yzhao062/pyodUna librería de Python para la detección de anomalías en datos tabulares, series temporales, grafos, texto, imágenes y audio. Más de 60 detectores, orquestación de ADEngine respaldada por benchmarks y un flujo de trabajo agéntico para agentes de IA.
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PyOD 3: Detección de anomalías agéntica a escala
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**PyOD está listo para agentes.** Claude Code y Codex pueden usar la habilidad ``od-expert`` para dirigir investigaciones de ADEngine, mientras que los agentes compatibles con MCP pueden consultar el conocimiento de detectores y las herramientas de planificación de PyOD. La API clásica ``fit``/``predict`` permanece sin cambios.
PyOD 3 es la librería de Python más completa para la detección de anomalías. Cuatro pilares:
=========================== ========================================================================================
Pilar Qué significa
=========================== ========================================================================================
Multi-Modal 61 detectores para datos tabulares, de series temporales, de grafos, de texto, de imágenes y de audio, una sola API
Ciclo de vida completo Desde datos sin procesar hasta anomalías explicadas y orientación para los siguientes pasos en una sola llamada
Agéntico od-expert convierte solicitudes en lenguaje natural en flujos de trabajo de ADEngine; MCP expone herramientas estructuradas para otros agentes
Más utilizado Más de 46 millones de descargas; enrutamiento respaldado por benchmarks (ADBench, TSB-AD, BOND, NLP-ADBench)
=========================== ========================================================================================
Instalación ^^^^^^^^^^^
Librería principal (requerida para todas las vías de activación):
.. code-block:: bash
pip install pyod
Después elige la vía de activación que coincida con tu stack de agentes:
.. 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
Ejecuta pyod info en cualquier momento para ver la versión, el número de detectores y el estado de instalación de cada vía de activación. pyod info también detecta qué stack de agentes tienes instalado (~/.claude/ para Claude Code, ~/.codex/ para Codex) y recomienda el comando de instalación adecuado.
Para conda, instalación desde el código fuente, detalles de dependencias y solución de problemas, consulta la guía de instalación completa <https://pyod.readthedocs.io/en/latest/install.html>__. El comando heredado pyod-install-skill de la v3.0.0 sigue funcionando como alias de pyod install skill.
Detección de anomalías con 5 líneas de código (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
Tres formas de usar PyOD:
========= ===================== ====================================================================== =======================================
Capa Nombre Cuándo usarla Punto de entrada
========= ===================== ====================================================================== =======================================
1 API clásica Sabes qué detector quieres Ejemplos de la capa 1 <https://pyod.readthedocs.io/en/latest/examples/tabular.html>__
2 ADEngine Quieres que PyOD elija, compare y evalúe automáticamente Tutorial de la capa 2 <https://pyod.readthedocs.io/en/latest/examples/adengine.html>__
3 Investigación agéntica Quieres que un agente de IA dirija la OD mediante conversación natural Tutorial de la capa 3 <https://pyod.readthedocs.io/en/latest/examples/agentic.html>__
========= ===================== ====================================================================== =======================================
Las capas 2 y 3 funcionan con ADEngine, el núcleo de orquestación del ciclo de vida de PyOD. El flujo completo de investigación multi-turno de la capa 3 está disponible a través de la habilidad od-expert para Claude Code y Codex. El servidor MCP (python -m pyod.mcp_server) expone diez herramientas sin estado para LLM compatibles con MCP, que abarcan consultas de conocimiento (list_detectors, explain_detector, compare_detectors, get_benchmarks), planificación (profile_data, plan_detection, build_detector) y detección (run_detection, analyze_results, explain_findings); las herramientas MCP con estado / se han pospuesto.
