pyoe.OEBench.ADBench.baseline package¶
Submodules¶
pyoe.OEBench.ADBench.baseline.PyOD module¶
- class pyoe.OEBench.ADBench.baseline.PyOD.PYOD(seed, model_name, tune=False)¶
Bases:
object- fit(X_train, y_train, ratio=None)¶
- grid_hp(model_name)¶
define the hyper-parameter search grid for different unsupervised mdoel
- grid_search(X_train, y_train, ratio=None)¶
implement the grid search for unsupervised models and return the best hyper-parameters the ratio could be the ground truth anomaly ratio of input dataset
- predict_score(X)¶