O R Logs

Description
Clear all H2O R command and error response logs from the local disk. Used primarily for debugging purposes.

Usage
h2o.clearLog()
See Also
h2o.startLogging, h2o.stopLogging,

h2o.openLog

Examples
library(h2o)
h2o.init()
h2o.startLogging()
ausPath = system.file("extdata", "australia.csv", package="h2o")
australia.hex = h2o.importFile(path = ausPath)
h2o.stopLogging()
h2o.clearLog()

h2o.clusterInfo

Description
Print H2O cluster info

Usage
h2o.clusterInfo()

Print H2O cluster info

32

h2o.clusterStatus

h2o.clusterIsUp

Determine if an H2O cluster is up or not

Description
Determine if an H2O cluster is up or not
Usage
h2o.clusterIsUp(conn = h2o.getConnection())
Arguments
conn

H2OConnection object

Value
TRUE if the cluster is up; FALSE otherwise

h2o.clusterStatus

Return the status of the cluster

Description
Retrieve information on the status of the cluster running H2O.
Usage
h2o.clusterStatus()
See Also
H2OConnection, h2o.init
Examples
h2o.init()
h2o.clusterStatus()

h2o.cluster_sizes

33

h2o.cluster_sizes

Retrieve the cluster sizes

Description
Retrieve the cluster sizes. If "train", "valid", and "xval" parameters are FALSE (default), then the
training cluster sizes value is returned. If more than one parameter is set to TRUE, then a named
list of cluster size vectors are returned, where the names are "train", "valid" or "xval".

Usage
h2o.cluster_sizes(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OClusteringModel object.

train

Retrieve the training cluster sizes

valid

Retrieve the validation cluster sizes

xval

Retrieve the cross-validation cluster sizes

h2o.coef

Return the coefficients that can be applied to the non-standardized
data.

Description
Note: standardize = True by default. If set to False, then coef() returns the coefficients that are fit
directly.

Usage
h2o.coef(object)
Arguments
object

an H2OModel object.

34

h2o.colnames

h2o.coef_norm

Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to
evaluate variable importance.

Description
Return coefficients fitted on the standardized data (requires standardize = True, which is on by
default). These coefficients can be used to evaluate variable importance.

Usage
h2o.coef_norm(object)

Arguments
object

h2o.colnames

an H2OModel object.

Return column names of an H2OFrame

Description
Return column names of an H2OFrame

Usage
h2o.colnames(x)

Arguments
x

An H2OFrame object.

See Also
colnames for the base R implementation.

h2o.columns_by_type

35

h2o.columns_by_type

Obtain a list of columns that are specified by ‘coltype‘

Description
Obtain a list of columns that are specified by ‘coltype‘
Usage
h2o.columns_by_type(object, coltype = "numeric", ...)
Arguments
object

H2OFrame object

coltype

A character string indicating which column type to filter by. This must be one of
the following: "numeric" - Numeric, but not categorical or time "categorical" Integer, with a categorical/factor String mapping "string" - String column "time"
- Long msec since the Unix Epoch - with a variety of display/parse options
"uuid" - UUID "bad" - No none-NA rows (triple negative! all NAs or zero rows)

...

Ignored

Value
A list of column indices that correspond to "type"
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.columns_by_type(prostate.hex,coltype="numeric")

h2o.computeGram

Compute weighted gram matrix.

Description
Compute weighted gram matrix.
Usage
h2o.computeGram(X, weights = "", use_all_factor_levels = FALSE,
standardize = TRUE, skip_missing = FALSE)

36

h2o.confusionMatrix

Arguments
X
an H2OModel corresponding to H2O framel.
weights
character corresponding to name of weight vector in frame.
use_all_factor_levels
logical flag telling h2o whether or not to skip first level of categorical variables
during one-hot encoding.
standardize
logical flag telling h2o whether or not to standardize data
skip_missing
logical flag telling h2o whether skip rows with missing data or impute them with
mean

h2o.confusionMatrix

Access H2O Confusion Matrices

Description
Retrieve either a single or many confusion matrices from H2O objects.
Usage
h2o.confusionMatrix(object, ...)
## S4 method for signature 'H2OModel'
h2o.confusionMatrix(object, newdata, valid = FALSE, ...)
## S4 method for signature 'H2OModelMetrics'
h2o.confusionMatrix(object, thresholds = NULL,
metrics = NULL)
Arguments
object
...
newdata
valid
thresholds
metrics

Either an H2OModel object or an H2OModelMetrics object.
Extra arguments for extracting train or valid confusion matrices.
An H2OFrame object that can be scored on. Requires a valid response column.
Retrieve the validation metric.
(Optional) A value or a list of valid values between 0.0 and 1.0. This value is
only used in the case of H2OBinomialMetrics objects.
(Optional) A metric or a list of valid metrics ("min_per_class_accuracy", "absolute_mcc", "tnr", "fnr", "fpr", "tpr", "precision", "accuracy", "f0point5", "f2",
"f1"). This value is only used in the case of H2OBinomialMetrics objects.

Details
The H2OModelMetrics version of this function will only take H2OBinomialMetrics or H2OMultinomialMetrics
objects. If no threshold is specified, all possible thresholds are selected.
Value
Calling this function on H2OModel objects returns a confusion matrix corresponding to the predict
function. If used on an H2OBinomialMetrics object, returns a list of matrices corresponding to the
number of thresholds specified.

h2o.connect

37

See Also
predict for generating prediction frames, h2o.performance for creating H2OModelMetrics.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
h2o.confusionMatrix(model, hex)
# Generating a ModelMetrics object
perf <- h2o.performance(model, hex)
h2o.confusionMatrix(perf)

h2o.connect

Connect to a running H2O instance.

Description
Connect to a running H2O instance.
Usage
h2o.connect(ip = "localhost", port = 54321, strict_version_check = TRUE,
proxy = NA_character_, https = FALSE, insecure = FALSE,
username = NA_character_, password = NA_character_,
cookies = NA_character_, context_path = NA_character_, config = NULL)
Arguments
ip

Object of class character representing the IP address of the server where H2O
is running.

port
Object of class numeric representing the port number of the H2O server.
strict_version_check
(Optional) Setting this to FALSE is unsupported and should only be done when
advised by technical support.
proxy

(Optional) A character string specifying the proxy path.

https

(Optional) Set this to TRUE to use https instead of http.

insecure

(Optional) Set this to TRUE to disable SSL certificate checking.

username

(Optional) Username to login with.

password

(Optional) Password to login with.

cookies

(Optional) Vector(or list) of cookies to add to request.

context_path

(Optional) The last part of connection URL: http://:/

config

(Optional) A list describing connection parameters.

38

h2o.cor

Value
an instance of H2OConnection object representing a connection to the running H2O instance.
Examples
## Not run:
library(h2o)
# Try to connect to a H2O instance running at http://localhost:54321/cluster_X
# If not found, start a local H2O instance from R with the default settings.
#h2o.connect(ip = "localhost", port = 54321, context_path = "cluster_X")
# Or
#config = list(ip = "localhost", port = 54321, context_path = "cluster_X")
#h2o.connect(config = config)
# Skip strict version check during connecting to the instance
#h2o.connect(config = c(strict_version_check = FALSE, config))
## End(Not run)

h2o.cor

Correlation of columns.

Description
Compute the correlation matrix of one or two H2OFrames.
Usage
h2o.cor(x, y = NULL, na.rm = FALSE, use)
cor(x, ...)
Arguments
x

An H2OFrame object.

y

NULL (default) or an H2OFrame. The default is equivalent to y = x.

na.rm

logical. Should missing values be removed?

use

An optional character string indicating how to handle missing values. This must
be one of the following: "everything" - outputs NaNs whenever one of its contributing observations is missing "all.obs" - presence of missing observations
will throw an error "complete.obs" - discards missing values along with all observations in their rows so that only complete observations are used

...

Further arguments to be passed down from other methods.

Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
cor(prostate.hex$AGE)

h2o.cos

39

h2o.cos

Compute the cosine of x

Description
Compute the cosine of x

Usage
h2o.cos(x)
Arguments
x

An H2OFrame object.

See Also
cos for the base R implementation.

h2o.cosh

Compute the hyperbolic cosine of x

Description
Compute the hyperbolic cosine of x

Usage
h2o.cosh(x)
Arguments
x

An H2OFrame object.

See Also
cosh for the base R implementation.

40

h2o.coxph

h2o.coxph

Trains a Cox Proportional Hazards Model (CoxPH) on an H2O
dataset

Description
Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset

Usage
h2o.coxph(x, event_column, training_frame, model_id = NULL,
start_column = NULL, stop_column = NULL, weights_column = NULL,
offset_column = NULL, ties = c("efron", "breslow"), init = 0,
lre_min = 9, iter_max = 20)
Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except event_column,
start_column and stop_column are used.

event_column

The name of binary data column in the training frame indicating the occurrence
of an event.

training_frame Id of the training data frame.
model_id

Destination id for this model; auto-generated if not specified.

start_column

start_column

stop_column

stop_column

weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed. Note: Weights are per-row observation weights and do not increase the
size of the data frame. This is typically the number of times a row is repeated,
but non-integer values are supported as well. During training, rows with higher
weights matter more, due to the larger loss function pre-factor.
offset_column

Offset column. This will be added to the combination of columns before applying the link function.

ties

ties Must be one of: "efron", "breslow". Defaults to efron.

init

init Defaults to 0.

lre_min

lre_min Defaults to 9.

iter_max

iter_max Defaults to 20.

h2o.createFrame

h2o.createFrame

41

Data H2OFrame Creation in H2O

Description
Creates a data frame in H2O with real-valued, categorical, integer, and binary columns specified by
the user.
Usage
h2o.createFrame(rows = 10000, cols = 10, randomize = TRUE, value = 0,
real_range = 100, categorical_fraction = 0.2, factors = 100,
integer_fraction = 0.2, integer_range = 100, binary_fraction = 0.1,
binary_ones_fraction = 0.02, time_fraction = 0, string_fraction = 0,
missing_fraction = 0.01, response_factors = 2, has_response = FALSE,
seed, seed_for_column_types)
Arguments
rows

The number of rows of data to generate.

cols

The number of columns of data to generate. Excludes the response column if
has_response = TRUE.

randomize

A logical value indicating whether data values should be randomly generated.
This must be TRUE if either categorical_fraction or integer_fraction is
non-zero.

value

If randomize = FALSE, then all real-valued entries will be set to this value.

real_range
The range of randomly generated real values.
categorical_fraction
The fraction of total columns that are categorical.
factors
The number of (unique) factor levels in each categorical column.
integer_fraction
The fraction of total columns that are integer-valued.
integer_range The range of randomly generated integer values.
binary_fraction
The fraction of total columns that are binary-valued.
binary_ones_fraction
The fraction of values in a binary column that are set to 1.
time_fraction The fraction of randomly created date/time columns.
string_fraction
The fraction of randomly created string columns.
missing_fraction
The fraction of total entries in the data frame that are set to NA.
response_factors
If has_response = TRUE, then this is the number of factor levels in the response
column.
has_response

A logical value indicating whether an additional response column should be prepended to the final H2O data frame. If set to TRUE, the total number of columns
will be cols+1.

42

h2o.cross_validation_fold_assignment
seed

A seed used to generate random values when randomize = TRUE.

seed_for_column_types
A seed used to generate random column types when randomize = TRUE.
Value
Returns an H2OFrame object.
Examples
library(h2o)
h2o.init()
hex <- h2o.createFrame(rows = 1000, cols = 100, categorical_fraction = 0.1,
factors = 5, integer_fraction = 0.5, integer_range = 1,
has_response = TRUE)
head(hex)
summary(hex)
hex2 <- h2o.createFrame(rows = 100, cols = 10, randomize = FALSE, value = 5,
categorical_fraction = 0, integer_fraction = 0)
summary(hex2)

h2o.cross_validation_fold_assignment
Retrieve the cross-validation fold assignment

Description
Retrieve the cross-validation fold assignment
Usage
h2o.cross_validation_fold_assignment(object)
Arguments
object

An H2OModel object.

Value
Returns a H2OFrame

h2o.cross_validation_holdout_predictions

h2o.cross_validation_holdout_predictions
Retrieve the cross-validation holdout predictions

Description
Retrieve the cross-validation holdout predictions
Usage
h2o.cross_validation_holdout_predictions(object)
Arguments
object

An H2OModel object.

Value
Returns a H2OFrame

h2o.cross_validation_models
Retrieve the cross-validation models

Description
Retrieve the cross-validation models
Usage
h2o.cross_validation_models(object)
Arguments
object

An H2OModel object.

Value
Returns a list of H2OModel objects

43

44

h2o.cummax

h2o.cross_validation_predictions
Retrieve the cross-validation predictions

Description
Retrieve the cross-validation predictions
Usage
h2o.cross_validation_predictions(object)
Arguments
object

An H2OModel object.

Value
Returns a list of H2OFrame objects

h2o.cummax

Return the cumulative max over a column or across a row

Description
Return the cumulative max over a column or across a row
Usage
h2o.cummax(x, axis = 0)
Arguments
x

An H2OFrame object.

axis

An int that indicates whether to do down a column (0) or across a row (1).

See Also
cummax for the base R implementation.

h2o.cummin

h2o.cummin

45

Return the cumulative min over a column or across a row

Description
Return the cumulative min over a column or across a row
Usage
h2o.cummin(x, axis = 0)
Arguments
x

An H2OFrame object.

axis

An int that indicates whether to do down a column (0) or across a row (1).

See Also
cummin for the base R implementation.

h2o.cumprod

Return the cumulative product over a column or across a row

Description
Return the cumulative product over a column or across a row
Usage
h2o.cumprod(x, axis = 0)
Arguments
x

An H2OFrame object.

axis

An int that indicates whether to do down a column (0) or across a row (1).

See Also
cumprod for the base R implementation.

46

h2o.cut

h2o.cumsum

Return the cumulative sum over a column or across a row

Description
Return the cumulative sum over a column or across a row
Usage
h2o.cumsum(x, axis = 0)
Arguments
x
axis

An H2OFrame object.
An int that indicates whether to do down a column (0) or across a row (1).

See Also
cumsum for the base R implementation.

h2o.cut

Cut H2O Numeric Data to Factor

Description
Divides the range of the H2O data into intervals and codes the values according to which interval
they fall in. The leftmost interval corresponds to the level one, the next is level two, etc.
Usage
h2o.cut(x, breaks, labels = NULL, include.lowest = FALSE, right = TRUE,
dig.lab = 3, ...)
## S3 method for class 'H2OFrame'
cut(x, breaks, labels = NULL, include.lowest = FALSE,
right = TRUE, dig.lab = 3, ...)
Arguments
x
breaks
labels

An H2OFrame object with a single numeric column.
A numeric vector of two or more unique cut points.
Labels for the levels of the resulting category. By default, labels are constructed
sing "(a,b]" interval notation.
include.lowest Logical, indicationg if an ’x[i]’ equal to the lowest (or highest, for right =
FALSE ’breaks’ value should be included
right
/codeLogical, indicating if the intervals should be closed on the right (opened
on the left) or vice versa.
dig.lab
Integer which is used when labels are not given, determines the number of digits
used in formatting the break numbers.
...
Further arguments passed to or from other methods.

h2o.day

47

Value
Returns an H2OFrame object containing the factored data with intervals as levels.
Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris_wheader.csv", package="h2o")
iris.hex <- h2o.uploadFile(path = irisPath, destination_frame = "iris.hex")
summary(iris.hex)
# Cut sepal length column into intervals determined by min/max/quantiles
sepal_len.cut <- cut(iris.hex$sepal_len, c(4.2, 4.8, 5.8, 6, 8))
head(sepal_len.cut)
summary(sepal_len.cut)

h2o.day

Convert Milliseconds to Day of Month in H2O Datasets

Description
Converts the entries of an H2OFrame object from milliseconds to days of the month (on a 1 to 31
scale).
Usage
h2o.day(x)
day(x)
## S3 method for class 'H2OFrame'
day(x)
Arguments
x

An H2OFrame object.

Value
An H2OFrame object containing the entries of x converted to days of the month.
See Also
h2o.month

48

h2o.dct

h2o.dayOfWeek

Convert Milliseconds to Day of Week in H2O Datasets

Description
Converts the entries of an H2OFrame object from milliseconds to days of the week (on a 0 to 6
scale).
Usage
h2o.dayOfWeek(x)
dayOfWeek(x)
## S3 method for class 'H2OFrame'
dayOfWeek(x)
Arguments
x

An H2OFrame object.

Value
An H2OFrame object containing the entries of x converted to days of the week.
See Also
h2o.day, h2o.month

h2o.dct

Compute DCT of an H2OFrame

Description
Compute the Discrete Cosine Transform of every row in the H2OFrame
Usage
h2o.dct(data, destination_frame, dimensions, inverse = FALSE)
Arguments
data
An H2OFrame object representing the dataset to transform
destination_frame
A frame ID for the result
dimensions

An array containing the 3 integer values for height, width, depth of each sample.
The product of HxWxD must total up to less than the number of columns. For
1D, use c(L,1,1), for 2D, use C(N,M,1).

inverse

Whether to perform the inverse transform

h2o.ddply

49

Value
Returns an H2OFrame object.
Examples
library(h2o)
h2o.init()
df <- h2o.createFrame(rows = 1000, cols = 8*16*24,
categorical_fraction = 0, integer_fraction = 0, missing_fraction = 0)
df1 <- h2o.dct(data=df, dimensions=c(8*16*24,1,1))
df2 <- h2o.dct(data=df1,dimensions=c(8*16*24,1,1),inverse=TRUE)
max(abs(df1-df2))
df1 <- h2o.dct(data=df, dimensions=c(8*16,24,1))
df2 <- h2o.dct(data=df1,dimensions=c(8*16,24,1),inverse=TRUE)
max(abs(df1-df2))
df1 <- h2o.dct(data=df, dimensions=c(8,16,24))
df2 <- h2o.dct(data=df1,dimensions=c(8,16,24),inverse=TRUE)
max(abs(df1-df2))

h2o.ddply

Split H2O Dataset, Apply Function, and Return Results

Description
For each subset of an H2O data set, apply a user-specified function, then combine the results. This
is an experimental feature.
Usage
h2o.ddply(X, .variables, FUN, ..., .progress = "none")
Arguments
X

An H2OFrame object to be processed.

.variables

Variables to split X by, either the indices or names of a set of columns.

FUN

Function to apply to each subset grouping.

...

Additional arguments passed on to FUN.

.progress

Name of the progress bar to use. #TODO: (Currently unimplemented)

Value
Returns an H2OFrame object containing the results from the split/apply operation, arranged
See Also
ddply for the plyr library implementation.

50

h2o.decryptionSetup

Examples
library(h2o)
h2o.init()
# Import iris dataset to H2O
irisPath <- system.file("extdata", "iris_wheader.csv", package = "h2o")
iris.hex <- h2o.uploadFile(path = irisPath, destination_frame = "iris.hex")
# Add function taking mean of sepal_len column
fun <- function(df) { sum(df[,1], na.rm = TRUE)/nrow(df) }
# Apply function to groups by class of flower
# uses h2o's ddply, since iris.hex is an H2OFrame object
res <- h2o.ddply(iris.hex, "class", fun)
head(res)

h2o.decryptionSetup

Setup a Decryption Tool

Description
If your source file is encrypted - setup a Decryption Tool and then provide the reference (result of
this function) to the import functions.
Usage
h2o.decryptionSetup(keystore, keystore_type = "JCEKS",
key_alias = NA_character_, password = NA_character_, decrypt_tool = "",
decrypt_impl = "water.parser.GenericDecryptionTool",
cipher_spec = NA_character_)
Arguments
keystore

An H2OFrame object referencing a loaded Java Keystore (see example).

keystore_type

(Optional) Specification of Keystore type, defaults to JCEKS.

key_alias

Which key from the keystore to use for decryption.

password

Password to the keystore and the key.

decrypt_tool

(Optional) Name of the decryption tool.

decrypt_impl

(Optional) Java class name implementing the Decryption Tool.

cipher_spec

Specification of a cipher (eg.: AES/ECB/PKCS5Padding).

See Also
h2o.importFile, h2o.parseSetup

h2o.deepfeatures

51

Examples
## Not run:
library(h2o)
h2o.init()
ksPath <- system.file("extdata", "keystore.jks", package = "h2o")
keystore <- h2o.importFile(path = ksPath, parse = FALSE) # don't parse, keep as a binary file
cipher <- "AES/ECB/PKCS5Padding"
pwd <- "Password123"
kAlias <- "secretKeyAlias"
dt <- h2o.decryptionSetup(keystore, key_alias = kAlias, password = pwd, cipher_spec = cipher)
dataPath <- system.file("extdata", "prostate.csv.aes", package = "h2o")
data <- h2o.importFile(dataPath, decrypt_tool = dt)
summary(data)
## End(Not run)

h2o.deepfeatures

Feature Generation via H2O Deep Learning or DeepWater Model

Description
Extract the non-linear feature from an H2O data set using an H2O deep learning model.

Usage
h2o.deepfeatures(object, data, layer)
Arguments
object

An H2OModel object that represents the deep learning model to be used for
feature extraction.

data

An H2OFrame object.

layer

Index (for DeepLearning, integer) or Name (for DeepWater, String) of the hidden layer to extract

Value
Returns an H2OFrame object with as many features as the number of units in the hidden layer of
the specified index.

See Also
link{h2o.deeplearning} for making H2O Deep Learning models.
link{h2o.deepwater} for making H2O DeepWater models.

52

h2o.deeplearning

Examples
library(h2o)
h2o.init()
prosPath = system.file("extdata", "prostate.csv", package = "h2o")
prostate.hex = h2o.importFile(path = prosPath)
prostate.dl = h2o.deeplearning(x = 3:9, y = 2, training_frame = prostate.hex,
hidden = c(100, 200), epochs = 5)
prostate.deepfeatures_layer1 = h2o.deepfeatures(prostate.dl, prostate.hex, layer = 1)
prostate.deepfeatures_layer2 = h2o.deepfeatures(prostate.dl, prostate.hex, layer = 2)
head(prostate.deepfeatures_layer1)
head(prostate.deepfeatures_layer2)
#if (h2o.deepwater.available()) {
# prostate.dl = h2o.deepwater(x = 3:9, y = 2, backend="mxnet", training_frame = prostate.hex,
#
hidden = c(100, 200), epochs = 5)
# prostate.deepfeatures_layer1 =
#
h2o.deepfeatures(prostate.dl, prostate.hex, layer = "fc1_w")
# prostate.deepfeatures_layer2 =
#
h2o.deepfeatures(prostate.dl, prostate.hex, layer = "fc2_w")
# head(prostate.deepfeatures_layer1)
# head(prostate.deepfeatures_layer2)
#}

h2o.deeplearning

Build a Deep Neural Network model using CPUs

Description
Builds a feed-forward multilayer artificial neural network on an H2OFrame.
Usage
h2o.deeplearning(x, y, training_frame, model_id = NULL,
validation_frame = NULL, nfolds = 0,
keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO",
"Random", "Modulo", "Stratified"), fold_column = NULL,
ignore_const_cols = TRUE, score_each_iteration = FALSE,
weights_column = NULL, offset_column = NULL, balance_classes = FALSE,
class_sampling_factors = NULL, max_after_balance_size = 5,
max_hit_ratio_k = 0, checkpoint = NULL, pretrained_autoencoder = NULL,
overwrite_with_best_model = TRUE, use_all_factor_levels = TRUE,
standardize = TRUE, activation = c("Tanh", "TanhWithDropout", "Rectifier",
"RectifierWithDropout", "Maxout", "MaxoutWithDropout"), hidden = c(200,
200), epochs = 10, train_samples_per_iteration = -2,
target_ratio_comm_to_comp = 0.05, seed = -1, adaptive_rate = TRUE,
rho = 0.99, epsilon = 1e-08, rate = 0.005, rate_annealing = 1e-06,
rate_decay = 1, momentum_start = 0, momentum_ramp = 1e+06,
momentum_stable = 0, nesterov_accelerated_gradient = TRUE,
input_dropout_ratio = 0, hidden_dropout_ratios = NULL, l1 = 0, l2 = 0,

h2o.deeplearning

53

max_w2 = 3.4028235e+38, initial_weight_distribution = c("UniformAdaptive",
"Uniform", "Normal"), initial_weight_scale = 1, initial_weights = NULL,
initial_biases = NULL, loss = c("Automatic", "CrossEntropy", "Quadratic",
"Huber", "Absolute", "Quantile"), distribution = c("AUTO", "bernoulli",
"multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace",
"quantile", "huber"), quantile_alpha = 0.5, tweedie_power = 1.5,
huber_alpha = 0.9, score_interval = 5, score_training_samples = 10000,
score_validation_samples = 0, score_duty_cycle = 0.1,
classification_stop = 0, regression_stop = 1e-06, stopping_rounds = 5,
stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE",
"RMSLE", "AUC", "lift_top_group", "misclassification",
"mean_per_class_error"), stopping_tolerance = 0, max_runtime_secs = 0,
score_validation_sampling = c("Uniform", "Stratified"),
diagnostics = TRUE, fast_mode = TRUE, force_load_balance = TRUE,
variable_importances = TRUE, replicate_training_data = TRUE,
single_node_mode = FALSE, shuffle_training_data = FALSE,
missing_values_handling = c("MeanImputation", "Skip"), quiet_mode = FALSE,
autoencoder = FALSE, sparse = FALSE, col_major = FALSE,
average_activation = 0, sparsity_beta = 0,
max_categorical_features = 2147483647, reproducible = FALSE,
export_weights_and_biases = FALSE, mini_batch_size = 1,
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
elastic_averaging = FALSE, elastic_averaging_moving_rate = 0.9,
elastic_averaging_regularization = 0.001, verbose = FALSE)
Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.

54

h2o.deeplearning
fold_column
Column with cross-validation fold index assignment per observation.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed. Note: Weights are per-row observation weights and do not increase the
size of the data frame. This is typically the number of times a row is repeated,
but non-integer values are supported as well. During training, rows with higher
weights matter more, due to the larger loss function pre-factor.
offset_column Offset column. This will be added to the combination of columns before applying the link function.
balance_classes
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). If not
specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
max_after_balance_size
Maximum relative size of the training data after balancing class counts (can be
less than 1.0). Requires balance_classes. Defaults to 5.0.
max_hit_ratio_k
Max. number (top K) of predictions to use for hit ratio computation (for multiclass only, 0 to disable). Defaults to 0.
checkpoint
Model checkpoint to resume training with.
pretrained_autoencoder
Pretrained autoencoder model to initialize this model with.
overwrite_with_best_model
Logical. If enabled, override the final model with the best model found during
training. Defaults to TRUE.
use_all_factor_levels
Logical. Use all factor levels of categorical variables. Otherwise, the first factor
level is omitted (without loss of accuracy). Useful for variable importances and
auto-enabled for autoencoder. Defaults to TRUE.
standardize
Logical. If enabled, automatically standardize the data. If disabled, the user
must provide properly scaled input data. Defaults to TRUE.
activation
Activation function. Must be one of: "Tanh", "TanhWithDropout", "Rectifier",
"RectifierWithDropout", "Maxout", "MaxoutWithDropout". Defaults to Rectifier.
hidden
Hidden layer sizes (e.g. [100, 100]). Defaults to [200, 200].
epochs
How many times the dataset should be iterated (streamed), can be fractional.
Defaults to 10.
train_samples_per_iteration
Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2:
automatic. Defaults to -2.

h2o.deeplearning

55

target_ratio_comm_to_comp
Target ratio of communication overhead to computation. Only for multi-node
operation and train_samples_per_iteration = -2 (auto-tuning). Defaults to 0.05.
seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Note: only reproducible
when running single threaded. Defaults to -1 (time-based random number).

adaptive_rate

Logical. Adaptive learning rate. Defaults to TRUE.

rho

Adaptive learning rate time decay factor (similarity to prior updates). Defaults
to 0.99.

epsilon

Adaptive learning rate smoothing factor (to avoid divisions by zero and allow
progress). Defaults to 1e-08.

rate

Learning rate (higher => less stable, lower => slower convergence). Defaults to
0.005.

rate_annealing Learning rate annealing: rate / (1 + rate_annealing * samples). Defaults to 1e06.
rate_decay

Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n 1). Defaults to 1.

momentum_start Initial momentum at the beginning of training (try 0.5). Defaults to 0.
momentum_ramp Number of training samples for which momentum increases. Defaults to 1000000.
momentum_stable
Final momentum after the ramp is over (try 0.99). Defaults to 0.
nesterov_accelerated_gradient
Logical. Use Nesterov accelerated gradient (recommended). Defaults to TRUE.
input_dropout_ratio
Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). Defaults
to 0.
hidden_dropout_ratios
Hidden layer dropout ratios (can improve generalization), specify one value per
hidden layer, defaults to 0.5.
l1

L1 regularization (can add stability and improve generalization, causes many
weights to become 0). Defaults to 0.

l2

L2 regularization (can add stability and improve generalization, causes many
weights to be small. Defaults to 0.

max_w2

Constraint for squared sum of incoming weights per unit (e.g. for Rectifier).
Defaults to 3.4028235e+38.
initial_weight_distribution
Initial weight distribution. Must be one of: "UniformAdaptive", "Uniform",
"Normal". Defaults to UniformAdaptive.
initial_weight_scale
Uniform: -value...value, Normal: stddev. Defaults to 1.
initial_weights
A list of H2OFrame ids to initialize the weight matrices of this model with.
initial_biases A list of H2OFrame ids to initialize the bias vectors of this model with.
loss

Loss function. Must be one of: "Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile". Defaults to Automatic.

distribution

Distribution function Must be one of: "AUTO", "bernoulli", "multinomial",
"gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.