.. image:: https://raw.githubusercontent.com/yzhao062/pyod/development/docs/figs/agentic-demo.png :alt: Demo de investigación agéntica de PyOD 3 sobre el conjunto de datos de cardiotocografía :align: center :width: 720
La figura anterior muestra una conversación agéntica real de 5 turnos sobre el conjunto de datos UCI Cardiotocography. Consulta el tutorial completo <https://pyod.readthedocs.io/en/latest/examples/agentic.html>, el ejemplo agéntico ejecutable <https://github.com/yzhao062/pyod/blob/development/examples/agentic_example.py> o la demo HTML interactiva <https://htmlpreview.github.io/?https://github.com/yzhao062/pyod/blob/development/examples/agentic_demo.html>__.
Ecosistema y recursos de PyOD:
NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>__ (detección de anomalías en NLP) | TODS <https://github.com/datamllab/tods>__ (series temporales) | PyGOD <https://pygod.org/>__ (grafos) | ADBench <https://github.com/Minqi824/ADBench>__ (benchmark) | AD-LLM <https://arxiv.org/abs/2412.11142>__ (AD basado en LLM) [#Yang2024ad]_ | Recursos <https://github.com/yzhao062/anomaly-detection-resources>__
Acerca de PyOD ^^^^^^^^^^^^^^
PyOD, creado en 2017, es la librería de Python para detección de anomalías que lleva más tiempo en funcionamiento y es la más utilizada. Con más de 46 millones de descargas <https://pepy.tech/project/pyod>, sirve tanto a la investigación académica (presente en 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>__ y Towards Data Science <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>__) como a productos comerciales.
V3 amplía la librería con ADEngine (orquestación del ciclo de vida) y la habilidad od-expert (flujo de trabajo agéntico), manteniendo la API clásica fit/predict totalmente compatible hacia atrás. V3 está construida sobre SUOD [#Zhao2021SUOD]_ para el entrenamiento paralelo rápido y numba JIT para aceleraciones por modelo.
Impacto y reconocimiento:
=================================== ===========================================================================
Área Ejemplos
=================================== ===========================================================================
Espacio y ciencia La Agencia Espacial Europea benchmark de telemetría de la nave espacial OPS-SAT <https://www.nature.com/articles/s41597-025-05035-3>__ (Nature Scientific Data, 2025) usa PyOD para los 30 algoritmos.
Despliegue empresarial Walmart (más de 1M de actualizaciones de precios diarias, KDD 2019), Databricks (framework Kakapo que integra PyOD con MLflow/Hyperopt; solución de detección de amenazas internas), IQVIA (más de 123K reclamaciones de farmacia), Altair AI Studio, Ericsson (patente WO2023166515A1 <https://patents.google.com/patent/WO2023166515A1>).
Libros 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).
Cursos DataCamp Anomaly Detection in Python <https://www.datacamp.com/courses/anomaly-detection-in-python>__ (más de 19M de estudiantes en la plataforma), Manning liveProject <https://www.manning.com/liveproject/using-pyod-and-ensembles-methods>, traducción completa de la documentación), japonés, coreano, alemán, español.
=================================== ===========================================================================
Consulta la página de impacto completa <https://pyod.readthedocs.io/en/latest/impact.html>__ en Read the Docs para ver la lista completa de citas, despliegues empresariales, patentes y cobertura mediática.
Cómo citar PyOD:
Si usas PyOD en una publicación científica, agradeceríamos citas a los siguientes artículos:
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection <https://arxiv.org/abs/2412.12154>__ está disponible como preprint. Si usas PyOD en una publicación científica, agradeceríamos citas al siguiente artículo::
@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}
}
El artículo de PyOD <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>__ se publicó en Journal of Machine Learning Research (JMLR) <http://www.jmlr.org/>__ (track MLOSS).::
@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}
}
o::
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.
Para una perspectiva más amplia sobre la detección de anomalías, consulta nuestros artículos de NeurIPS sobre ADBench <https://arxiv.org/abs/2206.09426>__ [#Han2022ADBench]_ y ADGym <https://arxiv.org/abs/2309.15376>__.
