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PyOD 3：大规模智能体异常检测
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**PyOD 已为智能体做好准备。** Claude Code 和 Codex 可以使用 ``od-expert`` 技能来驱动 ADEngine 调查，而兼容 MCP 的智能体可以查询 PyOD 的检测器知识与规划工具。经典的 ``fit``/``predict`` API 保持不变。
PyOD 3 是功能最全面的 Python 异常检测库。四大支柱：
=========================== ========================================================================================
支柱 含义
=========================== ========================================================================================
多模态 跨表格、时间序列、图、文本、图像和音频数据的 61 个检测器，一个 API
全生命周期 从原始数据到可解释的异常及下一步行动建议，一次调用即可完成
智能体 od-expert 将自然语言请求转化为 ADEngine 工作流；MCP 为其他智能体提供结构化工具
使用最广泛 下载量超过 4600 万次；基于基准测试的路由（ADBench、TSB-AD、BOND、NLP-ADBench）
=========================== ========================================================================================
安装 ^^^^^^^
核心库（每条激活路径都需要）：
.. code-block:: bash
pip install pyod
然后选择与你的智能体技术栈匹配的激活路径：
.. code-block:: bash
# 1. Claude Code / Codex — enables the od-expert skill
pyod install skill # Claude Code: user-global (~/.claude/skills/)
pyod install skill --project # Codex: project-local (./skills/, Codex has no user-global dir)
# 2. Any MCP-compatible LLM — requires the optional mcp extra
pip install pyod[mcp]
pyod mcp serve # alias for `python -m pyod.mcp_server`
# 3. Pure Python — no extra step
# from pyod.utils.ad_engine import ADEngine
随时运行 pyod info，即可查看版本、检测器数量以及每条激活路径的安装状态。pyod info 还会检测你已安装的智能体栈（~/.claude/ 对应 Claude Code，~/.codex/ 对应 Codex），并推荐合适的安装命令。
关于 conda、源码安装、依赖详情和故障排查，请参阅完整的安装指南 <https://pyod.readthedocs.io/en/latest/install.html>__。v3.0.0 中遗留的 pyod-install-skill 命令仍可作为 pyod install skill 的别名使用。
5 行代码实现异常检测（pip install pyod）：
.. code-block:: python
from pyod.models.iforest import IForest
clf = IForest()
clf.fit(X_train)
y_train_scores = clf.decision_scores_ # training anomaly scores
y_test_scores = clf.decision_function(X_test) # test anomaly scores
使用 PyOD 的三种方式：
========= ===================== ====================================================================== =======================================
层级 名称 适用场景 入口
========= ===================== ====================================================================== =======================================
1 经典 API 你已明确想用哪个检测器 第 1 层示例 <https://pyod.readthedocs.io/en/latest/examples/tabular.html>__
2 ADEngine 希望 PyOD 自动选择、比较与评估 第 2 层演练 <https://pyod.readthedocs.io/en/latest/examples/adengine.html>__
3 智能体调查 希望 AI 智能体通过自然对话驱动异常检测 第 3 层演练 <https://pyod.readthedocs.io/en/latest/examples/agentic.html>__
========= ===================== ====================================================================== =======================================
第 2 层和第 3 层由 PyOD 的生命周期编排核心 ADEngine 驱动。完整的第 3 层多轮调查流程可通过面向 Claude Code 和 Codex 的 od-expert 技能使用。MCP 服务器（python -m pyod.mcp_server）为兼容 MCP 的 LLM 提供十个无状态工具，涵盖知识查询（list_detectors、explain_detector、compare_detectors、get_benchmarks）、规划（profile_data、plan_detection、build_detector）和检测（run_detection、analyze_results、explain_findings）；有状态的 investigate / MCP 工具暂缓推出。
.. image:: https://raw.githubusercontent.com/yzhao062/pyod/development/docs/figs/agentic-demo.png :alt: PyOD 3 在 cardiotocography 数据集上的智能体调查演示 :align: center :width: 720
上图展示的是在 UCI Cardiotocography 数据集上进行的一次真实 5 轮智能体对话。另请参阅完整演练 <https://pyod.readthedocs.io/en/latest/examples/agentic.html>、可运行的智能体示例 <https://github.com/yzhao062/pyod/blob/development/examples/agentic_example.py>，或交互式 HTML 演示 <https://htmlpreview.github.io/?https://github.com/yzhao062/pyod/blob/development/examples/agentic_demo.html>__。