56

h2o.deeplearning
quantile_alpha Desired quantile for Quantile regression, must be between 0 and 1. Defaults to
0.5.
tweedie_power

Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to
1.5.

huber_alpha

Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). Defaults to 0.9.

score_interval Shortest time interval (in seconds) between model scoring. Defaults to 5.
score_training_samples
Number of training set samples for scoring (0 for all). Defaults to 10000.
score_validation_samples
Number of validation set samples for scoring (0 for all). Defaults to 0.
score_duty_cycle
Maximum duty cycle fraction for scoring (lower: more training, higher: more
scoring). Defaults to 0.1.
classification_stop
Stopping criterion for classification error fraction on training data (-1 to disable).
Defaults to 0.
regression_stop
Stopping criterion for regression error (MSE) on training data (-1 to disable).
Defaults to 1e-06.
stopping_rounds
Early stopping based on convergence of stopping_metric. Stop if simple moving
average of length k of the stopping_metric does not improve for k:=stopping_rounds
scoring events (0 to disable) Defaults to 5.
stopping_metric
Metric to use for early stopping (AUTO: logloss for classification, deviance for
regression) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE",
"MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error".
Defaults to AUTO.
stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
score_validation_sampling
Method used to sample validation dataset for scoring. Must be one of: "Uniform", "Stratified". Defaults to Uniform.
diagnostics
fast_mode

Logical. Enable diagnostics for hidden layers. Defaults to TRUE.

Logical. Enable fast mode (minor approximation in back-propagation). Defaults to TRUE.
force_load_balance
Logical. Force extra load balancing to increase training speed for small datasets
(to keep all cores busy). Defaults to TRUE.
variable_importances
Logical. Compute variable importances for input features (Gedeon method) can be slow for large networks. Defaults to TRUE.
replicate_training_data
Logical. Replicate the entire training dataset onto every node for faster training
on small datasets. Defaults to TRUE.

h2o.deeplearning

57

single_node_mode
Logical. Run on a single node for fine-tuning of model parameters. Defaults to
FALSE.
shuffle_training_data
Logical. Enable shuffling of training data (recommended if training data is
replicated and train_samples_per_iteration is close to #nodes x #rows, of if using
balance_classes). Defaults to FALSE.
missing_values_handling
Handling of missing values. Either MeanImputation or Skip. Must be one of:
"MeanImputation", "Skip". Defaults to MeanImputation.
quiet_mode
Logical. Enable quiet mode for less output to standard output. Defaults to
FALSE.
autoencoder
Logical. Auto-Encoder. Defaults to FALSE.
sparse
Logical. Sparse data handling (more efficient for data with lots of 0 values).
Defaults to FALSE.
col_major
Logical. #DEPRECATED Use a column major weight matrix for input layer.
Can speed up forward propagation, but might slow down backpropagation. Defaults to FALSE.
average_activation
Average activation for sparse auto-encoder. #Experimental Defaults to 0.
sparsity_beta Sparsity regularization. #Experimental Defaults to 0.
max_categorical_features
Max. number of categorical features, enforced via hashing. #Experimental Defaults to 2147483647.
reproducible
Logical. Force reproducibility on small data (will be slow - only uses 1 thread).
Defaults to FALSE.
export_weights_and_biases
Logical. Whether to export Neural Network weights and biases to H2O Frames.
Defaults to FALSE.
mini_batch_size
Mini-batch size (smaller leads to better fit, larger can speed up and generalize
better). Defaults to 1.
categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
elastic_averaging
Logical. Elastic averaging between compute nodes can improve distributed
model convergence. #Experimental Defaults to FALSE.
elastic_averaging_moving_rate
Elastic averaging moving rate (only if elastic averaging is enabled). Defaults to
0.9.
elastic_averaging_regularization
Elastic averaging regularization strength (only if elastic averaging is enabled).
Defaults to 0.001.
verbose
Logical. Print scoring history to the console (Metrics per tree for GBM, DRF,
& XGBoost. Metrics per epoch for Deep Learning). Defaults to FALSE.
See Also
predict.H2OModel for prediction

58

h2o.deepwater

Examples
library(h2o)
h2o.init()
iris.hex <- as.h2o(iris)
iris.dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris.hex, seed=123456)
# now make a prediction
predictions <- h2o.predict(iris.dl, iris.hex)

h2o.deepwater

Build a Deep Learning model using multiple native GPU backends

Description
Builds a deep neural network on an H2OFrame containing various data sources.
Usage
h2o.deepwater(x, y, training_frame, model_id = NULL, checkpoint = NULL,
autoencoder = FALSE, validation_frame = NULL, nfolds = 0,
balance_classes = FALSE, max_after_balance_size = 5,
class_sampling_factors = NULL, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO",
"Random", "Modulo", "Stratified"), fold_column = NULL,
offset_column = NULL, weights_column = NULL,
score_each_iteration = FALSE, categorical_encoding = c("AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder",
"SortByResponse", "EnumLimited"), overwrite_with_best_model = TRUE,
epochs = 10, train_samples_per_iteration = -2,
target_ratio_comm_to_comp = 0.05, seed = -1, standardize = TRUE,
learning_rate = 0.001, learning_rate_annealing = 1e-06,
momentum_start = 0.9, momentum_ramp = 10000, momentum_stable = 0.9,
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson",
"gamma", "tweedie", "laplace", "quantile", "huber"), score_interval = 5,
score_training_samples = 10000, score_validation_samples = 0,
score_duty_cycle = 0.1, classification_stop = 0, regression_stop = 0,
stopping_rounds = 5, stopping_metric = c("AUTO", "deviance", "logloss",
"MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification",
"mean_per_class_error"), stopping_tolerance = 0, max_runtime_secs = 0,
ignore_const_cols = TRUE, shuffle_training_data = TRUE,
mini_batch_size = 32, clip_gradient = 10, network = c("auto", "user",
"lenet", "alexnet", "vgg", "googlenet", "inception_bn", "resnet"),
backend = c("mxnet", "caffe", "tensorflow"), image_shape = c(0, 0),
channels = 3, sparse = FALSE, gpu = TRUE, device_id = c(0),
cache_data = TRUE, network_definition_file = NULL,
network_parameters_file = NULL, mean_image_file = NULL,
export_native_parameters_prefix = NULL, activation = c("Rectifier",
"Tanh"), hidden = NULL, input_dropout_ratio = 0,
hidden_dropout_ratios = NULL, problem_type = c("auto", "image",
"dataset"))

h2o.deepwater

59

Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
model_id

Destination id for this model; auto-generated if not specified.

checkpoint

Model checkpoint to resume training with.

autoencoder
Logical. Auto-Encoder. Defaults to FALSE.
validation_frame
Id of the validation data frame.
nfolds
balance_classes

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.

Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
max_after_balance_size
Maximum relative size of the training data after balancing class counts (can be
less than 1.0). Requires balance_classes. Defaults to 5.0.
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). If not
specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column

Column with cross-validation fold index assignment per observation.

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed. Note: Weights are per-row observation weights and do not increase the
size of the data frame. This is typically the number of times a row is repeated,
but non-integer values are supported as well. During training, rows with higher
weights matter more, due to the larger loss function pre-factor.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.

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categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
overwrite_with_best_model
Logical. If enabled, override the final model with the best model found during
training. Defaults to TRUE.
epochs

How many times the dataset should be iterated (streamed), can be fractional.
Defaults to 10.
train_samples_per_iteration
Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2:
automatic. Defaults to -2.
target_ratio_comm_to_comp
Target ratio of communication overhead to computation. Only for multi-node
operation and train_samples_per_iteration = -2 (auto-tuning). Defaults to 0.05.
seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Note: only reproducible
when running single threaded. Defaults to -1 (time-based random number).

standardize

Logical. If enabled, automatically standardize the data. If disabled, the user
must provide properly scaled input data. Defaults to TRUE.

learning_rate

Learning rate (higher => less stable, lower => slower convergence). Defaults to
0.001.
learning_rate_annealing
Learning rate annealing: rate / (1 + rate_annealing * samples). Defaults to 1e06.
momentum_start Initial momentum at the beginning of training (try 0.5). Defaults to 0.9.
momentum_ramp Number of training samples for which momentum increases. Defaults to 10000.
momentum_stable
Final momentum after the ramp is over (try 0.99). Defaults to 0.9.
distribution

Distribution function Must be one of: "AUTO", "bernoulli", "multinomial",
"gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.

score_interval Shortest time interval (in seconds) between model scoring. Defaults to 5.
score_training_samples
Number of training set samples for scoring (0 for all). Defaults to 10000.
score_validation_samples
Number of validation set samples for scoring (0 for all). Defaults to 0.
score_duty_cycle
Maximum duty cycle fraction for scoring (lower: more training, higher: more
scoring). Defaults to 0.1.
classification_stop
Stopping criterion for classification error fraction on training data (-1 to disable).
Defaults to 0.
regression_stop
Stopping criterion for regression error (MSE) on training data (-1 to disable).
Defaults to 0.

h2o.deepwater

61

stopping_rounds
Early stopping based on convergence of stopping_metric. Stop if simple moving
average of length k of the stopping_metric does not improve for k:=stopping_rounds
scoring events (0 to disable) Defaults to 5.
stopping_metric
Metric to use for early stopping (AUTO: logloss for classification, deviance for
regression) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE",
"MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error".
Defaults to AUTO.
stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
shuffle_training_data
Logical. Enable global shuffling of training data. Defaults to TRUE.
mini_batch_size
Mini-batch size (smaller leads to better fit, larger can speed up and generalize
better). Defaults to 32.
clip_gradient

Clip gradients once their absolute value is larger than this value. Defaults to 10.

network

Network architecture. Must be one of: "auto", "user", "lenet", "alexnet", "vgg",
"googlenet", "inception_bn", "resnet". Defaults to auto.

backend

Deep Learning Backend. Must be one of: "mxnet", "caffe", "tensorflow". Defaults to mxnet.

image_shape

Width and height of image. Defaults to [0, 0].

channels

Number of (color) channels. Defaults to 3.

sparse

Logical. Sparse data handling (more efficient for data with lots of 0 values).
Defaults to FALSE.

gpu

Logical. Whether to use a GPU (if available). Defaults to TRUE.

device_id

Device IDs (which GPUs to use). Defaults to [0].

cache_data

Logical. Whether to cache the data in memory (automatically disabled if data
size is too large). Defaults to TRUE.
network_definition_file
Path of file containing network definition (graph, architecture).
network_parameters_file
Path of file containing network (initial) parameters (weights, biases).
mean_image_file
Path of file containing the mean image data for data normalization.
export_native_parameters_prefix
Path (prefix) where to export the native model parameters after every iteration.
activation

Activation function. Only used if no user-defined network architecture file is
provided, and only for problem_type=dataset. Must be one of: "Rectifier",
"Tanh".

hidden

Hidden layer sizes (e.g. [200, 200]). Only used if no user-defined network
architecture file is provided, and only for problem_type=dataset.

62

h2o.describe
input_dropout_ratio
Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). Defaults
to 0.
hidden_dropout_ratios
Hidden layer dropout ratios (can improve generalization), specify one value per
hidden layer, defaults to 0.5.
problem_type

Problem type, auto-detected by default. If set to image, the H2OFrame must
contain a string column containing the path (URI or URL) to the images in
the first column. If set to text, the H2OFrame must contain a string column
containing the text in the first column. If set to dataset, Deep Water behaves
just like any other H2O Model and builds a model on the provided H2OFrame
(non-String columns). Must be one of: "auto", "image", "dataset". Defaults to
auto.

h2o.deepwater.available
Determines whether Deep Water is available

Description
Ask the H2O server whether a Deep Water model can be built. (Depends on availability of native
backends.) Returns TRUE if a Deep Water model can be built, or FALSE otherwise.
Usage
h2o.deepwater.available(h2oRestApiVersion = .h2o.__REST_API_VERSION)
Arguments
h2oRestApiVersion
(Optional) Specific version of the REST API to use.

h2o.describe

H2O Description of A Dataset

Description
Reports the "Flow" style summary rollups on an instance of H2OFrame. Includes information about
column types, mins/maxs/missing/zero counts/stds/number of levels
Usage
h2o.describe(frame)
Arguments
frame

An H2OFrame object.

Value
A table with the Frame stats.

h2o.difflag1

63

Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.importFile(path = prosPath)
h2o.describe(prostate.hex)

h2o.difflag1

Conduct a lag 1 transform on a numeric H2OFrame column

Description
Conduct a lag 1 transform on a numeric H2OFrame column
Usage
h2o.difflag1(object)
Arguments
object

H2OFrame object

Value
Returns an H2OFrame object.

h2o.dim

Returns the number of rows and columns for an H2OFrame object.

Description
Returns the number of rows and columns for an H2OFrame object.
Usage
h2o.dim(x)
Arguments
x

An H2OFrame object.

See Also
dim for the base R implementation.

64

h2o.distance

h2o.dimnames

Column names of an H2OFrame

Description
Column names of an H2OFrame
Usage
h2o.dimnames(x)
Arguments
x

An H2OFrame object.

See Also
dimnames for the base R implementation.

h2o.distance

Compute a pairwise distance measure between all rows of two numeric
H2OFrames.

Description
Compute a pairwise distance measure between all rows of two numeric H2OFrames.
Usage
h2o.distance(x, y, measure)
Arguments
x

An H2OFrame object (large, references).

y

An H2OFrame object (small, queries).

measure

An optional string indicating what distance measure to use. Must be one of:
"l1" - Absolute distance (L1-norm, >=0) "l2" - Euclidean distance (L2-norm,
>=0) "cosine" - Cosine similarity (-1...1) "cosine_sq" - Squared Cosine similarity (0...1)

Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.distance(prostate.hex[11:30,], prostate.hex[1:10,], "cosine")

h2o.downloadAllLogs

65

h2o.downloadAllLogs

Download H2O Log Files to Disk

Description
h2o.downloadAllLogs downloads all H2O log files to local disk in .zip format. Generally used for
debugging purposes.
Usage
h2o.downloadAllLogs(dirname = ".", filename = NULL)
Arguments
dirname

(Optional) A character string indicating the directory that the log file should be
saved in.

filename

(Optional) A character string indicating the name that the log file should be
saved to. Note that the saved format is .zip, so the file name must include the
.zip extension.

Examples
h2o.downloadAllLogs(dirname='./your_directory_name/', filename = 'autoh2o_log.zip')

h2o.downloadCSV

Download H2O Data to Disk

Description
Download an H2O data set to a CSV file on the local disk
Usage
h2o.downloadCSV(data, filename)
Arguments
data

an H2OFrame object to be downloaded.

filename

A string indicating the name that the CSV file should be should be saved to.

Warning
Files located on the H2O server may be very large! Make sure you have enough hard drive space to
accomodate the entire file.

66

h2o.download_mojo

Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris_wheader.csv", package = "h2o")
iris.hex <- h2o.uploadFile(path = irisPath)
myFile <- paste(getwd(), "my_iris_file.csv", sep = .Platform$file.sep)
h2o.downloadCSV(iris.hex, myFile)
file.info(myFile)
file.remove(myFile)

h2o.download_mojo

Download the model in MOJO format.

Description
Download the model in MOJO format.
Usage
h2o.download_mojo(model, path = getwd(), get_genmodel_jar = FALSE,
genmodel_name = "")
Arguments
model

An H2OModel

path

The path where MOJO file should be saved. Saved to current directory by default.
get_genmodel_jar
If TRUE, then also download h2o-genmodel.jar and store it in folder “path“.
genmodel_name

Custom name of genmodel jar.

Value
Name of the MOJO file written to the path.
Examples
library(h2o)
h <- h2o.init()
fr <- as.h2o(iris)
my_model <- h2o.gbm(x=1:4, y=5, training_frame=fr)
h2o.download_mojo(my_model) # save to the current working directory

h2o.download_pojo

h2o.download_pojo

67

Download the Scoring POJO (Plain Old Java Object) of an H2O
Model

Description
Download the Scoring POJO (Plain Old Java Object) of an H2O Model

Usage
h2o.download_pojo(model, path = NULL, getjar = NULL, get_jar = TRUE,
jar_name = "")
Arguments
model

An H2OModel

path

The path to the directory to store the POJO (no trailing slash). If NULL, then
print to to console. The file name will be a compilable java file name.

getjar

(DEPRECATED) Whether to also download the h2o-genmodel.jar file needed
to compile the POJO. This argument is now called ‘get_jar‘.

get_jar

Whether to also download the h2o-genmodel.jar file needed to compile the POJO

jar_name

Custom name of genmodel jar.

Value
If path is NULL, then pretty print the POJO to the console. Otherwise save it to the specified
directory and return POJO file name.

Examples
library(h2o)
h <- h2o.init()
fr <- as.h2o(iris)
my_model <- h2o.gbm(x=1:4, y=5, training_frame=fr)
h2o.download_pojo(my_model) # print the model to screen
# h2o.download_pojo(my_model, getwd()) # save the POJO and jar file to the current working
#
directory, NOT RUN
# h2o.download_pojo(my_model, getwd(), get_jar = FALSE ) # save only the POJO to the current
#
working directory, NOT RUN
h2o.download_pojo(my_model, getwd()) # save to the current working directory

68

h2o.exp

h2o.entropy

Shannon entropy

Description
Return the Shannon entropy of a string column. If the string is empty, the entropy is 0.
Usage
h2o.entropy(x)
Arguments
x

The column on which to calculate the entropy.

Examples
library(h2o)
h2o.init()
buys <- as.h2o(c("no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes","no"))
buys_entropy <- h2o.entropy(buys)

h2o.exp

Compute the exponential function of x

Description
Compute the exponential function of x
Usage
h2o.exp(x)
Arguments
x

An H2OFrame object.

See Also
exp for the base R implementation.

h2o.exportFile

h2o.exportFile

69

Export an H2O Data Frame (H2OFrame) to a File or to a collection
of Files.

Description
Exports an H2OFrame (which can be either VA or FV) to a file. This file may be on the H2O
instace’s local filesystem, or to HDFS (preface the path with hdfs://) or to S3N (preface the path
with s3n://).
Usage
h2o.exportFile(data, path, force = FALSE, parts = 1)
Arguments
data

An H2OFrame object.

path

The path to write the file to. Must include the directory and also filename if
exporting to a single file. May be prefaced with hdfs:// or s3n://. Each row of
data appears as line of the file.

force

logical, indicates how to deal with files that already exist.

parts

integer, number of part files to export to. Default is to write to a single file.
Large data can be exported to multiple ’part’ files, where each part file contains
subset of the data. User can specify the maximum number of part files or use
value -1 to indicate that H2O should itself determine the optimal number of files.
Parameter path will be considered to be a path to a directory if export to multiple
part files is desired. Part files conform to naming scheme ’part-m-?????’.

Details
In the case of existing files force = TRUE will overwrite the file. Otherwise, the operation will fail.
Examples
## Not run:
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris.csv", package = "h2o")
iris.hex <- h2o.uploadFile(path = irisPath)
#
#
#
#

These aren't real paths
h2o.exportFile(iris.hex, path = "/path/on/h2o/server/filesystem/iris.csv")
h2o.exportFile(iris.hex, path = "hdfs://path/in/hdfs/iris.csv")
h2o.exportFile(iris.hex, path = "s3n://path/in/s3/iris.csv")

## End(Not run)

70

h2o.fillna

h2o.exportHDFS

Export a Model to HDFS

Description
Exports an H2OModel to HDFS.
Usage
h2o.exportHDFS(object, path, force = FALSE)
Arguments
object

an H2OModel class object.

path

The path to write the model to. Must include the driectory and filename.

force

logical, indicates how to deal with files that already exist.

h2o.fillna

fillNA

Description
Fill NA’s in a sequential manner up to a specified limit
Usage
h2o.fillna(x, method = "forward", axis = 1, maxlen = 1L)
Arguments
x

an H2OFrame

method

A String: "forward" or "backward"

axis

An Integer 1 for row-wise fill (default), 2 for column-wise fill

maxlen

An Integer for maximum number of consecutive NA’s to fill

Value
An H2OFrame after filling missing values
Examples
library(h2o)
h2o.init()
fr.with.nas = h2o.createFrame(categorical_fraction=0.0,missing_fraction=0.7,rows=6,cols=2,seed=123)
fr <- h2o.fillna(fr.with.nas, "forward", axis=1, maxlen=2L)

h2o.filterNACols

h2o.filterNACols

71

Filter NA Columns

Description
Filter NA Columns
Usage
h2o.filterNACols(data, frac = 0.2)
Arguments
data

A dataset to filter on.

frac

The threshold of NAs to allow per column (columns >= this threshold are filtered)

Value
Returns a numeric vector of indexes that pertain to non-NA columns

h2o.findSynonyms

Find synonyms using a word2vec model.

Description
Find synonyms using a word2vec model.
Usage
h2o.findSynonyms(word2vec, word, count = 20)
Arguments
word2vec

A word2vec model.

word

A single word to find synonyms for.

count

The top ‘count‘ synonyms will be returned.

72

h2o.find_threshold_by_max_metric

h2o.find_row_by_threshold
Find the threshold, give the max metric. No duplicate thresholds allowed

Description
Find the threshold, give the max metric. No duplicate thresholds allowed

Usage
h2o.find_row_by_threshold(object, threshold)

Arguments
object

H2OBinomialMetrics

threshold

number between 0 and 1

h2o.find_threshold_by_max_metric
Find the threshold, give the max metric

Description
Find the threshold, give the max metric

Usage
h2o.find_threshold_by_max_metric(object, metric)

Arguments
object

H2OBinomialMetrics

metric

"F1," for example

h2o.floor

h2o.floor

73

Take a single numeric argument and return a numeric vector with the
largest integers

Description
floor takes a single numeric argument x and returns a numeric vector containing the largest integers
not greater than the corresponding elements of x.
Usage
h2o.floor(x)
Arguments
x

An H2OFrame object.

See Also
floor for the base R implementation.

h2o.flow

Open H2O Flow

Description
Open H2O Flow in your browser
Usage
h2o.flow()

h2o.gainsLift

Access H2O Gains/Lift Tables

Description
Retrieve either a single or many Gains/Lift tables from H2O objects.
Usage
h2o.gainsLift(object, ...)
## S4 method for signature 'H2OModel'
h2o.gainsLift(object, newdata, valid = FALSE,
xval = FALSE, ...)
## S4 method for signature 'H2OModelMetrics'
h2o.gainsLift(object)

74

h2o.gbm

Arguments
object

Either an H2OModel object or an H2OModelMetrics object.

...

further arguments to be passed to/from this method.

newdata

An H2OFrame object that can be scored on. Requires a valid response column.

valid

Retrieve the validation metric.

xval

Retrieve the cross-validation metric.

Details
The H2OModelMetrics version of this function will only take H2OBinomialMetrics objects.
Value
Calling this function on H2OModel objects returns a Gains/Lift table corresponding to the predict
function.
See Also
predict for generating prediction frames, h2o.performance for creating H2OModelMetrics.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, distribution = "bernoulli",
training_frame = hex, validation_frame = hex, nfolds=3)
h2o.gainsLift(model)
## extract training metrics
h2o.gainsLift(model, valid=TRUE) ## extract validation metrics (here: the same)
h2o.gainsLift(model, xval =TRUE) ## extract cross-validation metrics
h2o.gainsLift(model, newdata=hex) ## score on new data (here: the same)
# Generating a ModelMetrics object
perf <- h2o.performance(model, hex)
h2o.gainsLift(perf)
## extract from existing metrics object

h2o.gbm

Build gradient boosted classification or regression trees

Description
Builds gradient boosted classification trees and gradient boosted regression trees on a parsed data
set. The default distribution function will guess the model type based on the response column type.
In order to run properly, the response column must be an numeric for "gaussian" or an enum for
"bernoulli" or "multinomial".

h2o.gbm

75

Usage
h2o.gbm(x, y, training_frame, model_id = NULL, validation_frame = NULL,
nfolds = 0, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE,
score_each_iteration = FALSE, score_tree_interval = 0,
fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL,
weights_column = NULL, balance_classes = FALSE,
class_sampling_factors = NULL, max_after_balance_size = 5,
max_hit_ratio_k = 0, ntrees = 50, max_depth = 5, min_rows = 10,
nbins = 20, nbins_top_level = 1024, nbins_cats = 1024,
r2_stopping = Inf, stopping_rounds = 0, stopping_metric = c("AUTO",
"deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group",
"misclassification", "mean_per_class_error"), stopping_tolerance = 0.001,
max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE,
learn_rate = 0.1, learn_rate_annealing = 1, distribution = c("AUTO",
"bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma",
"tweedie", "laplace", "quantile", "huber"), quantile_alpha = 0.5,
tweedie_power = 1.5, huber_alpha = 0.9, checkpoint = NULL,
sample_rate = 1, sample_rate_per_class = NULL, col_sample_rate = 1,
col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1,
min_split_improvement = 1e-05, histogram_type = c("AUTO",
"UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin"),
max_abs_leafnode_pred = Inf, pred_noise_bandwidth = 0,
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
calibrate_model = FALSE, calibration_frame = NULL,
custom_metric_func = NULL, verbose = FALSE)
Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.
y
The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.
training_frame Id of the training data frame.
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
nfolds
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.

76

h2o.gbm
score_tree_interval
Score the model after every so many trees. Disabled if set to 0. Defaults to 0.
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column
Column with cross-validation fold index assignment per observation.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
offset_column Offset column. This will be added to the combination of columns before applying the link function.
weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed. Note: Weights are per-row observation weights and do not increase the
size of the data frame. This is typically the number of times a row is repeated,
but non-integer values are supported as well. During training, rows with higher
weights matter more, due to the larger loss function pre-factor.
balance_classes
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). If not
specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
max_after_balance_size
Maximum relative size of the training data after balancing class counts (can be
less than 1.0). Requires balance_classes. Defaults to 5.0.
max_hit_ratio_k
Max. number (top K) of predictions to use for hit ratio computation (for multiclass only, 0 to disable) Defaults to 0.
ntrees
Number of trees. Defaults to 50.
max_depth
Maximum tree depth. Defaults to 5.
min_rows
Fewest allowed (weighted) observations in a leaf. Defaults to 10.
nbins
For numerical columns (real/int), build a histogram of (at least) this many bins,
then split at the best point Defaults to 20.
nbins_top_level
For numerical columns (real/int), build a histogram of (at most) this many bins
at the root level, then decrease by factor of two per level Defaults to 1024.
nbins_cats
For categorical columns (factors), build a histogram of this many bins, then split
at the best point. Higher values can lead to more overfitting. Defaults to 1024.
r2_stopping
r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version
of H2O would stop making trees when the R^2 metric equals or exceeds this
Defaults to 1.797693135e+308.
stopping_rounds
Early stopping based on convergence of stopping_metric. Stop if simple moving
average of length k of the stopping_metric does not improve for k:=stopping_rounds
scoring events (0 to disable) Defaults to 0.

h2o.gbm

77

stopping_metric
Metric to use for early stopping (AUTO: logloss for classification, deviance for
regression) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE",
"MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error".
Defaults to AUTO.
stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).
build_tree_one_node
Logical. Run on one node only; no network overhead but fewer cpus used.
Suitable for small datasets. Defaults to FALSE.
learn_rate
Learning rate (from 0.0 to 1.0) Defaults to 0.1.
learn_rate_annealing
Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999) Defaults
to 1.
distribution

Distribution function Must be one of: "AUTO", "bernoulli", "quasibinomial",
"multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.

quantile_alpha Desired quantile for Quantile regression, must be between 0 and 1. Defaults to
0.5.
tweedie_power

Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to
1.5.

huber_alpha

Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). Defaults to 0.9.

checkpoint

Model checkpoint to resume training with.

sample_rate
Row sample rate per tree (from 0.0 to 1.0) Defaults to 1.
sample_rate_per_class
A list of row sample rates per class (relative fraction for each class, from 0.0 to
1.0), for each tree
col_sample_rate
Column sample rate (from 0.0 to 1.0) Defaults to 1.
col_sample_rate_change_per_level
Relative change of the column sampling rate for every level (must be > 0.0 and
<= 2.0) Defaults to 1.
col_sample_rate_per_tree
Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.
min_split_improvement
Minimum relative improvement in squared error reduction for a split to happen
Defaults to 1e-05.
histogram_type What type of histogram to use for finding optimal split points Must be one of:
"AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin".
Defaults to AUTO.

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h2o.getConnection
max_abs_leafnode_pred
Maximum absolute value of a leaf node prediction Defaults to 1.797693135e+308.
pred_noise_bandwidth
Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node
predictions Defaults to 0.
categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
calibrate_model
Logical. Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. Defaults to
FALSE.
calibration_frame
Calibration frame for Platt Scaling
custom_metric_func
Reference to custom evaluation function, format: ‘language:keyName=funcName‘
verbose

Logical. Print scoring history to the console (Metrics per tree for GBM, DRF,
& XGBoost. Metrics per epoch for Deep Learning). Defaults to FALSE.

