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
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'))