SDV
SDGym
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SDV | SDGym | |
---|---|---|
59 | 1 | |
2,080 | 239 | |
13.9% | 4.6% | |
9.3 | 7.5 | |
7 days ago | 9 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SDV
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Synthetic data generation for tabular data
Can someone help me understand the licensing of this?
https://github.com/sdv-dev/SDV/blob/main/LICENSE
It was MIT licensed up until 2022 where it was changed to what it is now, where they say that it will become MIT again 4 years after release... but is that from when the license was changed or the first release of the software in GitHub?
- FLaNK Stack Weekly for 30 April 2023
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What is the best way to generate synthetic OHLC data?
I have the same question so I cant give a direct answer. However, I've been thinking of using SDV and TimeSynth python packages to produce synthetic data for backtesting.
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Show HN: Augmenting tabular data with SDV to improve ML results
For those wanting to learn more about it, SDV can be found here: https://github.com/sdv-dev/SDV
SDV (The Synthetic Data Vault) is an ecosystem of Open Source Python libraries and tools for Synthetic Data Generation that works with single-table, multi-table and time-series data. One of the use cases of Synthetic Data is data augmentation for machine learning models, as shown in the example posted, but it also enables a multitude of other use cases such as privacy preserving methods for sharing data or the generation of data for software testing. More resources, tutorials and documentation can be also found here: https://sdv.dev
SDGym
We haven't tracked posts mentioning SDGym yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
CTGAN - Conditional GAN for generating synthetic tabular data.
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.
machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.
tsfresh - Automatic extraction of relevant features from time series:
Copulas - A library to model multivariate data using copulas.
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python
EigenGAN-Tensorflow - EigenGAN: Layer-Wise Eigen-Learning for GANs (ICCV 2021)
Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
genalog - Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
HR-Attrition - Will they stay or will they go? Predicting whether employees will leave + why.
pandas-ai - Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
orbstack - Fast, light, simple Docker containers & Linux machines for macOS