See Also
predict.H2OModel for prediction
Examples
library(h2o)
h2o.init()
# Run regression GBM on australia.hex data
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
independent <- c("premax", "salmax","minairtemp", "maxairtemp", "maxsst",
"maxsoilmoist", "Max_czcs")
dependent <- "runoffnew"
h2o.gbm(y = dependent, x = independent, training_frame = australia.hex,
ntrees = 3, max_depth = 3, min_rows = 2)

h2o.getConnection

Retrieve an H2O Connection

Description
Attempt to recover an h2o connection.
Usage
h2o.getConnection()

h2o.getFrame

79

Value
Returns an H2OConnection object.
h2o.getFrame

Get an R Reference to an H2O Dataset, that will NOT be GC’d by
default

Description
Get the reference to a frame with the given id in the H2O instance.
Usage
h2o.getFrame(id)
Arguments
id

A string indicating the unique frame of the dataset to retrieve.

h2o.getFutureModel

Get future model

Description
Get future model
Usage
h2o.getFutureModel(object, verbose = FALSE)
Arguments
object
verbose

H2OModel
Print model progress to console. Default is FALSE

h2o.getGLMFullRegularizationPath
Extract full regularization path from a GLM model

Description
Extract the full regularization path from a GLM model (assuming it was run with the lambda search
option).
Usage
h2o.getGLMFullRegularizationPath(model)
Arguments
model

an H2OModel corresponding from a h2o.glm call.

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h2o.getId

h2o.getGrid

Get a grid object from H2O distributed K/V store.

Description
Note that if neither cross-validation nor a validation frame is used in the grid search, then the training
metrics will display in the "get grid" output. If a validation frame is passed to the grid, and nfolds
= 0, then the validation metrics will display. However, if nfolds > 1, then cross-validation metrics
will display even if a validation frame is provided.
Usage
h2o.getGrid(grid_id, sort_by, decreasing)
Arguments
grid_id

ID of existing grid object to fetch

sort_by

Sort the models in the grid space by a metric. Choices are "logloss", "residual_deviance", "mse", "auc", "accuracy", "precision", "recall", "f1", etc.

decreasing

Specify whether sort order should be decreasing

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

h2o.getId

Get back-end distributed key/value store id from an H2OFrame.

Description
Get back-end distributed key/value store id from an H2OFrame.
Usage
h2o.getId(x)

h2o.getModel

81

Arguments
x

An H2OFrame

Value
The id of the H2OFrame

h2o.getModel

Get an R reference to an H2O model

Description
Returns a reference to an existing model in the H2O instance.
Usage
h2o.getModel(model_id)
Arguments
model_id

A string indicating the unique model_id of the model to retrieve.

Value
Returns an object that is a subclass of H2OModel.
Examples
library(h2o)
h2o.init()
iris.hex <- as.h2o(iris, "iris.hex")
model_id <- h2o.gbm(x = 1:4, y = 5, training_frame = iris.hex)@model_id
model.retrieved <- h2o.getModel(model_id)

h2o.getTimezone

Get the Time Zone on the H2O Cloud Returns a string

Description
Get the Time Zone on the H2O Cloud Returns a string
Usage
h2o.getTimezone()

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h2o.giniCoef

h2o.getTypes

Get the types-per-column

Description
Get the types-per-column
Usage
h2o.getTypes(x)
Arguments
x

An H2OFrame

Value
A list of types per column

h2o.getVersion

Get h2o version

Description
Get h2o version
Usage
h2o.getVersion()

h2o.giniCoef

Retrieve the GINI Coefficcient

Description
Retrieves the GINI coefficient from an H2OBinomialMetrics. If "train", "valid", and "xval" parameters are FALSE (default), then the training GINIvalue is returned. If more than one parameter is
set to TRUE, then a named vector of GINIs are returned, where the names are "train", "valid" or
"xval".
Usage
h2o.giniCoef(object, train = FALSE, valid = FALSE, xval = FALSE)

h2o.glm

83

Arguments
object

an H2OBinomialMetrics object.

train

Retrieve the training GINI Coefficcient

valid

Retrieve the validation GINI Coefficcient

xval

Retrieve the cross-validation GINI Coefficcient

See Also
h2o.auc for AUC, h2o.giniCoef for the GINI coefficient, and h2o.metric for the various. See
h2o.performance for creating H2OModelMetrics objects. threshold metrics.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
perf <- h2o.performance(model, hex)
h2o.giniCoef(perf)

h2o.glm

Fit a generalized linear model

Description
Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.
Usage
h2o.glm(x, y, training_frame, model_id = NULL, validation_frame = NULL,
nfolds = 0, seed = -1, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO",
"Random", "Modulo", "Stratified"), fold_column = NULL,
ignore_const_cols = TRUE, score_each_iteration = FALSE,
offset_column = NULL, weights_column = NULL, family = c("gaussian",
"binomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma",
"tweedie"), tweedie_variance_power = 0, tweedie_link_power = 1,
solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE",
"COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"),
alpha = NULL, lambda = NULL, lambda_search = FALSE,
early_stopping = TRUE, nlambdas = -1, standardize = TRUE,
missing_values_handling = c("MeanImputation", "Skip"),
compute_p_values = FALSE, remove_collinear_columns = FALSE,
intercept = TRUE, non_negative = FALSE, max_iterations = -1,

84

h2o.glm
objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1,
link = c("family_default", "identity", "logit", "log", "inverse", "tweedie",
"ologit", "oprobit", "ologlog"), prior = -1, lambda_min_ratio = -1,
beta_constraints = NULL, max_active_predictors = -1,
interactions = NULL, interaction_pairs = NULL, obj_reg = -1,
balance_classes = FALSE, class_sampling_factors = NULL,
max_after_balance_size = 5, max_hit_ratio_k = 0, max_runtime_secs = 0,
custom_metric_func = NULL)

Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.

seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column
Column with cross-validation fold index assignment per observation.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
offset_column

Offset column. This will be added to the combination of columns before applying the link function.

weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed. Note: Weights are per-row observation weights and do not increase the

h2o.glm

85

size of the data frame. This is typically the number of times a row is repeated,
but non-integer values are supported as well. During training, rows with higher
weights matter more, due to the larger loss function pre-factor.
family
Family. Use binomial for classification with logistic regression, others are for
regression problems. Must be one of: "gaussian", "binomial", "quasibinomial",
"ordinal", "multinomial", "poisson", "gamma", "tweedie". Defaults to gaussian.
tweedie_variance_power
Tweedie variance power Defaults to 0.
tweedie_link_power
Tweedie link power Defaults to 1.
solver
AUTO will set the solver based on given data and the other parameters. IRLSM
is fast on on problems with small number of predictors and for lambda-search
with L1 penalty, L_BFGS scales better for datasets with many columns. Coordinate descent is experimental (beta). Must be one of: "AUTO", "IRLSM",
"L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT",
"GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". Defaults
to AUTO.
alpha
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties.
A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge
regression, and anything in between specifies the amount of mixing between the
two. Default value of alpha is 0 when SOLVER = ’L-BFGS’; 0.5 otherwise.
lambda
Regularization strength
lambda_search Logical. Use lambda search starting at lambda max, given lambda is then interpreted as lambda min Defaults to FALSE.
early_stopping Logical. Stop early when there is no more relative improvement on train or
validation (if provided) Defaults to TRUE.
nlambdas
Number of lambdas to be used in a search. Default indicates: If alpha is zero,
with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas
are needed for ridge regression) otherwise it is set to 100. Defaults to -1.
standardize
Logical. Standardize numeric columns to have zero mean and unit variance
Defaults to TRUE.
missing_values_handling
Handling of missing values. Either MeanImputation or Skip. Must be one of:
"MeanImputation", "Skip". Defaults to MeanImputation.
compute_p_values
Logical. Request p-values computation, p-values work only with IRLSM solver
and no regularization Defaults to FALSE.
remove_collinear_columns
Logical. In case of linearly dependent columns, remove some of the dependent
columns Defaults to FALSE.
intercept
Logical. Include constant term in the model Defaults to TRUE.
non_negative
Logical. Restrict coefficients (not intercept) to be non-negative Defaults to
FALSE.
max_iterations Maximum number of iterations Defaults to -1.
objective_epsilon
Converge if objective value changes less than this. Default indicates: If lambda_search
is set to True the value of objective_epsilon is set to .0001. If the lambda_search
is set to False and lambda is equal to zero, the value of objective_epsilon is set
to .000001, for any other value of lambda the default value of objective_epsilon
is set to .0001. Defaults to -1.

86

h2o.glm
beta_epsilon

Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY
applies to IRLSM solver Defaults to 0.0001.
gradient_epsilon
Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default indicates: If lambda_search is set to False
and lambda is equal to zero, the default value of gradient_epsilon is equal to
.000001, otherwise the default value is .0001. If lambda_search is set to True,
the conditional values above are 1E-8 and 1E-6 respectively. Defaults to -1.
link

Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie",
"ologit", "oprobit", "ologlog". Defaults to family_default.

prior

Prior probability for y==1. To be used only for logistic regression iff the data
has been sampled and the mean of response does not reflect reality. Defaults to
-1.
lambda_min_ratio
Minimum lambda used in lambda search, specified as a ratio of lambda_max
(the smallest lambda that drives all coefficients to zero). Default indicates:
if the number of observations is greater than the number of variables, then
lambda_min_ratio is set to 0.0001; if the number of observations is less than
the number of variables, then lambda_min_ratio is set to 0.01. Defaults to -1.
beta_constraints
Beta constraints
max_active_predictors
Maximum number of active predictors during computation. Use as a stopping
criterion to prevent expensive model building with many predictors. Default
indicates: If the IRLSM solver is used, the value of max_active_predictors is set
to 5000 otherwise it is set to 100000000. Defaults to -1.
interactions

A list of predictor column indices to interact. All pairwise combinations will be
computed for the list.
interaction_pairs
A list of pairwise (first order) column interactions.
obj_reg
balance_classes

Likelihood divider in objective value computation, default is 1/nobs Defaults to
-1.

Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). If not
specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
max_after_balance_size
Maximum relative size of the training data after balancing class counts (can be
less than 1.0). Requires balance_classes. Defaults to 5.0.
max_hit_ratio_k
Maximum number (top K) of predictions to use for hit ratio computation (for
multi-class only, 0 to disable) Defaults to 0.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
custom_metric_func
Reference to custom evaluation function, format: ‘language:keyName=funcName‘

h2o.glrm

87

Value
A subclass of H2OModel is returned. The specific subclass depends on the machine learning task at
hand (if it’s binomial classification, then an H2OBinomialModel is returned, if it’s regression then
a H2ORegressionModel is returned). The default print- out of the models is shown, but further
GLM-specifc information can be queried out of the object. To access these various items, please
refer to the seealso section below. Upon completion of the GLM, the resulting object has coefficients, normalized coefficients, residual/null deviance, aic, and a host of model metrics including
MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices. Please refer to the
more in-depth GLM documentation available here: https://h2o-release.s3.amazonaws.com/
h2o-dev/rel-shannon/2/docs-website/h2o-docs/index.html#Data+Science+Algorithms-GLM
See Also
predict.H2OModel for prediction, h2o.mse, h2o.auc, h2o.confusionMatrix, h2o.performance,
h2o.giniCoef, h2o.logloss, h2o.varimp, h2o.scoreHistory
Examples
h2o.init()
# Run GLM of CAPSULE ~ AGE + RACE + PSA + DCAPS
prostatePath = system.file("extdata", "prostate.csv", package = "h2o")
prostate.hex = h2o.importFile(path = prostatePath, destination_frame = "prostate.hex")
h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), training_frame = prostate.hex,
family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE)
# Run GLM of VOL ~ CAPSULE + AGE + RACE + PSA + GLEASON
myX = setdiff(colnames(prostate.hex), c("ID", "DPROS", "DCAPS", "VOL"))
h2o.glm(y = "VOL", x = myX, training_frame = prostate.hex, family = "gaussian",
nfolds = 0, alpha = 0.1, lambda_search = FALSE)
# GLM variable importance
# Also see:
# https://github.com/h2oai/h2o/blob/master/R/tests/testdir_demos/runit_demo_VI_all_algos.R
data.hex = h2o.importFile(
path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv",
destination_frame = "data.hex")
myX = 1:20
myY="y"
my.glm = h2o.glm(x=myX, y=myY, training_frame=data.hex, family="binomial", standardize=TRUE,
lambda_search=TRUE)

h2o.glrm

Generalized low rank decomposition of an H2O data frame

Description
Builds a generalized low rank decomposition of an H2O data frame

88

h2o.glrm

Usage
h2o.glrm(training_frame, cols = NULL, model_id = NULL,
validation_frame = NULL, ignore_const_cols = TRUE,
score_each_iteration = FALSE, loading_name = NULL, transform = c("NONE",
"STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), k = 1,
loss = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic",
"Periodic"), loss_by_col = c("Quadratic", "Absolute", "Huber", "Poisson",
"Hinge", "Logistic", "Periodic", "Categorical", "Ordinal"),
loss_by_col_idx = NULL, multi_loss = c("Categorical", "Ordinal"),
period = 1, regularization_x = c("None", "Quadratic", "L2", "L1",
"NonNegative", "OneSparse", "UnitOneSparse", "Simplex"),
regularization_y = c("None", "Quadratic", "L2", "L1", "NonNegative",
"OneSparse", "UnitOneSparse", "Simplex"), gamma_x = 0, gamma_y = 0,
max_iterations = 1000, max_updates = 2000, init_step_size = 1,
min_step_size = 1e-04, seed = -1, init = c("Random", "SVD", "PlusPlus",
"User"), svd_method = c("GramSVD", "Power", "Randomized"), user_y = NULL,
user_x = NULL, expand_user_y = TRUE, impute_original = FALSE,
recover_svd = FALSE, max_runtime_secs = 0)
Arguments
training_frame Id of the training data frame.
cols

(Optional) A vector containing the data columns on which k-means operates.

model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
loading_name

Frame key to save resulting X

transform

Transformation of training data Must be one of: "NONE", "STANDARDIZE",
"NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE.

k

Rank of matrix approximation Defaults to 1.

loss

Numeric loss function Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic". Defaults to Quadratic.

loss_by_col

Loss function by column (override) Must be one of: "Quadratic", "Absolute",
"Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal".

loss_by_col_idx
Loss function by column index (override)
multi_loss

Categorical loss function Must be one of: "Categorical", "Ordinal". Defaults to
Categorical.

period
Length of period (only used with periodic loss function) Defaults to 1.
regularization_x
Regularization function for X matrix Must be one of: "None", "Quadratic",
"L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None.

h2o.glrm

89

regularization_y
Regularization function for Y matrix Must be one of: "None", "Quadratic",
"L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None.
gamma_x

Regularization weight on X matrix Defaults to 0.

gamma_y

Regularization weight on Y matrix Defaults to 0.

max_iterations Maximum number of iterations Defaults to 1000.
max_updates

Maximum number of updates, defaults to 2*max_iterations Defaults to 2000.

init_step_size Initial step size Defaults to 1.
min_step_size

Minimum step size Defaults to 0.0001.

seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).

init

Initialization mode Must be one of: "Random", "SVD", "PlusPlus", "User".
Defaults to PlusPlus.

svd_method

Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to Randomized.

user_y

User-specified initial Y

user_x

User-specified initial X

expand_user_y

Logical. Expand categorical columns in user-specified initial Y Defaults to
TRUE.

impute_original

recover_svd

Logical. Reconstruct original training data by reversing transform Defaults to
FALSE.
Logical. Recover singular values and eigenvectors of XY Defaults to FALSE.

max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
Value
Returns an object of class H2ODimReductionModel.
References
M. Udell, C. Horn, R. Zadeh, S. Boyd (2014). Generalized Low Rank Models[http://arxiv.org/abs/1410.0342].
Unpublished manuscript, Stanford Electrical Engineering Department N. Halko, P.G. Martinsson,
J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[http://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.
See Also
h2o.kmeans, h2o.svd, h2o.prcomp

90

h2o.grep

Examples
library(h2o)
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
h2o.glrm(training_frame = australia.hex, k = 5, loss = "Quadratic", regularization_x = "L1",
gamma_x = 0.5, gamma_y = 0, max_iterations = 1000)

h2o.grep

Search for matches to an argument pattern

Description
Searches for matches to argument ‘pattern‘ within each element of a string column.
Usage
h2o.grep(pattern, x, ignore.case = FALSE, invert = FALSE,
output.logical = FALSE)
Arguments
pattern

A character string containing a regular expression.

x

An H2O frame that wraps a single string column.

ignore.case

If TRUE case is ignored during matching.

invert

Identify elements that do not match the pattern.

output.logical If TRUE returns logical vector of indicators instead of list of matching positions
Details
This function has similar semantics as R’s native grep function and it supports a subset of its parameters. Default behavior is to return indices of the elements matching the pattern. Parameter
‘output.logical‘ can be used to return a logical vector indicating if the element matches the pattern
(1) or not (0).
Value
H2OFrame holding the matching positions or a logical vector if ‘output.logical‘ is enabled.
Examples
library(h2o)
h2o.init()
addresses <- as.h2o(c("2307", "Leghorn St", "Mountain View", "CA", "94043"))
zip.codes <- addresses[h2o.grep("[0-9]{5}", addresses, output.logical = TRUE),]

h2o.grid

h2o.grid

91

H2O Grid Support

Description
Provides a set of functions to launch a grid search and get its results.
Usage
h2o.grid(algorithm, grid_id, x, y, training_frame, ..., 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.

x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
...

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_
or list(strategy = "RandomDiscrete", stopping_metric = "AUTO", stopping_tolerance =
or list(strategy = "RandomDiscrete", stopping_metric = "misclassification", stoppin
Details
Launch grid search with given algorithm and parameters.

92

h2o.group_by

Examples
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)})

h2o.group_by

Group and Apply by Column

Description
Performs a group by and apply similar to ddply.
Usage
h2o.group_by(data, by, ..., gb.control = list(na.methods = NULL, col.names =
NULL))
Arguments
data

an H2OFrame object.

by

a list of column names

...

any supported aggregate function. See Details: for more help.

gb.control

a list of how to handle NA values in the dataset as well as how to name output columns. The method is specified using the rm.method argument. See
Details: for more help.

Details
In the case of na.methods within gb.control, there are three possible settings. "all" will include
NAs in computation of functions. "rm" will completely remove all NA fields. "ignore" will remove
NAs from the numerator but keep the rows for computational purposes. If a list smaller than the
number of columns groups is supplied, the list will be padded by "ignore".
Note that to specify a list of column names in the gb.control list, you must add the col.names
argument. Similar to na.methods, col.names will pad the list with the default column names if the
length is less than the number of colums groups supplied.
Supported functions include nrow. This function is required and accepts a string for the name of the
generated column. Other supported aggregate functions accept col and na arguments for specifying
columns and the handling of NAs ("all", "ignore", and GroupBy object; max calculates the maximum of each column specified in col for each group of a GroupBy object; mean calculates the mean
of each column specified in col for each group of a GroupBy object; min calculates the minimum of

h2o.gsub

93

each column specified in col for each group of a GroupBy object; mode calculates the mode of each
column specified in col for each group of a GroupBy object; sd calculates the standard deviation of
each column specified in col for each group of a GroupBy object; ss calculates the sum of squares
of each column specified in col for each group of a GroupBy object; sum calculates the sum of each
column specified in col for each group of a GroupBy object; and var calculates the variance of each
column specified in col for each group of a GroupBy object. If an aggregate is provided without a
value (for example, as max in sum(col="X1", na="all").mean(col="X5", na="all").max()),
then it is assumed that the aggregation should apply to all columns except the GroupBy columns.
Note again that nrow is required and cannot be empty.
Value
Returns a new H2OFrame object with columns equivalent to the number of groups created

h2o.gsub

String Global Substitute

Description
Creates a copy of the target column in which each string has all occurence of the regex pattern
replaced with the replacement substring.

Usage
h2o.gsub(pattern, replacement, x, ignore.case = FALSE)
Arguments
pattern

The pattern to replace.

replacement

The replacement pattern.

x

The column on which to operate.

ignore.case

Case sensitive or not

Examples
library(h2o)
h2o.init()
string_to_gsub <- as.h2o("r tutorial")
sub_string <- h2o.gsub("r ","H2O ",string_to_gsub)

94

h2o.hist

h2o.head

Return the Head or Tail of an H2O Dataset.

Description
Returns the first or last rows of an H2OFrame object.
Usage
h2o.head(x, n = 6L, ...)
## S3 method for class 'H2OFrame'
head(x, n = 6L, ...)
h2o.tail(x, n = 6L, ...)
## S3 method for class 'H2OFrame'
tail(x, n = 6L, ...)
Arguments
x

An H2OFrame object.

n

(Optional) A single integer. If positive, number of rows in x to return. If negative, all but the n first/last number of rows in x.

...

Ignored.

Value
An H2OFrame containing the first or last n rows of an H2OFrame object.
Examples
library(h2o)
h2o.init(ip <- "localhost", port = 54321, startH2O = TRUE)
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
head(australia.hex, 10)
tail(australia.hex, 10)

h2o.hist

Compute A Histogram

Description
Compute a histogram over a numeric column. If breaks=="FD", the MAD is used over the IQR in
computing bin width. Note that we do not beautify the breakpoints as R does.

h2o.hit_ratio_table

95

Usage
h2o.hist(x, breaks = "Sturges", plot = TRUE)
Arguments
x

A single numeric column from an H2OFrame.

breaks

Can be one of the following: A string: "Sturges", "Rice", "sqrt", "Doane", "FD",
"Scott" A single number for the number of breaks splitting the range of the vec
into number of breaks bins of equal width A vector of numbers giving the split
points, e.g., c(-50,213.2123,9324834)

plot

A logical value indicating whether or not a plot should be generated (default is
TRUE).

h2o.hit_ratio_table

Retrieve the Hit Ratios

Description
If "train", "valid", and "xval" parameters are FALSE (default), then the training Hit Ratios value
is returned. If more than one parameter is set to TRUE, then a named list of Hit Ratio tables are
returned, where the names are "train", "valid" or "xval".
Usage
h2o.hit_ratio_table(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel object.

train

Retrieve the training Hit Ratio

valid

Retrieve the validation Hit Ratio

xval

Retrieve the cross-validation Hit Ratio

h2o.hour

Convert Milliseconds to Hour of Day in H2O Datasets

Description
Converts the entries of an H2OFrame object from milliseconds to hours of the day (on a 0 to 23
scale).
Usage
h2o.hour(x)
hour(x)
## S3 method for class 'H2OFrame'
hour(x)

96

h2o.ifelse

Arguments
x

An H2OFrame object.

Value
An H2OFrame object containing the entries of x converted to hours of the day.
See Also
h2o.day

h2o.ifelse

H2O Apply Conditional Statement

Description
Applies conditional statements to numeric vectors in H2O parsed data objects when the data are
numeric.
Usage
h2o.ifelse(test, yes, no)
ifelse(test, yes, no)
Arguments
test

A logical description of the condition to be met (>, <, =, etc...)

yes

The value to return if the condition is TRUE.

no

The value to return if the condition is FALSE.

Details
Both numeric and categorical values can be tested. However when returning a yes and no condition
both conditions must be either both categorical or numeric.
Value
Returns a vector of new values matching the conditions stated in the ifelse call.
Examples
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.importFile(path = ausPath)
australia.hex[,9] <- ifelse(australia.hex[,3] < 279.9, 1, 0)
summary(australia.hex)

h2o.importFile

h2o.importFile

97

Import Files into H2O

Description
Imports files into an H2O cloud. The default behavior is to pass-through to the parse phase automatically.
Usage
h2o.importFile(path, destination_frame = "", parse = TRUE, header = NA,
sep = "", col.names = NULL, col.types = NULL, na.strings = NULL,
decrypt_tool = NULL)
h2o.importFolder(path, pattern = "", destination_frame = "", parse = TRUE,
header = NA, sep = "", col.names = NULL, col.types = NULL,
na.strings = NULL, decrypt_tool = NULL)
h2o.importHDFS(path, pattern = "", destination_frame = "", parse = TRUE,
header = NA, sep = "", col.names = NULL, na.strings = NULL)
h2o.uploadFile(path, destination_frame = "", parse = TRUE, header = NA,
sep = "", col.names = NULL, col.types = NULL, na.strings = NULL,
progressBar = FALSE, parse_type = NULL, decrypt_tool = NULL)
Arguments
path

The complete URL or normalized file path of the file to be imported. Each row
of data appears as one line of the file.
destination_frame
(Optional) The unique hex key assigned to the imported file. If none is given, a
key will automatically be generated based on the URL path.
parse

(Optional) A logical value indicating whether the file should be parsed after
import, for details see h2o.parseRaw.

header

(Optional) A logical value indicating whether the first line of the file contains
column headers. If left empty, the parser will try to automatically detect this.

sep

(Optional) The field separator character. Values on each line of the file are separated by this character. If sep = "", the parser will automatically detect the
separator.

col.names

(Optional) An H2OFrame object containing a single delimited line with the column names for the file.

col.types

(Optional) A vector to specify whether columns should be forced to a certain
type upon import parsing.

na.strings

(Optional) H2O will interpret these strings as missing.

decrypt_tool

(Optional) Specify a Decryption Tool (key-reference acquired by calling h2o.decryptionSetup.

pattern

(Optional) Character string containing a regular expression to match file(s) in
the folder.

98

h2o.import_sql_select
progressBar

(Optional) When FALSE, tell H2O parse call to block synchronously instead of
polling. This can be faster for small datasets but loses the progress bar.

parse_type

(Optional) Specify which parser type H2O will use. Valid types are "ARFF",
"XLS", "CSV", "SVMLight"

Details
h2o.importFile is a parallelized reader and pulls information from the server from a location
specified by the client. The path is a server-side path. This is a fast, scalable, highly optimized way
to read data. H2O pulls the data from a data store and initiates the data transfer as a read operation.
Unlike the import function, which is a parallelized reader, h2o.uploadFile is a push from the
client to the server. The specified path must be a client-side path. This is not scalable and is only
intended for smaller data sizes. The client pushes the data from a local filesystem (for example, on
your machine where R is running) to H2O. For big-data operations, you don’t want the data stored
on or flowing through the client.
h2o.importFolder imports an entire directory of files. If the given path is relative, then it will
be relative to the start location of the H2O instance. The default behavior is to pass-through to the
parse phase automatically.
h2o.importHDFS is deprecated. Instead, use h2o.importFile.
See Also
h2o.import_sql_select, h2o.import_sql_table, h2o.parseRaw
Examples
h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
prosPath = system.file("extdata", "prostate.csv", package = "h2o")
prostate.hex = h2o.importFile(path = prosPath, destination_frame = "prostate.hex")
class(prostate.hex)
summary(prostate.hex)
#Import files with a certain regex pattern by utilizing h2o.importFolder()
#In this example we import all .csv files in the directory prostate_folder
prosPath = system.file("extdata", "prostate_folder", package = "h2o")
prostate_pattern.hex = h2o.importFolder(path = prosPath, pattern = ".*.csv",
destination_frame = "prostate.hex")
class(prostate_pattern.hex)
summary(prostate_pattern.hex)

h2o.import_sql_select Import SQL table that is result of SELECT SQL query into H2O

Description

Creates a temporary SQL table from the specified sql_query. Runs multiple SELECT SQL queries
on the temporary table concurrently for parallel ingestion, then drops the table. Be sure to start the
h2o.jar in the terminal with your downloaded JDBC driver in the classpath: ‘java -cp :: water.H2OApp‘ Also see h2o.import_sql_select. Currently supported SQL databases are MySQL, PostgreSQL, and MariaDB. Support for Oracle 12g
and Microsoft SQL Server
Usage
h2o.import_sql_table(connection_url, table, username, password,
columns = NULL, optimize = NULL)
Arguments
connection_url URL of the SQL database connection as specified by the Java Database Connectivity (JDBC) Driver. For example, "jdbc:mysql://localhost:3306/menagerie?&useSSL=false"
table

Name of SQL table

username

Username for SQL server

password

Password for SQL server

columns

(Optional) Character vector of column names to import from SQL table. Default
is to import all columns.

optimize

(Optional) Optimize import of SQL table for faster imports. Experimental. Default is true.

