Resource-Efficient Supervised Anomaly Detection Classifier

Prerequisites:

• CMake 3.13 or higher
• numpy
• pandas
• sklearn
• xgboost

Prerequisities installation:

- Install CMake 3.13 or higher.

- The python packages can be installed manually or by running:

   pip3 install -r requirments.txt

Usage:

- This classifier is implemented as a sci-kit classifier.

- example_program.py contains an example that compares RADE to Random Forest and XGBoost.

- Another example code for using RADE is: 

    from rade_classifier import RadeClassifier

    from sklearn.datasets import make_classification

    from sklearn.metrics import classification_report

    from sklearn.model_selection import train_test_split



    X, y = make_classification(n_samples=1000000, n_features=4,

                                n_informative=2, n_redundant=0,

                                random_state=0, shuffle=False, weights=[0.99, 0.01])



    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)



    clf = RadeClassifier()



    clf.fit(X_train, y_train)

    y_predicted = clf.predict(X_test)

    print(classification_report(y_test, y_predicted, digits=5))

Version 1.0.0

• Added OSL file