How to use the sklearn2pmml.ruleset.RuleSetClassifier function in sklearn2pmml
To help you get started, we’ve selected a few sklearn2pmml examples, based on popular ways it is used in public projects.
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openscoring / papis.io / RuleSetIris.py View on Github 
from sklearn2pmml import sklearn2pmml
from sklearn2pmml.pipeline import PMMLPipeline
from sklearn2pmml.ruleset import RuleSetClassifier
import pandas
import sys
iris_df = pandas.read_csv("csv/Iris.csv")
#print(iris_df.head(5))
iris_X = iris_df[iris_df.columns.difference(["Species"])]
iris_y = iris_df["Species"]
classifier = RuleSetClassifier([
("X['Petal_Length'] < 2.45", "setosa"),
("X['Petal_Width'] < 1.75", "versicolor"),
], default_score = "virginica")
pipeline = PMMLPipeline([
("classifier", classifier)
])
pipeline.fit(iris_X, iris_y)
sklearn2pmml(pipeline, "pmml/RuleSetIris.pmml")
if "--deploy" in sys.argv:
from openscoring import Openscoring
os = Openscoring("http://localhost:8080/openscoring")
os.deployFile("RuleSetIris", "pmml/RuleSetIris.pmml")sklearn2pmml
Python library for converting Scikit-Learn pipelines to PMML
