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A library to parse PMML models into Scikit-learn estimators.

Project description

sklearn-pmml-model

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A library to effortlessly import models trained on different platforms and with programming languages into scikit-learn in Python. First export your model to PMML (widely supported). Next, load the exported PMML file with this library, and use the class as any other scikit-learn estimator.

Installation

The easiest way is to use pip:

$ pip install sklearn-pmml-model

Status

The library currently supports the following models:

Model Classification Regression Categorical features
Decision Trees 1
Random Forests 1
Gradient Boosting 1
Linear Regression 3
Ridge 2 3
Lasso 2 3
ElasticNet 2 3
Gaussian Naive Bayes 3
Support Vector Machines 3
Nearest Neighbors
Neural Networks

1 Categorical feature support using slightly modified internals, based on scikit-learn#12866.

2 These models differ only in training characteristics, the resulting model is of the same form. Classification is supported using PMMLLogisticRegression for regression models and PMMLRidgeClassifier for general regression models.

3 By one-hot encoding categorical features automatically.

Example

A minimal working example (using this PMML file) is shown below:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn_pmml_model.ensemble import PMMLForestClassifier

# Prepare data
iris = load_iris()
X = pd.DataFrame(iris.data)
X.columns = np.array(iris.feature_names)
y = pd.Series(np.array(iris.target_names)[iris.target])
y.name = "Class"
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.33, random_state=123)

clf = PMMLForestClassifier(pmml="models/randomForest.pmml")
clf.predict(Xte)
clf.score(Xte, yte)

More examples can be found in the subsequent packages: tree, ensemble, linear_model, naive_bayes, svm, neighbors and neural_network.

Benchmark

Depending on the data set and model, sklearn-pmml-model is between 5 and a 1000 times faster than competing libraries, by leveraging the optimization and industry-tested robustness of sklearn. Source code for this benchmark can be found in the corresponding jupyter notebook.

Running times (load + predict, in seconds)

Linear model Naive Bayes Decision tree Random Forest Gradient boosting
Wine PyPMML 0.773291 0.77384 0.777425 0.895204 0.902355
sklearn-pmml-model 0.005813 0.006357 0.002693 0.108882 0.121823
Breast cancer PyPMML 3.849855 3.878448 3.83623 4.16358 4.13766
sklearn-pmml-model 0.015723 0.011278 0.002807 0.146234 0.044016

Improvement

Linear model Naive Bayes Decision tree Random Forest Gradient boosting
Wine Improvement 133× 122× 289×
Breast cancer Improvement 245× 344× 1,367× 28× 94×

Development

Prerequisites

Tests can be run using Py.test. Grab a local copy of the source:

$ git clone http://github.com/iamDecode/sklearn-pmml-model
$ cd sklearn-pmml-model

create a virtual environment and activating it:

$ python3 -m venv venv
$ source venv/bin/activate

and install the dependencies:

$ pip install -r requirements.txt

The final step is to build the Cython extensions:

$ python setup.py build_ext --inplace

Testing

You can execute tests with py.test by running:

$ python setup.py pytest

Contributing

Feel free to make a contribution. Please read CONTRIBUTING.md for more details.

License

This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.

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