Examples

Directly Use Detection Models

DeepOD can be used in a few lines of code. This API style is the same with Sklean and PyOD.

for tabular anomaly detection:

# unsupervised methods
from deepod.models.tabular import DeepSVDD
clf = DeepSVDD()
clf.fit(X_train, y=None)
scores = clf.decision_function(X_test)

# weakly-supervised methods
from deepod.models.tabular import DevNet
clf = DevNet()
clf.fit(X_train, y=semi_y) # semi_y uses 1 for known anomalies, and 0 for unlabeled data
scores = clf.decision_function(X_test)

# evaluation of tabular anomaly detection
from deepod.metrics import tabular_metrics
auc, ap, f1 = tabular_metrics(y_test, scores)

for time series anomaly detection:

# time series anomaly detection methods
from deepod.models.time_series import TimesNet
clf = TimesNet()
clf.fit(X_train)
scores = clf.decision_function(X_test)

# evaluation of time series anomaly detection
from deepod.metrics import ts_metrics
from deepod.metrics import point_adjustment # execute point adjustment for time series ad
eval_metrics = ts_metrics(labels, scores)
adj_eval_metrics = ts_metrics(labels, point_adjustment(labels, scores))

Testbed

Testbed contains the whole process of testing an anomaly detection model, including data loading, preprocessing, anomaly detection, and evaluation.

Please refer to testbed/

testbed/testbed_unsupervised_ad.py is for testing unsupervised tabular anomaly detection models.
testbed/testbed_unsupervised_tsad.py is for testing unsupervised time-series anomaly detection models.

Key arguments:

--input_dir: name of the folder that contains datasets (.csv, .npy)
--dataset: “FULL” represents testing all the files within the folder, or a list of dataset names using commas to split them (e.g., “10_cover*,20_letter*”)
--model: anomaly detection model name
--runs: how many times running the detection model, finally report an average performance with standard deviation values

Example:

Download ADBench datasets.
modify the dataset_root variable as the directory of the dataset.
input_dir is the sub-folder name of the dataset_root, e.g., Classical or NLP_by_BERT.
use the following command in the bash

cd DeepOD
pip install .
cd testbed
python testbed_unsupervised_ad.py --model DeepIsolationForest --runs 5 --input_dir ADBench