R Tutorial¶

Getting Started¶

Installing and Starting H2O

Before beginning, be sure to have an instance of H₂O running. Additionally, users who are using H₂O for the first time can find help for installing the R package at http://docs.0xdata.com/Ruser/Rinstall.html.

Step 1

Call the H₂O package, and initialize H₂O in R. Note that an object “localh2o” is created. Assigning the H₂O initialization to an object is important, because the connection will be used later to tell R where to send data sets, model specification, and where to find results.

library(h2o)
localH2O <- h2o.init(ip = "localhost", port = 54321)

Users may see a response message in R indicating that the instance of H₂O running is a different version than that of the corresponding H₂O R package. This error will look similar to the picture below. If you get this error, it can be resolved by downloading the correct version of H₂O from http://0xdata.com/downloadtable/. Users should follow the installation instructions on the download page.

Cluster Info

Used to check that the H₂O instance is running and healthy.

library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
h2o.checkClient(localH2O)

Importing Data¶

Import File

Use this call when importing a data set that exists in a single file.

irisPath = system.file("extdata", "iris.csv", package="h2o")
iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
summary(iris.hex)

Import Folder

Use this call when importing a data set that exists in multiple files.

myPath = system.file("extdata", "prostate_folder", package = "h2o")
prostate_all.hex = h2o.importFolder(localH2O, path = myPath)
class(prostate_all.hex)
summary(prostate_all.hex)
prostate_all.fv = h2o.importFolder(localH2O, path = myPath, version = 2)

Import URL

Use this call when data are stored at a website accessible from the machine on which the instance of H₂O is running.

prostate.hex = h2o.importURL(localH2O, path = paste("https://raw.github.com",
"0xdata/h2o/master/smalldata/logreg/prostate.csv", sep = "/"), key = "prostate.hex")
class(prostate.hex)
summary(prostate.hex)

Data Manipulation and Description¶

Any Factor

Used to determine if any column in a data set is a factor.

irisPath = system.file("extdata", "iris_wheader.csv", package="h2o")
iris.hex = h2o.importFile(localH2O, path = irisPath)
h2o.anyFactor(iris.hex)

As Data Frame

Used to convert an H₂O parsed data object into an R data frame (which can subsequently be manipulated using R calls). While this is frequently useful, as.data.frame should be used with care when converting H₂O Parsed Data objects. Data sets that are easily and quickly handled by H₂O are often too large to be treated equivalently well in R.

prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath)
prostate.data.frame<- as.data.frame(prostate.hex)
summary(prostate.data.frame)
head(prostate.data.frame)

As Factor

Used to convert an integer into a non-ordered factor (alternatively called an enum or categorical).

prosPath = system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath)
prostate.hex[,4] = as.factor(prostate.hex[,4])
summary(prostate.hex)

As H2O

Used to pass a data frame from inside of the R environment to the H₂O instance.

data(iris)
summary(iris)
iris.r <- iris
iris.h2o <- as.h2o(localH2O, iris.r, key="iris.h2o")
class(iris.h2o)

Assign H2O

Used to create an hex key on the server where H₂O is running for data sets manipulated in R. For instance, in the example below, the prostate data set was uploaded to the H₂O instance, and was manipulated to remove outliers. Saving the new data set on the H₂O server so that it can be subsequently be analyzed with H₂O without overwriting the original data set relies on h2o.assign.

prosPath = system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath)
prostate.qs = quantile(prostate.hex$PSA)
PSA.outliers = prostate.hex[prostate.hex$PSA <= prostate.qs[2] | prostate.hex$PSA >=   prostate.qs[10],]
PSA.outliers = h2o.assign(PSA.outliers, "PSA.outliers")
nrow(prostate.hex)
nrow(PSA.outliers)

Colnames

Used to obtain a list of the column names in a data set.

irisPath = system.file("extdata", "iris.csv", package="h2o")
iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
summary(iris.hex)
colnames(iris.hex)

Extremes

Used to obtain the maximum and minimum values in real valued columns.

ausPath = system.file("extdata", "australia.csv", package="h2o")
australia.hex = h2o.importFile(localH2O, path = ausPath, key = "australia.hex")
min(australia.hex)
min(c(-1, 0.5, 0.2), FALSE, australia.hex[,1:4])

Quantiles

Used to request quantiles for an H₂O parsed data set. When requested for a full parsed data set quantiles() returns a matrix displaying quantile information for all numeric columns in the data set.

prosPath = system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath)
quantile(prostate.hex)

Summary

Used to generate an R like summary for each of the columns of a data set. For continuous reals this produces a summary that includes information on quartiles, min, max and mean. For factors this produces information on counts of elements within each factor level. For information on the Summary algorithm see Summary

prosPath = system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath)
summary(prostate.hex)
summary(prostate.hex$GLEASON)
summary(prostate.hex[,4:6])

H2O Table

Used to summarize information in data. Note that because H₂O handles such large data sets, it is possible for users to generate tables that are larger that R’s capacity. To minimize this risk and allow users to work uninterrupted, h2o.table is called inside of a call for head() or tail(). Within head() and tail() users can explicitly specify the number of rows in the table to return.

head(h2o.table(prostate.hex[,3]))
head(h2o.table(prostate.hex[,c(3,4)]))

Test Train Split and generating Random Numbers

Runif is used to append a column of random numbers to an H₂O data frame and facilitate creating test/ train splits of data for analysis and validation in H₂O.