100

h2o.impute

Details

For example, my_sql_conn_url <- "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false"
table <- "citibike20k" username <- "root" password <- "abc123" my_citibike_data <- h2o.import_sql_table(my_sql_conn_
table, username, password)

h2o.impute

Basic Imputation of H2O Vectors

Description
Perform inplace imputation by filling missing values with aggregates computed on the "na.rm’d"
vector. Additionally, it’s possible to perform imputation based on groupings of columns from within
data; these columns can be passed by index or name to the by parameter. If a factor column is
supplied, then the method must be "mode".
Usage
h2o.impute(data, column = 0, method = c("mean", "median", "mode"),
combine_method = c("interpolate", "average", "lo", "hi"), by = NULL,
groupByFrame = NULL, values = NULL)
Arguments
data

The dataset containing the column to impute.

column

A specific column to impute, default of 0 means impute the whole frame.

method

"mean" replaces NAs with the column mean; "median" replaces NAs with the
column median; "mode" replaces with the most common factor (for factor columns
only);

combine_method If method is "median", then choose how to combine quantiles on even sample
sizes. This parameter is ignored in all other cases.
by

group by columns

groupByFrame

Impute the column col with this pre-computed grouped frame.

values

A vector of impute values (one per column). NaN indicates to skip the column

Details
The default method is selected based on the type of the column to impute. If the column is numeric
then "mean" is selected; if it is categorical, then "mode" is selected. Other column types (e.g. String,
Time, UUID) are not supported.
Value
an H2OFrame with imputed values

h2o.init

101

Examples
h2o.init()
fr <- as.h2o(iris, destination_frame="iris")
fr[sample(nrow(fr),40),5] <- NA # randomly replace 50 values with NA
# impute with a group by
fr <- h2o.impute(fr, "Species", "mode", by=c("Sepal.Length", "Sepal.Width"))

h2o.init

Initialize and Connect to H2O

Description
Attempts to start and/or connect to and H2O instance.
Usage
h2o.init(ip = "localhost", port = 54321, startH2O = TRUE,
forceDL = FALSE, enable_assertions = TRUE, license = NULL,
nthreads = -1, max_mem_size = NULL, min_mem_size = NULL,
ice_root = tempdir(), strict_version_check = TRUE,
proxy = NA_character_, https = FALSE, insecure = FALSE,
username = NA_character_, password = NA_character_,
cookies = NA_character_, context_path = NA_character_,
ignore_config = FALSE, extra_classpath = NULL)
Arguments
ip

Object of class character representing the IP address of the server where H2O
is running.

port

Object of class numeric representing the port number of the H2O server.

startH2O

(Optional) A logical value indicating whether to try to start H2O from R if no
connection with H2O is detected. This is only possible if ip = "localhost"
or ip = "127.0.0.1". If an existing connection is detected, R does not start
H2O.

forceDL

(Optional) A logical value indicating whether to force download of the H2O
executable. Defaults to FALSE, so the executable will only be downloaded if it
does not already exist in the h2o R library resources directory h2o/java/h2o.jar.
This value is only used when R starts H2O.
enable_assertions
(Optional) A logical value indicating whether H2O should be launched with
assertions enabled. Used mainly for error checking and debugging purposes.
This value is only used when R starts H2O.
license

(Optional) A character string value specifying the full path of the license file.
This value is only used when R starts H2O.

nthreads

(Optional) Number of threads in the thread pool. This relates very closely to the
number of CPUs used. -1 means use all CPUs on the host (Default). A positive
integer specifies the number of CPUs directly. This value is only used when R
starts H2O.

102

h2o.init
max_mem_size

(Optional) A character string specifying the maximum size, in bytes, of the
memory allocation pool to H2O. This value must a multiple of 1024 greater
than 2MB. Append the letter m or M to indicate megabytes, or g or G to indicate
gigabytes. This value is only used when R starts H2O.

min_mem_size

(Optional) A character string specifying the minimum size, in bytes, of the
memory allocation pool to H2O. This value must a multiple of 1024 greater
than 2MB. Append the letter m or M to indicate megabytes, or g or G to indicate
gigabytes. This value is only used when R starts H2O.

ice_root
(Optional) A directory to handle object spillage. The defaul varies by OS.
strict_version_check
(Optional) Setting this to FALSE is unsupported and should only be done when
advised by technical support.
proxy

(Optional) A character string specifying the proxy path.

https

(Optional) Set this to TRUE to use https instead of http.

insecure

(Optional) Set this to TRUE to disable SSL certificate checking.

username

(Optional) Username to login with.

password

(Optional) Password to login with.

cookies

(Optional) Vector(or list) of cookies to add to request.

context_path

(Optional) The last part of connection URL: http://:/

ignore_config

(Optional) A logical value indicating whether a search for a .h2oconfig file
should be conducted or not. Default value is FALSE.

extra_classpath
(Optional) A vector of paths to libraries to be added to the Java classpath when
H2O is started from R.
Details
By default, this method first checks if an H2O instance is connectible. If it cannot connect and
start = TRUE with ip = "localhost", it will attempt to start and instance of H2O at localhost:54321. If an open ip and port of your choice are passed in, then this method will attempt to
start an H2O instance at that specified ip port.

When initializing H2O locally, this method searches for h2o.jar in the R library resources (system.file("java", "h2o.
and if the file does not exist, it will automatically attempt to download the correct version from
Amazon S3. The user must have Internet access for this process to be successful.
Once connected, the method checks to see if the local H2O R package version matches the version
of H2O running on the server. If there is a mismatch and the user indicates she wishes to upgrade,
it will remove the local H2O R package and download/install the H2O R package from the server.
Value
this method will load it and return a H2OConnection object containing the IP address and port
number of the H2O server.
Note
Users may wish to manually upgrade their package (rather than waiting until being prompted),
which requires that they fully uninstall and reinstall the H2O package, and the H2O client package.
You must unload packages running in the environment before upgrading. It’s recommended that
users restart R or R studio after upgrading

h2o.insertMissingValues

103

See Also
H2O R package documentation for more details. h2o.shutdown for shutting down from R.
Examples
## Not run:
# Try to connect to a local H2O instance that is already running.
# If not found, start a local H2O instance from R with the default settings.
h2o.init()
# Try to connect to a local H2O instance.
# If not found, raise an error.
h2o.init(startH2O = FALSE)
# Try to connect to a local H2O instance that is already running.
# If not found, start a local H2O instance from R with 5 gigabytes of memory.
h2o.init(max_mem_size = "5g")
# Try to connect to a local H2O instance that is already running.
# If not found, start a local H2O instance from R that uses 5 gigabytes of memory.
h2o.init(max_mem_size = "5g")
## End(Not run)

h2o.insertMissingValues
Insert Missing Values into an H2OFrame

Description
Randomly replaces a user-specified fraction of entries in an H2O dataset with missing values.
Usage
h2o.insertMissingValues(data, fraction = 0.1, seed = -1)
Arguments
data

An H2OFrame object representing the dataset.

fraction

A number between 0 and 1 indicating the fraction of entries to replace with
missing.

seed

A random number used to select which entries to replace with missing values.
Default of seed = -1 will automatically generate a seed in H2O.

Value
Returns an H2OFrame object.
WARNING
This will modify the original dataset. Unless this is intended, this function should only be called on
a subset of the original.

104

h2o.interaction

Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris.csv", package = "h2o")
iris.hex <- h2o.importFile(path = irisPath)
summary(iris.hex)
irismiss.hex <- h2o.insertMissingValues(iris.hex, fraction = 0.25)
head(irismiss.hex)
summary(irismiss.hex)

h2o.interaction

Categorical Interaction Feature Creation in H2O

Description
Creates a data frame in H2O with n-th order interaction features between categorical columns, as
specified by the user.
Usage
h2o.interaction(data, destination_frame, factors, pairwise, max_factors,
min_occurrence)
Arguments
data
An H2OFrame object containing the categorical columns.
destination_frame
A string indicating the destination key. If empty, this will be auto-generated by
H2O.
factors

Factor columns (either indices or column names).

pairwise

Whether to create pairwise interactions between factors (otherwise create one
higher-order interaction). Only applicable if there are 3 or more factors.

max_factors

Max. number of factor levels in pair-wise interaction terms (if enforced, one
extra catch-all factor will be made)

min_occurrence Min. occurrence threshold for factor levels in pair-wise interaction terms
Value
Returns an H2OFrame object.
Examples
library(h2o)
h2o.init()
# Create some random data
myframe <- h2o.createFrame(rows = 20, cols = 5,
seed = -12301283, randomize = TRUE, value = 0,

h2o.isax

105

categorical_fraction = 0.8, factors = 10, real_range = 1,
integer_fraction = 0.2, integer_range = 10,
binary_fraction = 0, binary_ones_fraction = 0.5,
missing_fraction = 0.2,
response_factors = 1)
# Turn integer column into a categorical
myframe[,5] <- as.factor(myframe[,5])
head(myframe, 20)
# Create pairwise interactions
pairwise <- h2o.interaction(myframe, destination_frame = 'pairwise',
factors = list(c(1,2),c("C2","C3","C4")),
pairwise=TRUE, max_factors = 10, min_occurrence = 1)
head(pairwise, 20)
h2o.levels(pairwise,2)
# Create 5-th order interaction
higherorder <- h2o.interaction(myframe, destination_frame = 'higherorder', factors = c(1,2,3,4,5),
pairwise=FALSE, max_factors = 10000, min_occurrence = 1)
head(higherorder, 20)
# Limit the number of factors of the "categoricalized" integer column
# to at most 3 factors, and only if they occur at least twice
head(myframe[,5], 20)
trim_integer_levels <- h2o.interaction(myframe, destination_frame = 'trim_integers', factors = "C5",
pairwise = FALSE, max_factors = 3, min_occurrence = 2)
head(trim_integer_levels, 20)
# Put all together
myframe <- h2o.cbind(myframe, pairwise, higherorder, trim_integer_levels)
myframe
head(myframe,20)
summary(myframe)

h2o.isax

iSAX

Description
Compute the iSAX index for a DataFrame which is assumed to be numeric time series data
Usage
h2o.isax(x, num_words, max_cardinality, optimize_card = FALSE)
Arguments
x

an H2OFrame

num_words
Number of iSAX words for the timeseries. ie granularity along the time series
max_cardinality
Maximum cardinality of the iSAX word. Each word can have less than the max
optimize_card

An optimization flag that will find the max cardinality regardless of what is
passed in for max_cardinality.

106

h2o.isfactor

Value
An H2OFrame with the name of time series, string representation of iSAX word, followed by binary
representation
References
http://www.cs.ucr.edu/~eamonn/iSAX_2.0.pdf
http://www.cs.ucr.edu/~eamonn/SAX.pdf

h2o.ischaracter

Check if character

Description
Check if character
Usage
h2o.ischaracter(x)
Arguments
x

An H2OFrame object.

See Also
is.character for the base R implementation.

h2o.isfactor

Check if factor

Description
Check if factor
Usage
h2o.isfactor(x)
Arguments
x

An H2OFrame object.

See Also
is.factor for the base R implementation.

h2o.isnumeric

107

h2o.isnumeric

Check if numeric

Description
Check if numeric
Usage
h2o.isnumeric(x)
Arguments
x

An H2OFrame object.

See Also
is.numeric for the base R implementation.

h2o.is_client

Check Client Mode Connection

Description
Check Client Mode Connection
Usage
h2o.is_client()

h2o.kfold_column

Produce a k-fold column vector.

Description
Create a k-fold vector useful for H2O algorithms that take a fold_assignments argument.
Usage
h2o.kfold_column(data, nfolds, seed = -1)
Arguments
data
nfolds
seed

A dataframe against which to create the fold column.
The number of desired folds.
A random seed, -1 indicates that H2O will choose one.

Value
Returns an H2OFrame object with fold assignments.

108

h2o.kmeans

h2o.killMinus3

Dump the stack into the JVM’s stdout.

Description
A poor man’s profiler, but effective.
Usage
h2o.killMinus3()

h2o.kmeans

Performs k-means clustering on an H2O dataset

Description
Performs k-means clustering on an H2O dataset
Usage
h2o.kmeans(training_frame, x, model_id = NULL, validation_frame = NULL,
nfolds = 0, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO",
"Random", "Modulo", "Stratified"), fold_column = NULL,
ignore_const_cols = TRUE, score_each_iteration = FALSE, k = 1,
estimate_k = FALSE, user_points = NULL, max_iterations = 10,
standardize = TRUE, seed = -1, init = c("Random", "PlusPlus",
"Furthest", "User"), max_runtime_secs = 0,
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"))
Arguments
training_frame Id of the training data frame.
x

A vector containing the character names of the predictors in the model.

model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.

h2o.kmeans

109

fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column
Column with cross-validation fold index assignment per observation.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
k

The max. number of clusters. If estimate_k is disabled, the model will find k
centroids, otherwise it will find up to k centroids. Defaults to 1.

estimate_k

Logical. Whether to estimate the number of clusters (<=k) iteratively and deterministically. Defaults to FALSE.

user_points

This option allows you to specify a dataframe, where each row represents an
initial cluster center. The user- specified points must have the same number
of columns as the training observations. The number of rows must equal the
number of clusters

max_iterations Maximum training iterations (if estimate_k is enabled, then this is for each inner
Lloyds iteration) Defaults to 10.
standardize

Logical. Standardize columns before computing distances Defaults to TRUE.

seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).

init

Initialization mode Must be one of: "Random", "PlusPlus", "Furthest", "User".
Defaults to Furthest.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
Value
Returns an object of class H2OClusteringModel.
See Also
h2o.cluster_sizes, h2o.totss, h2o.num_iterations, h2o.betweenss, h2o.tot_withinss,
h2o.withinss, h2o.centersSTD, h2o.centers
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)

110

h2o.levels
h2o.kmeans(training_frame = prostate.hex, k = 10, x = c("AGE", "RACE", "VOL", "GLEASON"))

h2o.kurtosis

Kurtosis of a column

Description
Obtain the kurtosis of a column of a parsed H2O data object.
Usage
h2o.kurtosis(x, ..., na.rm = TRUE)
kurtosis.H2OFrame(x, ..., na.rm = TRUE)
Arguments
x

An H2OFrame object.

...

Further arguments to be passed from or to other methods.

na.rm

A logical value indicating whether NA or missing values should be stripped before the computation.

Value
Returns a list containing the kurtosis for each column (NaN for non-numeric columns).
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.kurtosis(prostate.hex$AGE)

h2o.levels

Return the levels from the column requested column.

Description
Return the levels from the column requested column.
Usage
h2o.levels(x, i)
Arguments
x

An H2OFrame object.

i

Optional, the index of the column whose domain is to be returned.

h2o.listTimezones

111

See Also
levels for the base R method.
Examples
iris.hex <- as.h2o(iris)
h2o.levels(iris.hex, 5) # returns "setosa"

h2o.listTimezones

"versicolor" "virginica"

List all of the Time Zones Acceptable by the H2O Cloud.

Description
List all of the Time Zones Acceptable by the H2O Cloud.
Usage
h2o.listTimezones()

h2o.list_all_extensions
List all H2O registered extensions

Description
List all H2O registered extensions
Usage
h2o.list_all_extensions()

h2o.list_api_extensions
List registered API extensions

Description
List registered API extensions
Usage
h2o.list_api_extensions()

112

h2o.loadModel

h2o.list_core_extensions
List registered core extensions

Description
List registered core extensions
Usage
h2o.list_core_extensions()

h2o.loadModel

Load H2O Model from HDFS or Local Disk

Description
Load a saved H2O model from disk. (Note that ensemble binary models can now be loaded using
this method.)
Usage
h2o.loadModel(path)
Arguments
path

The path of the H2O Model to be imported. and port of the server running H2O.

Value
Returns a H2OModel object of the class corresponding to the type of model built.
See Also
h2o.saveModel, H2OModel
Examples
## Not run:
# library(h2o)
# h2o.init()
# prosPath = system.file("extdata", "prostate.csv", package = "h2o")
# prostate.hex = h2o.importFile(path = prosPath, destination_frame = "prostate.hex")
# prostate.glm = h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"),
# training_frame = prostate.hex, family = "binomial", alpha = 0.5)
# glmmodel.path = h2o.saveModel(prostate.glm, dir = "/Users/UserName/Desktop")
# glmmodel.load = h2o.loadModel(glmmodel.path)
## End(Not run)

h2o.log

113

h2o.log

Compute the logarithm of x

Description
Compute the logarithm of x

Usage
h2o.log(x)
Arguments
x

An H2OFrame object.

See Also
log for the base R implementation.

h2o.log10

Compute the log10 of x

Description
Compute the log10 of x

Usage
h2o.log10(x)
Arguments
x

An H2OFrame object.

See Also
log10 for the base R implementation.

114

h2o.log2

h2o.log1p

Compute the log1p of x

Description
Compute the log1p of x

Usage
h2o.log1p(x)
Arguments
x

An H2OFrame object.

See Also
log1p for the base R implementation.

h2o.log2

Compute the log2 of x

Description
Compute the log2 of x

Usage
h2o.log2(x)
Arguments
x

An H2OFrame object.

See Also
log2 for the base R implementation.

h2o.logAndEcho

h2o.logAndEcho

115

Log a message on the server-side logs

Description
This is helpful when running several pieces of work one after the other on a single H2O cluster and
you want to make a notation in the H2O server side log where one piece of work ends and the next
piece of work begins.
Usage
h2o.logAndEcho(message)
Arguments
message

A character string with the message to write to the log.

Details
h2o.logAndEcho sends a message to H2O for logging. Generally used for debugging purposes.

h2o.logloss

Retrieve the Log Loss Value

Description
Retrieves the log loss output for a H2OBinomialMetrics or H2OMultinomialMetrics object If "train",
"valid", and "xval" parameters are FALSE (default), then the training Log Loss value is returned.
If more than one parameter is set to TRUE, then a named vector of Log Losses are returned, where
the names are "train", "valid" or "xval".
Usage
h2o.logloss(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

a H2OModelMetrics object of the correct type.

train

Retrieve the training Log Loss

valid

Retrieve the validation Log Loss

xval

Retrieve the cross-validation Log Loss

116

h2o.lstrip

h2o.ls

List Keys on an H2O Cluster

Description
Accesses a list of object keys in the running instance of H2O.
Usage
h2o.ls()
Value
Returns a list of hex keys in the current H2O instance.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.ls()

h2o.lstrip

Strip set from left

Description
Return a copy of the target column with leading characters removed. The set argument is a string
specifying the set of characters to be removed. If omitted, the set argument defaults to removing
whitespace.
Usage
h2o.lstrip(x, set = " ")
Arguments
x

The column whose strings should be lstrip-ed.

set

string of characters to be removed

Examples
library(h2o)
h2o.init()
string_to_lstrip <- as.h2o("1234567890")
lstrip_string <- h2o.lstrip(string_to_lstrip,"123") #Remove "123"

h2o.mae

117

h2o.mae

Retrieve the Mean Absolute Error Value

Description
Retrieves the mean absolute error (MAE) value from an H2O model. If "train", "valid", and "xval"
parameters are FALSE (default), then the training MAE value is returned. If more than one parameter is set to TRUE, then a named vector of MAEs are returned, where the names are "train", "valid"
or "xval".
Usage
h2o.mae(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel object.

train

Retrieve the training MAE

valid

Retrieve the validation set MAE if a validation set was passed in during model
build time.

xval

Retrieve the cross-validation MAE

Examples
library(h2o)
h <- h2o.init()
fr <- as.h2o(iris)
m <- h2o.deeplearning(x=2:5,y=1,training_frame=fr)
h2o.mae(m)

h2o.makeGLMModel

Set betas of an existing H2O GLM Model

Description
This function allows setting betas of an existing glm model.
Usage
h2o.makeGLMModel(model, beta)
Arguments
model

an H2OModel corresponding from a h2o.glm call.

beta

a new set of betas (a named vector)

118

h2o.match

h2o.make_metrics

Create Model Metrics from predicted and actual values in H2O

Description
Given predicted values (target for regression, class-1 probabilities or binomial or per-class probabilities for multinomial), compute a model metrics object
Usage
h2o.make_metrics(predicted, actuals, domain = NULL, distribution = NULL)
Arguments
predicted

An H2OFrame containing predictions

actuals

An H2OFrame containing actual values

domain

Vector with response factors for classification.

distribution

Distribution for regression.

Value
Returns an object of the H2OModelMetrics subclass.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
prostate.hex$CAPSULE <- as.factor(prostate.hex$CAPSULE)
prostate.gbm <- h2o.gbm(3:9, "CAPSULE", prostate.hex)
pred <- h2o.predict(prostate.gbm, prostate.hex)[,3] ## class-1 probability
h2o.make_metrics(pred,prostate.hex$CAPSULE)

h2o.match

Value Matching in H2O

Description
match and %in% return values similar to the base R generic functions.
Usage
h2o.match(x, table, nomatch = 0, incomparables = NULL)
match.H2OFrame(x, table, nomatch = 0, incomparables = NULL)
x %in% table

h2o.max

119

Arguments
x

a categorical vector from an H2OFrame object with values to be matched.

table

an R object to match x against.

nomatch

the value to be returned in the case when no match is found.

incomparables

a vector of calues that cannot be matched. Any value in x matching a value in
this vector is assigned the nomatch value.

Value
Returns a vector of the positions of (first) matches of its first argument in its second
See Also
match for base R implementation.
Examples
h2o.init()
hex <- as.h2o(iris)
h2o.match(hex[,5], c("setosa", "versicolor"))

h2o.max

Returns the maxima of the input values.

Description
Returns the maxima of the input values.
Usage
h2o.max(x, na.rm = FALSE)
Arguments
x

An H2OFrame object.

na.rm

logical. indicating whether missing values should be removed.

See Also
max for the base R implementation.

120

h2o.mean

h2o.mean

Compute the frame’s mean by-column (or by-row).

Description
Compute the frame’s mean by-column (or by-row).
Usage
h2o.mean(x, na.rm = FALSE, axis = 0, return_frame = FALSE, ...)
## S3 method for class 'H2OFrame'
mean(x, na.rm = FALSE, axis = 0, return_frame = FALSE,
...)
Arguments
x

An H2OFrame object.

na.rm

logical. Indicate whether missing values should be removed.

axis

integer. Indicate whether to calculate the mean down a column (0) or across a
row (1). NOTE: This is only applied when return_frame is set to TRUE. Otherwise, this parameter is ignored.

return_frame

logical. Indicate whether to return an H2O frame or a list. Default is FALSE
(returns a list).

...

Further arguments to be passed from or to other methods.

Value
Returns a list containing the mean for each column (NaN for non-numeric columns) if return_frame
is set to FALSE. If return_frame is set to TRUE, then it will return an H2O frame with means per
column or row (depends on axis argument).
See Also
mean , rowMeans, or colMeans for the base R implementation
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
# Default behavior. Will return list of means per column.
h2o.mean(prostate.hex$AGE)
# return_frame set to TRUE. This will return an H2O Frame
# with mean per row or column (depends on axis argument)
h2o.mean(prostate.hex,na.rm=TRUE,axis=1,return_frame=TRUE)

h2o.mean_per_class_error

121

h2o.mean_per_class_error
Retrieve the mean per class error

Description
Retrieves the mean per class error from an H2OBinomialMetrics. If "train", "valid", and "xval"
parameters are FALSE (default), then the training mean per class error value is returned. If more
than one parameter is set to TRUE, then a named vector of mean per class errors are returned, where
the names are "train", "valid" or "xval".

Usage
h2o.mean_per_class_error(object, train = FALSE, valid = FALSE,
xval = FALSE)
Arguments
object

An H2OBinomialMetrics object.

train

Retrieve the training mean per class error

valid

Retrieve the validation mean per class error

xval

Retrieve the cross-validation mean per class error

See Also
h2o.mse for MSE, and h2o.metric for the various threshold metrics. See h2o.performance for
creating H2OModelMetrics objects.

Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
perf <- h2o.performance(model, hex)
h2o.mean_per_class_error(perf)
h2o.mean_per_class_error(model, train=TRUE)

122

h2o.median

h2o.mean_residual_deviance
Retrieve the Mean Residual Deviance value

Description
Retrieves the Mean Residual Deviance value from an H2O model. If "train", "valid", and "xval"
parameters are FALSE (default), then the training Mean Residual Deviance value is returned. If
more than one parameter is set to TRUE, then a named vector of Mean Residual Deviances are
returned, where the names are "train", "valid" or "xval".
Usage
h2o.mean_residual_deviance(object, train = FALSE, valid = FALSE,
xval = FALSE)
Arguments
object

An H2OModel object.

train

Retrieve the training Mean Residual Deviance

valid

Retrieve the validation Mean Residual Deviance

xval

Retrieve the cross-validation Mean Residual Deviance

Examples
library(h2o)
h <- h2o.init()
fr <- as.h2o(iris)
m <- h2o.deeplearning(x=2:5,y=1,training_frame=fr)
h2o.mean_residual_deviance(m)

h2o.median

H2O Median

Description
Compute the median of an H2OFrame.
Usage
h2o.median(x, na.rm = TRUE)
## S3 method for class 'H2OFrame'
median(x, na.rm = TRUE)

h2o.merge

123

Arguments
x

An H2OFrame object.

na.rm

a logical, indicating whether na’s are omitted.

Value
Returns a list containing the median for each column (NaN for non-numeric columns)
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath, destination_frame = "prostate.hex")
h2o.median(prostate.hex)

h2o.merge

Merge Two H2O Data Frames

Description
Merges two H2OFrame objects with the same arguments and meanings as merge() in base R. However, we do not support all=TRUE, all.x=TRUE and all.y=TRUE. The default method is auto and it
will default to the radix method. The radix method will return the correct merge result regardless
of duplicated rows in the right frame. In addition, the radix method can perform merge even if you
have string columns in your frames. If there are duplicated rows in your rite frame, they will not
be included if you use the hash method. The hash method cannot perform merge if you have string
columns in your left frame. Hence, we consider the radix method superior to the hash method and
is the default method to use.
Usage
h2o.merge(x, y, by = intersect(names(x), names(y)), by.x = by, by.y = by,
all = FALSE, all.x = all, all.y = all, method = "auto")
Arguments
x, y

H2OFrame objects

by

columns used for merging by default the common names

by.x

x columns used for merging by name or number

by.y

y columns used for merging by name or number

all

TRUE includes all rows in x and all rows in y even if there is no match to the
other

all.x

If all.x is true, all rows in the x will be included, even if there is no matching
row in y, and vice-versa for all.y.

all.y

see all.x

method

auto(default), radix, hash

124

h2o.metric

Examples
h2o.init()
left <- data.frame(fruit = c('apple', 'orange', 'banana', 'lemon', 'strawberry', 'blueberry'),
color <- c('red', 'orange', 'yellow', 'yellow', 'red', 'blue'))
right <- data.frame(fruit = c('apple', 'orange', 'banana', 'lemon', 'strawberry', 'watermelon'),
citrus <- c(FALSE, TRUE, FALSE, TRUE, FALSE, FALSE))
l.hex <- as.h2o(left)
r.hex <- as.h2o(right)
left.hex <- h2o.merge(l.hex, r.hex, all.x = TRUE)

h2o.metric

H2O Model Metric Accessor Functions

Description
A series of functions that retrieve model metric details.
Usage
h2o.metric(object, thresholds, metric)
h2o.F0point5(object, thresholds)
h2o.F1(object, thresholds)
h2o.F2(object, thresholds)
h2o.accuracy(object, thresholds)
h2o.error(object, thresholds)
h2o.maxPerClassError(object, thresholds)
h2o.mean_per_class_accuracy(object, thresholds)
h2o.mcc(object, thresholds)
h2o.precision(object, thresholds)
h2o.tpr(object, thresholds)
h2o.fpr(object, thresholds)
h2o.fnr(object, thresholds)
h2o.tnr(object, thresholds)
h2o.recall(object, thresholds)

h2o.metric

125

h2o.sensitivity(object, thresholds)
h2o.fallout(object, thresholds)
h2o.missrate(object, thresholds)
h2o.specificity(object, thresholds)

Arguments
object

An H2OModelMetrics object of the correct type.

thresholds

(Optional) A value or a list of values between 0.0 and 1.0.

metric

(Optional) A specified paramter to retrieve.

Details
Many of these functions have an optional thresholds parameter. Currently only increments of 0.1
are allowed. If not specified, the functions will return all possible values. Otherwise, the function
will return the value for the indicated threshold.
Currently, the these functions are only supported by H2OBinomialMetrics objects.

Value
Returns either a single value, or a list of values.

See Also
h2o.auc for AUC, h2o.giniCoef for the GINI coefficient, and h2o.mse for MSE. See h2o.performance
for creating H2OModelMetrics objects.

Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
perf <- h2o.performance(model, hex)
h2o.F1(perf)

126

h2o.mktime

h2o.min

Returns the minima of the input values.

Description
Returns the minima of the input values.
Usage
h2o.min(x, na.rm = FALSE)
Arguments
x

An H2OFrame object.

na.rm

logical. indicating whether missing values should be removed.

See Also
min for the base R implementation.

h2o.mktime

Compute msec since the Unix Epoch

Description
Compute msec since the Unix Epoch
Usage
h2o.mktime(year = 1970, month = 0, day = 0, hour = 0, minute = 0,
second = 0, msec = 0)
Arguments
year

Defaults to 1970

month

zero based (months are 0 to 11)

day

zero based (days are 0 to 30)

hour

hour

minute

minute

second

second

msec

msec

h2o.month

h2o.month

127

Convert Milliseconds to Months in H2O Datasets

Description
Converts the entries of an H2OFrame object from milliseconds to months (on a 1 to 12 scale).
Usage
h2o.month(x)
month(x)
## S3 method for class 'H2OFrame'
month(x)
Arguments
x

An H2OFrame object.

Value
An H2OFrame object containing the entries of x converted to months of the year.
See Also
h2o.year

h2o.mse

Retrieves Mean Squared Error Value

Description
Retrieves the mean squared error value from an H2OModelMetrics object. If "train", "valid", and
"xval" parameters are FALSE (default), then the training MSEvalue is returned. If more than one
parameter is set to TRUE, then a named vector of MSEs are returned, where the names are "train",
"valid" or "xval".
Usage
h2o.mse(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModelMetrics object of the correct type.

train

Retrieve the training MSE

valid

Retrieve the validation MSE

xval

Retrieve the cross-validation MSE

128

h2o.nacnt

Details
This function only supports H2OBinomialMetrics, H2OMultinomialMetrics, and H2ORegressionMetrics
objects.
See Also
h2o.auc for AUC, h2o.mse for MSE, and h2o.metric for the various threshold metrics. See
h2o.performance for creating H2OModelMetrics objects.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
perf <- h2o.performance(model, hex)
h2o.mse(perf)

h2o.nacnt

Count of NAs per column

Description
Gives the count of NAs per column.
Usage
h2o.nacnt(x)
Arguments
x

An H2OFrame object.