PyOD 生态系统与资源：
NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>（NLP 异常检测） | TODS <https://github.com/datamllab/tods>（时间序列） | PyGOD <https://pygod.org/>（图） | ADBench <https://github.com/Minqi824/ADBench>（基准测试） | AD-LLM <https://arxiv.org/abs/2412.11142>（基于 LLM 的异常检测）[#Yang2024ad]_ | 资源 <https://github.com/yzhao062/anomaly-detection-resources>
关于 PyOD ^^^^^^^^^^
PyOD 成立于 2017 年，是运行时间最长、使用最广泛的 Python 异常检测库。凭借 4600 万+ 次下载 <https://pepy.tech/project/pyod>，它同时服务于学术研究（曾被 Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>、KDnuggets <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>__ 和 Towards Data Science <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>__ 专题报道）与商业产品。
V3 通过 ADEngine（生命周期编排）和 od-expert 技能（智能体工作流）扩展了该库，同时保持经典 fit/predict API 完全向后兼容。V3 基于 SUOD [#Zhao2021SUOD]_ 构建，以实现快速并行训练，并通过 numba JIT 为各模型加速。
影响力与认可：
=================================== ===========================================================================
领域 示例
=================================== ===========================================================================
航天与科学 欧洲航天局的 OPS-SAT 航天器遥测基准 <https://www.nature.com/articles/s41597-025-05035-3>（Nature Scientific Data，2025）使用 PyOD 执行全部 30 种算法。
企业部署 沃尔玛（每日 100 万+ 次定价更新，KDD 2019）、Databricks（将 PyOD 与 MLflow/Hyperopt 集成的 Kakapo 框架；内部威胁检测解决方案）、IQVIA（12.3 万+ 条药房理赔）、Altair AI Studio、爱立信（专利 WO2023166515A1 <https://patents.google.com/patent/WO2023166515A1>）。
书籍 Outlier Detection in Python <https://www.manning.com/books/outlier-detection-in-python>（Brett Kennedy，Manning）；Handbook of Anomaly Detection with Python（Chris Kuo，Columbia）；Finding Ghosts in Your Data <https://link.springer.com/book/10.1007/978-1-4842-8870-2>（Kevin Feasel，Apress）。
课程 DataCamp Anomaly Detection in Python <https://www.datacamp.com/courses/anomaly-detection-in-python>（平台学习者 1900 万+）、Manning liveProject <https://www.manning.com/liveproject/using-pyod-and-ensembles-methods>、O'Reilly 视频版、多个 Udemy 课程。
播客 Talk Python To Me #497 <https://talkpython.fm/episodes/show/497/outlier-detection-with-python>。
国际 提供 5 种非英语语言教程：中文（CSDN、知乎、搜狐、机器之心、__ 完整文档翻译）、日语、韩语、德语、西班牙语。
=================================== ===========================================================================
如需完整的引用文献、企业部署、专利和媒体报道列表，请参阅 Read the Docs 上的完整影响力页面 <https://pyod.readthedocs.io/en/latest/impact.html>__。
引用 PyOD：
如果您在科学出版物中使用 PyOD，我们将不胜感激您引用以下论文：
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection <https://arxiv.org/abs/2412.12154>__ 以预印本形式提供。如果您在科学出版物中使用 PyOD，我们恳请您引用以下论文::
@inproceedings{chen2025pyod,
title={Pyod 2: A python library for outlier detection with llm-powered model selection},
author={Chen, Sihan and Qian, Zhuangzhuang and Siu, Wingchun and Hu, Xingcan and Li, Jiaqi and Li, Shawn and Qin, Yuehan and Yang, Tiankai and Xiao, Zhuo and Ye, Wanghao and others},
booktitle={Companion Proceedings of the ACM on Web Conference 2025},
pages={2807--2810},
year={2025}
}
PyOD 论文 <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>__ 发表于 Journal of Machine Learning Research (JMLR) <http://www.jmlr.org/>__（MLOSS 专栏）。::
@article{zhao2019pyod,
author = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
title = {PyOD: A Python Toolbox for Scalable Outlier Detection},
journal = {Journal of Machine Learning Research},
year = {2019},
volume = {20},
number = {96},
pages = {1-7},
url = {http://jmlr.org/papers/v20/19-011.html}
}
或::
Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.
