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yzhao062/pyod

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9,878 نجوم·1,482 تفرعات·Python·BSD-2-Clause·5 مشاهداتpyod.readthedocs.io↗

Pyod

PyOD is a Python anomaly detection library used to identify outliers in tabular, time series, graph, text, and image data. It provides a collection of algorithms for detecting anomalous data points and includes a unified detector interface that standardizes input and output signatures across its available detection algorithms.

The project features a multi-modal outlier detector for identifying anomalies across diverse formats including unstructured text and images, as well as a specialized toolkit for graph-based and time-series anomaly detection. It includes an ensemble framework for combining multiple outlier detection models to improve processing speed and prediction accuracy, and an agentic machine learning workflow tool that translates natural language requests into automated detection pipelines.

The library covers a broad range of capabilities, including performance evaluation suites for quantifying detection accuracy against ground truth labels and visualization tools for plotting the distribution of outliers. It also provides model management utilities for persisting artifacts and monitoring tools for feature importance analysis.

Features

  • Anomaly Detection - Provides a comprehensive collection of algorithms to identify outliers and assign binary labels in datasets.
  • Agentic Planning - Provides an AI-driven orchestration layer that translates natural language into structured detection plans.
  • Anomaly Degree Scoring - Provides raw numerical values for each data point to quantify the degree of outlierness.
  • Graph Anomaly Detectors - Identifies structural and attribute outliers in graph-structured data using connectivity analysis.

