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README.md

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 # ADBench
 Official Implement of "ADBench: Anomaly Detection Benchmark".
+
+  ### Quickly Implementation of ADBench
+
+    # 1. 设置路径
+    import os
+    import sys
+
+    os.chdir('...') # 设置成你本地repo的地址
+    sys.path.append('...')
+
+    # 2. 加载目前已有的模型(如下表**Import**列所示)
+    from baseline.PyOD import PYOD
+
+    # 3. 实例化
+    '''
+    PyOD相关的模型, 以及supervised相关的模型, 需要指定模型名称(如下表**Model**列所示)
+    '''
+    model = PYOD(model_name='...')
+
+    # 4. 训练
+    model.fit(X_train, y_train)
+
+    # 5. 预测
+    score_test = model.predict_score(X_test)
+
+    # 6. 评价
+    from myutils import Utils
+    utils = Utils()
+
+    result = utils.metric(y_true=y_test, y_score=score_test, pos_label=1) # 结果中包含计算的AUC-ROC以及AUC-PR
+
+
+
+  ### Supported Benchmark Algorithms
+|  Model  | Paper/Year | Type |  DL  |       Import        |  Source  |
+| :-----: | :--------: | :--: | :--: | :-----------------: | :------: |
+| [MCD]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [PCA]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [OCSVM]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [LOF]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [CBLOF]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [COF]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [HBOS]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [KNN]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [SOD]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [COPOD](https://arxiv.org/abs/2009.09463) | ICDM, 2020  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [ECOD](https://arxiv.org/abs/2201.00382) | TKDE, 2022  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [IForest*]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [FeatureBagging*]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [LSCP*]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [LODA*]() | xxx, xxx  | Unsupervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [SOGAAL](https://arxiv.org/pdf/1809.10816.pdf) | TKDE, 2019  | Unsupervised |  ✓   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [MOGAAL*](https://arxiv.org/pdf/1809.10816.pdf) | TKDE, 2019  | Unsupervised |  ✓   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [DAGMM](https://openreview.net/forum?id=BJJLHbb0-) | ICLR, 2018  | Unsupervised |  ✓   | from baseline.DAGMM.run import DAGMM | [Link](https://github.com/mperezcarrasco/PyTorch-DAGMM) |
+| [AutoEncoder]() | xxx, xxx  | Semi-supervised^ |  ✓   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [GANomaly](https://arxiv.org/abs/1805.06725) | ACCV, 2018  | Semi-supervised^ |  ✓   | from baseline.GANomaly.run import GANomaly | [Link]() |
+| [XGBOD](https://arxiv.org/abs/1912.00290) | IJCNN, 2018  | Semi-supervised |  ✗   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |
+| [DeepSAD](https://arxiv.org/abs/1906.02694) | ICLR, 2019  | Semi-supervised |  ✓   | from baseline.DeepSAD.src.run import DeepSAD | [Link](https://github.com/lukasruff/Deep-SAD-PyTorch) |
+| [REPEN](https://arxiv.org/abs/1806.04808) | KDD, 2018  | Weakly-supervised |  ✓   | from baseline.REPEN.run import REPEN | [Link]() |
+| [DevNet](https://arxiv.org/abs/1911.08623) | KDD, 2019  | Weakly-supervised |  ✓   | from baseline.DevNet.run import DevNet | [Link](https://github.com/GuansongPang/deviation-network) |
+| [PReNet](https://arxiv.org/abs/1910.13601) | arxiv, 2020  | Weakly-supervised |  ✓   | from baseline.PReNet.run import PReNet | [Link]() |
+| [FEAWAD](https://arxiv.org/abs/2105.10500) | TNNLS, 2021  | Weakly-supervised |  ✓   | from baseline.FEAWAD.run import FEAWAD | [Link](https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection/blob/main/FEAWAD.py) |
+| [LR]() | xxx, xxx  | Supervised |  ✗   | from baseline.Supervised import supervised | [Link]() |
+| [NB]() | xxx, xxx  | Supervised |  ✗   | from baseline.Supervised import supervised | [Link]() |
+| [SVM]() | xxx, xxx  | Supervised |  ✗   | from baseline.Supervised import supervised | [Link]() |
+| [MLP]() | xxx, xxx  | Supervised |  ✓   | from baseline.Supervised import supervised | [Link]() |
+| [RF](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) | xxx, xxx  | Supervised |  ✗   | from baseline.Supervised import supervised | [Link]() |
+| [LGB](https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf) | NIPS, 2017  | Supervised |  ✗   | from baseline.Supervised import supervised | [Link](https://lightgbm.readthedocs.io/en/latest/) |
+| [XGB](https://arxiv.org/abs/1603.02754) | KDD, 2016  | Supervised |  ✗   | from baseline.Supervised import supervised | [Link](https://xgboost.readthedocs.io/en/stable/) |
+| [ResNet](https://arxiv.org/pdf/2106.11959.pdf) | NIPS, 2019  | Supervised |  ✓   | from baseline.FTTransformer.run import FTTransformer | [Link](https://yura52.github.io/rtdl/stable/index.html) |
+| [FTTransformer](https://arxiv.org/pdf/2106.11959.pdf) | NIPS, 2019  | Supervised |  ✓   | from baseline.FTTransformer.run import FTTransformer | [Link](https://yura52.github.io/rtdl/stable/index.html) |
+- '*' denotes that this model is ensembled.
+- '^' denotes that this semi-supervised model only uses normal samples in the training stage.
+