sergiogaiotto / sklearn2pmml

Python library for converting Scikit-Learn pipelines to PMML

SkLearn2PMML

Python library for converting Scikit-Learn pipelines to PMML.

Features

This library is a thin wrapper around the JPMML-SkLearn command-line application.

For a list of supported Estimator and Transformer types, please refer to JPMML-SkLearn supported packages.

Prerequisites

• Python 2.7, 3.4 or newer.
• Java 1.8 or newer. The Java executable must be available on system path.

Installation

Installing a release version from PyPI:

pip install sklearn2pmml

Alternatively, installing the latest snapshot version from GitHub:

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

Usage

A typical workflow can be summarized as follows:

1. Create a PMMLPipeline object, and populate it with pipeline steps as usual. Class sklearn2pmml.pipeline.PMMLPipeline extends class sklearn.pipeline.Pipeline with the following functionality:

• If the PMMLPipeline.fit(X, y) method is invoked with pandas.DataFrame or pandas.Series object as an X argument, then its column names are used as feature names. Otherwise, feature names default to "x1", "x2", .., "x{number_of_features}".
• If the PMMLPipeline.fit(X, y) method is invoked with pandas.Series object as an y argument, then its name is used as the target name (for supervised models). Otherwise, the target name defaults to "y".

2. Fit and validate the pipeline as usual.
3. Optionally, compute and embed verification data into the PMMLPipeline object by invoking PMMLPipeline.verify(X) method with a small but representative subset of training data.
4. Convert the PMMLPipeline object to a PMML file in local filesystem by invoking utility method sklearn2pmml.sklearn2pmml(pipeline, pmml_destination_path).

Developing a simple decision tree model for the classification of iris species:

import pandas

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

iris_X = iris_df[iris_df.columns.difference(["Species"])]
iris_y = iris_df["Species"]

from sklearn.tree import DecisionTreeClassifier
from sklearn2pmml.pipeline import PMMLPipeline

pipeline = PMMLPipeline([
	("classifier", DecisionTreeClassifier())
])
pipeline.fit(iris_X, iris_y)

from sklearn2pmml import sklearn2pmml

sklearn2pmml(pipeline, "DecisionTreeIris.pmml", with_repr = True)

Developing a more elaborate logistic regression model for the same:

import pandas

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

iris_X = iris_df[iris_df.columns.difference(["Species"])]
iris_y = iris_df["Species"]

from sklearn_pandas import DataFrameMapper
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn2pmml.decoration import ContinuousDomain
from sklearn2pmml.pipeline import PMMLPipeline

pipeline = PMMLPipeline([
	("mapper", DataFrameMapper([
		(["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"], [ContinuousDomain(), SimpleImputer()])
	])),
	("pca", PCA(n_components = 3)),
	("selector", SelectKBest(k = 2)),
	("classifier", LogisticRegression(multi_class = "ovr"))
])
pipeline.fit(iris_X, iris_y)
pipeline.verify(iris_X.sample(n = 15))

from sklearn2pmml import sklearn2pmml

sklearn2pmml(pipeline, "LogisticRegressionIris.pmml", with_repr = True)

Documentation

Up-to-date:

• Benchmarking Scikit-Learn against JPMML-Evaluator in Java and Python environments
• Extending Scikit-Learn with outlier detector transformer type
• Analyzing Scikit-Learn feature importances via PMML
• Training Scikit-Learn based TF(-IDF) plus XGBoost pipelines
• Converting Scikit-Learn based TF(-IDF) pipelines to PMML documents
• Converting Scikit-Learn based Imbalanced-Learn (imblearn) pipelines to PMML documents
• Extending Scikit-Learn with date and datetime features
• Extending Scikit-Learn with feature specifications
• Converting logistic regression models to PMML documents
• Stacking Scikit-Learn, LightGBM and XGBoost models
• Converting Scikit-Learn hyperparameter-tuned pipelines to PMML documents
• Extending Scikit-Learn with GBDT plus LR ensemble (GBDT+LR) model type
• Converting Scikit-Learn based TPOT automated machine learning (AutoML) pipelines to PMML documents
• Converting Scikit-Learn based LightGBM pipelines to PMML documents
• Extending Scikit-Learn with business rules (BR) model type

Slightly outdated:

• Converting Scikit-Learn to PMML

De-installation

Uninstalling:

pip uninstall sklearn2pmml

License

SkLearn2PMML is licensed under the terms and conditions of the GNU Affero General Public License, Version 3.0.

If you would like to use SkLearn2PMML in a proprietary software project, then it is possible to enter into a licensing agreement which makes SkLearn2PMML available under the terms and conditions of the BSD 3-Clause License instead.

Additional information

SkLearn2PMML is developed and maintained by Openscoring Ltd, Estonia.

Interested in using Java PMML API software in your company? Please contact [e-mail]