Anomaly detection lifecycle engine. Parameters ---------- knowledge_dir : str or None Path to knowledge base directory. If None,

Wrapper of scikit-learn LOF Class with more functionalities. Unsupervised Outlier Detection using Local Outlier Factor (LOF). The anomaly sco

Anomaly detection on raw data via embedding + detector pipeline. Chains any embedding encoder with any PyOD detector. Encode raw data (text,

kNN class for outlier detection. For an observation, its distance to its kth nearest neighbor could be viewed as the outlying score. It could

Subspace outlier detection (SOD) schema aims to detect outlier in varying subspaces of a high dimensional feature space. For each data object,

Wrapper of scikit-learn Isolation Forest with more functionalities. The IsolationForest 'isolates' observations by randomly selecting a featu

KPCA class for outlier detection. PCA is performed on the feature space uniquely determined by the kernel, and the reconstruction error on th

LUNAR class for outlier detection. See https://www.aaai.org/AAAI22Papers/AAAI-51.GoodgeA.pdf for details. For an observation, its ordered lis

Multi-modal anomaly detection via score fusion. Runs a separate detector per modality and combines their anomaly scores. Each modality can us

COPOD class for Copula Based Outlier Detector. COPOD is a parameter-free, highly interpretable outlier detection algorithm based on empirical

Encode multiple modalities and concatenate into a single embedding. Each modality is encoded by its own encoder. The resulting embeddings are

Histogram- based outlier detection (HBOS) is an efficient unsupervised method. It assumes the feature independence and calculates the degree o

Connectivity-Based Outlier Factor (COF) COF uses the ratio of average chaining distance of data point and the average of average chaining dist

ECOD class for Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (ECOD) ECOD is a parameter-free, highly interp

Linear Method for Deviation-based Outlier Detection. LMDD employs the concept of the smoothing factor which indicates how much the dissimilar

SCAN: Structural Clustering Algorithm for Networks. Implements the full SCAN procedure: computes structural similarity ``sigma(u,v) = |N[u

Sampling class for outlier detection. Sugiyama, M., Borgwardt, K. M.: Rapid Distance-Based Outlier Detection via Sampling, Advances in Neural

Windowed bridge that wraps any PyOD detector for time series anomaly detection. Takes a time series, creates sliding windows, runs any PyOD d

ABOD class for Angle-base Outlier Detection. For an observation, the variance of its weighted cosine scores to all neighbors could be viewed a

Encoder that wraps a user-provided callable. Parameters ---------- fn : callable A function that accepts raw input and returns a

GUIDE: Higher-order Structure Based Anomaly Detection. Constructs a motif adjacency from triangle participation (binarized in v1: edges in

k-Shape clustering-based time series anomaly detector. Extracts sliding-window subsequences, clusters them using the k-Shape algorithm (shape

Loda: Lightweight on-line detector of anomalies. See :cite:`pevny2016loda` for more information. Two versions of LODA are supported:

ANOMALOUS: Joint Modeling for Anomaly Detection. CUR-style decomposition: ``min_{W,R} ||X - XWX - R||_F^2 + alpha * ||W||_F^2 + lambda_r *

AnomalyDAE: Dual Autoencoder for Anomaly Detection. Uses GATConv for structure encoding and an MLP for attribute encoding. Reconstructs ad

Handcrafted acoustic-feature encoder (librosa, no GPU). Each audio clip becomes a 74-dimensional vector: 20 MFCC, 12 chroma, and 5 spectral d

DOMINANT: Deep Anomaly Detection on Attributed Networks. GCN encoder maps nodes to embeddings. Structure decoder reconstructs adjacency vi

Median Absolute Deviation: for measuring the distances between data points and the median in terms of median distance. See :cite:`iglewicz1993

Matrix Profile time series anomaly detector using STOMP. Computes the Matrix Profile via the STOMP algorithm, identifying anomalous subsequen

Radar: Residual Analysis for Anomaly Detection. Solves ``min_{W,R} ||X - WX - R||_F^2 + alpha * ||W||_F^2 + gamma * (||R||_{2,1} + tr(R^T

CONAD: Contrastive + Reconstruction Anomaly Detection. Constructs an anomalous view via attribute swapping and random edge injection, enco

CoLA: Contrastive Anomaly Detection on Attributed Networks. GCN encoder maps nodes to embeddings. A bilinear discriminator scores how well

KDE class for outlier detection. For an observation, its negative log probability density could be viewed as the outlying score. See :ci

Wrapper of scikit-learn one-class SVM Class with more functionalities. Unsupervised Outlier Detection. Estimate the support of a high-dimensi

Rotation-based Outlier Detection (ROD), is a robust and parameter-free algorithm that requires no statistical distribution assumptions and wor