Value
Returns a list containing the count of NAs per column
Examples
h2o.init()
iris.hex <- as.h2o(iris)
h2o.nacnt(iris.hex) # should return all 0s
h2o.insertMissingValues(iris.hex)
h2o.nacnt(iris.hex)

h2o.naiveBayes

h2o.naiveBayes

129

Compute naive Bayes probabilities on an H2O dataset.

Description
The naive Bayes classifier assumes independence between predictor variables conditional on the
response, and a Gaussian distribution of numeric predictors with mean and standard deviation computed from the training dataset. When building a naive Bayes classifier, every row in the training
dataset that contains at least one NA will be skipped completely. If the test dataset has missing
values, then those predictors are omitted in the probability calculation during prediction.
Usage
h2o.naiveBayes(x, y, training_frame, model_id = NULL, nfolds = 0,
seed = -1, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
fold_column = NULL, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE, validation_frame = NULL,
ignore_const_cols = TRUE, score_each_iteration = FALSE,
balance_classes = FALSE, class_sampling_factors = NULL,
max_after_balance_size = 5, max_hit_ratio_k = 0, laplace = 0,
threshold = 0.001, min_sdev = 0.001, eps = 0, eps_sdev = 0,
min_prob = 0.001, eps_prob = 0, compute_metrics = TRUE,
max_runtime_secs = 0)
Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
model_id

Destination id for this model; auto-generated if not specified.

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.

seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).

fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column
Column with cross-validation fold index assignment per observation.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.

130

h2o.naiveBayes
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.
validation_frame
Id of the validation data frame.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
balance_classes
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). If not
specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
max_after_balance_size
Maximum relative size of the training data after balancing class counts (can be
less than 1.0). Requires balance_classes. Defaults to 5.0.
max_hit_ratio_k
Max. number (top K) of predictions to use for hit ratio computation (for multiclass only, 0 to disable) Defaults to 0.
laplace
Laplace smoothing parameter Defaults to 0.
threshold
This argument is deprecated, use ‘min_sdev‘ instead. The minimum standard
deviation to use for observations without enough data. Must be at least 1e-10.
min_sdev
The minimum standard deviation to use for observations without enough data.
Must be at least 1e-10.
eps
This argument is deprecated, use ‘eps_sdev‘ instead. A threshold cutoff to deal
with numeric instability, must be positive.
eps_sdev
A threshold cutoff to deal with numeric instability, must be positive.
min_prob
Min. probability to use for observations with not enough data.
eps_prob
Cutoff below which probability is replaced with min_prob.
compute_metrics
Logical. Compute metrics on training data Defaults to TRUE.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.

Details
The naive Bayes classifier assumes independence between predictor variables conditional on the
response, and a Gaussian distribution of numeric predictors with mean and standard deviation computed from the training dataset. When building a naive Bayes classifier, every row in the training
dataset that contains at least one NA will be skipped completely. If the test dataset has missing
values, then those predictors are omitted in the probability calculation during prediction.
Value
Returns an object of class H2OBinomialModel if the response has two categorical levels, and
H2OMultinomialModel otherwise.

h2o.names

131

Examples
h2o.init()
votesPath <- system.file("extdata", "housevotes.csv", package="h2o")
votes.hex <- h2o.uploadFile(path = votesPath, header = TRUE)
h2o.naiveBayes(x = 2:17, y = 1, training_frame = votes.hex, laplace = 3)

h2o.names

Column names of an H2OFrame

Description
Column names of an H2OFrame
Usage
h2o.names(x)
Arguments
x

An H2OFrame object.

See Also
names for the base R implementation.

h2o.na_omit

Remove Rows With NAs

Description
Remove Rows With NAs
Usage
h2o.na_omit(object, ...)
Arguments
object

H2OFrame object

...

Ignored

Value
Returns an H2OFrame object containing non-NA rows.

132

h2o.ncol

h2o.nchar

String length

Description
String length
Usage
h2o.nchar(x)
Arguments
x

The column whose string lengths will be returned.

Examples
library(h2o)
h2o.init()
string_to_nchar <- as.h2o("r tutorial")
nchar_string <- h2o.nchar(string_to_nchar)

h2o.ncol

Return the number of columns present in x.

Description
Return the number of columns present in x.
Usage
h2o.ncol(x)
Arguments
x

An H2OFrame object.

See Also
ncol for the base R implementation.

h2o.networkTest

133

h2o.networkTest

View Network Traffic Speed

Description
View speed with various file sizes.
Usage
h2o.networkTest()
Value
Returns a table listing the network speed for 1B, 10KB, and 10MB.

h2o.nlevels

Get the number of factor levels for this frame.

Description
Get the number of factor levels for this frame.
Usage
h2o.nlevels(x)
Arguments
x

An H2OFrame object.

See Also
nlevels for the base R method.

h2o.no_progress

Description
Disable Progress Bar
Usage
h2o.no_progress()

Disable Progress Bar

134

h2o.null_deviance

h2o.nrow

Return the number of rows present in x.

Description
Return the number of rows present in x.
Usage
h2o.nrow(x)
Arguments
x

An H2OFrame object.

See Also
nrow for the base R implementation.

h2o.null_deviance

Retrieve the null deviance

Description
If "train", "valid", and "xval" parameters are FALSE (default), then the training null deviance value
is returned. If more than one parameter is set to TRUE, then a named vector of null deviances are
returned, where the names are "train", "valid" or "xval".
Usage
h2o.null_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel or H2OModelMetrics

train

Retrieve the training null deviance

valid

Retrieve the validation null deviance

xval

Retrieve the cross-validation null deviance

h2o.null_dof

135

h2o.null_dof

Retrieve the null degrees of freedom

Description
If "train", "valid", and "xval" parameters are FALSE (default), then the training null degrees of
freedom value is returned. If more than one parameter is set to TRUE, then a named vector of null
degrees of freedom are returned, where the names are "train", "valid" or "xval".

Usage
h2o.null_dof(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel or H2OModelMetrics

train

Retrieve the training null degrees of freedom

valid

Retrieve the validation null degrees of freedom

xval

Retrieve the cross-validation null degrees of freedom

h2o.num_iterations

Retrieve the number of iterations.

Description
Retrieve the number of iterations.

Usage
h2o.num_iterations(object)
Arguments
object

An H2OClusteringModel object.

...

further arguments to be passed on (currently unimplemented)

136

h2o.openLog

h2o.num_valid_substrings
Count of substrings >= 2 chars that are contained in file

Description
Find the count of all possible substrings >= 2 chars that are contained in the specified line-separated
text file.
Usage
h2o.num_valid_substrings(x, path)
Arguments
x
path

The column on which to calculate the number of valid substrings.
Path to text file containing line-separated strings to be referenced.

h2o.openLog

View H2O R Logs

Description
Open existing logs of H2O R POST commands and error resposnes on local disk. Used primarily
for debugging purposes.
Usage
h2o.openLog(type)
Arguments
type

Currently unimplemented.

See Also
h2o.startLogging, h2o.stopLogging,

h2o.clearLog

Examples
## Not run:
h2o.init()
h2o.startLogging()
ausPath = system.file("extdata", "australia.csv", package="h2o")
australia.hex = h2o.importFile(path = ausPath)
h2o.stopLogging()
# Not run to avoid windows being opened during R CMD check
# h2o.openLog("Command")
# h2o.openLog("Error")
## End(Not run)

h2o.parseRaw

h2o.parseRaw

137

H2O Data Parsing

Description
The second phase in the data ingestion step.
Usage
h2o.parseRaw(data, pattern = "", destination_frame = "", header = NA,
sep = "", col.names = NULL, col.types = NULL, na.strings = NULL,
blocking = FALSE, parse_type = NULL, chunk_size = NULL,
decrypt_tool = NULL)
Arguments
data

An H2OFrame object to be parsed.

pattern

(Optional) Character string containing a regular expression to match file(s) in
the folder.
destination_frame
(Optional) The hex key assigned to the parsed file.
header

(Optional) A logical value indicating whether the first row is the column header.
If missing, H2O will automatically try to detect the presence of a header.

sep

(Optional) The field separator character. Values on each line of the file are separated by this character. If sep = "", the parser will automatically detect the
separator.

col.names

(Optional) An H2OFrame object containing a single delimited line with the column names for the file.

col.types

(Optional) A vector specifying the types to attempt to force over columns.

na.strings

(Optional) H2O will interpret these strings as missing.

blocking

(Optional) Tell H2O parse call to block synchronously instead of polling. This
can be faster for small datasets but loses the progress bar.

parse_type

(Optional) Specify which parser type H2O will use. Valid types are "ARFF",
"XLS", "CSV", "SVMLight"

chunk_size

size of chunk of (input) data in bytes

decrypt_tool

(Optional) Specify a Decryption Tool (key-reference acquired by calling h2o.decryptionSetup.

Details
Parse the Raw Data produced by the import phase.
See Also
h2o.importFile, h2o.parseSetup

138

h2o.parseSetup

h2o.parseSetup

Get a parse setup back for the staged data.

Description
Get a parse setup back for the staged data.

Usage
h2o.parseSetup(data, pattern = "", destination_frame = "", header = NA,
sep = "", col.names = NULL, col.types = NULL, na.strings = NULL,
parse_type = NULL, chunk_size = NULL, decrypt_tool = NULL)

Arguments
data

An H2OFrame object to be parsed.

pattern

(Optional) Character string containing a regular expression to match file(s) in
the folder.

destination_frame
(Optional) The hex key assigned to the parsed file.
header

(Optional) A logical value indicating whether the first row is the column header.
If missing, H2O will automatically try to detect the presence of a header.

sep

(Optional) The field separator character. Values on each line of the file are separated by this character. If sep = "", the parser will automatically detect the
separator.

col.names

(Optional) An H2OFrame object containing a single delimited line with the column names for the file.

col.types

(Optional) A vector specifying the types to attempt to force over columns.

na.strings

(Optional) H2O will interpret these strings as missing.

parse_type

(Optional) Specify which parser type H2O will use. Valid types are "ARFF",
"XLS", "CSV", "SVMLight"

chunk_size

size of chunk of (input) data in bytes

decrypt_tool

(Optional) Specify a Decryption Tool (key-reference acquired by calling h2o.decryptionSetup.

See Also
h2o.parseRaw

h2o.partialPlot

h2o.partialPlot

139

Partial Dependence Plots

Description
Partial dependence plot gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. Note: Unlike randomForest’s partialPlot when plotting partial dependence the mean response (probabilities) is returned
rather than the mean of the log class probability.
Usage
h2o.partialPlot(object, data, cols, destination_key, nbins = 20,
plot = TRUE, plot_stddev = TRUE)
Arguments
object

An H2OModel object.

data

An H2OFrame object used for scoring and constructing the plot.

cols
Feature(s) for which partial dependence will be calculated.
destination_key
An key reference to the created partial dependence tables in H2O.
nbins

Number of bins used. For categorical columns make sure the number of bins
exceed the level count.

plot

A logical specifying whether to plot partial dependence table.

plot_stddev

A logical specifying whether to add std err to partial dependence plot.

Value
Plot and list of calculated mean response tables for each feature requested.
Examples
library(h2o)
h2o.init()
prostate.path <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prostate.path, destination_frame = "prostate.hex")
prostate.hex[, "CAPSULE"] <- as.factor(prostate.hex[, "CAPSULE"] )
prostate.hex[, "RACE"] <- as.factor(prostate.hex[,"RACE"] )
prostate.gbm <- h2o.gbm(x = c("AGE","RACE"),
y = "CAPSULE",
training_frame = prostate.hex,
ntrees = 10,
max_depth = 5,
learn_rate = 0.1)
h2o.partialPlot(object = prostate.gbm, data = prostate.hex, cols = c("AGE", "RACE"))

140

h2o.performance

h2o.performance

Model Performance Metrics in H2O

Description
Given a trained h2o model, compute its performance on the given dataset
Usage
h2o.performance(model, newdata = NULL, train = FALSE, valid = FALSE,
xval = FALSE, data = NULL)
Arguments
model

An H2OModel object

newdata

An H2OFrame. The model will make predictions on this dataset, and subsequently score them. The dataset should match the dataset that was used to train
the model, in terms of column names, types, and dimensions. If newdata is
passed in, then train, valid, and xval are ignored.

train

A logical value indicating whether to return the training metrics (constructed
during training).
Note: when the trained h2o model uses balance_classes, the training metrics
constructed during training will be from the balanced training dataset. For more
information visit: https://0xdata.atlassian.net/browse/TN-9

valid

A logical value indicating whether to return the validation metrics (constructed
during training).

xval

A logical value indicating whether to return the cross-validation metrics (constructed during training).

data

(DEPRECATED) An H2OFrame. This argument is now called ‘newdata‘.

Value
Returns an object of the H2OModelMetrics subclass.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
prostate.hex$CAPSULE <- as.factor(prostate.hex$CAPSULE)
prostate.gbm <- h2o.gbm(3:9, "CAPSULE", prostate.hex)
h2o.performance(model = prostate.gbm, newdata=prostate.hex)
## If model uses balance_classes
## the results from train = TRUE will not match the results from newdata = prostate.hex
prostate.gbm.balanced <- h2o.gbm(3:9, "CAPSULE", prostate.hex, balance_classes = TRUE)
h2o.performance(model = prostate.gbm.balanced, newdata = prostate.hex)
h2o.performance(model = prostate.gbm.balanced, train = TRUE)

h2o.pivot

h2o.pivot

141

Pivot a frame

Description
Pivot the frame designated by the three columns: index, column, and value. Index and column
should be of type enum, int, or time. For cases of multiple indexes for a column label, the aggregation method is to pick the first occurrence in the data frame
Usage
h2o.pivot(x, index, column, value)
Arguments
x

an H2OFrame

index

the column where pivoted rows should be aligned on

column

the column to pivot

value

values of the pivoted table

Value
An H2OFrame with columns from the columns arg, aligned on the index arg, with values from
values arg

h2o.prcomp

Principal component analysis of an H2O data frame

Description
Principal components analysis of an H2O data frame using the power method to calculate the singular value decomposition of the Gram matrix.
Usage
h2o.prcomp(training_frame, x, model_id = NULL, validation_frame = NULL,
ignore_const_cols = TRUE, score_each_iteration = FALSE,
transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"),
pca_method = c("GramSVD", "Power", "Randomized", "GLRM"), k = 1,
max_iterations = 1000, use_all_factor_levels = FALSE,
compute_metrics = TRUE, impute_missing = FALSE, seed = -1,
max_runtime_secs = 0)

142

h2o.prcomp

Arguments
training_frame Id of the training data frame.
x

A vector containing the character names of the predictors in the model.

model_id

Destination id for this model; auto-generated if not specified.

validation_frame
Id of the validation data frame.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
transform

Transformation of training data Must be one of: "NONE", "STANDARDIZE",
"NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE.

pca_method

Method for computing PCA (Caution: GLRM is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized", "GLRM". Defaults to GramSVD.

k

Rank of matrix approximation Defaults to 1.

max_iterations Maximum training iterations Defaults to 1000.
use_all_factor_levels
Logical. Whether first factor level is included in each categorical expansion
Defaults to FALSE.
compute_metrics
Logical. Whether to compute metrics on the training data Defaults to TRUE.
impute_missing Logical. Whether to impute missing entries with the column mean Defaults to
FALSE.
seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).

max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
Value
Returns an object of class H2ODimReductionModel.
References
N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms
for constructing approximate matrix decompositions[http://arxiv.org/abs/0909.4061]. SIAM Rev.,
Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.
See Also
h2o.svd, h2o.glrm

h2o.predict_json

143

Examples
library(h2o)
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
h2o.prcomp(training_frame = australia.hex, k = 8, transform = "STANDARDIZE")

h2o.predict_json

H2O Prediction from R without having H2O running

Description
Provides the method h2o.predict with which you can predict a MOJO or POJO Jar model from R.
Usage
h2o.predict_json(model, json, genmodelpath, labels, classpath, javaoptions)
Arguments
model

String with file name of MOJO or POJO Jar

json

JSON String with inputs to model

genmodelpath

(Optional) path name to h2o-genmodel.jar, if not set defaults to same dir as
MOJO

labels

(Optional) if TRUE then show output labels in result

classpath

(Optional) Extra items for the class path of where to look for Java classes, e.g.,
h2o-genmodel.jar

javaoptions

(Optional) Java options string, default if "-Xmx4g"

Value
Returns an object with the prediction result
Examples
library(h2o)
h2o.predict_json('~/GBM_model_python_1473313897851_6.zip', '{"C7":1}')
h2o.predict_json('~/GBM_model_python_1473313897851_6.zip', '{"C7":1}', c(".", "lib"))

144

h2o.prod

h2o.print

Print An H2OFrame

Description
Print An H2OFrame

Usage
h2o.print(x, n = 6L)
Arguments
x

An H2OFrame object

n

An (Optional) A single integer. If positive, number of rows in x to return. If
negative, all but the n first/last number of rows in x. Anything bigger than 20
rows will require asking the server (first 20 rows are cached on the client).

...

Further arguments to be passed from or to other methods.

h2o.prod

Return the product of all the values present in its arguments.

Description
Return the product of all the values present in its arguments.

Usage
h2o.prod(x)
Arguments
x

An H2OFrame object.

See Also
prod for the base R implementation.

h2o.proj_archetypes

145

h2o.proj_archetypes

Convert Archetypes to Features from H2O GLRM Model

Description
Project each archetype in an H2O GLRM model into the corresponding feature space from the H2O
training frame.

Usage
h2o.proj_archetypes(object, data, reverse_transform = FALSE)
Arguments
object

An H2ODimReductionModel object that represents the model containing archetypes
to be projected.

data

An H2OFrame object representing the training data for the H2O GLRM model.

reverse_transform
(Optional) A logical value indicating whether to reverse the transformation from
model-building by re-scaling columns and adding back the offset to each column
of the projected archetypes.

Value
Returns an H2OFrame object containing the projection of the archetypes down into the original
feature space, where each row is one archetype.

See Also
h2o.glrm for making an H2ODimReductionModel.
Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris_wheader.csv", package="h2o")
iris.hex <- h2o.uploadFile(path = irisPath)
iris.glrm <- h2o.glrm(training_frame = iris.hex, k = 4, loss = "Quadratic",
multi_loss = "Categorical", max_iterations = 1000)
iris.parch <- h2o.proj_archetypes(iris.glrm, iris.hex)
head(iris.parch)

146

h2o.quantile

h2o.quantile

Quantiles of H2O Frames.

Description
Obtain and display quantiles for H2O parsed data.
Usage
h2o.quantile(x, probs = c(0.001, 0.01, 0.1, 0.25, 0.333, 0.5, 0.667, 0.75,
0.9, 0.99, 0.999), combine_method = c("interpolate", "average", "avg",
"low", "high"), weights_column = NULL, ...)
## S3 method for class 'H2OFrame'
quantile(x, probs = c(0.001, 0.01, 0.1, 0.25, 0.333, 0.5,
0.667, 0.75, 0.9, 0.99, 0.999), combine_method = c("interpolate", "average",
"avg", "low", "high"), weights_column = NULL, ...)
Arguments
x

An H2OFrame object with a single numeric column.

probs

Numeric vector of probabilities with values in [0,1].

combine_method How to combine quantiles for even sample sizes. Default is to do linear interpolation. E.g., If method is "lo", then it will take the lo value of the quantile.
Abbreviations for average, low, and high are acceptable (avg, lo, hi).
weights_column (Optional) String name of the observation weights column in x or an H2OFrame
object with a single numeric column of observation weights.
...

Further arguments passed to or from other methods.

Details
quantile.H2OFrame, a method for the quantile generic. Obtain and return quantiles for an
H2OFrame object.
Value
A vector describing the percentiles at the given cutoffs for the H2OFrame object.
Examples
# Request quantiles for an H2O parsed data set:
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
# Request quantiles for a subset of columns in an H2O parsed data set
quantile(prostate.hex[,3])
for(i in 1:ncol(prostate.hex))
quantile(prostate.hex[,i])

h2o.r2

147

h2o.r2

Retrieve the R2 value

Description
Retrieves the R2 value from an H2O model. Will return R^2 for GLM Models and will return NaN
otherwise. If "train", "valid", and "xval" parameters are FALSE (default), then the training R2 value
is returned. If more than one parameter is set to TRUE, then a named vector of R2s are returned,
where the names are "train", "valid" or "xval".
Usage
h2o.r2(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel object.

train

Retrieve the training R2

valid

Retrieve the validation set R2 if a validation set was passed in during model
build time.

xval

Retrieve the cross-validation R2

Examples
library(h2o)
h <- h2o.init()
fr <- as.h2o(iris)
m <- h2o.glm(x=2:5,y=1,training_frame=fr)
h2o.r2(m)

h2o.randomForest

Build a Random Forest model

Description
Builds a Random Forest model on an H2OFrame.
Usage
h2o.randomForest(x, y, training_frame, model_id = NULL,
validation_frame = NULL, nfolds = 0,
keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE,
score_each_iteration = FALSE, score_tree_interval = 0,
fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),

148

h2o.randomForest
fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL,
weights_column = NULL, balance_classes = FALSE,
class_sampling_factors = NULL, max_after_balance_size = 5,
max_hit_ratio_k = 0, ntrees = 50, max_depth = 20, min_rows = 1,
nbins = 20, nbins_top_level = 1024, nbins_cats = 1024,
r2_stopping = Inf, stopping_rounds = 0, stopping_metric = c("AUTO",
"deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group",
"misclassification", "mean_per_class_error"), stopping_tolerance = 0.001,
max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE,
mtries = -1, sample_rate = 0.6320000291, sample_rate_per_class = NULL,
binomial_double_trees = FALSE, checkpoint = NULL,
col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1,
min_split_improvement = 1e-05, histogram_type = c("AUTO",
"UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin"),
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
calibrate_model = FALSE, calibration_frame = NULL,
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson",
"gamma", "tweedie", "laplace", "quantile", "huber"),
custom_metric_func = NULL, verbose = FALSE)

Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
score_tree_interval
Score the model after every so many trees. Disabled if set to 0. Defaults to 0.
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.

h2o.randomForest

149

fold_column
Column with cross-validation fold index assignment per observation.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
offset_column Offset column. This argument is deprecated and has no use for Random Forest.
weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed. Note: Weights are per-row observation weights and do not increase the
size of the data frame. This is typically the number of times a row is repeated,
but non-integer values are supported as well. During training, rows with higher
weights matter more, due to the larger loss function pre-factor.
balance_classes
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). If not
specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
max_after_balance_size
Maximum relative size of the training data after balancing class counts (can be
less than 1.0). Requires balance_classes. Defaults to 5.0.
max_hit_ratio_k
Max. number (top K) of predictions to use for hit ratio computation (for multiclass only, 0 to disable) Defaults to 0.
ntrees
Number of trees. Defaults to 50.
max_depth
Maximum tree depth. Defaults to 20.
min_rows
Fewest allowed (weighted) observations in a leaf. Defaults to 1.
nbins
For numerical columns (real/int), build a histogram of (at least) this many bins,
then split at the best point Defaults to 20.
nbins_top_level
For numerical columns (real/int), build a histogram of (at most) this many bins
at the root level, then decrease by factor of two per level Defaults to 1024.
nbins_cats
For categorical columns (factors), build a histogram of this many bins, then split
at the best point. Higher values can lead to more overfitting. Defaults to 1024.
r2_stopping
r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version
of H2O would stop making trees when the R^2 metric equals or exceeds this
Defaults to 1.797693135e+308.
stopping_rounds
Early stopping based on convergence of stopping_metric. Stop if simple moving
average of length k of the stopping_metric does not improve for k:=stopping_rounds
scoring events (0 to disable) Defaults to 0.
stopping_metric
Metric to use for early stopping (AUTO: logloss for classification, deviance for
regression) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE",
"MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error".
Defaults to AUTO.
stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001.

150

h2o.randomForest
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).
build_tree_one_node
Logical. Run on one node only; no network overhead but fewer cpus used.
Suitable for small datasets. Defaults to FALSE.
mtries

Number of variables randomly sampled as candidates at each split. If set to -1,
defaults to sqrtp for classification and p/3 for regression (where p is the # of
predictors Defaults to -1.

sample_rate
Row sample rate per tree (from 0.0 to 1.0) Defaults to 0.6320000291.
sample_rate_per_class
A list of row sample rates per class (relative fraction for each class, from 0.0 to
1.0), for each tree
binomial_double_trees
Logical. For binary classification: Build 2x as many trees (one per class) - can
lead to higher accuracy. Defaults to FALSE.
checkpoint
Model checkpoint to resume training with.
col_sample_rate_change_per_level
Relative change of the column sampling rate for every level (must be > 0.0 and
<= 2.0) Defaults to 1.
col_sample_rate_per_tree
Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.
min_split_improvement
Minimum relative improvement in squared error reduction for a split to happen
Defaults to 1e-05.
histogram_type What type of histogram to use for finding optimal split points Must be one of:
"AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin".
Defaults to AUTO.
categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
calibrate_model
Logical. Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. Defaults to
FALSE.
calibration_frame
Calibration frame for Platt Scaling
distribution
Distribution. This argument is deprecated and has no use for Random Forest.
custom_metric_func
Reference to custom evaluation function, format: ‘language:keyName=funcName‘
verbose

Logical. Print scoring history to the console (Metrics per tree for GBM, DRF,
& XGBoost. Metrics per epoch for Deep Learning). Defaults to FALSE.

Value
Creates a H2OModel object of the right type.

h2o.range

151

See Also
predict.H2OModel for prediction

h2o.range

Returns a vector containing the minimum and maximum of all the
given arguments.

Description
Returns a vector containing the minimum and maximum of all the given arguments.
Usage
h2o.range(x, na.rm = FALSE, finite = FALSE)
Arguments
x

An H2OFrame object.

na.rm

logical. indicating whether missing values should be removed.

finite

logical. indicating if all non-finite elements should be omitted.

See Also
range for the base R implementation.

h2o.rbind

Combine H2O Datasets by Rows

Description
Takes a sequence of H2O data sets and combines them by rows
Usage
h2o.rbind(...)
Arguments
...

A sequence of H2OFrame arguments. All datasets must exist on the same H2O
instance (IP and port) and contain the same number and types of columns.

Value
An H2OFrame object containing the combined . . . arguments row-wise.
See Also
rbind for the base R method.

152

h2o.reconstruct

Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
prostate.cbind <- h2o.rbind(prostate.hex, prostate.hex)
head(prostate.cbind)

h2o.reconstruct

Reconstruct Training Data via H2O GLRM Model

Description
Reconstruct the training data and impute missing values from the H2O GLRM model by computing
the matrix product of X and Y, and transforming back to the original feature space by minimizing
each column’s loss function.
Usage
h2o.reconstruct(object, data, reverse_transform = FALSE)
Arguments
object

An H2ODimReductionModel object that represents the model to be used for
reconstruction.
data
An H2OFrame object representing the training data for the H2O GLRM model.
Used to set the domain of each column in the reconstructed frame.
reverse_transform
(Optional) A logical value indicating whether to reverse the transformation from
model-building by re-scaling columns and adding back the offset to each column
of the reconstructed frame.
Value
Returns an H2OFrame object containing the approximate reconstruction of the training data;
See Also
h2o.glrm for making an H2ODimReductionModel.
Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris_wheader.csv", package="h2o")
iris.hex <- h2o.uploadFile(path = irisPath)
iris.glrm <- h2o.glrm(training_frame = iris.hex, k = 4, transform = "STANDARDIZE",
loss = "Quadratic", multi_loss = "Categorical", max_iterations = 1000)
iris.rec <- h2o.reconstruct(iris.glrm, iris.hex, reverse_transform = TRUE)
head(iris.rec)

h2o.relevel

153

h2o.relevel

Reorders levels of an H2O factor, similarly to standard R’s relevel.

Description
The levels of a factor are reordered os that the reference level is at level 0, remaining levels are
moved down as needed.
Usage
h2o.relevel(x, y)
Arguments
x

factor column in h2o frame

y

reference level (string)

Value
new reordered factor column

h2o.removeAll

Remove All Objects on the H2O Cluster

Description
Removes the data from the h2o cluster, but does not remove the local references.
Usage
h2o.removeAll(timeout_secs = 0)
Arguments
timeout_secs

Timeout in seconds. Default is no timeout.

See Also
h2o.rm
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package = "h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.ls()
h2o.removeAll()
h2o.ls()

154

h2o.rep_len

h2o.removeVecs

Delete Columns from an H2OFrame

Description
Delete the specified columns from the H2OFrame. Returns an H2OFrame without the specified
columns.

Usage
h2o.removeVecs(data, cols)
Arguments
data

The H2OFrame.

cols

The columns to remove.

h2o.rep_len

Replicate Elements of Vectors or Lists into H2O

Description
h2o.rep_len performs just as rep does. It replicates the values in x in the H2O backend.
Usage
h2o.rep_len(x, length.out)
Arguments
x

an H2O frame

length.out

non negative integer. The desired length of the output vector.

