Save XGBoost Model in PMML Format

PMML (Predictive Model Markup Language) is an XML-based format for representing machine learning models.

Saving your XGBoost models in PMML format enables interoperability with other tools and platforms that support PMML.

First, we must install the sklearn2pmml library using our preferred package manager, such as pip:

pip install sklearn2pmml

Next, we can save our XGBoost model in PMML format.

import numpy as np
import xgboost as xgb
from sklearn.datasets import make_classification
from sklearn.pipeline import Pipeline
from sklearn2pmml import sklearn2pmml, PMMLPipeline
from sklearn2pmml.decoration import ContinuousDomain

# Generate a synthetic dataset
X, y = make_classification(n_samples=100, n_features=20, random_state=42)

# Create a PMMLPipeline with a single XGBoost model
pipeline = PMMLPipeline([
    ("domain", ContinuousDomain()),
    ("classifier", xgb.XGBClassifier(eval_metric='logloss'))
])

# Fit the pipeline
pipeline.fit(X, y)

# Export the pipeline to a PMML file
sklearn2pmml(pipeline, "xgboost_model.pmml", with_repr=True)

Here’s what we’re doing:

1. We generate a synthetic classification dataset using make_classification with 100 samples and 20 features.
2. We create a PMMLPipeline that includes a ContinuousDomain transformer and an XGBClassifier. The eval_metric='logloss' parameter specifies the evaluation metric for the model.
3. We fit the pipeline on the generated dataset using pipeline.fit(X, y).
4. We use sklearn2pmml to export the fitted pipeline to a PMML file named xgboost_model.pmml. The with_repr=True argument includes a human-readable representation of the model in the PMML file.

By using a PMMLPipeline, we can include additional preprocessing steps, such as feature scaling or selection, alongside the XGBoost model. The entire pipeline is then exported to PMML format, ensuring that the preprocessing steps are also captured in the PMML representation.

Once saved in PMML format, the model can be used in other environments or tools that support PMML. However, the specifics of loading and using PMML models may vary depending on the tool or platform.