Java library and command-line application for converting [Scikit-Learn] (http://scikit-learn.org/) models to PMML.

• Supported Estimator and Transformer types:
  • Clustering:
    • [cluster.KMeans] (http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)
    • [cluster.MiniBatchKMeans] (http://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html)
  • Matrix Decomposition:
    • [decomposition.PCA] (http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)
    • [decomposition.IncrementalPCA] (http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.IncrementalPCA.html)
  • Discriminant Analysis:
    • [discriminant_analysis.LinearDiscriminantAnalysis] (http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html)
  • Ensemble Methods:
    • [ensemble.AdaBoostRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html)
    • [ensemble.BaggingClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html)
    • [ensemble.BaggingRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html)
    • [ensemble.ExtraTreesClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)
    • [ensemble.ExtraTreesRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html)
    • [ensemble.GradientBoostingClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html)
    • [ensemble.GradientBoostingRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
    • [ensemble.IsolationForest] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html)
    • [ensemble.RandomForestClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
    • [ensemble.RandomForestRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)
    • [ensemble.VotingClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html)
  • Feature Selection:
    • [feature_selection.SelectKBest] (http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html)
  • Generalized Linear Models:
    • [linear_model.ElasticNet] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html)
    • [linear_model.ElasticNetCV] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html)
    • [linear_model.Lasso] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html)
    • [linear_model.LassoCV] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html)
    • [linear_model.LinearRegression] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)
    • [linear_model.LogisticRegression] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)
    • [linear_model.LogisticRegressionCV] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html)
    • [linear_model.Ridge] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html)
    • [linear_model.RidgeCV] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html)
    • [linear_model.RidgeClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html)
    • [linear_model.RidgeClassifierCV] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifierCV.html)
    • [linear_model.SGDClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html)
    • [linear_model.SGDRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html)
  • Naive Bayes:
    • [naive_bayes.GaussianNB] (http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html)
  • Nearest Neighbors:
    • [neighbors.KNeighborsClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)
    • [neighbors.KNeighborsRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html)
  • Pipelines:
    • [pipeline.Pipeline] (http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)
  • Neural network models:
    • [neural_network.MLPClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)
    • [neural_network.MLPRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html)
  • Preprocessing and Normalization:
    • [preprocessing.Binarizer] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html)
    • [preprocessing.FunctionTransformer] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html)
    • [preprocessing.Imputer] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html)
    • [preprocessing.LabelBinarizer] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html)
    • [preprocessing.LabelEncoder] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)
    • [preprocessing.MaxAbsScaler] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html)
    • [preprocessing.MinMaxScaler] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
    • [preprocessing.OneHotEncoder] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)
    • [preprocessing.RobustScaler] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html)
    • [preprocessing.StandardScaler] (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)
  • Support Vector Machines:
    • [svm.OneClassSVM] (http://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html)
    • [svm.SVC] (http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)
    • [svm.NuSVC] (http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html)
    • [svm.SVR] (http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html)
    • [svm.LinearSVR] (http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html)
    • [svm.NuSVR] (http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html)
  • Decision Trees:
    • [tree.DecisionTreeClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)
    • [tree.DecisionTreeRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html)
    • [tree.ExtraTreeClassifier] (http://scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html)
    • [tree.ExtraTreeRegressor] (http://scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeRegressor.html)
• Supported third-party Estimator and Transformer types:
  • [SkLearn2PMML] (https://github.com/jpmml/sklearn2pmml):
    • decoration.CategoricalDomain
    • decoration.ContinuousDomain
  • [XGBoost] (https://github.com/dmlc/xgboost):
    • [xgboost.XGBClassifier] (http://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier)
    • [xgboost.XGBRegressor] (http://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressor)
• Production quality:
  • Complete test coverage.
  • Fully compliant with the [JPMML-Evaluator] (https://github.com/jpmml/jpmml-evaluator) library.

• Python 2.7, 3.4 or newer.
• [scikit-learn] (https://pypi.python.org/pypi/scikit-learn) 0.16.0 or newer.
• [pandas] (https://pypi.python.org/pypi/pandas) 0.16.2 or newer.
• [sklearn-pandas] (https://pypi.python.org/pypi/sklearn-pandas) 0.0.10 or newer.
• [joblib] (https://pypi.python.org/pypi/joblib) 0.8.4 or newer.
• [numpy] (https://pypi.python.org/pypi/numpy) 1.9.2 or newer.

Python installation can be validated as follows:

import sklearn, pandas, sklearn_pandas, joblib, numpy

print(sklearn.__version__)
print(pandas.__version__)
print(sklearn_pandas.__version__)
print(joblib.__version__)
print(numpy.__version__)

• Java 1.7 or newer.

Enter the project root directory and build using [Apache Maven] (http://maven.apache.org/):

mvn clean install

The build produces an executable uber-JAR file target/converter-executable-1.2-SNAPSHOT.jar.

A typical workflow can be summarized as follows:

1. Use Python to train a model.
2. Serialize the model in pickle data format to a file in a local filesystem.
3. Use the JPMML-SkLearn command-line converter application to turn the pickle file to a PMML file.

Load data to a pandas.DataFrame object:

import pandas

iris_df = pandas.read_csv("Iris.csv")

Describe data and data pre-processing actions by creating an appropriate sklearn_pandas.DataFrameMapper object:

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn_pandas import DataFrameMapper
from sklearn2pmml.decoration import ContinuousDomain

iris_mapper = DataFrameMapper([
    (["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"], [ContinuousDomain(), StandardScaler(), PCA(n_components = 3)]),
    ("Species", None)
])

iris = iris_mapper.fit_transform(iris_df)

Train an appropriate estimator object:

from sklearn.feature_selection import SelectKBest
from sklearn.ensemble.forest import RandomForestClassifier
from sklearn.pipeline import Pipeline

iris_X = iris[:, 0:3]
iris_y = iris[:, 3]

iris_estimator = Pipeline([
    ("selector", SelectKBest(k = 2)),
    ("estimator", RandomForestClassifier(min_samples_leaf = 5))
])
iris_estimator.fit(iris_X, iris_y)

Serialize the sklearn_pandas.DataFrameMapper object and estimator object in pickle data format:

from sklearn.externals import joblib

joblib.dump(iris_mapper, "mapper.pkl", compress = 9)
joblib.dump(iris_estimator, "estimator.pkl", compress = 9)

Please see the test script file [main.py] (https://github.com/jpmml/jpmml-sklearn/blob/master/src/test/resources/main.py) for more classification (binary and multi-class) and regression workflows.

Converting the estimator pickle file estimator.pkl to a PMML file estimator.pmml:

java -jar target/converter-executable-1.2-SNAPSHOT.jar --pkl-input estimator.pkl --pmml-output estimator.pmml

Converting the sklearn_pandas.DataFrameMapper pickle file mapper.pkl and the estimator pickle file estimator.pkl to a PMML file mapper-estimator.pmml:

java -jar target/converter-executable-1.2-SNAPSHOT.jar --pkl-mapper-input mapper.pkl --pkl-estimator-input estimator.pkl --pmml-output mapper-estimator.pmml

Getting help:

java -jar target/converter-executable-1.2-SNAPSHOT.jar --help

JPMML-SkLearn is licensed under the [GNU Affero General Public License (AGPL) version 3.0] (http://www.gnu.org/licenses/agpl-3.0.html). Other licenses are available on request.

Please contact [[email protected]] (mailto:[email protected])