Value
Creates an H2OFrame of the same type as x

h2o.residual_deviance

155

h2o.residual_deviance Retrieve the residual deviance

Description
If "train", "valid", and "xval" parameters are FALSE (default), then the training residual deviance
value is returned. If more than one parameter is set to TRUE, then a named vector of residual
deviances are returned, where the names are "train", "valid" or "xval".
Usage
h2o.residual_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel or H2OModelMetrics

train

Retrieve the training residual deviance

valid

Retrieve the validation residual deviance

xval

Retrieve the cross-validation residual deviance

h2o.residual_dof

Retrieve the residual degrees of freedom

Description
If "train", "valid", and "xval" parameters are FALSE (default), then the training residual degrees
of freedom value is returned. If more than one parameter is set to TRUE, then a named vector of
residual degrees of freedom are returned, where the names are "train", "valid" or "xval".
Usage
h2o.residual_dof(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel or H2OModelMetrics

train

Retrieve the training residual degrees of freedom

valid

Retrieve the validation residual degrees of freedom

xval

Retrieve the cross-validation residual degrees of freedom

156

h2o.rmse

h2o.rm

Delete Objects In H2O

Description
Remove the h2o Big Data object(s) having the key name(s) from ids.
Usage
h2o.rm(ids)
Arguments
ids

The object or hex key associated with the object to be removed or a vector/list
of those things.

See Also
h2o.assign, h2o.ls

h2o.rmse

Retrieves Root Mean Squared Error Value

Description
Retrieves the root mean squared error value from an H2OModelMetrics object. If "train", "valid",
and "xval" parameters are FALSE (default), then the training RMSEvalue is returned. If more than
one parameter is set to TRUE, then a named vector of RMSEs are returned, where the names are
"train", "valid" or "xval".
Usage
h2o.rmse(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModelMetrics object of the correct type.

train

Retrieve the training RMSE

valid

Retrieve the validation RMSE

xval

Retrieve the cross-validation RMSE

Details
This function only supports H2OBinomialMetrics, H2OMultinomialMetrics, and H2ORegressionMetrics
objects.
See Also
h2o.auc for AUC, h2o.mse for RMSE, and h2o.metric for the various threshold metrics. See
h2o.performance for creating H2OModelMetrics objects.

h2o.rmsle

157

Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
perf <- h2o.performance(model, hex)
h2o.rmse(perf)

h2o.rmsle

Retrieve the Root Mean Squared Log Error

Description
Retrieves the root mean squared log error (RMSLE) value from an H2O model. If "train", "valid",
and "xval" parameters are FALSE (default), then the training rmsle value is returned. If more than
one parameter is set to TRUE, then a named vector of rmsles are returned, where the names are
"train", "valid" or "xval".
Usage
h2o.rmsle(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OModel object.

train

Retrieve the training rmsle

valid

Retrieve the validation set rmsle if a validation set was passed in during model
build time.

xval

Retrieve the cross-validation rmsle

Examples
library(h2o)
h <- h2o.init()
fr <- as.h2o(iris)
m <- h2o.deeplearning(x=2:5,y=1,training_frame=fr)
h2o.rmsle(m)

158

h2o.rstrip

h2o.round

Round doubles/floats to the given number of decimal places.

Description
Round doubles/floats to the given number of decimal places.
Usage
h2o.round(x, digits = 0)
round(x, digits = 0)
Arguments
x

An H2OFrame object.

digits

Number of decimal places to round doubles/floats. Rounding to a negative number of decimal places is

See Also
round for the base R implementation.

h2o.rstrip

Strip set from right

Description
Return a copy of the target column with trailing characters removed. The set argument is a string
specifying the set of characters to be removed. If omitted, the set argument defaults to removing
whitespace.
Usage
h2o.rstrip(x, set = " ")
Arguments
x

The column whose strings should be rstrip-ed.

set

string of characters to be removed

Examples
library(h2o)
h2o.init()
string_to_rstrip <- as.h2o("1234567890")
rstrip_string <- h2o.rstrip(string_to_rstrip,"890") #Remove "890"

h2o.runif

h2o.runif

159

Produce a Vector of Random Uniform Numbers

Description
Creates a vector of random uniform numbers equal in length to the length of the specified H2O
dataset.
Usage
h2o.runif(x, seed = -1)
Arguments
x

An H2OFrame object.

seed

A random seed used to generate draws from the uniform distribution.

Value
A vector of random, uniformly distributed numbers. The elements are between 0 and 1.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.importFile(path = prosPath, destination_frame = "prostate.hex")
s <- h2o.runif(prostate.hex)
summary(s)
prostate.train <- prostate.hex[s <= 0.8,]
prostate.train <- h2o.assign(prostate.train, "prostate.train")
prostate.test <- prostate.hex[s > 0.8,]
prostate.test <- h2o.assign(prostate.test, "prostate.test")
nrow(prostate.train) + nrow(prostate.test)

h2o.saveModel

Save an H2O Model Object to Disk

Description
Save an H2OModel to disk. (Note that ensemble binary models can be saved.)
Usage
h2o.saveModel(object, path = "", force = FALSE)

160

h2o.saveModelDetails

Arguments
object

an H2OModel object.

path

string indicating the directory the model will be written to.

force

logical, indicates how to deal with files that already exist.

Details
In the case of existing files force = TRUE will overwrite the file. Otherwise, the operation will fail.
See Also
h2o.loadModel for loading a model to H2O from disk
Examples
## Not run:
# library(h2o)
# h2o.init()
# prostate.hex <- h2o.importFile(path = paste("https://raw.github.com",
#
"h2oai/h2o-2/master/smalldata/logreg/prostate.csv", sep = "/"),
#
destination_frame = "prostate.hex")
# prostate.glm <- h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"),
#
training_frame = prostate.hex, family = "binomial", alpha = 0.5)
# h2o.saveModel(object = prostate.glm, path = "/Users/UserName/Desktop", force=TRUE)
## End(Not run)

h2o.saveModelDetails

Save an H2O Model Details

Description
Save Model Details of an H2O Model in JSON Format
Usage
h2o.saveModelDetails(object, path = "", force = FALSE)
Arguments
object

an H2OModel object.

path

string indicating the directory the model details will be written to.

force

logical, indicates how to deal with files that already exist.

Details
Model Details will download as a JSON file. In the case of existing files force = TRUE will
overwrite the file. Otherwise, the operation will fail.

h2o.saveMojo

161

Examples
## Not run:
# library(h2o)
# h2o.init()
# prostate.hex <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package="h2o"))
# prostate.glm <- h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"),
#
training_frame = prostate.hex, family = "binomial", alpha = 0.5)
# h2o.saveModelDetails(object = prostate.glm, path = "/Users/UserName/Desktop", force=TRUE)
## End(Not run)

h2o.saveMojo

Save an H2O Model Object as Mojo to Disk

Description
Save an MOJO (Model Object, Optimized) to disk.
Usage
h2o.saveMojo(object, path = "", force = FALSE)
Arguments
object

an H2OModel object.

path

string indicating the directory the model will be written to.

force

logical, indicates how to deal with files that already exist.

Details
MOJO will download as a zip file. In the case of existing files force = TRUE will overwrite the
file. Otherwise, the operation will fail.
See Also
h2o.saveModel for saving a model to disk as a binary object.
Examples
## Not run:
# library(h2o)
# h2o.init()
# prostate.hex <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package="h2o"))
# prostate.glm <- h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"),
#
training_frame = prostate.hex, family = "binomial", alpha = 0.5)
# h2o.saveMojo(object = prostate.glm, path = "/Users/UserName/Desktop", force=TRUE)
## End(Not run)

162

h2o.scoreHistory

h2o.scale

Scaling and Centering of an H2OFrame

Description
Centers and/or scales the columns of an H2O dataset.
Usage
h2o.scale(x, center = TRUE, scale = TRUE)
## S3 method for class 'H2OFrame'
scale(x, center = TRUE, scale = TRUE)
Arguments
x

An H2OFrame object.

center

either a logical value or numeric vector of length equal to the number of
columns of x.

scale

either a logical value or numeric vector of length equal to the number of
columns of x.

Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris_wheader.csv", package="h2o")
iris.hex <- h2o.uploadFile(path = irisPath, destination_frame = "iris.hex")
summary(iris.hex)
# Scale and center all the numeric columns in iris data set
scale(iris.hex[, 1:4])

h2o.scoreHistory

Retrieve Model Score History

Description
Retrieve Model Score History
Usage
h2o.scoreHistory(object)
Arguments
object

An H2OModel object.

h2o.sd

163

h2o.sd

Standard Deviation of a column of data.

Description
Obtain the standard deviation of a column of data.
Usage
h2o.sd(x, na.rm = FALSE)
sd(x, na.rm = FALSE)
Arguments
x

An H2OFrame object.

na.rm

logical. Should missing values be removed?

See Also
h2o.var for variance, and sd for the base R implementation.
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
sd(prostate.hex$AGE)

h2o.sdev

Retrieve the standard deviations of principal components

Description
Retrieve the standard deviations of principal components
Usage
h2o.sdev(object)
Arguments
object

An H2ODimReductionModel object.

164

h2o.setTimezone

h2o.setLevels

Set Levels of H2O Factor Column

Description
Works on a single categorical vector. New domains must be aligned with the old domains. This call
has SIDE EFFECTS and mutates the column in place (change of the levels will also affect all the
frames that are referencing this column). If you want to make a copy of the column instead, use
parameter in.place = FALSE.
Usage
h2o.setLevels(x, levels, in.place = TRUE)
Arguments
x

A single categorical column.

levels

A character vector specifying the new levels. The number of new levels must
match the number of old levels.

in.place

Indicates whether new domain will be directly applied to the column (in place
change) or if a copy of the column will be created with the given domain levels.

Examples
h2o.init()
iris.hex <- as.h2o(iris)
new.levels <- c("setosa", "versicolor", "caroliniana")
iris.hex$Species <- h2o.setLevels(iris.hex$Species, new.levels, in.place = FALSE)
h2o.levels(iris.hex$Species)

h2o.setTimezone

Set the Time Zone on the H2O Cloud

Description
Set the Time Zone on the H2O Cloud
Usage
h2o.setTimezone(tz)
Arguments
tz

The desired timezone.

h2o.show_progress

165

h2o.show_progress

Enable Progress Bar

Description
Enable Progress Bar
Usage
h2o.show_progress()

h2o.shutdown

Shut Down H2O Instance

Description
Shut down the specified instance. All data will be lost.
Usage
h2o.shutdown(prompt = TRUE)
Arguments
prompt

A logical value indicating whether to prompt the user before shutting down
the H2O server.

Details
This method checks if H2O is running at the specified IP address and port, and if it is, shuts down
that H2O instance.
WARNING
All data, models, and other values stored on the server will be lost! Only call this function if you
and all other clients connected to the H2O server are finished and have saved your work.
Note
Users must call h2o.shutdown explicitly in order to shut down the local H2O instance started by R.
If R is closed before H2O, then an attempt will be made to automatically shut down H2O. This only
applies to local instances started with h2o.init, not remote H2O servers.
See Also
h2o.init

166

h2o.sin

Examples
# Don't run automatically to prevent accidentally shutting down a cloud
## Not run:
library(h2o)
h2o.init()
h2o.shutdown()
## End(Not run)

h2o.signif

Round doubles/floats to the given number of significant digits.

Description
Round doubles/floats to the given number of significant digits.
Usage
h2o.signif(x, digits = 6)
signif(x, digits = 6)
Arguments
x

An H2OFrame object.

digits

Number of significant digits to round doubles/floats.

See Also
signif for the base R implementation.

h2o.sin

Compute the sine of x

Description
Compute the sine of x
Usage
h2o.sin(x)
Arguments
x

An H2OFrame object.

See Also
sin for the base R implementation.

h2o.skewness

h2o.skewness

167

Skewness of a column

Description
Obtain the skewness of a column of a parsed H2O data object.
Usage
h2o.skewness(x, ..., na.rm = TRUE)
skewness.H2OFrame(x, ..., na.rm = TRUE)
Arguments
x

An H2OFrame object.

...

Further arguments to be passed from or to other methods.

na.rm

A logical value indicating whether NA or missing values should be stripped before the computation.

Value
Returns a list containing the skewness for each column (NaN for non-numeric columns).
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.skewness(prostate.hex$AGE)

h2o.splitFrame

Split an H2O Data Set

Description
Split an existing H2O data set according to user-specified ratios. The number of subsets is always 1
more than the number of given ratios. Note that this does not give an exact split. H2O is designed
to be efficient on big data using a probabilistic splitting method rather than an exact split. For
example, when specifying a split of 0.75/0.25, H2O will produce a test/train split with an expected
value of 0.75/0.25 rather than exactly 0.75/0.25. On small datasets, the sizes of the resulting splits
will deviate from the expected value more than on big data, where they will be very close to exact.
Usage
h2o.splitFrame(data, ratios = 0.75, destination_frames, seed = -1)

168

h2o.sqrt

Arguments
data

An H2OFrame object representing the dataste to split.

ratios

A numeric value or array indicating the ratio of total rows contained in each
split. Must total up to less than 1.

destination_frames
An array of frame IDs equal to the number of ratios specified plus one.
seed

Random seed.

Value
Returns a list of split H2OFrame’s
Examples
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris.csv", package = "h2o")
iris.hex <- h2o.importFile(path = irisPath)
iris.split <- h2o.splitFrame(iris.hex, ratios = c(0.2, 0.5))
head(iris.split[[1]])
summary(iris.split[[1]])

h2o.sqrt

Compute the square root of x

Description
Compute the square root of x
Usage
h2o.sqrt(x)
Arguments
x

An H2OFrame object.

See Also
sqrt for the base R implementation.

h2o.stackedEnsemble

169

h2o.stackedEnsemble

Builds a Stacked Ensemble

Description
Build a stacked ensemble (aka. Super Learner) using the H2O base learning algorithms specified
by the user.
Usage
h2o.stackedEnsemble(x, y, training_frame, model_id = NULL,
validation_frame = NULL, base_models = list(),
metalearner_algorithm = c("AUTO", "glm", "gbm", "drf", "deeplearning"),
metalearner_nfolds = 0, metalearner_fold_assignment = c("AUTO", "Random",
"Modulo", "Stratified"), metalearner_fold_column = NULL,
keep_levelone_frame = FALSE, seed = -1, metalearner_params = NULL)
Arguments
x

(Optional). A vector containing the names or indices of the predictor variables
to use in building the model. If x is missing, then all columns except y are used.
Training frame is used only to compute ensemble training metrics.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
base_models

List of models (or model ids) to ensemble/stack together. Models must have
been cross-validated using nfolds > 1, and folds must be identical across models.
Defaults to [].
metalearner_algorithm
Type of algorithm to use as the metalearner. Options include ’AUTO’ (GLM
with non negative weights; if validation_frame is present, a lambda search is
performed), ’glm’ (GLM with default parameters), ’gbm’ (GBM with default
parameters), ’drf’ (Random Forest with default parameters), or ’deeplearning’
(Deep Learning with default parameters). Must be one of: "AUTO", "glm",
"gbm", "drf", "deeplearning". Defaults to AUTO.
metalearner_nfolds
Number of folds for K-fold cross-validation of the metalearner algorithm (0 to
disable or >= 2). Defaults to 0.
metalearner_fold_assignment
Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The ’Stratified’ option will
stratify the folds based on the response variable, for classification problems.
Must be one of: "AUTO", "Random", "Modulo", "Stratified".

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h2o.startLogging
metalearner_fold_column
Column with cross-validation fold index assignment per observation for crossvalidation of the metalearner.
keep_levelone_frame
Logical. Keep level one frame used for metalearner training. Defaults to
FALSE.
seed

Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number) Defaults to -1 (time-based random
number).
metalearner_params
Parameters for metalearner algorithm Defaults to NULL.
Examples
# See example R code here:
# http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html

h2o.startLogging

Start Writing H2O R Logs

Description
Begin logging H2o R POST commands and error responses to local disk. Used primarily for debuggin purposes.
Usage
h2o.startLogging(file)
Arguments
file

a character string name for the file, automatically generated

See Also
h2o.stopLogging, h2o.clearLog,

h2o.openLog

Examples
library(h2o)
h2o.init()
h2o.startLogging()
ausPath = system.file("extdata", "australia.csv", package="h2o")
australia.hex = h2o.importFile(path = ausPath)
h2o.stopLogging()

h2o.std_coef_plot

h2o.std_coef_plot

171

Plot Standardized Coefficient Magnitudes

Description
Plot a GLM model’s standardized coefficient magnitudes.
Usage
h2o.std_coef_plot(model, num_of_features = NULL)
Arguments
model
A trained generalized linear model
num_of_features
The number of features to be shown in the plot
See Also
h2o.varimp_plot for variable importances plot of random forest, GBM, deep learning.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.importFile(prosPath)
prostate.hex[,2] <- as.factor(prostate.hex[,2])
prostate.glm <- h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"),
training_frame = prostate.hex, family = "binomial",
nfolds = 0, alpha = 0.5, lambda_search = FALSE)
h2o.std_coef_plot(prostate.glm)

h2o.stopLogging

Stop Writing H2O R Logs

Description
Halt logging of H2O R POST commands and error responses to local disk. Used primarily for
debugging purposes.
Usage
h2o.stopLogging()
See Also
h2o.startLogging, h2o.clearLog,

h2o.openLog

172

h2o.stringdist

Examples
library(h2o)
h2o.init()
h2o.startLogging()
ausPath = system.file("extdata", "australia.csv", package="h2o")
australia.hex = h2o.importFile(path = ausPath)
h2o.stopLogging()

h2o.str

Display the structure of an H2OFrame object

Description
Display the structure of an H2OFrame object
Usage
h2o.str(object, ..., cols = FALSE)
Arguments
object
...
cols

h2o.stringdist

An H2OFrame.
Further arguments to be passed from or to other methods.
Print the per-column str for the H2OFrame

Compute element-wise string distances between two H2OFrames

Description
Compute element-wise string distances between two H2OFrames. Both frames need to have the
same shape (N x M) and only contain string/factor columns. Return a matrix (H2OFrame) of shape
N x M.
Usage
h2o.stringdist(x, y, method = c("lv", "lcs", "qgram", "jaccard", "jw",
"soundex"), compare_empty = TRUE)
Arguments
x
y
method

compare_empty

An H2OFrame
A comparison H2OFrame
A string identifier indicating what string distance measure to use. Must be
one of: "lv" - Levenshtein distance "lcs" - Longest common substring distance
"qgram" - q-gram distance "jaccard" - Jaccard distance between q-gram profiles
"jw" - Jaro, or Jaro-Winker distance "soundex" - Distance based on soundex
encoding
if set to FALSE, empty strings will be handled as NaNs

h2o.strsplit

173

Examples
h2o.init()
x <- as.h2o(c("Martha", "Dwayne", "Dixon"))
y <- as.character(as.h2o(c("Marhta", "Duane", "Dicksonx")))
h2o.stringdist(x, y, method = "jw")

h2o.strsplit

String Split

Description
String Split
Usage
h2o.strsplit(x, split)
Arguments
x

The column whose strings must be split.

split

The pattern to split on.

Value
An H2OFrame where each column is the outcome of the string split.
Examples
library(h2o)
h2o.init()
string_to_split <- as.h2o("Split at every character.")
split_string <- h2o.strsplit(string_to_split,"")

h2o.sub

String Substitute

Description
Creates a copy of the target column in which each string has the first occurence of the regex pattern
replaced with the replacement substring.
Usage
h2o.sub(pattern, replacement, x, ignore.case = FALSE)

174

h2o.substring

Arguments
pattern

The pattern to replace.

replacement

The replacement pattern.

x

The column on which to operate.

ignore.case

Case sensitive or not

Examples
library(h2o)
h2o.init()
string_to_sub <- as.h2o("r tutorial")
sub_string <- h2o.sub("r ","H2O ",string_to_sub)

h2o.substring

Substring

Description
Returns a copy of the target column that is a substring at the specified start and stop indices, inclusive. If the stop index is not specified, then the substring extends to the end of the original string. If
start is longer than the number of characters in the original string, or is greater than stop, an empty
string is returned. Negative start is coerced to 0.
Usage
h2o.substring(x, start, stop = "[]")
h2o.substr(x, start, stop = "[]")
Arguments
x

The column on which to operate.

start

The index of the first element to be included in the substring.

stop

Optional, The index of the last element to be included in the substring.

Examples
library(h2o)
h2o.init()
string_to_substring <- as.h2o("1234567890")
substr <- h2o.substring(string_to_substring,2) #Get substring from second index onwards

h2o.sum

h2o.sum

175

Compute the frame’s sum by-column (or by-row).

Description
Compute the frame’s sum by-column (or by-row).
Usage
h2o.sum(x, na.rm = FALSE, axis = 0, return_frame = FALSE)
Arguments
x

An H2OFrame object.

na.rm

logical. indicating whether missing values should be removed.

axis

An int that indicates whether to do down a column (0) or across a row (1).

return_frame

A boolean that indicates whether to return an H2O frame or a list. Default is
FALSE.

See Also
sum for the base R implementation.

h2o.summary

Summarizes the columns of an H2OFrame.

Description
A method for the summary generic. Summarizes the columns of an H2O data frame or subset of
columns and rows using vector notation (e.g. dataset[row, col]).
Usage
h2o.summary(object, factors = 6L, exact_quantiles = FALSE, ...)
## S3 method for class 'H2OFrame'
summary(object, factors, exact_quantiles, ...)
Arguments
object

An H2OFrame object.

factors
The number of factors to return in the summary. Default is the top 6.
exact_quantiles
Compute exact quantiles or use approximation. Default is to use approximation.
...

Further arguments passed to or from other methods.

176

h2o.svd

Details
By default it uses approximated version of quantiles computation, however, user can modify this
behavior by setting up exact_quantiles argument to true.
Value
A table displaying the minimum, 1st quartile, median, mean, 3rd quartile and maximum for each
numeric column, and the levels and category counts of the levels in each categorical column.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.importFile(path = prosPath)
summary(prostate.hex)
summary(prostate.hex$GLEASON)
summary(prostate.hex[,4:6])
summary(prostate.hex, exact_quantiles=TRUE)

h2o.svd

Singular value decomposition of an H2O data frame using the power
method

Description
Singular value decomposition of an H2O data frame using the power method
Usage
h2o.svd(training_frame, x, destination_key, model_id = NULL,
validation_frame = NULL, ignore_const_cols = TRUE,
score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE",
"NORMALIZE", "DEMEAN", "DESCALE"), svd_method = c("GramSVD", "Power",
"Randomized"), nv = 1, max_iterations = 1000, seed = -1,
keep_u = TRUE, u_name = NULL, use_all_factor_levels = TRUE,
max_runtime_secs = 0)
Arguments
training_frame Id of the training data frame.
x
A vector containing the character names of the predictors in the model.
destination_key
(Optional) The unique hex key assigned to the resulting model. Automatically
generated if none is provided.
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.

h2o.svd

177

ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
transform

Transformation of training data Must be one of: "NONE", "STANDARDIZE",
"NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE.

svd_method

Method for computing SVD (Caution: Randomized is currently experimental
and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults
to GramSVD.

nv

Number of right singular vectors Defaults to 1.

max_iterations Maximum iterations Defaults to 1000.
seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).

keep_u

Logical. Save left singular vectors? Defaults to TRUE.

u_name

Frame key to save left singular vectors

use_all_factor_levels
Logical. Whether first factor level is included in each categorical expansion
Defaults to TRUE.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.

Value
Returns an object of class H2ODimReductionModel.

References
N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms
for constructing approximate matrix decompositions[http://arxiv.org/abs/0909.4061]. SIAM Rev.,
Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.

Examples
library(h2o)
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
h2o.svd(training_frame = australia.hex, nv = 8)

178

h2o.tabulate

h2o.table

Cross Tabulation and Table Creation in H2O

Description
Uses the cross-classifying factors to build a table of counts at each combination of factor levels.
Usage
h2o.table(x, y = NULL, dense = TRUE)
table.H2OFrame(x, y = NULL, dense = TRUE)
Arguments
x

An H2OFrame object with at most two columns.

y

An H2OFrame similar to x, or NULL.

dense

A logical for dense representation, which lists only non-zero counts, 1 combination per row. Set to FALSE to expand counts across all combinations.

Value
Returns a tabulated H2OFrame object.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath, destination_frame = "prostate.hex")
summary(prostate.hex)
# Counts of the ages of all patients
head(h2o.table(prostate.hex[,3]))
h2o.table(prostate.hex[,3])
# Two-way table of ages (rows) and race (cols) of all patients
head(h2o.table(prostate.hex[,c(3,4)]))
h2o.table(prostate.hex[,c(3,4)])

h2o.tabulate

Tabulation between Two Columns of an H2OFrame

Description
Simple Co-Occurrence based tabulation of X vs Y, where X and Y are two Vecs in a given dataset.
Uses histogram of given resolution in X and Y. Handles numerical/categorical data and missing
values. Supports observation weights.

h2o.tan

179

Usage
h2o.tabulate(data, x, y, weights_column = NULL, nbins_x = 50,
nbins_y = 50)
Arguments
data

An H2OFrame object.

x

predictor column

y

response column

weights_column (optional) observation weights column
nbins_x

number of bins for predictor column

nbins_y

number of bins for response column

Value
Returns two TwoDimTables of 3 columns each count_table: X Y counts response_table: X meanY
counts
Examples
library(h2o)
h2o.init()
df <- as.h2o(iris)
tab <- h2o.tabulate(data = df, x = "Sepal.Length", y = "Petal.Width",
weights_column = NULL, nbins_x = 10, nbins_y = 10)
plot(tab)

h2o.tan

Compute the tangent of x

Description
Compute the tangent of x
Usage
h2o.tan(x)
Arguments
x

An H2OFrame object.

See Also
tan for the base R implementation.

180

h2o.target_encode_apply

h2o.tanh

Compute the hyperbolic tangent of x

Description
Compute the hyperbolic tangent of x
Usage
h2o.tanh(x)
Arguments
x

An H2OFrame object.

See Also
tanh for the base R implementation.

h2o.target_encode_apply
Apply Target Encoding Map to Frame

Description
Applies a target encoding map to an H2OFrame object. Computing target encoding for high cardinality categorical columns can improve performance of supervised learning models.
Usage
h2o.target_encode_apply(data, x, y, target_encode_map, holdout_type,
fold_column = NULL, blended_avg = TRUE, noise_level = NULL, seed = -1)
Arguments
data

An H2OFrame object with which to apply the target encoding map.

x

A list containing the names or indices of the variables to encode. A target encoding column will be created for each element in the list. Items in the list can
be multiple columns. For example, if ‘x = list(c("A"), c("B", "C"))‘, then the
resulting frame will have a target encoding column for A and a target encoding
column for B & C (in this case, we group by two columns).

y

The name or column index of the response variable in the data. The response
variable can be either numeric or binary.
target_encode_map
A list of H2OFrame objects that is the results of the h2o.target_encode_create
function.
holdout_type

The holdout type used. Must be one of: "LeaveOneOut", "KFold", "None".

fold_column

(Optional) The name or column index of the fold column in the data. Defaults
to NULL (no ‘fold_column‘). Only required if ‘holdout_type‘ = "KFold".

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181

blended_avg

Logical. (Optional) Whether to perform blended average.

noise_level

(Optional) The amount of random noise added to the target encoding. This helps
prevent overfitting. Defaults to 0.01 * range of y.

seed

(Optional) A random seed used to generate draws from the uniform distribution
for random noise. Defaults to -1.

Value
Returns an H2OFrame object containing the target encoding per record.
See Also
h2o.target_encode_create for creating the target encoding map
Examples
library(h2o)
h2o.init()
# Get Target Encoding Frame on bank-additional-full data with numeric `y`
data <- h2o.importFile(
path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv",
destination_frame = "data")
splits <- h2o.splitFrame(data, seed = 1234)
train <- splits[[1]]
test <- splits[[2]]
mapping <- h2o.target_encode_create(data = train, x = list(c("job"), c("job", "marital")),
y = "age")
# Apply mapping to the training dataset
train_encode <- h2o.target_encode_apply(data = train, x = list(c("job"), c("job", "marital")),
y = "age", mapping, holdout_type = "LeaveOneOut")
# Apply mapping to a test dataset
test_encode <- h2o.target_encode_apply(data = test, x = list(c("job"), c("job", "marital")),
y = "age", target_encode_map = mapping, holdout_type = "None")

h2o.target_encode_create
Create Target Encoding Map

Description
Creates a target encoding map based on group-by columns (‘x‘) and a numeric or binary target
column (‘y‘). Computing target encoding for high cardinality categorical columns can improve
performance of supervised learning models.
Usage
h2o.target_encode_create(data, x, y, fold_column = NULL)

182

h2o.toFrame

Arguments
data

An H2OFrame object with which to create the target encoding map.

x

A list containing the names or indices of the variables to encode. A target encoding map will be created for each element in the list. Items in the list can be
multiple columns. For example, if ‘x = list(c("A"), c("B", "C"))‘, then there will
be one mapping frame for A and one mapping frame for B & C (in this case, we
group by two columns).

y

The name or column index of the response variable in the data. The response
variable can be either numeric or binary.

fold_column

(Optional) The name or column index of the fold column in the data. Defaults
to NULL (no ‘fold_column‘).

