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

• C++ / embedded systems
• Javascript
• 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:

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