abdoutobblaze/everywhereml

EverywhereML

A Python package to train Machine Learning models that run (almost) everywhere, including:

[X] C++ / embedded systems [X] Javascript [X] PHP [] Go / TinyGo [] Micropython [] ... other languages

This means you can deploy your models to:

• Edge devices
• Web servers
• Web browsers
• ... other environments

Components

The package implements most of the tools you need to develop a fully functional model, including:

[X] Data loading and visualization [X] Preprocessing [] Pipeline [X] BoxCox (power transform) [X] CrossDiff [X] MinMaxScaler [X] Normalizer [X] PolynomialFeatures [X] RateLimit [X] StandardScaler [X] YeoJohnson (power transform) [] Audio [] MelSpectrogram [X] Feature selection [X] RFE [X] SelectKBest [] Time series analysis [X] Diff [X] Fourier transform [X] Rolling window [] TSFRESH [] Classification [X] RandomForest [X] LogisticRegression [X] GaussianNB [] BernoulliNB [] SVM (not tested) [] LinearSVM [X] DecisionTree [X] XGBoost [] Catboost [] Regression [] LinearRegression

Each of these components can be trained in Python and exported to any of the supported languages with no (or as few as possible) external dependencies.

For example:

from everywhereml.data.preprocessing import MinMaxScaler
from sklearn.datasets import load_iris

transformer = MinMaxScaler()
X, y = load_iris(return_X_y=True)
Xt, yt = transformer.fit_transform(X, y)

print('Original range', (X.min(), X.max()))
print('Transformed range', (Xt.min(), Xt.max()))

# port to C++
print(transformer.port(language='cpp'))

# port to Js
print(transformer.port(language='js'))

# port to PHP
print(transformer.port(language='php'))