如需更全面的异常检测视角，请参阅我们在 NeurIPS 上发表的关于 ADBench <https://arxiv.org/abs/2206.09426>__ [#Han2022ADBench]_ 和 ADGym <https://arxiv.org/abs/2309.15376>__ 的论文。
目录：
API 速查表与参考 <#api-cheatsheet--reference>__基准测试 <#benchmarks>__已实现的算法 <#implemented-algorithms>__（表格、时间序列、图、嵌入）其他主题 <#additional-topics>__（模型保存/加载、SUOD、阈值化）异常检测快速入门 <#quick-start-for-outlier-detection>__如何贡献 <#how-to-contribute>__收录标准 <#inclusion-criteria>__API 速查表与参考 ^^^^^^^^^^^^^^^^^^^^^^^^^^
完整的 API 参考按模态拆分，位于 PyOD 文档 <https://pyod.readthedocs.io/en/latest/>：表格 <https://pyod.readthedocs.io/en/latest/pyod.models.tabular.html>、时间序列 <https://pyod.readthedocs.io/en/latest/pyod.models.timeseries.html>、图 <https://pyod.readthedocs.io/en/latest/pyod.models.graph.html>、嵌入 <https://pyod.readthedocs.io/en/latest/pyod.models.embedding.html>、ADEngine <https://pyod.readthedocs.io/en/latest/pyod.ad_engine.html>、工具函数 <https://pyod.readthedocs.io/en/latest/pyod.utils.html>__。以下是所有检测器的快速速查表：
已拟合模型的关键属性：
基准测试 ^^^^^^^^^^
ADBench <https://github.com/Minqi824/ADBench>__ [#Han2022ADBench]_：57 个表格数据集上的 30 种算法。参见对比 <https://github.com/yzhao062/pyod/blob/master/examples/compare_all_models.py>__。NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>__：8 个文本数据集上的 19 种方法。两步法（嵌入 + 检测器）优于端到端方法。TSB-AD <https://github.com/TheDatumOrg/TSB-AD>__ [#Liu2024TSB]_：1070 个时间序列数据集上的 40 种算法（NeurIPS 2024）。BOND <https://arxiv.org/abs/2206.10071>__ [#Liu2022BOND]_：14 个数据集上的 14 种图异常检测算法（NeurIPS 2022）。其他主题 ^^^^^^^^^
模型保存与加载 <https://pyod.readthedocs.io/en/latest/model_persistence.html>：使用 joblib 或 pickle 保存和加载 PyOD 模型。参见示例 <https://github.com/yzhao062/pyod/blob/master/examples/save_load_model_example.py>。使用 SUOD 快速训练 <https://pyod.readthedocs.io/en/latest/fast_train.html>：利用 SUOD 框架 [#Zhao2021SUOD]_ 加速训练与预测。参见示例 <https://github.com/yzhao062/pyod/blob/master/examples/suod_example.py>。异常分数阈值化 <https://pyod.readthedocs.io/en/latest/thresholding.html>：通过 PyThresh <https://github.com/KulikDM/pythresh> 以数据驱动的方式设置污染率水平。已实现的算法 ^^^^^^^^^^^^^^
PyOD 分为两个功能组：（i）检测算法，针对表格、时间序列、图与音频数据设有专门的小节（表格表中的 EmbeddingOD 通过基础模型编码器增加了对文本和图像的支持）；以及**（ii）工具函数**，用于数据生成、评估与生命周期编排。
（i-a）表格与多模态检测算法：
.. list-table:: :widths: 15 14 58 5 8 :header-rows: 1* - 类型 - 缩写 - 算法 - 年份 - 引用
示例 <https://github.com/yzhao062/pyod/blob/development/examples/ecod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/abod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/abod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/copod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/mad_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/sos_example.py>__)集成方法（IForest、INNE、DIF、FB、LSCP、LODA、SUOD、XGBOD）已包含在上表中。分数组合函数（平均值、最大化、AOM、MOA、中位数、多数投票）位于 pyod.models.combination 中。有关详细信息，请参阅 API 文档 <https://pyod.readthedocs.io/en/latest/pyod.models.tabular.html>__。
(i-b) 时间序列异常检测 :
所有时间序列检测器都使用与表格检测器相同的 fit/predict/decision_function API，但有一个例外：MatrixProfile 是转导式的（仅训练；在 fit() 后使用 decision_scores_ 和 labels_，没有样本外 predict）。
输入格式：单变量为形状 (n_timestamps,) 的 numpy 数组，多变量为 (n_timestamps, n_channels)。每行是一个时间步；列是通道/特征。Pandas DataFrame 和列表会自动转换。输出：形状为 (n_timestamps,) 的 decision_scores_，每个时间步对应一个异常分数。
3 行代码实现时间序列检测:
.. code-block:: python
from pyod.models.ts_kshape import KShape # or any TS detector
clf = KShape(window_size=20)
clf.fit(X_train) # shape (n_timestamps,) or (n_timestamps, n_channels)
scores = clf.decision_scores_ # per-timestamp anomaly scores
算法排名来自 TSB-AD 基准 <https://github.com/TheDatumOrg/TSB-AD>__ [#Liu2024TSB]_ (NeurIPS 2024, 1070 个数据集):
.. list-table:: :widths: 15 18 50 5 12 :header-rows: 1
示例 <https://github.com/yzhao062/pyod/blob/development/examples/ts_od_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/ts_matrix_profile_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/ts_spectral_residual_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/ts_kshape_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/ts_sand_example.py>__)(i-c) 图异常检测 (pip install pyod[graph]):