Value
Returns a list of H2OFrame objects containing the target encoding mapping for each column in ‘x‘.
See Also
h2o.target_encode_apply for applying the target encoding mapping to a frame.
Examples
library(h2o)
h2o.init()
# Get Target Encoding Map on bank-additional-full data with numeric response
data <- h2o.importFile(
path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv",
destination_frame = "data")
mapping_age <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")),
y = "age")
head(mapping_age)
# Get Target Encoding Map on bank-additional-full data with binary response
mapping_y <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")),
y = "y")
head(mapping_y)

h2o.toFrame

Convert a word2vec model into an H2OFrame

Description
Converts a given word2vec model into an H2OFrame. The frame represents learned word embeddings
Usage
h2o.toFrame(word2vec)

h2o.tokenize

183

Arguments
word2vec

A word2vec model.

Examples
h2o.init()
# Build a dummy word2vec model
data <- as.character(as.h2o(c("a", "b", "a")))
w2v.model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2)
# Transform words to vectors and return average vector for each sentence
h2o.toFrame(w2v.model) # -> Frame made of 2 rows and 2 columns

h2o.tokenize

Tokenize String

Description
h2o.tokenize is similar to h2o.strsplit, the difference between them is that h2o.tokenize will store
the tokenized text into a single column making it easier for additional processing (filtering stop
words, word2vec algo, ...).
Usage
h2o.tokenize(x, split)
Arguments
x

The column or columns whose strings to tokenize.

split

The regular expression to split on.

Value
An H2OFrame with a single column representing the tokenized Strings. Original rows of the input
DF are separated by NA.
Examples
library(h2o)
h2o.init()
string_to_tokenize <- as.h2o("Split at every character and tokenize.")
tokenize_string <- h2o.tokenize(as.character(string_to_tokenize),"")

184

h2o.topN

h2o.tolower

Convert strings to lowercase

Description
Convert strings to lowercase
Usage
h2o.tolower(x)
Arguments
x

An H2OFrame object whose strings should be lower cased

Value
An H2OFrame with all entries in lowercase format
Examples
library(h2o)
h2o.init()
string_to_lower <- as.h2o("ABCDE")
lowered_string <- h2o.tolower(string_to_lower)

h2o.topN

H2O topN

Description
Extract the top N percent of values of a column and return it in a H2OFrame.
Usage
h2o.topN(x, column, nPercent)
Arguments
x

an H2OFrame

column

is a column name or column index to grab the top N percent value from

nPercent

is a top percentage value to grab

Value
An H2OFrame with 2 columns. The first column is the original row indices, second column contains
the topN values

h2o.totss

h2o.totss

185

Get the total sum of squares.

Description
If "train", "valid", and "xval" parameters are FALSE (default), then the training totss value is returned. If more than one parameter is set to TRUE, then a named vector of totss’ are returned, where
the names are "train", "valid" or "xval".
Usage
h2o.totss(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OClusteringModel object.

train

Retrieve the training total sum of squares

valid

Retrieve the validation total sum of squares

xval

Retrieve the cross-validation total sum of squares

h2o.tot_withinss

Get the total within cluster sum of squares.

Description
If "train", "valid", and "xval" parameters are FALSE (default), then the training tot_withinss value
is returned. If more than one parameter is set to TRUE, then a named vector of tot_withinss’ are
returned, where the names are "train", "valid" or "xval".
Usage
h2o.tot_withinss(object, train = FALSE, valid = FALSE, xval = FALSE)
Arguments
object

An H2OClusteringModel object.

train

Retrieve the training total within cluster sum of squares

valid

Retrieve the validation total within cluster sum of squares

xval

Retrieve the cross-validation total within cluster sum of squares

186

h2o.transform

h2o.toupper

Convert strings to uppercase

Description
Convert strings to uppercase
Usage
h2o.toupper(x)
Arguments
x

An H2OFrame object whose strings should be upper cased

Value
An H2OFrame with all entries in uppercase format
Examples
library(h2o)
h2o.init()
string_to_upper <- as.h2o("abcde")
upper_string <- h2o.toupper(string_to_upper)

h2o.transform

Transform words (or sequences of words) to vectors using a word2vec
model.

Description
Transform words (or sequences of words) to vectors using a word2vec model.
Usage
h2o.transform(word2vec, words, aggregate_method = c("NONE", "AVERAGE"))
Arguments
word2vec

A word2vec model.

words
An H2OFrame made of a single column containing source words.
aggregate_method
Specifies how to aggregate sequences of words. If method is ‘NONE‘ then no
aggregation is performed and each input word is mapped to a single word-vector.
If method is ’AVERAGE’ then input is treated as sequences of words delimited
by NA. Each word of a sequences is internally mapped to a vector and vectors
belonging to the same sentence are averaged and returned in the result.

h2o.trim

187

Examples
h2o.init()
# Build a dummy word2vec model
data <- as.character(as.h2o(c("a", "b", "a")))
w2v.model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2)
# Transform words to vectors without aggregation
sentences <- as.character(as.h2o(c("b", "c", "a", NA, "b")))
h2o.transform(w2v.model, sentences) # -> 5 rows total, 2 rows NA ("c" is not in the vocabulary)
# Transform words to vectors and return average vector for each sentence
h2o.transform(w2v.model, sentences, aggregate_method = "AVERAGE") # -> 2 rows

h2o.trim

Trim Space

Description
Trim Space
Usage
h2o.trim(x)
Arguments
x

The column whose strings should be trimmed.

Examples
library(h2o)
h2o.init()
string_to_trim <- as.h2o("r tutorial")
trim_string <- h2o.trim(string_to_trim)

h2o.trunc

Truncate values in x toward 0

Description
trunc takes a single numeric argument x and returns a numeric vector containing the integers formed
by truncating the values in x toward 0.
Usage
h2o.trunc(x)

188

h2o.var

Arguments
x

An H2OFrame object.

See Also
trunc for the base R implementation.

h2o.unique

H2O Unique

Description
Extract unique values in the column.
Usage
h2o.unique(x)
Arguments
x

An H2OFrame object.

Value
Returns an H2OFrame object.

h2o.var

Variance of a column or covariance of columns.

Description
Compute the variance or covariance matrix of one or two H2OFrames.
Usage
h2o.var(x, y = NULL, na.rm = FALSE, use)
var(x, y = NULL, na.rm = FALSE, use)
Arguments
x

An H2OFrame object.

y

NULL (default) or an H2OFrame. The default is equivalent to y = x.

na.rm

logical. Should missing values be removed?

use

An optional character string indicating how to handle missing values. This must
be one of the following: "everything" - outputs NaNs whenever one of its contributing observations is missing "all.obs" - presence of missing observations
will throw an error "complete.obs" - discards missing values along with all observations in their rows so that only complete observations are used

h2o.varimp

189

See Also
var for the base R implementation. h2o.sd for standard deviation.
Examples
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
var(prostate.hex$AGE)

h2o.varimp

Retrieve the variable importance.

Description
Retrieve the variable importance.
Usage
h2o.varimp(object)
Arguments
object

An H2OModel object.

h2o.varimp_plot

Plot Variable Importances

Description
Plot Variable Importances
Usage
h2o.varimp_plot(model, num_of_features = NULL)
Arguments
model

A trained model (accepts a trained random forest, GBM, or deep learning model,
will use h2o.std_coef_plot for a trained GLM

num_of_features
The number of features shown in the plot (default is 10 or all if less than 10).
See Also
h2o.std_coef_plot for GLM.

190

h2o.week

Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.importFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
h2o.varimp_plot(model)
# for deep learning set the variable_importance parameter to TRUE
iris.hex <- as.h2o(iris)
iris.dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris.hex,
variable_importances = TRUE)
h2o.varimp_plot(iris.dl)

h2o.week

Convert Milliseconds to Week of Week Year in H2O Datasets

Description
Converts the entries of an H2OFrame object from milliseconds to weeks of the week year (starting
from 1).
Usage
h2o.week(x)
week(x)
## S3 method for class 'H2OFrame'
week(x)
Arguments
x

An H2OFrame object.

Value
An H2OFrame object containing the entries of x converted to weeks of the week year.
See Also
h2o.month

h2o.weights

h2o.weights

191

Retrieve the respective weight matrix

Description
Retrieve the respective weight matrix
Usage
h2o.weights(object, matrix_id = 1)
Arguments
object

An H2OModel or H2OModelMetrics

matrix_id

An integer, ranging from 1 to number of layers + 1, that specifies the weight
matrix to return.

h2o.which

Which indices are TRUE?

Description
Give the TRUE indices of a logical object, allowing for array indices.
Usage
h2o.which(x)
Arguments
x

An H2OFrame object.

Value
Returns an H2OFrame object.
See Also
which for the base R method.
Examples
h2o.init()
iris.hex <- as.h2o(iris)
h2o.which(iris.hex[,1]==4.4)

192

h2o.which_min

h2o.which_max

Which indice contains the max value?

Description
Get the index of the max value in a column or row
Usage
h2o.which_max(x, na.rm = TRUE, axis = 0)
which.max.H2OFrame(x, na.rm = TRUE, axis = 0)
which.min.H2OFrame(x, na.rm = TRUE, axis = 0)
Arguments
x

An H2OFrame object.

na.rm

logical. Indicate whether missing values should be removed.

axis

integer. Indicate whether to calculate the mean down a column (0) or across a
row (1).

Value
Returns an H2OFrame object.
See Also
which.max for the base R method.

h2o.which_min

Which index contains the min value?

Description
Get the index of the min value in a column or row
Usage
h2o.which_min(x, na.rm = TRUE, axis = 0)
Arguments
x

An H2OFrame object.

na.rm

logical. Indicate whether missing values should be removed.

axis

integer. Indicate whether to calculate the mean down a column (0) or across a
row (1).

h2o.withinss

193

Value
Returns an H2OFrame object.
See Also
which.min for the base R method.

h2o.withinss

Get the Within SS

Description
Get the Within SS
Usage
h2o.withinss(object)
Arguments
object

h2o.word2vec

An H2OClusteringModel object.

Trains a word2vec model on a String column of an H2O data frame

Description
Trains a word2vec model on a String column of an H2O data frame
Usage
h2o.word2vec(training_frame = NULL, model_id = NULL, min_word_freq = 5,
word_model = c("SkipGram"), norm_model = c("HSM"), vec_size = 100,
window_size = 5, sent_sample_rate = 0.001, init_learning_rate = 0.025,
epochs = 5, pre_trained = NULL, max_runtime_secs = 0)
Arguments
training_frame Id of the training data frame.
model_id

Destination id for this model; auto-generated if not specified.

min_word_freq

This will discard words that appear less than  times Defaults to 5.

word_model

Use the Skip-Gram model Must be one of: "SkipGram". Defaults to SkipGram.

norm_model

Use Hierarchical Softmax Must be one of: "HSM". Defaults to HSM.

vec_size

Set size of word vectors Defaults to 100.

window_size

Set max skip length between words Defaults to 5.

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h2o.xgboost
sent_sample_rate
Set threshold for occurrence of words. Those that appear with higher frequency
in the training data will be randomly down-sampled; useful range is (0, 1e-5)
Defaults to 0.001.
init_learning_rate
Set the starting learning rate Defaults to 0.025.
epochs

Number of training iterations to run Defaults to 5.

pre_trained

Id of a data frame that contains a pre-trained (external) word2vec model

max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.

h2o.xgboost

Build an eXtreme Gradient Boosting model

Description
Builds a eXtreme Gradient Boosting model using the native XGBoost backend.
Usage
h2o.xgboost(x, y, training_frame, model_id = NULL, validation_frame = NULL,
nfolds = 0, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE,
score_each_iteration = FALSE, fold_assignment = c("AUTO", "Random",
"Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE,
offset_column = NULL, weights_column = NULL, stopping_rounds = 0,
stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE",
"RMSLE", "AUC", "lift_top_group", "misclassification",
"mean_per_class_error"), stopping_tolerance = 0.001, max_runtime_secs = 0,
seed = -1, distribution = c("AUTO", "bernoulli", "multinomial",
"gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"),
tweedie_power = 1.5, categorical_encoding = c("AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder",
"SortByResponse", "EnumLimited"), quiet_mode = TRUE, ntrees = 50,
max_depth = 6, min_rows = 1, min_child_weight = 1, learn_rate = 0.3,
eta = 0.3, sample_rate = 1, subsample = 1, col_sample_rate = 1,
colsample_bylevel = 1, col_sample_rate_per_tree = 1,
colsample_bytree = 1, max_abs_leafnode_pred = 0, max_delta_step = 0,
score_tree_interval = 0, min_split_improvement = 0, gamma = 0,
max_bins = 256, max_leaves = 0, min_sum_hessian_in_leaf = 100,
min_data_in_leaf = 0, sample_type = c("uniform", "weighted"),
normalize_type = c("tree", "forest"), rate_drop = 0, one_drop = FALSE,
skip_drop = 0, tree_method = c("auto", "exact", "approx", "hist"),
grow_policy = c("depthwise", "lossguide"), booster = c("gbtree",
"gblinear", "dart"), reg_lambda = 0, reg_alpha = 0,
dmatrix_type = c("auto", "dense", "sparse"), backend = c("auto", "gpu",
"cpu"), gpu_id = 0, verbose = FALSE)

h2o.xgboost

195

Arguments
x

(Optional) A vector containing the names or indices of the predictor variables to
use in building the model. If x is missing, then all columns except y are used.

y

The name or column index of the response variable in the data. The response
must be either a numeric or a categorical/factor variable. If the response is
numeric, then a regression model will be trained, otherwise it will train a classification model.

training_frame Id of the training data frame.
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to
0.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to
FALSE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to
FALSE.
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The
’Stratified’ option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column
Column with cross-validation fold index assignment per observation.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
offset_column

Offset column. This will be added to the combination of columns before applying the link function.

weights_column Column with observation weights. Giving some observation a weight of zero
is equivalent to excluding it from the dataset; giving an observation a relative
weight of 2 is equivalent to repeating that row twice. Negative weights are not
allowed. Note: Weights are per-row observation weights and do not increase the
size of the data frame. This is typically the number of times a row is repeated,
but non-integer values are supported as well. During training, rows with higher
weights matter more, due to the larger loss function pre-factor.
stopping_rounds
Early stopping based on convergence of stopping_metric. Stop if simple moving
average of length k of the stopping_metric does not improve for k:=stopping_rounds
scoring events (0 to disable) Defaults to 0.
stopping_metric
Metric to use for early stopping (AUTO: logloss for classification, deviance for
regression) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE",
"MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error".
Defaults to AUTO.

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h2o.xgboost
stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to 0.
seed

Seed for random numbers (affects certain parts of the algo that are stochastic
and those might or might not be enabled by default) Defaults to -1 (time-based
random number).

distribution

Distribution function Must be one of: "AUTO", "bernoulli", "multinomial",
"gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.

tweedie_power

Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to
1.5.
categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
quiet_mode

Logical. Enable quiet mode Defaults to TRUE.

ntrees

(same as n_estimators) Number of trees. Defaults to 50.

max_depth

Maximum tree depth. Defaults to 6.

min_rows

(same as min_child_weight) Fewest allowed (weighted) observations in a leaf.
Defaults to 1.
min_child_weight
(same as min_rows) Fewest allowed (weighted) observations in a leaf. Defaults
to 1.
learn_rate

(same as eta) Learning rate (from 0.0 to 1.0) Defaults to 0.3.

eta

(same as learn_rate) Learning rate (from 0.0 to 1.0) Defaults to 0.3.

sample_rate

(same as subsample) Row sample rate per tree (from 0.0 to 1.0) Defaults to 1.

subsample
(same as sample_rate) Row sample rate per tree (from 0.0 to 1.0) Defaults to 1.
col_sample_rate
(same as colsample_bylevel) Column sample rate (from 0.0 to 1.0) Defaults to
1.
colsample_bylevel
(same as col_sample_rate) Column sample rate (from 0.0 to 1.0) Defaults to 1.
col_sample_rate_per_tree
(same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.
colsample_bytree
(same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to
1.0) Defaults to 1.
max_abs_leafnode_pred
(same as max_delta_step) Maximum absolute value of a leaf node prediction
Defaults to 0.0.
max_delta_step (same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction Defaults to 0.0.
score_tree_interval
Score the model after every so many trees. Disabled if set to 0. Defaults to 0.

h2o.xgboost.available

197

min_split_improvement
(same as gamma) Minimum relative improvement in squared error reduction for
a split to happen Defaults to 0.0.
gamma

(same as min_split_improvement) Minimum relative improvement in squared
error reduction for a split to happen Defaults to 0.0.

max_bins

For tree_method=hist only: maximum number of bins Defaults to 256.

max_leaves
For tree_method=hist only: maximum number of leaves Defaults to 0.
min_sum_hessian_in_leaf
For tree_method=hist only: the mininum sum of hessian in a leaf to keep splitting Defaults to 100.0.
min_data_in_leaf
For tree_method=hist only: the mininum data in a leaf to keep splitting Defaults
to 0.0.
sample_type

For booster=dart only: sample_type Must be one of: "uniform", "weighted".
Defaults to uniform.

normalize_type For booster=dart only: normalize_type Must be one of: "tree", "forest". Defaults
to tree.
rate_drop

For booster=dart only: rate_drop (0..1) Defaults to 0.0.

one_drop

Logical. For booster=dart only: one_drop Defaults to FALSE.

skip_drop

For booster=dart only: skip_drop (0..1) Defaults to 0.0.

tree_method

Tree method Must be one of: "auto", "exact", "approx", "hist". Defaults to auto.

grow_policy

Grow policy - depthwise is standard GBM, lossguide is LightGBM Must be one
of: "depthwise", "lossguide". Defaults to depthwise.

booster

Booster type Must be one of: "gbtree", "gblinear", "dart". Defaults to gbtree.

reg_lambda

L2 regularization Defaults to 0.0.

reg_alpha

L1 regularization Defaults to 0.0.

dmatrix_type

Type of DMatrix. For sparse, NAs and 0 are treated equally. Must be one of:
"auto", "dense", "sparse". Defaults to auto.

backend

Backend. By default (auto), a GPU is used if available. Must be one of: "auto",
"gpu", "cpu". Defaults to auto.

gpu_id

Which GPU to use. Defaults to 0.

verbose

Logical. Print scoring history to the console (Metrics per tree for GBM, DRF,
& XGBoost. Metrics per epoch for Deep Learning). Defaults to FALSE.

h2o.xgboost.available Determines whether an XGBoost model can be built

Description
Ask the H2O server whether a XGBoost model can be built. (Depends on availability of native
backend.) Returns True if a XGBoost model can be built, or False otherwise.
Usage
h2o.xgboost.available()

198

H2OAutoML-class

h2o.year

Convert Milliseconds to Years in H2O Datasets

Description
Convert the entries of an H2OFrame object from milliseconds to years, indexed starting from 1900.

Usage
h2o.year(x)
year(x)
## S3 method for class 'H2OFrame'
year(x)
Arguments
x

An H2OFrame object.

Details
This method calls the function of the MutableDateTime class in Java.

Value
An H2OFrame object containing the entries of x converted to years
See Also
h2o.month

H2OAutoML-class

The H2OAutoML class

Description
This class represents an H2OAutoML object

H2OClusteringModel-class

199

H2OClusteringModel-class
The H2OClusteringModel object.

Description
This virtual class represents a clustering model built by H2O.
Details
This object has slots for the key, which is a character string that points to the model key existing in
the H2O cloud, the data used to build the model (an object of class H2OFrame).
Slots
model_id A character string specifying the key for the model fit in the H2O cloud’s key-value
store.
algorithm A character string specifying the algorithm that was used to fit the model.
parameters A list containing the parameter settings that were used to fit the model that differ
from the defaults.
allparameters A list containing all parameters used to fit the model.
model A list containing the characteristics of the model returned by the algorithm.
size The number of points in each cluster.
totss Total sum of squared error to grand mean.
withinss A vector of within-cluster sum of squared error.
tot_withinss Total within-cluster sum of squared error.
betweenss Between-cluster sum of squared error.

H2OConnection-class

The H2OConnection class.

Description
This class represents a connection to an H2O cloud.
Usage
## S4 method for signature 'H2OConnection'
show(object)
Arguments
object

an H2OConnection object.

200

H2OCoxPHModel-class

Details
Because H2O is not a master-slave architecture, there is no restriction on which H2O node is used
to establish the connection between R (the client) and H2O (the server).
A new H2O connection is established via the h2o.init() function, which takes as parameters the ‘ip‘
and ‘port‘ of the machine running an instance to connect with. The default behavior is to connect
with a local instance of H2O at port 54321, or to boot a new local instance if one is not found at
port 54321.
Slots
ip A character string specifying the IP address of the H2O cloud.
port A numeric value specifying the port number of the H2O cloud.
proxy A character specifying the proxy path of the H2O cloud.
https Set this to TRUE to use https instead of http.
insecure Set this to TRUE to disable SSL certificate checking.
username Username to login with.
password Password to login with.
cookies Cookies to add to request
context_path Context path which is appended to H2O server location.
mutable An H2OConnectionMutableState object to hold the mutable state for the H2O connection.
H2OConnectionMutableState
The H2OConnectionMutableState class

Description
This class represents the mutable aspects of a connection to an H2O cloud.
Slots
session_id A character string specifying the H2O session identifier.
key_count A integer value specifying count for the number of keys generated for the session_id.
H2OCoxPHModel-class

The H2OCoxPHModel object.

Description
Virtual object representing H2O’s CoxPH Model.
Usage
## S4 method for signature 'H2OCoxPHModel'
show(object)
Arguments
object

an H2OCoxPHModel object.

H2OCoxPHModelSummary-class

201

H2OCoxPHModelSummary-class
The H2OCoxPHModelSummary object.

Description
Wrapper object for summary information compatible with survival package.
Slots
summary A list containing the a summary compatible with CoxPH summary used in the survival
package.

H2OFrame-class

The H2OFrame class

Description
This class represents an H2OFrame object

H2OFrame-Extract

Extract or Replace Parts of an H2OFrame Object

Description
Operators to extract or replace parts of H2OFrame objects.
Usage
## S3 method for class 'H2OFrame'
data[row, col, drop = TRUE]
## S3 method for class 'H2OFrame'
x$name
## S3 method for class 'H2OFrame'
x[[i, exact = TRUE]]
## S3 method for class 'H2OFrame'
x$name
## S3 method for class 'H2OFrame'
x[[i, exact = TRUE]]
## S3 replacement method for class 'H2OFrame'
data[row, col, ...] <- value

202

H2OGrid-class
## S3 replacement method for class 'H2OFrame'
data$name <- value
## S3 replacement method for class 'H2OFrame'
data[[name]] <- value

Arguments
data
row
col
drop
x
name
i
exact
...
value

H2OGrid-class

object from which to extract element(s) or in which to replace element(s).
index specifying row element(s) to extract or replace. Indices are numeric or
character vectors or empty (missing) or will be matched to the names.
index specifying column element(s) to extract or replace.
Unused
An H2OFrame
a literal character string or a name (possibly backtick quoted).
index
controls possible partial matching of [[ when extracting a character
Further arguments passed to or from other methods.
To be assigned

H2O Grid

Description
A class to contain the information about grid results
Format grid object in user-friendly way
Usage
## S4 method for signature 'H2OGrid'
show(object)
Arguments
object

an H2OGrid object.

Slots
grid_id the final identifier of grid
model_ids list of model IDs which are included in the grid object
hyper_names list of parameter names used for grid search
failed_params list of model parameters which caused a failure during model building, it can
contain a null value
failure_details list of detailed messages which correspond to failed parameters field
failure_stack_traces list of stack traces corresponding to model failures reported by failed_params
and failure_details fields
failed_raw_params list of failed raw parameters
summary_table table of models built with parameters and metric information.

H2OModel-class

203

See Also
H2OModel for the final model types.
H2OModel-class

The H2OModel object.

Description
This virtual class represents a model built by H2O.
Usage
## S4 method for signature 'H2OModel'
show(object)
Arguments
object

an H2OModel object.

Details
This object has slots for the key, which is a character string that points to the model key existing in
the H2O cloud, the data used to build the model (an object of class H2OFrame).
Slots
model_id A character string specifying the key for the model fit in the H2O cloud’s key-value
store.
algorithm A character string specifying the algorithm that were used to fit the model.
parameters A list containing the parameter settings that were used to fit the model that differ
from the defaults.
allparameters A list containg all parameters used to fit the model.
have_pojo A logical indicating whether export to POJO is supported
have_mojo A logical indicating whether export to MOJO is supported
model A list containing the characteristics of the model returned by the algorithm.
H2OModelFuture-class

H2O Future Model

Description
A class to contain the information for background model jobs.
Slots
job_key a character key representing the identification of the job process.
model_id the final identifier for the model
See Also
H2OModel for the final model types.

204

housevotes

H2OModelMetrics-class The H2OModelMetrics Object.

Description
A class for constructing performance measures of H2O models.
Usage
## S4 method for signature 'H2OModelMetrics'
show(object)
## S4 method for signature 'H2OBinomialMetrics'
show(object)
## S4 method for signature 'H2OMultinomialMetrics'
show(object)
## S4 method for signature 'H2OOrdinalMetrics'
show(object)
## S4 method for signature 'H2ORegressionMetrics'
show(object)
## S4 method for signature 'H2OClusteringMetrics'
show(object)
## S4 method for signature 'H2OAutoEncoderMetrics'
show(object)
## S4 method for signature 'H2ODimReductionMetrics'
show(object)
Arguments
object

housevotes

An H2OModelMetrics object

United States Congressional Voting Records 1984

Description
This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16
key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired
for, and announced for (these three simplified to yea), voted against, paired against, and announced
against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and
did not vote or otherwise make a position known (these three simplified to an unknown disposition).

iris

205

Format
A data frame with 435 rows and 17 columns
Source
Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional
Quarterly Inc., Washington, D.C., 1985
References
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine
learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University
of California, Department of Information and Computer Science.

iris

Edgar Anderson’s Iris Data

Description
Measurements in centimeters of the sepal length and width and petal length and width, respectively,
for three species of iris flowers.
Format
A data frame with 150 rows and 5 columns
Source
Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics,
7, Part II, 179-188.
The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of
the American Iris Society, 59, 2-5.

is.character

Check if character

Description
Check if character
Usage
is.character(x)
Arguments
x

An H2OFrame object

206

is.numeric

is.factor

Check if factor

Description
Check if factor
Usage
is.factor(x)
Arguments
x

is.h2o

An H2OFrame object

Is H2O Frame object

Description
Test if object is H2O Frame.
Usage
is.h2o(x)
Arguments
x

is.numeric

An R object.

Check if numeric

Description
Check if numeric
Usage
is.numeric(x)
Arguments
x

An H2OFrame object

Logical-or

207

Logical-or

Logical or for H2OFrames

Description
Logical or for H2OFrames
Usage
"||"(x, y)
Arguments
x

An H2OFrame object

y

An H2OFrame object

ModelAccessors

Accessor Methods for H2OModel Object

Description
Function accessor methods for various H2O output fields.
Usage
getParms(object)
## S4 method for signature 'H2OModel'
getParms(object)
getCenters(object)
getCentersStd(object)
getWithinSS(object)
getTotWithinSS(object)
getBetweenSS(object)
getTotSS(object)
getIterations(object)
getClusterSizes(object)
## S4 method for signature 'H2OClusteringModel'
getCenters(object)

208

Ops.H2OFrame
## S4 method for signature 'H2OClusteringModel'
getCentersStd(object)
## S4 method for signature 'H2OClusteringModel'
getWithinSS(object)
## S4 method for signature 'H2OClusteringModel'
getTotWithinSS(object)
## S4 method for signature 'H2OClusteringModel'
getBetweenSS(object)
## S4 method for signature 'H2OClusteringModel'
getTotSS(object)
## S4 method for signature 'H2OClusteringModel'
getIterations(object)
## S4 method for signature 'H2OClusteringModel'
getClusterSizes(object)

Arguments
object

names.H2OFrame

an H2OModel class object.

Column names of an H2OFrame

Description
Column names of an H2OFrame
Usage
## S3 method for class 'H2OFrame'
names(x)
Arguments
x

Ops.H2OFrame

An H2OFrame

S3 Group Generic Functions for H2O

Description
Methods for group generic functions and H2O objects.

Ops.H2OFrame
Usage

## S3 method for class 'H2OFrame'
Ops(e1, e2)
## S3 method for class 'H2OFrame'
Math(x, ...)
## S3 method for class 'H2OFrame'
Math(x, ...)
## S3 method for class 'H2OFrame'
Math(x, ...)
## S3 method for class 'H2OFrame'
Summary(x, ..., na.rm)
## S3 method for class 'H2OFrame'
!x
## S3 method for class 'H2OFrame'
is.na(x)
## S3 method for class 'H2OFrame'
t(x)
log(x, ...)
log10(x)
log2(x)
log1p(x)
trunc(x, ...)
x %*% y
nrow.H2OFrame(x)
ncol.H2OFrame(x)
## S3 method for class 'H2OFrame'
length(x)
h2o.length(x)
## S3 replacement method for class 'H2OFrame'
names(x) <- value
colnames(x) <- value

209

210

plot.H2OModel

Arguments
e1

object

e2

object

x

object

...

