omega-models

Omega Models is a Python package that provides a collection of generated machine learning classifiers with a scikit-learn style API.

Install

pip install omega-models

Quick start

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from omega_models import AdaptiveAbstractionForest

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = AdaptiveAbstractionForest()
model.fit(X_train, y_train)
print("accuracy:", model.score(X_test, y_test))

More examples

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from omega_models import DirectionalForest

X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

model = DirectionalForest(n_estimators=200)
model.fit(X_train, y_train)
preds = model.predict(X_test)
print(preds[:10])

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from omega_models import ResidualMLPEnsemble

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)

model = ResidualMLPEnsemble()
model.fit(X_train, y_train)
print("score:", model.score(X_test, y_test))

Available models

The public API re-exports models for convenient imports:

• AdaptiveAbstractionForest
• AdaptiveAggressivenessClassifier
• AdaptiveDensityWeightedClassifier
• AdaptiveResidualLearningClassifier
• DimAwareForest
• DirectionalForest
• ResidualMLPEnsemble

Requirements

• Python 3.6+
• NumPy, SciPy, scikit-learn

License

MIT License. See LICENSE.