PMML, the Predictive Model Markup Language, is the de facto standard to represent predictive models. With PMML, models can be exported from one tool and easily imported by another, without all the hassle of dealing with proprietary code and incompatibilities.
Converting from one version to another
More often than not though, auto-generated PMML code is represented in different versions of PMML. A tool may export PMML 2.1 and another import PMML 3.2. This problem raises the issue of conversion. For true interoperability, PMML needs to be easily converted from one version to another.
Validating code against the schema
PMML is an XML-based language. The Data Mining Group (DMG) publishes a PMML Schema (.xsd file) that is specific on how PMML elements should be used. Unfortunately, some tools do not adhere 100% to the schema. For true interoperability, PMML needs to be successfully validated against the schema and if any problems are found, these need to be pointed out so that they can be fixed.
Correcting files so that they conform to the schema
Once schema incompatibilities are identified, life becomes a lot easier if problems are correctly automatically so that any PMML code that won’t .. …
PMML, the Predictive Model Markup Language, is the de facto standard to represent predictive models. With PMML, models can be exported from one tool and easily imported by another, without all the hassle of dealing with proprietary code and incompatibilities.
Converting from one version to another
More often than not though, auto-generated PMML code is represented in different versions of PMML. A tool may export PMML 2.1 and another import PMML 3.2. This problem raises the issue of conversion. For true interoperability, PMML needs to be easily converted from one version to another.
Validating code against the schema
PMML is an XML-based language. The Data Mining Group (DMG) publishes a PMML Schema (.xsd file) that is specific on how PMML elements should be used. Unfortunately, some tools do not adhere 100% to the schema. For true interoperability, PMML needs to be successfully validated against the schema and if any problems are found, these need to be pointed out so that they can be fixed.
Correcting files so that they conform to the schema
Once schema incompatibilities are identified, life becomes a lot easier if problems are correctly automatically so that any PMML code that won’t validate against the schema at first is successfully validated after being corrected.
Obviously, one may wonder why not have perfect PMML code at all times, and in its latest version (version 4.0 was just release! June 2009). This is the ideal scenario, but in reality, PMML producers and consumers have different levels of support for the standard and have a tendency to lag behind when it comes to updating importers and exporters to accompany the latest release.
Given that we don’t leave on an ideal PMML world, the emergence of a PMML tool that can validate, correct, and convert PMML code is to be celebrated.
The PMML Converter
Besides schema validation, the PMML Converter automatically corrects known issues with PMML code from several sources/vendors. The aim is to successfully validate code in older versions of PMML (2.1, 3.0, 3.1) and convert them to PMML 3.2. Files in PMML 3.2 can also be passed through the converter so that they can be corrected and validated against the 3.2 schema.
If the PMML code cannot be converted, that usually means that it could not be automatically corrected. In that case, the PMML Converter embeds comments into the PMML code pinpointing the problem so that they can be fixed manually before being submitted again for conversion. This is done via a hyper-link in the converter which allows for the PMML file to be download after failed conversion.
For a list of modeling elements covered by the PMML Converter, click HERE.
For a guide on how to use the PMML Converter as well as a how-to video, click HERE.
Schema validation and convertion to PMML 4.0 is coming soon!
Comprehensive blog featuring topics related to predictive analytics with an emphasis on open standards, Predictive Model Markup Language (PMML), cloud computing, as well as the deployment and integration of predictive models in any business process.