Loading and executing a PMML file

The Predictive Model Markup Language (PMML) is an XML-based file format developed by the Data Mining Group to provide a way for applications to describe and exchange models produced by data mining and machine learning algorithms.

Yacs provides an elementary python node called PyLoadPMML that generates an object of type pyobj from a model read in a PMML file. This pyobj is a PyFunction that can be executed in a python node created by the user.

Authorized PMML model types

Node PyLoadPMML uses the swig/python interface to library libpmmlLib.so (Linux) or pmmllib.dll (Windows). This library handles :

  • Neuronal Network models and
  • Linear Regression models.

Description of PyLoadPMML

  • Input ports
Input port name YACS type Comment
filename string Name of the PMML file, including its path if the file is not in the current directory
modelname string Name of the model to load
pmmltype string Type of the model. Value is one of kLR (linear regression) or kANN (neural network)
  • Output ports
Output port name YACS type Comment
pyFunc pyobj PyFunction representing the model This function takes a vector of doubles as input parameter and returns a value of type double

Example of model execution

Create a YACS schema that uses node PyLoadPMML and add a python node that will execute the the pyfunction created by PyLoadPMML. The YACS schema with the execution python node code is shown below :


The characteristics of the execution node are the following:

  • Input ports
Input port name YACS type Comment
myFunc pyobj Linked to the PyFunction generated by PyLoadPMML
params dblevec Vector of doubles, input of the PyFunction
  • Output ports
Output port name YACS type Comment
o5 double Result of the model execution