pmml: Generate PMML for an R object

Description

'pmml' is a generic function implementing S3 methods used to produce the PMML (Predictive Model Markup Language) representation of an R model. The resulting PMML file can then be imported into other systems that accept PMML. The same function can also be used to output variable transformations in PMML format. In particular, it can be used as a transformations generator. Various transformation operations can be implemented in R and those transformations can then be output in PMML format by calling the function with a NULL value for the model input and a pmmlTransformations object as the transforms input. Please see the R 'pmmlTransformations' package for more information on how to create the pmmlTransformations object.

The 'pmml' function can also be called using a pre-existing PMML model as the first input and a pmmlTransformations object as the transforms input. The result is a new PMML model with the transformation inserted as a 'LocalTransformations' element in the original model. If the original model already had a 'LocalTransformations' element, the new information will be appended to that element. If the model variables are derived directly from or from a chain of transformations defined in the transforms input, the field names in the model are replaced with the original field names with the correct data types to make a consistent model. The covered cases include model fields derived from an original field, model fields derived from a chain of transforms starting from an original field and mutiple fields derived from the same original field. Please see the description of the 'addLT' function for more details on how the 'pmml' function adds a new transformations information.

Usage

pmml(model, model.name="Rattle_Model", app.name="Rattle/PMML",
     description=NULL, copyright=NULL, transforms=NULL, dataset=NULL,
     ...)

Arguments

model

an object to be converted to PMML.

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.

the copyright notice for the model.

transforms

a coded list of transforms performed.

dataset

the orginal training dataset, if available.

...

further arguments passed to or from other methods.

Details

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. The generated PMML can be imported into any PMML consuming application, 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

Rattle home page: http://rattle.togaware.com

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 A. Guazzelli, T. Jena, W. Lin, M. Zeller (2013). Extending the Naive Bayes Model Element in PMML: Adding Support for Continuous Input Variables. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. T. Jena, A. Guazzelli, W. Lin, M. Zeller (2013). The R pmmlTransformations Package. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

Examples

Run this code

# Build a simple lm model
(iris.lm <- lm(Sepal.Length ~ ., data=iris))

# Convert to pmml
pmml(iris.lm)

# Create a pmmlTransformations object
library(pmmlTransformations)
xo <- WrapData(iris)

# Transform the 'Sepal.Length' variable
xo <- MinMaxXform(xo,xformInfo="column1->d_sl")

# Output the tranformation in PMML format
pmml(NULL, transforms=xo)

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