Examples

All three modules can be executed separately and the demo codes are in /examples/module_examples/:

• M1_RP: demo_random_projection.py

• M2_PSA: demo_pseudo_sup_approximation.py

• M3_BPS: demo_balanced_scheduling.py

For instance, you could navigate to /M1_RP/demo_random_projection.py. Demo codes all start with “demo_*.py”.

The examples for the full framework can be found under /examples folder; run “demo_base.py” for a simplified example. Run “demo_full.py” for a full example.

API Demo:

from suod.models.base import SUOD

# initialize a set of base outlier detectors to train and predict on
base_estimators = [
    LOF(n_neighbors=5, contamination=contamination),
    LOF(n_neighbors=15, contamination=contamination),
    LOF(n_neighbors=25, contamination=contamination),
    HBOS(contamination=contamination),
    PCA(contamination=contamination),
    OCSVM(contamination=contamination),
    KNN(n_neighbors=5, contamination=contamination),
    KNN(n_neighbors=15, contamination=contamination),
    KNN(n_neighbors=25, contamination=contamination)]

# initialize a SUOD model with all features turned on
model = SUOD(base_estimators=base_estimators, n_jobs=6,  # number of workers
             rp_flag_global=True,  # global flag for random projection
             bps_flag=True,  # global flag for balanced parallel scheduling
             approx_flag_global=False,  # global flag for model approximation
             contamination=contamination)

model.fit(X_train)  # fit all models with X
model.approximate(X_train)  # conduct model approximation if it is enabled
predicted_labels = model.predict(X_test)  # predict labels
predicted_scores = model.decision_function(X_test)  # predict scores
predicted_probs = model.predict_proba(X_test)  # predict outlying probability