Raised when :func:`parse_routing_response` cannot extract a plan. The engine catches this and falls back to rule-driven routing unless the en

SAND streaming anomaly detector for time series. Extracts z-normalized sliding-window subsequences, initializes k centroids via k-Shape on th

Spectral Residual anomaly detector for time series. Computes a saliency map via the spectral residual of the log-amplitude spectrum. This is

Variational auto encoder Encoder maps X onto a latent space Z Decoder samples Z from N(0,1) VAE_loss = Reconstruction_loss + KL_loss

Auto Encoder (AE) is a type of neural networks for learning useful data representations in an unsupervised manner. Similar to PCA, AE could b

Cook's distance can be used to identify points that negatively affect a regression model. A combination of each observation’s leverage a

Deep Isolation Forest (DIF) is an extension of iForest. It uses deep representation ensemble to achieve non-linear isolation on original data

Wrapper of scikit-learn Gaussian Mixture Model with more functionalities. Unsupervised Outlier Detection. See :cite:`aggarwal2015outlier` Cha

Principal component analysis (PCA) can be used in detecting outliers. PCA is a linear dimensionality reduction using Singular Value Decomposition

Adversarially Learned Anomaly Detection (ALAD). Paper: https://arxiv.org/pdf/1812.02288.pdf See :cite:`zenati2018adversarially` for details.

r"""The CBLOF operator calculates the outlier score based on cluster-based local outlier factor. CBLOF takes as an input the data set and the

A feature bagging detector is a meta estimator that fits a number of base detectors on various sub-samples of the dataset and use averaging o

Isolation-based anomaly detection using nearest-neighbor ensembles. The INNE algorithm uses the nearest neighbour ensemble to isolate anomal

LSTM-based anomaly detector for time series. Trains a stacked LSTM to predict the next timestep, then scores each timestamp by the Mahalanobi

Detecting outliers in a Gaussian distributed dataset using Minimum Covariance Determinant (MCD): robust estimator of covariance. The Minimum

The Wrap-around Quasi-Monte Carlo discrepancy is a uniformity criterion which is used to assess the space filling of a number of samples in a

SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large scale unsupervised outlier detector training and predict

A pattern that matches user feedback and produces a structured action.

Anomaly Transformer for time series anomaly detection. Implements the Anomaly Transformer (Xu et al., ICLR 2022), a Transformer-based archite

Log-mel reconstruction autoencoder for audio anomaly detection. The detector extracts overlapping log-mel context windows from each clip, fit

Deep One-Class Classifier with AutoEncoder (AE) is a type of neural networks for learning useful data representations in an unsupervised way.

Locally Selection Combination in Parallel Outlier Ensembles LSCP is an unsupervised parallel outlier detection ensemble which selects compet

Multi-Objective Generative Adversarial Active Learning. MO_GAAL directly generates informative potential outliers to assist the classifier in

Encoder using OpenAI Embeddings API. Produces text embeddings via the OpenAI API. Handles batching (max 2048 items per request) internally.

Stochastic Outlier Selection. SOS employs the concept of affinity to quantify the relationship from one data point to another data point.

Auto Encoder with One-class SVM for anomaly detection. Note: self.device is needed or all tensors may not be on the same device (if device w/

Wrapper of scikit-learn HDBSCAN for outlier detection. HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) per

Typed state object for an ADEngine investigation session. Tracks the full workflow: profiling, planning, detection, analysis, and iteration.

Loader and accessor for PyOD's structured knowledge base. Reads JSON files from the knowledge directory and provides query methods for algori

Local Correlation Integral. LOCI is highly effective for detecting outliers and groups of outliers ( a.k.a.micro-clusters), which offers

Linear block with activation and batch normalization Parameters ---------- in_features : int Number of input features

Outlier Detection via R-graph. Paper: https://openaccess.thecvf.com/content_cvpr_2017/papers/You_Provable_Self-Representation_Based_CVPR_2017_pap

Single-Objective Generative Adversarial Active Learning. SO-GAAL directly generates informative potential outliers to assist the classifier i

Single-Objective Generative Adversarial Active Learning. SO-GAAL directly generates informative potential outliers to assist the classifier i

Encoder using sentence-transformers library. Wraps ``sentence_transformers.SentenceTransformer`` to produce text embeddings compatible with P

r"""XGBOD class for outlier detection. It first uses the passed in unsupervised outlier detectors to extract richer representation of the data

Anomaly Detection with Generative Adversarial Networks (AnoGAN). See the original paper "Unsupervised anomaly detection with generative adver

Abstract base class for embedding encoders. All encoders must implement the ``encode`` method, which converts raw input data to a 2D numpy ar