prosPath = system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath, key = "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)

Running Models¶

GBM

Gradient Boosted Models. For information on the GBM algorithm see Gradient Boosted Regression and Classification

ausPath = system.file("extdata", "australia.csv", package="h2o")
australia.hex = h2o.importFile(localH2O, path = ausPath)
independent <- c("premax", "salmax","minairtemp", "maxairtemp",
"maxsst", "maxsoilmoist", "Max_czcs")
dependent <- "runoffnew"
h2o.gbm(y = dependent, x = independent, data = australia.hex,
n.trees = 10, interaction.depth = 3,
   n.minobsinnode = 2, shrinkage = 0.2, distribution= "gaussian")

Run multinomial classification GBM on abalone data

h2o.gbm(y = dependent, x = independent, data = australia.hex, n.trees
= 15, interaction.depth = 5,
 n.minobsinnode = 2, shrinkage = 0.01, distribution= "multinomial")

Generalized Linear Models

Generalized linear models, which are used to develop linear models for exponential distributions. Regularization can be applied. For information on the GBM algorithm see Generalized Linear Model

prostate.hex = h2o.importURL.VA(localH2O, path =
"https://raw.github.com/0xdata/h2o/master/smalldata/logreg/prostate.csv",
key = "prostate.hex")
h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), data =
prostate.hex, family = "binomial", nfolds = 10, alpha = 0.5)

myX = setdiff(colnames(prostate.hex), c("ID", "DPROS", "DCAPS", "VOL"))
h2o.glm(y = "VOL", x = myX, data = prostate.hex, family = "gaussian", nfolds = 5, alpha = 0.1)

K-Means

K means is a clustering algorithm that allows users to characterize data. This algorithm does not rely on a dependent variable. For information on the K-Means algorithm see K-Means

prosPath = system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath)
h2o.kmeans(data = prostate.hex, centers = 10, cols = c("AGE", "RACE", "VOL", "GLEASON"))
covPath = system.file("extdata", "covtype.csv", package="h2o")
covtype.hex = h2o.importFile(localH2O, path = covPath)
covtype.km = h2o.kmeans(data = covtype.hex, centers = 5, cols = c(1, 2, 3))
print(covtype.km)

Principal Components Analysis

Principal Components Analysis maps a set of variables onto a subspace via linear transformations. PCA is the first step in Principal Components Regression. For more information on PCA see Principal Components Analysis.

ausPath = system.file("extdata", "australia.csv", package="h2o")
australia.hex = h2o.importFile(localH2O, path = ausPath)
australia.pca = h2o.prcomp(data = australia.hex, standardize = TRUE)
print(australia.pca)
summary(australia.pca)

Principal Components Regression

PCR is an algorithm that allows users to map a set of variables to a new set of linearly independent variables. The new set of variables are linearly independent linear combinations of the original variables and exist in a subspace of lower dimension. This transformation is then prepended to a regression model, often improving results. For more information on PCA see Principal Components Analysis.

prostate.hex = h2o.importFile(localH2O,
path =
"https://raw.github.com/0xdata/h2o/master/smalldata/logreg/prostate.csv",
key = "prostate.hex")
h2o.pcr(x = c("AGE","RACE","PSA","DCAPS"), y = "CAPSULE", data =
prostate.hex, family = "binomial",
nfolds = 10, alpha = 0.5, ncomp = 3)

Obtaining Predictions¶

Predict

Used to apply an H₂O model to a holdout set to obtain predictions based on model results. In the examples below models are first generated, and then the predictions for that model are obtained.

prostate.hex = h2o.importURL.VA(localH2O,
path =
"https://raw.github.com/0xdata/h2o/master/smalldata/logreg/prostate.csv",
key = "prostate.hex")
prostate.glm = h2o.glm(y = "CAPSULE", x =
c("AGE","RACE","PSA","DCAPS"), data = prostate.hex,
family = "binomial", nfolds = 10, alpha = 0.5)
prostate.fit = h2o.predict(object = prostate.glm, newdata = prostate.hex)
summary(prostate.fit)

covPath = system.file("extdata", "covtype.csv", package="h2o")
covtype.hex = h2o.importFile(localH2O, path = covPath)
covtype.km = h2o.kmeans(data = covtype.hex, centers = 5, cols = c(1, 2, 3))
covtype.clusters = h2o.predict(object = covtype.km, newdata = covtype.hex)

Other Useful Functions¶

List all H2O Objects

Used to generate a list of all H₂O objects that have been generated during a work session, along with each objects byte size.

prostate.hex = h2o.importFile(localH2O, path = prosPath, key = "prostate.hex")
s = runif(nrow(prostate.hex))
prostate.train = prostate.hex[s <= 0.8,]
prostate.train = h2o.assign(prostate.train, "prostate.train")
h2o.ls(localH2O)

Remove an H2O object from the server where H2O is running

Users may wish to remove an H₂O object on the server that is associated with an object in the R environment. Recommended behavior is to also remove the object in the R environment.

localH2O = h2o.init()
prosPath = system.file("extdata", "prostate.csv", package="h2o")
prostate.hex = h2o.importFile(localH2O, path = prosPath, key = "prostate.hex")
s = runif(nrow(prostate.hex))
prostate.train = prostate.hex[s <= 0.8,]
prostate.train = h2o.assign(prostate.train, "prostate.train")
h2o.ls(localH2O)
h2o.rm(object= localH2O, keys= "Last.value.0")
h2o.ls(localH2O)