grape
snorkel
| grape | snorkel | |
|---|---|---|
| 3 | 5 | |
| 482 | 5,712 | |
| 2.9% | 0.5% | |
| 6.4 | 5.5 | |
| 2 months ago | 2 months ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
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grape
- Grape (Graph Representation LeArning, Predictions and Evaluation)
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Zoomable, animated scatterplots in the browser that scales over a billion points
Ideally, you'd embed the graph into 2 or 3d first, then visualize it as a scatterplot.
Visualizing the edges at scale doesnt yield nice results in general.
The way to do it is to reduce the graph to some 300d or 500d embeddings, then use TSNE/UMAP/PACMAP to reduce that to 3d. Then visualize.
My prefered way is to use some first order embedding method like GGVec in this library [1] (disclaimer I wrote it). Node2Vec and ProNE don't yield great embeddings for visualization (the first is too filamented, the second too close to the unit ball).
Another great library to do this work is GRAPE [2]. Try first-order embedding methods, or short walks on second order methods to avoid the embeddings being too filamented by long random walk sampling.
[1] https://github.com/VHRanger/nodevectors
[2] https://github.com/AnacletoLAB/grape/
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
snorkel
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
- [Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
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Can't use load_data from utils
Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo
- [D] A hand-picked selection of the best Python ML Libraries of 2021
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[Discussion] Methods for enhancing high-quality dataset A with low-quality dataset
Snorkel (https://github.com/snorkel-team/snorkel) might provide you exactly what you are looking for. From the docs:
What are some alternatives?
deodel - A mixed attributes predictive algorithm implemented in Python.
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
deepscatter - Zoomable, animated scatterplots in the browser that scales over a billion points
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)
nanocube
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]