Tabla de contenido:
Referencia rápida de la API <#api-cheatsheet--reference>__Benchmarks <#benchmarks>__Algoritmos implementados <#implemented-algorithms>__ (Tabular, Series temporales, Grafos, Embedding)Temas adicionales <#additional-topics>__ (Guardar/cargar modelos, SUOD, Umbrales)Inicio rápido para detección de anomalías <#quick-start-for-outlier-detection>__Cómo contribuir <#how-to-contribute>__Criterios de inclusión <#inclusion-criteria>__Referencia rápida de la API ^^^^^^^^^^^^^^^^^^^^^^^^^^^
La referencia completa de la API está dividida por modalidad en Documentación de PyOD <https://pyod.readthedocs.io/en/latest/>: Tabular <https://pyod.readthedocs.io/en/latest/pyod.models.tabular.html>, Series temporales <https://pyod.readthedocs.io/en/latest/pyod.models.timeseries.html>, Grafos <https://pyod.readthedocs.io/en/latest/pyod.models.graph.html>, Embedding <https://pyod.readthedocs.io/en/latest/pyod.models.embedding.html>, ADEngine <https://pyod.readthedocs.io/en/latest/pyod.ad_engine.html>, Utilidades <https://pyod.readthedocs.io/en/latest/pyod.utils.html>__. A continuación se muestra una hoja de referencia rápida para todos los detectores:
Atributos clave de un modelo ajustado:
Benchmarks ^^^^^^^^^^
ADBench <https://github.com/Minqi824/ADBench>__ [#Han2022ADBench]_: 30 algoritmos en 57 conjuntos de datos tabulares. Consulta la comparación <https://github.com/yzhao062/pyod/blob/master/examples/compare_all_models.py>__.NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>__: 19 métodos en 8 conjuntos de datos de texto. El enfoque de dos pasos (embedding + detector) supera al extremo a extremo.TSB-AD <https://github.com/TheDatumOrg/TSB-AD>__ [#Liu2024TSB]_: 40 algoritmos en 1070 conjuntos de datos de series temporales (NeurIPS 2024).BOND <https://arxiv.org/abs/2206.10071>__ [#Liu2022BOND]_: 14 algoritmos de detección de anomalías en grafos en 14 conjuntos de datos (NeurIPS 2022).Temas adicionales ^^^^^^^^^^^^^^^^^
Guardado y carga de modelos <https://pyod.readthedocs.io/en/latest/model_persistence.html>: Usa joblib o pickle para guardar y cargar modelos de PyOD. Consulta el ejemplo <https://github.com/yzhao062/pyod/blob/master/examples/save_load_model_example.py>.Entrenamiento rápido con SUOD <https://pyod.readthedocs.io/en/latest/fast_train.html>: Acelera el entrenamiento y la predicción con el framework SUOD [#Zhao2021SUOD]_. Consulta el ejemplo <https://github.com/yzhao062/pyod/blob/master/examples/suod_example.py>.Umbrales para puntuaciones de anomalías <https://pyod.readthedocs.io/en/latest/thresholding.html>: Enfoques basados en datos para establecer niveles de contaminación mediante PyThresh <https://github.com/KulikDM/pythresh>.Algoritmos implementados ^^^^^^^^^^^^^^^^^^^^^^^^
PyOD está organizado en dos grupos funcionales: (i) Algoritmos de detección, con subsecciones dedicadas para datos tabulares, de series temporales, de grafos y de audio (EmbeddingOD dentro de la tabla tabular añade soporte para texto e imágenes mediante codificadores de modelos fundacionales); y (ii) Funciones de utilidad para generación de datos, evaluación y orquestación del ciclo de vida.
(i-a) Algoritmos de detección tabulares y multimodales :
.. list-table:: :widths: 15 14 58 5 8 :header-rows: 1* - Tipo - Abrev - Algoritmo - Año - Ref
ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ecod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/abod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/abod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/copod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/mad_example.py>__)Los métodos de ensemble (IForest, INNE, DIF, FB, LSCP, LODA, SUOD, XGBOD) están incluidos en la tabla anterior. Las funciones de combinación de puntuaciones (average, maximization, AOM, MOA, median, majority vote) se encuentran en pyod.models.combination. Consulta documentación de la API <https://pyod.readthedocs.io/en/latest/pyod.models.tabular.html>__ para más detalles.
