Export python scikit learn models into pmml

Question

I want to export python scikit-learn models into PMML.

What python package is best suited?

I read about Augustus, but I was not able to find any example using

Answer 1

SkLearn2PMML is

a thin wrapper around the JPMML-SkLearn command-line application. For a list of supported Scikit-Learn Estimator and Transformer types, please refer to the documentation of the JPMML-SkLearn project.

As @user1808924 notes, it supports Python 2.7 or 3.4+. It also requires Java 1.7+

Installed via: (requires git)

pip install git+https://github.com/jpmml/sklearn2pmml.git

Example of how export a classifier tree to PMML. First grow the tree:

# example tree & viz from http://scikit-learn.org/stable/modules/tree.html
from sklearn import datasets, tree
iris = datasets.load_iris()
clf = tree.DecisionTreeClassifier() 
clf = clf.fit(iris.data, iris.target)

There are two parts to an SkLearn2PMML conversion, an estimator (our clf) and a mapper (for preprocessing steps such as discretization or PCA). Our mapper is pretty basic, since we are not doing any transformations.

from sklearn_pandas import DataFrameMapper
default_mapper = DataFrameMapper([(i, None) for i in iris.feature_names + ['Species']])

from sklearn2pmml import sklearn2pmml
sklearn2pmml(estimator=clf, 
             mapper=default_mapper, 
             pmml="D:/workspace/IrisClassificationTree.pmml")

It is possible (though not documented) to pass mapper=None, but you will see that the predictor names get lost (returning x1 not sepal length etc.).

Let's look at the .pmml file:

    

        
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The first split (Node 1) is on petal width at 0.8. Node 2 (petal width <= 0.8) captures all of the setosa, with nothing else.

You can compare the pmml output to the graphviz output:

from sklearn.externals.six import StringIO
import pydotplus # this might be pydot for python 2.7
dot_data = StringIO() 
tree.export_graphviz(clf, 
                     out_file=dot_data,  
                     feature_names=iris.feature_names,  
                     class_names=iris.target_names,  
                     filled=True, rounded=True,  
                     special_characters=True) 
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("D:/workspace/iris.pdf") 
# for in-line display, you can also do:
# from IPython.display import Image  
# Image(graph.create_png())