iterate_threshold = True
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s P%(process)d %(levelname)s %(message)s",
)
# load dataset
data_dict = load_dataset(
dataset,
subdataset,
"all",
)
x_train = data_dict["train"]
x_test = data_dict["test"]
x_test_labels = data_dict["test_labels"]
# data preprocessing for MSCRED
od = LODA(n_bins=n_bins, n_random_cuts=n_random_cuts)
od.fit(x_train)
# get outlier scores
anomaly_score = od.decision_function(x_test)
anomaly_label = x_test_labels
# Make evaluation
evaluate_all(anomaly_score, anomaly_label)
import sys
sys.path.append("../")
import numpy as np
from common.evaluation import evaluate_all
from common.utils import pprint
if __name__ == "__main__":
num_points = 100
anomaly_label = np.random.choice([0, 1], size=num_points)
anomaly_score = np.random.uniform(0, 1, size=num_points)
anomaly_score_train = np.random.uniform(0, 1, size=num_points)
metrics_iter, metrics_evt, theta_iter, theta_evt = evaluate_all(
anomaly_score, anomaly_label, anomaly_score_train)