corex_topic
pyod
| corex_topic | pyod | |
|---|---|---|
| 5 | 7 | |
| 622 | 7,962 | |
| - | - | |
| 3.8 | 7.5 | |
| about 3 years ago | 4 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 2-clause "Simplified" License |
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corex_topic
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[NLP] Topic Identification & Quantisation
We ended up settling on a HuggingFace transformer + HDBSCAN pipeline from BERTopic. I like this because it makes it straightforward to tune and test, and you probabilistically assign documents to clusters, so you can do interesting aggregation and sampling after you have your inference done, like selecting text. Other options include top2vec which basically does the same thing without some guiding tools available in BERTopic. Either is suitable for what you’re doing. Older techniques include things like the Latent Dirichlet Allocation and COREX.
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NLP: How to visualise the main context (in the form of words, sentences etc) of a text document?
If I understand correctly, it sounds like you are trying to do Topic Modeling on documents. One python library I’ve enjoyed using is https://github.com/gregversteeg/corex_topic which, besides doing general topic modeling, also learns hierarchies of topics.
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Are topic models reliable or useful?
We started off by trying LDA and NMF, but the topics were too messy so we wound up switching to CorEx (https://github.com/gregversteeg/corex_topic), which is a semi-supervised algo that lets you "nudge" the model in the right direction using anchor terms. By the time our topics started looking coherent, it turned out that a regex with the anchor terms we'd picked outperformed the model itself. This case study was on a relatively small sample of relatively short documents (~4k survey open-ends) but for what it's worth, we also tried to use topic models to classify congressional Facebook posts (much larger corpus and longer documents) and the results were the same.
Overfitting is certainly part of the problem - in one of my earlier posts I talk about "conceptually spurious words," which are essentially the product of overfitting - but the more difficult problem is polysemy. I'm sure there are ways to mitigate that - expanding the feature space with POS tagging, etc. - but ultimately I think the solution is to simply avoid using a dimensionality reduction method for text classification. Supervised models are clearly the way to go - even if those "models" are just keyword dictionaries curated based on domain knowledge.
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Trying to read text documents and allow for up to m labels per documents, like suggested tags, but the number of labels can be different for each document. Any advice?
Unsupervised is also possible for topic modelling: CorEX
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NLP Problem
You could use CorEX for topic modelling. With that you can find the type of causes and from there their distribution. Then you can join it with other non-textual meta-data.
pyod
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A Comprehensive Guide for Building Rag-Based LLM Applications
This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod
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Analyze defects and errors in the created images
PyOD
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Multivariate Outlier Detection in Python
Check out the algorithms and documentation in this toolkit. It’ll give you a list of methods to read up on to understand their mechanisms. https://github.com/yzhao062/pyod
- Pyod – A Comprehensive and Scalable Python Library for Outlier Detection
- Predictive Maintenance and Anomaly Detection Resources
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[D] Unsupervised Outlier Detection - Advise Requested
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
- PyOD: ~50 anomaly detection algorithms in one framework.
What are some alternatives?
GuidedLDA - semi supervised guided topic model with custom guidedLDA
tods - TODS: An Automated Time-series Outlier Detection System
gensim - Topic Modelling for Humans
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
ennemi - Easy Nearest Neighbor Estimation of Mutual Information
alibi-detect - Algorithms for outlier, adversarial and drift detection
pycaret - An open-source, low-code machine learning library in Python
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis
pymiere - Python for Premiere pro
loglizer - A machine learning toolkit for log-based anomaly detection [ISSRE'16]
kafkaml-anomaly-detection - Project for real-time anomaly detection using Kafka and python