code: def make_pmml_pipeline(obj, active_fields = None, target_fields = None):
  	
  	steps = _filter_steps(_get_steps(obj))
  	pipeline = PMMLPipeline(steps)
  	if active_fields is not None:
  		pipeline.active_fields = numpy.asarray(active_fields)
  	if target_fields is not None:
  		pipeline.target_fields = numpy.asarray(target_fields)
  	return pipeline
docs: Translates a regular Scikit-Learn estimator or pipeline to a PMML pipeline.
  
  	Parameters:
  	----------
  	obj: BaseEstimator
  		The object.
  
  	active_fields: list of strings, optional
  		Feature names. If missing, "x1", "x2", .., "xn" are assumed.
  
  	target_fields: list of strings, optional
  		Label name(s). If missing, "y" is assumed.