(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

Related tags

Deep Learningmachine-learninggpu-accelerationoutlier-detectionanomaly-detectiongpu-systems
Overview

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

Background: Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.

To scale outlier detection (OD) to large-scale, high-dimensional datasets, we propose TOD, a novel system that abstracts OD algorithms into basic tensor operations for efficient GPU acceleration.

The corresponding paper. The code is being cleaned up and released. Please watch and star!

One reason to use it:

On average, TOD is 11 times faster than PyOD!

If you need another reason: it can handle much larger datasets:more than a million sample OD within an hour!

TOD is featured for:

  • Unified APIs, detailed documentation, and examples for the easy use (under construction)
  • Supports more than 10 different OD algorithms and more are being added
  • TOD supports multi-GPU acceleration
  • Advanced techniques like provable quantization

Programming Model Interface

Complex OD algorithms can be abstracted into common tensor operators.

https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction.png

For instance, ABOD and COPOD can be assembled by the basic tensor operators.

https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction_example.png

End-to-end Performance Comparison with PyOD

Overall, it is much (on avg. 11 times) faster than PyOD takes way less run time.

https://raw.githubusercontent.com/yzhao062/pytod/master/figs/run_time.png

Code is being released. Watch and star for the latest news!

Comments
  • Error while installing package

    Error while installing package

    I installed Pytorch 1.10 from their site. It seen in virtual environment. I try pip install pytod but when searching for pytorch, it cannot find it because it searches with the "pytorch" package, not the "torch" package.

    ERROR: Could not find a version that satisfies the requirement pytorch>=1.7 (from pytod) (from versions: 0.1.2, 1.0.2)
ERROR: No matching distribution found for pytorch>=1.7
    opened by nuriakiin 1
  • decision_function() returns None

    decision_function() returns None

    Thanks for the package. When I try to implement LOF (or KNN) decision_function() on test data returns empty object. Is there a fix to this? Following is the code that replicates the issue (on GPU):

    from pytod.models.lof import LOF import torch import numpy as np

    x = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [75,80]], dtype=np.float32) x = torch.from_numpy(x)

    y = np.array([[6, 5], [1, 2], [3, 4], [5, 1], [11,12]], dtype=np.float32) y = torch.from_numpy(y)

    lof = LOF(n_neighbors=2, device = 'cuda:0')

    lof.fit(x)

    print(lof.decision_function(y))

    opened by sugatc 0
  • Support for novelty detection and changing distance metric with local outlier factor

    Support for novelty detection and changing distance metric with local outlier factor

    The current implementation of LOF doesn't allow changing the distance metric to 'cosine', for example or setting novelty = True which prevents it from being used for novelty detection task. It will be great if support can be added for these.

    opened by sugatc 2
  • can't fit model in colab

    can't fit model in colab

    when i try fit on any model in colab gpu instance i get the following error. my dataset has 2 columns and 1 million rows:

    AttributeError Traceback (most recent call last) in () 4 clf_name = 'KNN' 5 clf = LOF() ----> 6 clf.fit(X)

    3 frames /usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in getattr(self, name) 5485 ): 5486 return self[name] -> 5487 return object.getattribute(self, name) 5488 5489 def setattr(self, name: str, value) -> None:

    AttributeError: 'DataFrame' object has no attribute 'to'

    opened by yairVanti 0
  • clean up reproducibility scripts

    clean up reproducibility scripts

    We are cleaning up these scripts for an easy run, while the primary results are reproducible with the compare_real_data.py (https://github.com/yzhao062/pytod/tree/main/reproducibility)

    enhancement 
    opened by yzhao062 0
Releases(v0.0.2)

  • v0.0.2(Jun 19, 2022)

    v<0.0.1>, <04/12/2021> -- Add LOF. v<0.0.1>, <04/23/2021> -- Add ABOD. v<0.0.2>, <06/19/2021> -- Add PCA and HBOS. v<0.0.2>, <06/19/2021> -- Turn on test suites.

    Now we have updated both the paper the repo to cover more algorithms.