(i-b) Detección de anomalías en series temporales :
Todos los detectores de series temporales usan la misma API fit/predict/decision_function que los detectores tabulares, con una excepción: MatrixProfile es transductivo (solo entrenamiento; use decision_scores_ y labels_ después de fit(), sin predict fuera de la muestra).
Formato de entrada: array de numpy de forma (n_timestamps,) para univariante o (n_timestamps, n_channels) para multivariante. Cada fila es un paso de tiempo; las columnas son canales/características. Los Pandas DataFrames y las listas se convierten automáticamente. Salida: decision_scores_ de forma (n_timestamps,) con una puntuación de anomalía por paso de tiempo.
Detección de series temporales en 3 líneas:
.. 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
Ranking de algoritmos del benchmark TSB-AD <https://github.com/TheDatumOrg/TSB-AD>__ [#Liu2024TSB]_ (NeurIPS 2024, 1070 conjuntos de datos):
.. list-table:: :widths: 15 18 50 5 12 :header-rows: 1
ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ts_od_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ts_matrix_profile_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ts_spectral_residual_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ts_kshape_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ts_sand_example.py>__)(i-c) Detección de anomalías en grafos (pip install pyod[graph]):
Todos los detectores de grafos son transductivos en v1: use decision_scores_ y labels_ después de fit(). No hay predict fuera de la muestra. Entrada: objeto Data de PyG con x (características de nodos) y edge_index (aristas COO). SCAN funciona sin características.
Detección en grafos en 3 líneas (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
Ranking de algoritmos del benchmark BOND <https://arxiv.org/abs/2206.10071>__ [#Liu2022BOND]_ (NeurIPS 2022, 14 conjuntos de datos):
.. list-table:: :widths: 18 18 45 5 14 :header-rows: 1
ejemplo de dominant <https://github.com/yzhao062/pyod/blob/development/examples/pyg_dominant_example.py>__)ejemplo de cola <https://github.com/yzhao062/pyod/blob/development/examples/pyg_cola_example.py>__)ejemplo de conad <https://github.com/yzhao062/pyod/blob/development/examples/pyg_conad_example.py>__)ejemplo de anomalydae <https://github.com/yzhao062/pyod/blob/development/examples/pyg_anomalydae_example.py>__)ejemplo de guide <https://github.com/yzhao062/pyod/blob/development/examples/pyg_guide_example.py>__)(i-d) Detección de anomalías de audio (pip install pyod[audio]):
Los clips de audio usan la misma API fit/decision_function. Hay dos vías disponibles: una vía ligera de incrustar y luego detectar (EmbeddingOD.for_audio() convierte cada clip en un vector acústico artesanal de 74 dimensiones y ejecuta cualquier detector clásico), y un detector profundo dedicado (AudioAE, un autoencoder de reconstrucción log-mel). Las entradas son rutas de archivo, arrays de forma de onda o tuplas (waveform, sample_rate). Salida: una puntuación de anomalía por clip.
