Working with Python and the Machine Learning and Data Science ecosystem is fun, but when it comes to deployment, you may not want to have to use Python. The goal of this repository (and its siblings) is to address this need; you can experiment and train models in the rich Python ecosystem, but deploy your models in other languages and platforms. scikit-learn4x is a free an open source library that allows you to deploy scikit-learn model in other programming languages.

Important Links

Release on pypi.org: https://pypi.org/project/sklearn4x/

scikit-learn 4 JVM Repository: https://github.com/scikitlearn4x/scikit-learn-4-jvm The scikit-learn-4-jvm library uses the models serialized by this repository to allow inference in JVM based languages.

scikit-learn 4 .NET Repository: https://github.com/scikitlearn4x/scikit-learn-4-net The scikit-learn-4-net library uses the models serialized by this repository to allow inference in .NET based languages.

Installation

sklearn4x requires only the following 2 dependencies:

• scikit-learn
• numpy

Note for the version of scikit-learn, there is no limitation. The library detects the vesion of scikit-learn and looks for the classifier fields based on the installed version.

To install from pypi.org, run this command:

pip install sklearn4x

Usage Example

Here is an example on how to use the library.

The Python Code

from sklearn import datasets
from sklearn.naive_bayes import GaussianNB
from sklearn4x.sklearn4x import save_scikit_learn_model
import pandas as pd

ds = datasets.load_iris()
X = ds.data
y = ds.target

train_data = pd.DataFrame(data=X, index=None, columns=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], dtype=X.dtype, copy=False)

classifier = GaussianNB()
classifier.fit(train_data, y)

predictions = classifier.predict(X)
prediction_probabilities = classifier.predict_proba(X)
prediction_log_probabilities = classifier.predict_log_proba(X)

test_data = {
    "training_data": X,
    "predictions": predictions,
    "prediction_probabilities": prediction_probabilities,
    "prediction_log_probabilities": prediction_log_probabilities,
}

print(f'First data point prediction: {predictions[0]}')
print(f'First data point probabilities: {prediction_probabilities[0, 0]:.3f}, {prediction_probabilities[0, 1]:.3f}')
print(f'First data point log probabilities: {prediction_log_probabilities[0, 0]:.3f}, {prediction_log_probabilities[0, 1]:.3f}')


save_scikit_learn_model({'classifier_to_deploy_in_java': classifier}, '/some/path/on/disk.skx', test_data)

# You should see the following outputs:
#
# First data point prediction: 0
# First data point probabilities: 1.000, 0.000
# First data point log probabilities: 0.000, -41.141

The Java Code

String path = "/same/path/on/disk.skx";
IScikitLearnPackage binaryPackage = ScikitLearnPackageFactory.loadFromFile(path);

// Check actual computed values
GaussianNaiveBayes classifier = (GaussianNaiveBayes)binaryPackage.getModel("classifier_to_deploy_in_java");

NumpyArray<Double> x = (NumpyArray<Double>)binaryPackage.getExtraValues().get("training_data");

NumpyArray<Long> predictions = classifier.predict(x);
NumpyArray<Double> probabilities = classifier.predictProbabilities(x);
NumpyArray<Double> logProbabilities = classifier.predictLogProbabilities(x);

System.out.println("First data point prediction: " + predictions.get(0));
System.out.println(String.format("First data point probabilities: %.3f, %.3f", probabilities.get(0, 0), probabilities.get(0, 1)));
System.out.println(String.format("First data point log probabilities: %.3f, %.3f", logProbabilities.get(0, 0), logProbabilities.get(0, 1)));

/*
    You should see the same outputs as Python's:

    First data point prediction: 0
    First data point probabilities: 1.000, 0.000
    First data point log probabilities: 0.000, -41.141
*/

The C# Code

String path = "/same/path/on/disk.skx";
IScikitLearnPackage binaryPackage = ScikitLearnPackageFactory.LoadFromFile(path);

// Check actual computed values
GaussianNaiveBayes classifier = (GaussianNaiveBayes)binaryPackage.GetModel("classifier_to_deploy_in_java");

NumpyArray<double> x = (NumpyArray<double>)binaryPackage.ExtraValues["training_data"];

NumpyArray<long> predictions = classifier.Predict(x);
NumpyArray<double> probabilities = classifier.PredictProbabilities(x);
NumpyArray<double> logProbabilities = classifier.PredictLogProbabilities(x);

Console.Writeline($"First data point prediction: {predictions.Get(0)}:N3");
Console.Writeline($"First data point probabilities: {probabilities.Get(0, 0):N3}, {probabilities.Get(0, 1):N3}");
Console.Writeline($"First data point log probabilities: {logProbabilities.Get(0, 0):N3}, {logProbabilities.Get(0, 1):N3}");

/*
    You should see the same outputs as Python's:

    First data point prediction: 0
    First data point probabilities: 1.000, 0.000
    First data point log probabilities: 0.000, -41.141
*/

Supported Models

See the release notes for most updated support.

Help and Support

Feel free to contact me with my email address: ma (initials for Mohammad Ali), then underscore and then my last name (Yektaie). Finally, add at outlook.com.