    Source code(tar.gz)
    Source code(zip)
Owner
Yue Zhao
Ph.D. Student @ CMU. Outlier Detection Systems | ML Systems (MLSys) | Anomaly/Outlier Detection | AutoML. Twitter@ yzhao062
Yue Zhao
GitHub Repository https://www.andrew.cmu.edu/user/yuezhao2/papers/21-preprint-tod.pdf
A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

ARES This repository contains the code for ARES (Adversarial Robustness Evaluation for Safety), a Python library for adversarial machine learning rese

Tsinghua Machine Learning Group 377 Dec 20, 2022
Official code repository for "Exploring Neural Models for Query-Focused Summarization"

Query-Focused Summarization Official code repository for "Exploring Neural Models for Query-Focused Summarization" This is a work in progress. Expect

Salesforce 29 Dec 18, 2022
Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network"

This is a Pytorch Lightning version PSMNet which is based on JiaRenChang/PSMNet. use python main.py to start training. PSM-Net Pytorch reimplementatio

XIAOTIAN LIU 1 Nov 25, 2021
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners This repository is built upon BEiT, thanks very much! Now, we on

Zhiliang Peng 2.3k Jan 04, 2023
UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac protocols on unmanned aerial vehicle networks.

UAV-Networks Simulator - Autonomous Networking - A.A. 20/21 UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac pr

0 Nov 13, 2021
A template repository for submitting a job to the Slurm Cluster installed at the DISI - University of Bologna

Cluster di HPC con GPU per esperimenti di calcolo (draft version 1.0) Per poter utilizzare il cluster il primo passo è abilitare l'account istituziona

20 Dec 16, 2022
Official git for "CTAB-GAN: Effective Table Data Synthesizing"

CTAB-GAN This is the official git paper CTAB-GAN: Effective Table Data Synthesizing. The paper is published on Asian Conference on Machine Learning (A

30 Dec 26, 2022
Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral

Temporally Efficient Vision Transformer for Video Instance Segmentation Temporally Efficient Vision Transformer for Video Instance Segmentation (CVPR

Hust Visual Learning Team 203 Dec 31, 2022
A small library for doing fluid simulation with neural networks.

Neural Fluid Fields This is a small library for doing fluid simulation with neural fields. Check out our review paper, Neural Fields in Visual Computi

Towaki 23 Jun 23, 2022
Geometric Algebra package for JAX

JAXGA - JAX Geometric Algebra GitHub | Docs JAXGA is a Geometric Algebra package on top of JAX. It can handle high dimensional algebras by storing onl

Robin Kahlow 36 Dec 22, 2022
A Python module for the generation and training of an entry-level feedforward neural network.

ff-neural-network A Python module for the generation and training of an entry-level feedforward neural network. This repository serves as a repurposin

Riadh 2 Jan 31, 2022
A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

3k Jan 02, 2023
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
Code for STFT Transformer used in BirdCLEF 2021 competition.

STFT_Transformer Code for STFT Transformer used in BirdCLEF 2021 competition. The STFT Transformer is a new way to use Transformers similar to Vision

Jean-François Puget 69 Sep 29, 2022
Programming with Neural Surrogates of Programs

Programming with Neural Surrogates of Programs

0 Dec 12, 2021
Defending against Model Stealing via Verifying Embedded External Features

Defending against Model Stealing Attacks via Verifying Embedded External Features This is the official implementation of our paper Defending against M

20 Dec 30, 2022
Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.

openpifpaf Continuously tested on Linux, MacOS and Windows: New 2021 paper: OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Te

VITA lab at EPFL 50 Dec 29, 2022
SmoothGrad implementation in PyTorch

SmoothGrad implementation in PyTorch PyTorch implementation of SmoothGrad: removing noise by adding noise. Vanilla Gradients SmoothGrad Guided backpro

SSKH 143 Jan 05, 2023
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization

Benjamin Biggs 29 Dec 28, 2022
Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics".

Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics This repository is the official PyTorch implementation of "Physics-aware Differ

USC-Melady 46 Nov 20, 2022