PMML model conversion

Converting models to the PMML format

PMML is the predictive model markup language. You can read more about it here. Machine learning models converted to the PMML format can be leveraged by IBM Snap ML; as described here, tree based models can utilize the IBM z16 Integrated Accelerator for AI.

Additionally, Machine Learning for z/OS can deploy PMML models.

Generally, the best approach to converting models to PMML is to use sklearn2pmml. This allows the user to create a PMMLPipeline object, which can contain either scikit-learn, xgboost, or lightGBM models as part of a pipeline.

For considerations for using PMML models in Machine Learning for z/OS, see the WMLz documentation

Samples

For an example of converting a scikit-learn model to PMML, see the IBM Snap ML examples; one such example can be found here

Additionally, here is a brief code snippet showing the process:

# Create a scikit-learn Random Forest Classifier model
model = RandomForestClassifier(n_estimators = 200, max_depth=6, n_jobs=4, random_state=42)

# Train a PMML pipeline that uses the scikit-learn model defined above
pipeline = PMMLPipeline([("model", model)]).fit(X_train, y_train)

# Save the trained PMML pipeline to a file, e.g., "model.pmml"
sklearn2pmml(pipeline, "model.pmml", with_repr=True)