pmml (version 1.3)

pmml.cv.glmnet: Generate PMML for a glmnet object

Description

Generate the Predictive Model Markup Language (PMML) representation of a glmnet elasticnet general linear regression object. In particular, this gives the PMML representation of the object created by the cv.glmnet function.

Usage

## S3 method for class 'cv.glmnet':
pmml(model, model.name="Elasticnet_Model", app.name="Rattle/PMML",
     description="Generalized Linear Regression Model", 
     copyright = NULL, transforms = NULL, dataset = NULL, s = NULL, \dots)

Arguments

model

the cv.glmnet object contained in an object of class glmnet, as contained in the object returned by the function cv.glmnet.

model.name

a name to give to the model in the PMML.

app.name

the name of the application that generated the PMML.

description

a descriptive text for the header of the PMML.

copyright

the copyright notice for the model.

transforms

data transforms.

dataset

the dataset using which the model was built.

s

the 'lambda' parameter at which to output the model. If not given, the lambda.min parameter from the model is used.

...

further arguments passed to or from other methods.

Value

• An object of class XMLNode as that defined by the XML package. This represents the top level, or root node, of the XML document and is of type PMML. It can be written to file with saveXML.

Details

The glmnet package expects the input and predicted values in a matrix format; they cannot be used as arrays or data frames. As of now, it will also accept numerical values only. As such, any string variables must be converted to numerical ones. One possible way to do so is to use data transformation functions, such as from the 'pmmlTransformations' package. However the result is a data frame. In all cases, lists, arrays and data frames can be converted to a matrix format using the data.matrix function from the base package. Given a data frame df, a matrix m can thus be created by using m <- data.matrix(df).

The PMML language requires variable names which will be read in as the column names of the input matrix. If the matrix does not have variable names, they will be given the default values of "X1", "X2", ... PMML is an XML based language which provides a way for applications to define statistical and data mining models and to share models between PMML compliant applications. More information about PMML and the Data Mining Group can be found at http://www.dmg.org.

Use of PMML and pmml.cv.glmnet requires the XML package. Be aware that XML is a very verbose data format. This function is used to export the structure of a general regression model to any PMML consuming applications, such as the Zementis ADAPA and UPPI scoring engines which allow for predictive models built in R to be deployed and executed on site, in the cloud (Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal, Sybase IQ, Teradata and Teradata Aster) or Hadoop (Datameer and Hive).

References

T. Hastie, R. Tibshirani, J. Friedman (2008), The Elements of Statistical Learning. Springer Series in Statistics

PMML home page: http://www.dmg.org

A. Guazzelli, W. Lin, T. Jena (2012), PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics. CreativeSpace (Second Edition) - Available on Amazon.com - http://www.amazon.com/dp/1470003244.

A. Guazzelli, M. Zeller, W. Lin, G. Williams (2009), PMML: An Open Standard for Sharing Models. The R journal, Volume 1/1, 60-65

See Also

pmml.

Examples

library(glmnet)

# create a simple predictor (x) and response(y) matrices
x=matrix(rnorm(100*20),100,20)
y=rnorm(100)

# Build a simple gaussian model
model1 = cv.glmnet(x,y)
# Output the model in PMML format
pmml(model1)

# shift y between 0 and 1 to create a poisson response
y = y - min(y)
# give the predictor variables names (default values are V1,V2,...)
name <- NULL
for(i in 1:20){
  name <- c(name,paste("variable",i,sep=""))
}
colnames(x) <- name
# create a simple poisson model
model2 <- cv.glmnet(x,y,family="poisson")
# output in PMML format the regression model at the lambda parameter = 0.006
pmml(model2,s=0.006)