Further arguments passed to or from other methods.

na.rm

logical. whether or not missing values should be removed

y

object

value

To be assigned

plot.H2OModel

Plot an H2O Model

Description
Plots training set (and validation set if available) scoring history for an H2O Model
Usage
## S3 method for class 'H2OModel'
plot(x, timestep = "AUTO", metric = "AUTO", ...)
Arguments
x

A fitted H2OModel object for which the scoring history plot is desired.

timestep

A unit of measurement for the x-axis.

metric

A unit of measurement for the y-axis.

...

additional arguments to pass on.

Details
This method dispatches on the type of H2O model to select the correct scoring history. The
timestep and metric arguments are restricted to what is available in the scoring history for a
particular type of model.
Value
Returns a scoring history plot.
See Also
h2o.deeplearning, h2o.gbm, h2o.glm, h2o.randomForest for model generation in h2o.

plot.H2OTabulate

211

Examples
if (requireNamespace("mlbench", quietly=TRUE)) {
library(h2o)
h2o.init()
df <- as.h2o(mlbench::mlbench.friedman1(10000,1))
rng <- h2o.runif(df, seed=1234)
train <- df[rng<0.8,]
valid <- df[rng>=0.8,]

}

gbm <- h2o.gbm(x = 1:10, y = "y", training_frame = train, validation_frame = valid,
ntrees=500, learn_rate=0.01, score_each_iteration = TRUE)
plot(gbm)
plot(gbm, timestep = "duration", metric = "deviance")
plot(gbm, timestep = "number_of_trees", metric = "deviance")
plot(gbm, timestep = "number_of_trees", metric = "rmse")
plot(gbm, timestep = "number_of_trees", metric = "mae")

plot.H2OTabulate

Plot an H2O Tabulate Heatmap

Description
Plots the simple co-occurrence based tabulation of X vs Y as a heatmap, where X and Y are two
Vecs in a given dataset. This function requires suggested ggplot2 package.
Usage
## S3 method for class 'H2OTabulate'
plot(x, xlab = x$cols[1], ylab = x$cols[2],
base_size = 12, ...)
Arguments
x

An H2OTabulate object for which the heatmap plot is desired.

xlab

A title for the x-axis. Defaults to what is specified in the given H2OTabulate
object.

ylab

A title for the y-axis. Defaults to what is specified in the given H2OTabulate
object.

base_size

Base font size for plot.

...

additional arguments to pass on.

Value
Returns a ggplot2-based heatmap of co-occurance.
See Also
link{h2o.tabulate}

212

predict.H2OModel

Examples
library(h2o)
h2o.init()
df <- as.h2o(iris)
tab <- h2o.tabulate(data = df, x = "Sepal.Length", y = "Petal.Width",
weights_column = NULL, nbins_x = 10, nbins_y = 10)
plot(tab)

predict.H2OAutoML

Predict on an AutoML object

Description
Obtains predictions from an AutoML object.
Usage
## S3 method for class 'H2OAutoML'
predict(object, newdata, ...)
Arguments
object

a fitted H2OAutoML object for which prediction is desired

newdata

An H2OFrame object in which to look for variables with which to predict.

...

additional arguments to pass on.

Details
This method generated predictions on the leader model from an AutoML run. The order of the rows
in the results is the same as the order in which the data was loaded, even if some rows fail (for
example, due to missing values or unseen factor levels).
Value
Returns an H2OFrame object with probabilites and default predictions.

predict.H2OModel

Predict on an H2O Model

Description
Obtains predictions from various fitted H2O model objects.
Usage
## S3 method for class 'H2OModel'
predict(object, newdata, ...)
h2o.predict(object, newdata, ...)

predict_leaf_node_assignment.H2OModel

213

Arguments
object

a fitted H2OModel object for which prediction is desired

newdata

An H2OFrame object in which to look for variables with which to predict.

...

additional arguments to pass on.

Details
This method dispatches on the type of H2O model to select the correct prediction/scoring algorithm.
The order of the rows in the results is the same as the order in which the data was loaded, even if
some rows fail (for example, due to missing values or unseen factor levels).
Value
Returns an H2OFrame object with probabilites and default predictions.
See Also
h2o.deeplearning, h2o.gbm, h2o.glm, h2o.randomForest for model generation in h2o.

predict_leaf_node_assignment.H2OModel
Predict the Leaf Node Assignment on an H2O Model

Description
Obtains leaf node assignment from fitted H2O model objects.
Usage
predict_leaf_node_assignment.H2OModel(object, newdata, ...)
h2o.predict_leaf_node_assignment(object, newdata, ...)
Arguments
object

a fitted H2OModel object for which prediction is desired

newdata

An H2OFrame object in which to look for variables with which to predict.

...

additional arguments to pass on.

Details
For every row in the test set, return a set of factors that identify the leaf placements of the row in all
the trees in the model. The order of the rows in the results is the same as the order in which the data
was loaded
Value
Returns an H2OFrame object with categorical leaf assignment identifiers for each tree in the model.

214

print.H2OTable

See Also
h2o.gbm and h2o.randomForest for model generation in h2o.
Examples
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
prostate.hex$CAPSULE <- as.factor(prostate.hex$CAPSULE)
prostate.gbm <- h2o.gbm(3:9, "CAPSULE", prostate.hex)
h2o.predict(prostate.gbm, prostate.hex)
h2o.predict_leaf_node_assignment(prostate.gbm, prostate.hex)

print.H2OFrame

Print An H2OFrame

Description
Print An H2OFrame
Usage
## S3 method for class 'H2OFrame'
print(x, n = 6L, ...)
Arguments
x

An H2OFrame object

n

An (Optional) A single integer. If positive, number of rows in x to return. If
negative, all but the n first/last number of rows in x. Anything bigger than 20
rows will require asking the server (first 20 rows are cached on the client).

...

Further arguments to be passed from or to other methods.

print.H2OTable

Print method for H2OTable objects

Description
This will print a truncated view of the table if there are more than 20 rows.
Usage
## S3 method for class 'H2OTable'
print(x, header = TRUE, ...)

prostate

215

Arguments
x

An H2OTable object

header

A logical value dictating whether or not the table name should be printed.

...

Further arguments passed to or from other methods.

Value
The original x object

prostate

Prostate Cancer Study

Description
Baseline exam results on prostate cancer patients from Dr. Donn Young at The Ohio State University Comprehensive Cancer Center.
Format
A data frame with 380 rows and 9 columns
Source
Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition.

range.H2OFrame

Range of an H2O Column

Description
Range of an H2O Column
Usage
## S3 method for class 'H2OFrame'
range(..., na.rm = TRUE)
Arguments
...

An H2OFrame object.

na.rm

ignore missing values

216

str.H2OFrame

show,H2OCoxPHModelSummary-method
Print the CoxPH Model Summary

Description
Print the CoxPH Model Summary

Usage
## S4 method for signature 'H2OCoxPHModelSummary'
show(object)

Arguments
object

An H2OCoxPHModelSummary object.

...

further arguments to be passed on (currently unimplemented)

str.H2OFrame

Display the structure of an H2OFrame object

Description
Display the structure of an H2OFrame object

Usage
## S3 method for class 'H2OFrame'
str(object, ..., cols = FALSE)

Arguments
object

An H2OFrame.

...

Further arguments to be passed from or to other methods.

cols

Print the per-column str for the H2OFrame

summary,H2OCoxPHModel-method

summary,H2OCoxPHModel-method
Print the CoxPH Model Summary

Description
Print the CoxPH Model Summary

Usage
## S4 method for signature 'H2OCoxPHModel'
summary(object, conf.int = 0.95, scale = 1)
Arguments
object

an H2OCoxPHModel object.

conf.int

a specification of the confidence interval.

scale

a scale.

summary,H2OGrid-method
Format grid object in user-friendly way

Description
Format grid object in user-friendly way

Usage
## S4 method for signature 'H2OGrid'
summary(object, show_stack_traces = FALSE)
Arguments
object

an H2OGrid object.

show_stack_traces
a flag to show stack traces for model failures

217

218

use.package

summary,H2OModel-method
Print the Model Summary

Description
Print the Model Summary
Usage
## S4 method for signature 'H2OModel'
summary(object, ...)
Arguments
object

An H2OModel object.

...

further arguments to be passed on (currently unimplemented)

use.package

Use optional package

Description
Testing availability of optional package, its version, and extra global default. This function is used
internally. It is exported and documented because user can control behavior of the function by
global option.
Usage
use.package(package, version = "1.9.8"[package == "data.table"],
use = getOption("h2o.use.data.table", FALSE)[package == "data.table"])
Arguments
package

character scalar name of a package that we Suggests or Enhances on.

version

character scalar required version of a package.

use

logical scalar, extra escape option, to be used as global option.

Details
We use this function to control csv read/write with optional data.table package. Currently data.table
is disabled by default, to enable it set options("h2o.use.data.table"=TRUE). It is possible
to control just fread or fwrite with options("h2o.fread"=FALSE, "h2o.fwrite"=FALSE).
h2o.fread and h2o.fwrite options are not handled in this function but next to fread and fwrite
calls.
See Also
as.h2o.data.frame, as.data.frame.H2OFrame

walking

219

Examples
op <- options("h2o.use.data.table" = TRUE)
if (use.package("data.table")) {
cat("optional package data.table 1.9.8+ is available\n")
} else {
cat("optional package data.table 1.9.8+ is not available\n")
}
options(op)

walking

Muscular Actuations for Walking Subject

Description
The musculoskeletal model, experimental data, settings files, and results for three-dimensional,
muscle-actuated simulations at walking speed as described in Hamner and Delp (2013). Simulations
were generated using OpenSim 2.4. The data is available from https://simtk.org/project/
xml/downloads.xml?group_id=603.
Format
A data frame with 151 rows and 124 columns
References
Hamner, S.R., Delp, S.L. Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds. Journal of Biomechanics, vol 46, pp 780-787. (2013)

zzz

Shutdown H2O cloud after examples run

Description
Shutdown H2O cloud after examples run
Examples
library(h2o)
h2o.init()
h2o.shutdown(prompt = FALSE)
Sys.sleep(3)

220

&&

&&

Logical and for H2OFrames

Description
Logical and for H2OFrames
Usage
"&&"(x, y)
Arguments
x

An H2OFrame object

y

An H2OFrame object

Index
!.H2OFrame (Ops.H2OFrame), 208
∗Topic datasets
australia, 14
housevotes, 204
iris, 205
prostate, 215
walking, 219
∗Topic package
h2o-package, 7
[,H2OFrame-method (H2OFrame-Extract),
201
[.H2OFrame (H2OFrame-Extract), 201
[<-.H2OFrame (H2OFrame-Extract), 201
[[.H2OFrame (H2OFrame-Extract), 201
[[<-.H2OFrame (H2OFrame-Extract), 201
$.H2OFrame (H2OFrame-Extract), 201
$<-.H2OFrame (H2OFrame-Extract), 201
%*% (Ops.H2OFrame), 208
%in% (h2o.match), 118
&&, 220

cosh, 39
cummax, 44
cummin, 45
cumprod, 45
cumsum, 46
cut.H2OFrame (h2o.cut), 46
data.table, 218
day (h2o.day), 47
dayOfWeek (h2o.dayOfWeek), 48
ddply, 49
dim, 15, 63
dim.H2OFrame, 15
dimnames, 64
dimnames.H2OFrame, 15
exp, 68
floor, 73
fread, 10, 218
fwrite, 11, 218

aaa, 8
abs, 16
acos, 17
all, 20, 21
apply, 8, 8
as.character, 22
as.character.H2OFrame, 9
as.data.frame.H2OFrame, 9, 218
as.factor, 10, 10, 23
as.h2o, 11
as.h2o.data.frame, 218
as.matrix.H2OFrame, 12
as.numeric, 13, 23
as.vector.H2OFrame, 13
australia, 14

getBetweenSS (ModelAccessors), 207
getBetweenSS,H2OClusteringModel-method
(ModelAccessors), 207
getCenters (ModelAccessors), 207
getCenters,H2OClusteringModel-method
(ModelAccessors), 207
getCentersStd (ModelAccessors), 207
getCentersStd,H2OClusteringModel-method
(ModelAccessors), 207
getClusterSizes (ModelAccessors), 207
getClusterSizes,H2OClusteringModel-method
(ModelAccessors), 207
getIterations (ModelAccessors), 207
getIterations,H2OClusteringModel-method
(ModelAccessors), 207
getParms (ModelAccessors), 207
getParms,H2OModel-method
(ModelAccessors), 207
getTotSS (ModelAccessors), 207
getTotSS,H2OClusteringModel-method
(ModelAccessors), 207
getTotWithinSS (ModelAccessors), 207

cbind, 29
ceiling, 30
colMeans, 120
colnames, 14, 34
colnames<- (Ops.H2OFrame), 208
cor (h2o.cor), 38
cos, 39
221

222
getTotWithinSS,H2OClusteringModel-method
(ModelAccessors), 207
getWithinSS (ModelAccessors), 207
getWithinSS,H2OClusteringModel-method
(ModelAccessors), 207
h2o (h2o-package), 7
h2o-package, 7
h2o.abs, 16
h2o.accuracy (h2o.metric), 124
h2o.acos, 17
h2o.aggregated_frame, 17
h2o.aggregator, 18
h2o.aic, 19
h2o.all, 20
h2o.anomaly, 20
h2o.any, 21
h2o.anyFactor, 21
h2o.arrange, 22
h2o.as_date, 24
h2o.ascharacter, 22
h2o.asfactor, 23
h2o.asnumeric, 23
h2o.assign, 24, 156
h2o.auc, 25, 83, 87, 125, 128, 156
h2o.automl, 25
h2o.betweenss, 27, 109
h2o.biases, 28
h2o.bottomN, 28
h2o.cbind, 29
h2o.ceiling, 29
h2o.centers, 30, 109
h2o.centersSTD, 30, 109
h2o.centroid_stats, 30
h2o.clearLog, 31, 136, 170, 171
h2o.cluster_sizes, 33, 109
h2o.clusterInfo, 31
h2o.clusterIsUp, 32
h2o.clusterStatus, 32
h2o.coef, 33
h2o.coef_norm, 34
h2o.colnames, 34
h2o.columns_by_type, 35
h2o.computeGram, 35
h2o.confusionMatrix, 36, 87
h2o.confusionMatrix,H2OModel-method
(h2o.confusionMatrix), 36
h2o.confusionMatrix,H2OModelMetrics-method
(h2o.confusionMatrix), 36
h2o.connect, 37
h2o.cor, 38
h2o.cos, 39
h2o.cosh, 39

INDEX
h2o.coxph, 40
h2o.createFrame, 41
h2o.cross_validation_fold_assignment,
42
h2o.cross_validation_holdout_predictions,
43
h2o.cross_validation_models, 43
h2o.cross_validation_predictions, 44
h2o.cummax, 44
h2o.cummin, 45
h2o.cumprod, 45
h2o.cumsum, 46
h2o.cut, 46
h2o.day, 47, 48, 96
h2o.dayOfWeek, 48
h2o.dct, 48
h2o.ddply, 49
h2o.decryptionSetup, 50, 97, 137, 138
h2o.deepfeatures, 51
h2o.deeplearning, 20, 52, 210, 213
h2o.deepwater, 58
h2o.deepwater.available, 62
h2o.describe, 62
h2o.difflag1, 63
h2o.dim, 63
h2o.dimnames, 64
h2o.distance, 64
h2o.download_mojo, 66
h2o.download_pojo, 67
h2o.downloadAllLogs, 65
h2o.downloadCSV, 65
h2o.entropy, 68
h2o.error (h2o.metric), 124
h2o.exp, 68
h2o.exportFile, 69
h2o.exportHDFS, 70
h2o.F0point5 (h2o.metric), 124
h2o.F1 (h2o.metric), 124
h2o.F2 (h2o.metric), 124
h2o.fallout (h2o.metric), 124
h2o.fillna, 70
h2o.filterNACols, 71
h2o.find_row_by_threshold, 72
h2o.find_threshold_by_max_metric, 72
h2o.findSynonyms, 71
h2o.floor, 73
h2o.flow, 73
h2o.fnr (h2o.metric), 124
h2o.fpr (h2o.metric), 124
h2o.gainsLift, 73
h2o.gainsLift,H2OModel-method
(h2o.gainsLift), 73

INDEX
h2o.gainsLift,H2OModelMetrics-method
(h2o.gainsLift), 73
h2o.gbm, 74, 210, 213, 214
h2o.getConnection, 78
h2o.getFrame, 79
h2o.getFutureModel, 79
h2o.getGLMFullRegularizationPath, 79
h2o.getGrid, 80
h2o.getId, 80
h2o.getModel, 81
h2o.getTimezone, 81
h2o.getTypes, 82
h2o.getVersion, 82
h2o.giniCoef, 25, 82, 83, 87, 125
h2o.glm, 7, 83, 210, 213
h2o.glrm, 87, 142, 145, 152
h2o.grep, 90
h2o.grid, 91
h2o.group_by, 92
h2o.gsub, 93
h2o.head, 94
h2o.hist, 94
h2o.hit_ratio_table, 95
h2o.hour, 95
h2o.ifelse, 96
h2o.import_sql_select, 98, 98
h2o.import_sql_table, 98, 99
h2o.importFile, 50, 97, 137
h2o.importFolder (h2o.importFile), 97
h2o.importHDFS (h2o.importFile), 97
h2o.impute, 100
h2o.init, 32, 101, 165
h2o.insertMissingValues, 103
h2o.interaction, 104
h2o.is_client, 107
h2o.isax, 105
h2o.ischaracter, 106
h2o.isfactor, 106
h2o.isnumeric, 107
h2o.kfold_column, 107
h2o.killMinus3, 108
h2o.kmeans, 89, 108
h2o.kurtosis, 110
h2o.length (Ops.H2OFrame), 208
h2o.levels, 110
h2o.list_all_extensions, 111
h2o.list_api_extensions, 111
h2o.list_core_extensions, 112
h2o.listTimezones, 111
h2o.loadModel, 112, 160
h2o.log, 113
h2o.log10, 113

223
h2o.log1p, 114
h2o.log2, 114
h2o.logAndEcho, 115
h2o.logloss, 87, 115
h2o.ls, 116, 156
h2o.lstrip, 116
h2o.mae, 117
h2o.make_metrics, 118
h2o.makeGLMModel, 117
h2o.match, 118
h2o.max, 119
h2o.maxPerClassError (h2o.metric), 124
h2o.mcc (h2o.metric), 124
h2o.mean, 120
h2o.mean_per_class_accuracy
(h2o.metric), 124
h2o.mean_per_class_error, 121
h2o.mean_residual_deviance, 122
h2o.median, 122
h2o.merge, 123
h2o.metric, 25, 83, 121, 124, 128, 156
h2o.min, 126
h2o.missrate (h2o.metric), 124
h2o.mktime, 126
h2o.month, 47, 48, 127, 190, 198
h2o.mse, 25, 87, 121, 125, 127, 128, 156
h2o.na_omit, 131
h2o.nacnt, 128
h2o.naiveBayes, 129
h2o.names, 131
h2o.nchar, 132
h2o.ncol, 132
h2o.networkTest, 133
h2o.nlevels, 133
h2o.no_progress, 133
h2o.nrow, 134
h2o.null_deviance, 134
h2o.null_dof, 135
h2o.num_iterations, 109, 135
h2o.num_valid_substrings, 136
h2o.openLog, 31, 136, 170, 171
h2o.parseRaw, 97, 98, 137, 138
h2o.parseSetup, 50, 137, 138
h2o.partialPlot, 139
h2o.performance, 25, 37, 74, 83, 87, 121,
125, 128, 140, 156
h2o.pivot, 141
h2o.prcomp, 89, 141
h2o.precision (h2o.metric), 124
h2o.predict (predict.H2OModel), 212
h2o.predict_json, 143
h2o.predict_leaf_node_assignment

224

INDEX

(predict_leaf_node_assignment.H2OModel),
h2o.sum, 175
213
h2o.summary, 175
h2o.print, 144
h2o.svd, 89, 142, 176
h2o.prod, 144
h2o.table, 178
h2o.proj_archetypes, 145
h2o.tabulate, 178
h2o.quantile, 146
h2o.tail (h2o.head), 94
h2o.r2, 147
h2o.tan, 179
h2o.randomForest, 147, 210, 213, 214
h2o.tanh, 180
h2o.range, 151
h2o.target_encode_apply, 180, 182
h2o.rbind, 151
h2o.target_encode_create, 180, 181, 181
h2o.recall (h2o.metric), 124
h2o.tnr (h2o.metric), 124
h2o.reconstruct, 152
h2o.toFrame, 182
h2o.relevel, 153
h2o.tokenize, 183
h2o.removeAll, 153
h2o.tolower, 184
h2o.removeVecs, 154
h2o.topN, 184
h2o.rep_len, 154
h2o.tot_withinss, 109, 185
h2o.residual_deviance, 155
h2o.totss, 109, 185
h2o.residual_dof, 155
h2o.toupper, 186
h2o.rm, 153, 156
h2o.tpr (h2o.metric), 124
h2o.rmse, 156
h2o.transform, 186
h2o.rmsle, 157
h2o.trim, 187
h2o.round, 158
h2o.trunc, 187
h2o.rstrip, 158
h2o.unique, 188
h2o.runif, 159
h2o.uploadFile (h2o.importFile), 97
h2o.saveModel, 112, 159, 161
h2o.var, 163, 188
h2o.saveModelDetails, 160
h2o.varimp, 87, 189
h2o.saveMojo, 161
h2o.varimp_plot, 171, 189
h2o.scale, 162
h2o.week, 190
h2o.scoreHistory, 87, 162
h2o.weights, 191
h2o.sd, 163, 189
h2o.which, 191
h2o.sdev, 163
h2o.which_max, 192
h2o.sensitivity (h2o.metric), 124
h2o.which_min, 192
h2o.setLevels, 164
h2o.withinss, 109, 193
h2o.setTimezone, 164
h2o.word2vec, 193
h2o.show_progress, 165
h2o.xgboost, 194
h2o.shutdown, 103, 165
h2o.xgboost.available, 197
h2o.signif, 166
h2o.year, 127, 198
h2o.sin, 166
H2OAutoEncoderMetrics-class
h2o.skewness, 167
(H2OModelMetrics-class), 204
h2o.specificity (h2o.metric), 124
H2OAutoEncoderModel, 20
h2o.splitFrame, 167
H2OAutoEncoderModel-class
h2o.sqrt, 168
(H2OModel-class), 203
h2o.stackedEnsemble, 169
H2OAutoML, 27, 212
h2o.startLogging, 31, 136, 170, 171
H2OAutoML-class, 198
h2o.std_coef_plot, 171, 189
H2OBinomialMetrics, 25, 36, 74, 82, 83, 115,
h2o.stopLogging, 31, 136, 170, 171
121, 125, 128, 156
h2o.str, 172
H2OBinomialMetrics-class
h2o.stringdist, 172
(H2OModelMetrics-class), 204
h2o.strsplit, 173
H2OBinomialModel, 87, 130
h2o.sub, 173
H2OBinomialModel-class
(H2OModel-class), 203
h2o.substr (h2o.substring), 174
H2OClusteringMetrics-class
h2o.substring, 174

INDEX
(H2OModelMetrics-class), 204
H2OClusteringModel, 17, 28, 30, 33, 109,
135, 185, 193
H2OClusteringModel-class, 199
H2OConnection, 32, 79
H2OConnection (H2OConnection-class), 199
H2OConnection-class, 199
H2OConnectionMutableState, 200
H2OCoxPHMetrics-class
(H2OModelMetrics-class), 204
H2OCoxPHModel (H2OCoxPHModel-class), 200
H2OCoxPHModel-class, 200
H2OCoxPHModelSummary, 216
H2OCoxPHModelSummary
(H2OCoxPHModelSummary-class),
201
H2OCoxPHModelSummary-class, 201
H2ODimReductionMetrics-class
(H2OModelMetrics-class), 204
H2ODimReductionModel, 89, 142, 145, 152,
163, 177
H2ODimReductionModel-class
(H2OModel-class), 203
H2OFrame-class, 201
H2OFrame-Extract, 201
H2OGrid (H2OGrid-class), 202
H2OGrid-class, 202
H2OModel, 19, 28, 33, 34, 36, 42–44, 51, 70,
74, 79, 81, 87, 95, 112, 117, 122,
134, 135, 139, 140, 147, 150, 155,
157, 159–162, 189, 191, 203, 208,
210, 213, 218
H2OModel (H2OModel-class), 203
H2OModel-class, 203
H2OModelFuture-class, 203
H2OModelMetrics, 19, 28, 36, 37, 74, 115,
118, 125, 127, 134, 135, 140, 155,
156, 191
H2OModelMetrics
(H2OModelMetrics-class), 204
H2OModelMetrics-class, 204
H2OMultinomialMetrics, 36, 115, 128, 156
H2OMultinomialMetrics-class
(H2OModelMetrics-class), 204
H2OMultinomialModel, 130
H2OMultinomialModel-class
(H2OModel-class), 203
H2OOrdinalMetrics-class
(H2OModelMetrics-class), 204
H2OOrdinalModel-class (H2OModel-class),
203
H2ORegressionMetrics, 128, 156

225
H2ORegressionMetrics-class
(H2OModelMetrics-class), 204
H2ORegressionModel, 87
H2ORegressionModel-class
(H2OModel-class), 203
H2OUnknownMetrics-class
(H2OModelMetrics-class), 204
H2OUnknownModel-class (H2OModel-class),
203
H2OWordEmbeddingMetrics-class
(H2OModelMetrics-class), 204
H2OWordEmbeddingModel-class
(H2OModel-class), 203
head.H2OFrame (h2o.head), 94
hour (h2o.hour), 95
housevotes, 204
ifelse (h2o.ifelse), 96
iris, 205
is.character, 106, 205
is.factor, 106, 206
is.h2o, 206
is.na.H2OFrame (Ops.H2OFrame), 208
is.numeric, 107, 206
kurtosis.H2OFrame (h2o.kurtosis), 110
length.H2OFrame (Ops.H2OFrame), 208
levels, 111
log, 113
log (Ops.H2OFrame), 208
log10, 113
log10 (Ops.H2OFrame), 208
log1p, 114
log1p (Ops.H2OFrame), 208
log2, 114
log2 (Ops.H2OFrame), 208
Logical-or, 207
match, 119
match.H2OFrame (h2o.match), 118
Math.H2OFrame (Ops.H2OFrame), 208
max, 119
mean, 120
mean.H2OFrame (h2o.mean), 120
median.H2OFrame (h2o.median), 122
min, 126
ModelAccessors, 207
month (h2o.month), 127
names, 131
names.H2OFrame, 208
names<-.H2OFrame (Ops.H2OFrame), 208

226
ncol, 132
ncol.H2OFrame (Ops.H2OFrame), 208
nlevels, 133
nrow, 134
nrow.H2OFrame (Ops.H2OFrame), 208
Ops.H2OFrame, 208
plot.H2OModel, 210
plot.H2OTabulate, 211
predict, 36, 37, 74
predict.H2OAutoML, 212
predict.H2OModel, 57, 78, 87, 151, 212
predict_leaf_node_assignment.H2OModel,
213
print.H2OFrame, 214
print.H2OTable, 214
prod, 144
prostate, 215
quantile, 146
quantile.H2OFrame (h2o.quantile), 146
range, 151
range.H2OFrame, 215
rbind, 151
round, 158
round (h2o.round), 158
rowMeans, 120
scale.H2OFrame (h2o.scale), 162
sd, 163
sd (h2o.sd), 163
show,H2OAutoEncoderMetrics-method
(H2OModelMetrics-class), 204
show,H2OBinomialMetrics-method
(H2OModelMetrics-class), 204
show,H2OClusteringMetrics-method
(H2OModelMetrics-class), 204
show,H2OConnection-method
(H2OConnection-class), 199
show,H2OCoxPHModel-method
(H2OCoxPHModel-class), 200
show,H2OCoxPHModelSummary-method, 216
show,H2ODimReductionMetrics-method
(H2OModelMetrics-class), 204
show,H2OGrid-method (H2OGrid-class), 202
show,H2OModel-method (H2OModel-class),
203
show,H2OModelMetrics-method
(H2OModelMetrics-class), 204
show,H2OMultinomialMetrics-method
(H2OModelMetrics-class), 204

INDEX
show,H2OOrdinalMetrics-method
(H2OModelMetrics-class), 204
show,H2ORegressionMetrics-method
(H2OModelMetrics-class), 204
signif, 166
signif (h2o.signif), 166
sin, 166
skewness.H2OFrame (h2o.skewness), 167
sqrt, 168
str.H2OFrame, 216
sum, 175
summary, 175
summary,H2OCoxPHModel-method, 217
summary,H2OGrid-method, 217
summary,H2OModel-method, 218
Summary.H2OFrame (Ops.H2OFrame), 208
summary.H2OFrame (h2o.summary), 175
t.H2OFrame (Ops.H2OFrame), 208
table.H2OFrame (h2o.table), 178
tail.H2OFrame (h2o.head), 94
tan, 179
tanh, 180
trunc, 188
trunc (Ops.H2OFrame), 208
use.package, 10, 11, 218
var, 189
var (h2o.var), 188
walking, 219
week (h2o.week), 190
which, 191
which.max, 192
which.max.H2OFrame (h2o.which_max), 192
which.min, 193
which.min.H2OFrame (h2o.which_max), 192
year (h2o.year), 198
zzz, 219



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