在 v1 中，所有图检测器都是转导式的：在 fit() 后使用 decision_scores_ 和 labels_。没有样本外 predict。输入：包含 x（节点特征）和 edge_index（COO 边）的 PyG Data 对象。SCAN 无需特征即可工作。
3 行代码实现图检测 (pip install pyod[graph]):
.. code-block:: python
from pyod.models.pyg_dominant import DOMINANT
clf = DOMINANT(hidden_dim=64, epochs=100)
clf.fit(data) # PyG Data object
scores = clf.decision_scores_ # per-node anomaly scores
算法排名来自 BOND 基准 <https://arxiv.org/abs/2206.10071>__ [#Liu2022BOND]_ (NeurIPS 2022, 14 个数据集):
.. list-table:: :widths: 18 18 45 5 14 :header-rows: 1
dominant 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_dominant_example.py>__)cola 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_cola_example.py>__)conad 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_conad_example.py>__)anomalydae 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_anomalydae_example.py>__)guide 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_guide_example.py>__)(i-d) 音频异常检测 (pip install pyod[audio]):
音频片段使用相同的 fit/decision_function API。提供两条路径：轻量级的“先嵌入后检测”路径（EmbeddingOD.for_audio() 将每个片段转换为 74 维手工声学特征向量，并运行任意经典检测器），以及专用的深度检测器（AudioAE，一个 log-mel 重构自编码器）。输入为文件路径、波形数组或 (waveform, sample_rate) 元组。输出：每个片段一个异常分数。
3 行代码实现音频检测 (pip install pyod[audio]):
.. code-block:: python
from pyod.models.embedding import EmbeddingOD
clf = EmbeddingOD.for_audio('balanced') # 74-dim handcrafted features + KNN
clf.fit(train_clips) # list of file paths or waveform arrays
scores = clf.decision_scores_ # per-clip anomaly scores
.. list-table:: :widths: 18 18 45 5 14 :header-rows: 1
for_audio(): 74 维 MFCC、chroma 和频谱特征，可与任意检测器配合使用(ii) 实用函数:=================== ============================ ===================================================================================================================================================== 类型 名称 功能 =================== ============================ ===================================================================================================================================================== 数据 generate_data 合成数据生成；正常数据来自多元高斯分布，异常值来自均匀分布 数据 generate_data_clusters 分簇的合成数据生成，用于更复杂的模式 评估 evaluate_print 打印检测器的 ROC-AUC 和 Precision @ Rank n 评估 precision_n_scores 计算 Precision @ Rank n 工具 get_label_n 将原始异常值得分转换为二元标签，将前 n 个最高分标记为 1 统计 wpearsonr 计算两个样本的加权 Pearson 相关系数 编码 resolve_encoder 从字符串名称、BaseEncoder 实例或可调用对象解析编码器 编码 SentenceTransformerEncoder 通过 sentence-transformers 模型（例如 MiniLM、mpnet）编码文本 编码 OpenAIEncoder 通过 OpenAI Embeddings API（text-embedding-3-small/large）编码文本 编码 HuggingFaceEncoder 通过 HuggingFace transformers（BERT、DINOv2、CLIP）编码文本或图像 =================== ============================ =====================================================================================================================================================
异常检测快速入门 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
PyOD 已获得机器学习社区的广泛认可，并有多篇专题文章和教程。
Analytics Vidhya：使用 PyOD 库在 Python 中学习异常检测的精彩教程 <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>__
KDnuggets：异常检测方法的直观可视化 <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>、来自 PyOD 的异常检测方法概述 <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>
Towards Data Science：异常检测入门指南 <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>__
"examples/knn_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/knn_example.py>__ 演示了使用 kNN 检测器的基本 API。需要注意的是，所有其他算法的 API 都是一致/相似的。
有关运行示例的更详细说明，请参阅 examples 目录 <https://github.com/yzhao062/pyod/blob/master/examples>__。
#. 初始化一个 kNN 检测器，拟合模型，并进行预测。
.. code-block:: python
from pyod.models.knn import KNN # kNN detector
from pyod.utils.data import generate_data
contamination = 0.1 # percentage of outliers
n_train = 200 # number of training points
n_test = 100 # number of testing points
# generate sample data
X_train, X_test, y_train, y_test = generate_data(
n_train=n_train, n_test=n_test, n_features=2,
contamination=contamination, random_state=42)
# train kNN detector
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)
