Dual-MGAN: An Efficient Approach for Semi-supervised Outlier Detection with Few Identified Anomalies
- Python 3.5
- Tensorflow (version: 1.0.1)
- Keras (version: 2.0.2)
The instruction of commands has been clearly stated in the codes (see the parse_args function).
Run Dual-MGAN:
python Dual-MGAN.py --path_out Data/out10.csv --path_unl Data/unl10.csv --path_test Data/test.csv --lr_d 0.001
Use python RCC-Dual-GAN.py -h to get more argument setting details.
-h, --help show this help message and exit
--path_out Input the path of the identified anomalies
--path_unl Input the path of the unlabeled data
--path_test Input the path of the test data
--lr_sg Learning rate of sub_generators
--lr_sd Learning rate of sub_discriminators
--lr_d Learning rate of the detector
--k_means The k in k-means
--max_iter_MGAOS Stop training sub_generators in MGAOS after max_iter_MGAOS
--max_iter_MGAAL Stop training sub_generators in MGAAL after max_iter_MGAAL
--nnr_MGAOS The thresholds of Nnr in MGAOS
--nnr_MGAAL The thresholds of Nnr in MGAAL