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Zementis Support Team September 4, 2015 Predictive Model Markup Language (PMML) / PMML Support in R

R PMML Transformations Package

This is a brand new R package. Called pmmlTranformations, this package transforms data and when used in conjunction with the pmml package, it allows for data transformations to be exported together with the predictive model in a single PMML file. Transformations currently supported are:
  • Min-max normalization
  • Z-score normalization
  • Dummy-fication of categorical variables
  • Value Mapping
  • Discretization (binning)
  • Variable renaming
If you would like to contribute code to the pmmlTransformations package, please feel free to contact us.

New Features

The released package version 1.3 has new functions to translate arbitrary mathematical expressions into a PMML representation. These new functions are:

  • FunctionXform
  • FunctionToPMML

To use these functions, one simply supplies an arbitrary mathematical expression, written in a normal way, as the input and the equivalent PMML expression is given as the output.

The 'FunctionToPMML' function is meant to be used to get a quick look at the PMML expressions output while the 'FunctionXform' function is meant to be used just as the other pmmlTransformations functions, as described below, to add those expressions to the 'LocalTransformations' element automatically.

For more information, simply load the pmmlTransformations library in R and type 'vignettes("FunctionXform")


How does it work?

The pmmlTransformations package works in tandem with the pmml package so that data pre-processing can be represented together with the model in the resulting PMML code. 
In R, as shown in the figure below, this process includes three steps:
  1. With the use of the pmmlTransformations package, transform the raw input data as appropriate
  2. Use transformed and raw data as inputs to the modeling function/package (hclust, nnet, glm, ...)
  3. Output the entire solution (data pre-processing + model) in PMML using the pmml package


Example - sequence of R commands used to build a linear regression model using lm with transformed data



For more on the pmmlTransformations package, please take a look at the paper we wrote for the KDD 2013 PMML Workshop. For that, just follow the link below:

1) KDD Paper: The R pmmlTransformations Package

Also, make sure to check out the package's documentation from CRAN:

2) CRAN: pmmlTransformations Package

Zementis Support Team September 4, 2015 Predictive Model Markup Language (PMML) / PMML Support in R


A PMML package for R that exports all kinds of predictive models is available directly from CRAN.

The pmml package offers support for the following model types:

  • ksvm (kernlab): Support Vector Machines
  • nnet: Neural Networks
  • rpart: C&RT Decision Trees 
  • lm & glm (stats): Linear and Binary Logistic Regression Models 
  • arules: Association Rules
  • kmeans and hclust: Clustering Models
  • multinom (nnet): Multinomial Logistic Regression Models
  • glm (stats): Generalized Linear Models for classification and regression with a wide variety of link functions
  • randomForest: Random Forest Models for classification and regression
  • coxph (survival): Cox Regression Models to calculate survival and stratified cumulative hazards
  • naiveBayes (e1071): Naive Bayes Classifiers
  • glmnet: Linear ElasticNet Regression Models
  • ada: Stochastic Boosting
  • svm (e1071): Support Vector Machines

New Features

The new pmml package offers additional features:
  • Add and insert arbitrary attributes to already created PMML models
  • Add and insert arbitrary elements to already created PMML models
  • Create various child elements allowed in the PMML schema so as to add them using the above functions
  • Such elements include DataField, Output, OutputField, Interval, Value
The pmml package can also export data transformations built with the pmmlTransformations package (see below). It can also be used to merge two disctinct PMML files into one. For example, if transformations and model were saved into separate PMML files, it can combine both files into one, as described in Chapter 5 of the PMML book - PMML in Action.

How does it work?

Simple, once you build your model using any of the supported model types, pass the model object as an input parameter to the pmml function as shown in the figure below:
Example - sequence of R commands used to build a linear regression model using lm and the Iris dataset:


For more on the pmml package, please take a look at the paper we published in The R Journal. For that, just follow the link below:

1) Paper: PMML: An Open Standard for Sharing Models

Also, make sure to check out the package's documentation from CRAN:

2) CRAN: pmml Package


Support Team July 10, 2014 Zementis Products / ADAPA

By using the AWS Marketplace, ADAPA users can benefit from using the AWS Management Console to launch one or more ADAPA instances on the Amazon Cloud. Users can also do that by benefiting from the 1-Click Launch feature provided by the AWS Marketplace.

In this posting we cover:

  1. Initial log in into ADAPA - finding the initial password and changing it
  2. How to ssh into the ADAPA instance

Watch our how-to video

Initial log in into ADAPA

In both cases, once the ADAPA instance is up and running, users can then access ADAPA directly through its Web Console. To log in the first time around into ADAPA though, users need to provide the Instance ID as the initial password (password can then be changed later on as depicted below). 

If the ADAPA instance was launched through the AWS Management Console, the Instance ID (the initial password) can be obtained from the Instances table itself:


Note that to access the ADAPA Console, you will need to copy and paste the Public DNS for the instance you just launched into a new tab of your Web browser. Remember to type https:// in front of the Public DNS. In this case, if the Public DNS for your instance is ec2-54-81-130-28.compute-1.amazonaws.com, you will need to type the following in your Web browser: 


If the ADAPA instance was launched through the 1-Click Launch feature, the Instance ID can be obtained as shown below:


To access the ADAPA Console, simply click on the Access Software hyperlink (last link on the table of instances). If it doesn't work, wait a few minutes. The instance may still be initializing.

Once logged in, users can then change the password directly in the ADAPA Console by following the hyperlink provided on the top right corner:



SSH Access to ADAPA Instance

Finally, once the ADAPA instance is up and running, an user can ssh into the instance by using the following command:

ssh -i /location/to/private_key ubuntu@public_dns


  • /location/to/private_key points to the pem file which contains the private key that was configured when the instance was launched
  • ubuntu is the default username for ssh access into ADAPA instance
  • public_dns is the DNS that is assigned to your instance once it is up

Support Team June 23, 2014 Predictive Model Markup Language (PMML) / About PMML

Alex Guazzelli, Zementis CTO, wrote a four-part article series about predictive analytics entitled Predicting the Future. The four articles have been published by IBM in their entirety in the developerWorks website. They are:

  1. Predicting the future, Part 1: What is predictive analytics?
  2. Predicting the future, Part 2: Predictive modeling techniques
  3. Predicting the future, Part 3: Create a predictive solution
  4. Predicting the future, Part 4: Put a predictive solution to work

And, if you are interested in learning about open-standards and predictive analytics, we also recommend the following articles:



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