# get the prediction label and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# it is possible to get the prediction confidence as well
y_test_pred, y_test_pred_confidence = clf.predict(X_test, return_confidence=True) # outlier labels (0 or 1) and confidence in the range of [0,1]
#. 通过 ROC 和 Precision @ Rank n (p@n) 评估预测结果。
.. code-block:: python
from pyod.utils.data import evaluate_print
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
#. 查看示例输出与可视化结果。
.. code-block:: python
On Training Data:
KNN ROC:0.9992, precision @ rank n:0.95
On Test Data:
KNN ROC:1.0, precision @ rank n:1.0
.. code-block:: python
from pyod.utils.example import visualize
visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred, show_figure=True, save_figure=False)
致谢 ^^^^^^^^^^^^^^^
本材料基于美国国家科学基金会（National Science Foundation）资助的工作，
奖项编号 2346158 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=2346158>_，
用于"NSF POSE：第二阶段：OpenAD：一个集成的开源异常检测生态系统"。
该奖项将伊利诺伊大学芝加哥分校列为主导机构，
伊利诺伊理工学院、理海大学和南加州大学列为次级受奖机构。
本材料中表达的任何观点、发现、结论或建议均为作者（们）的观点， 并不一定反映美国国家科学基金会的意见。
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iterateReal Python Podcast #208 <https://realpython.com/podcasts/rpp/208/>aidoczh.com <https://www.aidoczh.com>示例 <https://github.com/yzhao062/pyod/blob/development/examples/qmcd_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/kde_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/sampling_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/gmm_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/pca_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/kpca_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/mcd_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/cd_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/ocsvm_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/lmdd_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/lof_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/cof_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/cof_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/cblof_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/loci_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/hbos_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/hdbscan_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/knn_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/knn_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/knn_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/sod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/rod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/iforest_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/inne_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/dif_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/feature_bagging_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/lscp_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/xgbod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/loda_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/suod_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/auto_encoder_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/vae_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/vae_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/so_gaal_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/mo_gaal_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/deepsvdd_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/alad_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/ae1svm_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/devnet_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/rgraph_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/lunar_example.py>__)示例 <https://github.com/yzhao062/pyod/blob/development/examples/embedding_od_example.py>__)radar 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_radar_example.py>__)anomalous 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_anomalous_example.py>__)scan 示例 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_scan_example.py>__)