Detección de audio en 3 líneas (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(): características MFCC, chroma y espectrales de 74 dimensiones con cualquier detector(ii) Funciones de utilidad:=================== ============================ ===================================================================================================================================================== Tipo Nombre Función =================== ============================ ===================================================================================================================================================== Datos generate_data Generación de datos sintetizados; datos normales de una Gaussiana multivariante, valores atípicos de una distribución uniforme Datos generate_data_clusters Generación de datos sintetizados en clústeres para patrones más complejos Evaluación evaluate_print Imprime ROC-AUC y Precision @ Rank n de un detector Evaluación precision_n_scores Calcula Precision @ Rank n Utilidad get_label_n Convierte las puntuaciones de anomalía brutas en etiquetas binarias asignando 1 a las n puntuaciones más altas Estadística wpearsonr Calcula la correlación de Pearson ponderada de dos muestras Codificación resolve_encoder Resuelve un codificador a partir de un nombre de cadena, una instancia de BaseEncoder o un invocable Codificación SentenceTransformerEncoder Codifica texto mediante modelos de sentence-transformers (p. ej., MiniLM, mpnet) Codificación OpenAIEncoder Codifica texto mediante la API de OpenAI Embeddings (text-embedding-3-small/large) Codificación HuggingFaceEncoder Codifica texto o imágenes mediante transformers de HuggingFace (BERT, DINOv2, CLIP) =================== ============================ =====================================================================================================================================================
Inicio rápido para la detección de anomalías ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
PyOD ha sido ampliamente reconocido por la comunidad de aprendizaje automático con varios artículos destacados y tutoriales.
Analytics Vidhya: Un tutorial increíble para aprender detección de anomalías en Python usando la librería PyOD <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>__
KDnuggets: Visualización intuitiva de métodos de detección de anomalías <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>, Una visión general de los métodos de detección de anomalías de PyOD <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>
Towards Data Science: Detección de anomalías para principiantes <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>__
"examples/knn_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/knn_example.py>__
demuestra la API básica del uso del detector kNN. Cabe señalar que la API en todos los demás algoritmos es consistente/similar.
Puede encontrar instrucciones más detalladas para ejecutar los ejemplos en el directorio de ejemplos <https://github.com/yzhao062/pyod/blob/master/examples>__.
#. Inicializa un detector kNN, ajusta el modelo y realiza la predicción.
.. 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]
#. Evalúa la predicción mediante ROC y Precision @ Rank n (p@n).
.. 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)
#. Consulta un ejemplo de salida y visualización.
.. 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)
Agradecimientos ^^^^^^^^^^^^^^^
Este material se basa en el trabajo financiado por la National Science Foundation
bajo la subvención N.º 2346158 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=2346158>_
para "NSF POSE: Phase II: OpenAD: An Integrated Open-Source Ecosystem for
Anomaly Detection." La subvención menciona a la University of Illinois Chicago como la
organización principal y al Illinois Institute of Technology, Lehigh University y
la University of Southern California como organizaciones subreceptoras.
Las opiniones, hallazgos y conclusiones o recomendaciones expresados en este material son de los autores y no reflejan necesariamente los puntos de vista de la National Science Foundation.
Referencias ^^^^^^^^^^^
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investigateiterateTalk Python To Me #497 <https://talkpython.fm/episodes/show/497/outlier-detection-with-python>Real Python Podcast #208 <https://realpython.com/podcasts/rpp/208/>aidoczh.com <https://www.aidoczh.com>ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/sos_example.py>ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/qmcd_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/kde_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/sampling_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/gmm_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/pca_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/kpca_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/mcd_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/cd_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ocsvm_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/lmdd_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/lof_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/cof_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/cof_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/cblof_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/loci_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/hbos_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/hdbscan_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/knn_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/knn_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/knn_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/sod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/rod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/iforest_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/inne_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/dif_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/feature_bagging_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/lscp_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/xgbod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/loda_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/suod_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/auto_encoder_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/vae_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/vae_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/so_gaal_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/mo_gaal_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/deepsvdd_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/alad_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/ae1svm_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/devnet_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/rgraph_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/lunar_example.py>__)ejemplo <https://github.com/yzhao062/pyod/blob/development/examples/embedding_od_example.py>__)ejemplo de radar <https://github.com/yzhao062/pyod/blob/development/examples/pyg_radar_example.py>__)ejemplo de anomalous <https://github.com/yzhao062/pyod/blob/development/examples/pyg_anomalous_example.py>__)ejemplo de scan <https://github.com/yzhao062/pyod/blob/development/examples/pyg_scan_example.py>__)