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  • Graph-Based Outlier Detection - Identifies outliers in datasets by analyzing data structures through a graph-based approach.
  • Multi-Modal Detection - Provides the ability to identify outliers across various data formats including unstructured text and images.
  • Unstructured Data Analysis - Chains foundation model encoders with outlier detectors to identify anomalies in text and images.
  • Unified Interfaces - Standardizes input and output signatures across numerous anomaly detection algorithms for consistent model swapping.
  • Multi-Modal AI Toolkits - Offers a toolkit for detecting anomalies across diverse formats including unstructured text, images, and graph data.
  • Outlier Detection - Implements deep learning architectures to distinguish between inliers and outliers.
  • Time Series Detection - Identifies outliers within sequential data by analyzing observation windows to determine if a point is anomalous.
  • MLOps Pipeline Automation - Translates natural-language requests into automated detection pipelines using AI agents.
  • Agentic Workflow Automation - Provides a framework that uses AI agents to translate natural language into automated detection pipelines.
  • Anomaly Detection Metrics - Compares predicted outlier labels and scores against ground-truth data to quantify the performance of a detection model in the project.
  • Anomaly Scoring - Calculates raw anomaly scores and probability estimates for data samples using fitted models.
  • Ensembles - Combines multiple outlier detection models into an ensemble to improve processing speed and prediction accuracy.
  • Histogram-Based Outlier Detection - Identifies anomalies in datasets using histogram-based outlier detection to assign labels and scores.
  • Influence-Based Outlier Detection - Identifies anomalous data points by calculating the influence of each observation on a regression model.
  • Kernel Principal Component Analysis - Uses Kernel PCA to project data into higher-dimensional spaces to identify non-linear patterns and outliers.
  • Median Absolute Deviation - Detects anomalies by calculating the Median Absolute Deviation to determine distance from the median.
  • Minimum Covariance Determinant - Utilizes the Minimum Covariance Determinant algorithm to produce binary labels and anomaly scores.
  • One-Class SVM - Isolates outliers by fitting a boundary around the majority of training data using One-Class SVM.
  • Sampling-Based Detection - Analyzes the distance or density of samples to distinguish outliers from normal observations.
  • Transductive Graph - Identifies outliers in graph data using transductive learning and decomposition.
  • Dual-Autoencoder Graph Detectors - Identifies anomalies in graph data by comparing structural connectivity and node attributes through symmetric reconstruction.
  • Cluster-Based Outlier Detection - Identifies outliers using a cluster-based local outlier factor approach to calculate deviation scores.
  • HDBSCAN Density-Based Detection - Identifies anomalous data points using density-based clustering to separate inliers from outliers.
  • Proximity and Density Analysis - Separates outliers from inliers using proximity-based and density-based detection models.
  • Bagging Ensembles - Uses a feature bagging meta-framework to combine multiple detection models for improved robustness.
  • Feature Bagging Outlier Detection - Identifies anomalous data points by training an ensemble of detectors on random subsets of features.
  • Ensemble Execution Acceleration - Combines multiple outlier detectors into a single model to increase processing speed through parallel execution.
  • Ensemble Result Aggregation - Combines multiple anomaly models into a single ensemble to improve efficiency through parallel result aggregation.
  • Contamination Threshold Optimization - Determines optimal contamination levels for outlier scores using data-driven thresholding approaches.
  • Contamination Threshold Optimizers - Includes logic to determine optimal contamination levels by analyzing the statistical distribution of raw anomaly scores.
  • Prediction Confidence Assessment - Assesses model certainty for each single-sample prediction to reject highly uncertain results.
  • Feature Importance Attribution - Calculates the contribution of each input feature to the detection process to identify which variables influence results in the project.
  • Gaussian Mixture Model Detection - Identifies anomalous data points by fitting a Gaussian Mixture Model and calculating sample likelihood.
  • Generative Adversarial Network Detection - Identifies anomalous data points by training a GAN to learn normal data distributions.
  • Outlier Detection - Identifies anomalous data points by training gradient boosting models on feature sets.
  • k-Nearest Neighbors Outlier Detection - Identifies anomalous data points by calculating the distance to the nearest neighbors.
  • Outlier Detection - Implements kernel density estimation to flag data points in low-density regions as anomalies.
  • Outlier Labeling - Assigns binary labels to distinguish between normal observations and outliers based on learned thresholds.
  • Performance Metrics - Computes performance metrics by comparing predicted outlier labels and scores against known ground truth data in the project.
  • Model Performance Evaluators - Compares predicted outlier scores against ground truth labels to quantify the accuracy of the detection model in the project.
  • Model Prediction Evaluation - Quantifies detection accuracy by comparing predicted outlier scores against ground truth labels.
  • Multi-modal Embedding Generation - Transforms diverse data formats into a common representation for unified outlier scoring.
  • Performance Evaluation Tools - Provides tools for calculating accuracy metrics and visualizing the distribution of detected anomalies.
  • Performance Metrics - Calculates performance metrics by comparing predicted outlier scores against known ground truth labels in the project.
  • Accuracy Calculators - Compares predicted outlier scores against ground truth labels to quantify the accuracy of a detection algorithm in the project.
  • Foundation-Model Encoder Chains - Pairs pretrained deep learning encoders with outlier detectors to extract features from unstructured text and image data.
  • Spectral Residual Analysis - Identifies outliers in sequential data using spectral residual analysis to find abnormal patterns.
  • Severity Scoring - Quantifies the degree of anomaly for data points by calculating raw decision scores.
  • Structural Graph Anomaly Detection - Identifies outliers in graph-structured data by analyzing structural connectivity and node attributes using dual autoencoders.
  • Variational Autoencoders - Implements anomaly detection using Variational Autoencoder architectures to identify outliers via reconstruction error.
  • Connectivity-Based Outlier Analysis - Identifies anomalous data points by analyzing the local density and connectivity of points.
  • Distance-Based Outlier Detection - Identifies anomalous data points by calculating distances between neighbors to detect deviations.
  • PCA-Based Outlier Detection - Identifies anomalous data points by analyzing principal components and calculating deviation scores.
  • Quasi-Monte Carlo Discrepancy Analysis - Identifies anomalous data points by calculating the Quasi-Monte Carlo Discrepancy to determine distribution deviation.
  • Parallel Processing Pipelines - Combines multiple detection models into a parallel execution pipeline to improve processing throughput.
  • Statistical Distribution Visualizers - Plots the distribution of inliers and outliers in a dataset to provide a visual representation of the model in the project.
  • General Machine Learning - Toolkit for detecting outliers in multivariate data.
  • Data Quality and Validation - Outlier and anomaly detection.
  • Data Validation and Anomaly Detection - Toolbox for scalable outlier and anomaly detection.

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    الأسئلة الشائعة

    ما هي وظيفة yzhao062/pyod؟

    PyOD is a Python anomaly detection library used to identify outliers in tabular, time series, graph, text, and image data. It provides a collection of algorithms for detecting anomalous data points and includes a unified detector interface that standardizes input and output signatures across its available detection algorithms.

    ما هي الميزات الرئيسية لـ yzhao062/pyod؟

    الميزات الرئيسية لـ yzhao062/pyod هي: Anomaly Detection, Agentic Planning, Anomaly Degree Scoring, Graph Anomaly Detectors, Graph-Based Outlier Detection, Multi-Modal Detection, Unstructured Data Analysis, Unified Interfaces.

    ما هي البدائل مفتوحة المصدر لـ yzhao062/pyod؟

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