Robyn
EconML
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| Robyn | EconML | |
|---|---|---|
| 61 | 8 | |
| 3,520 | 3,540 | |
| 6.8% | 2.0% | |
| 9.1 | 8.3 | |
| 7 days ago | about 22 hours ago | |
| Python | Jupyter Notebook | |
| BSD 2-clause "Simplified" License | 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.
Robyn
- Robyn – Innovator Friendly, and Community Driven Python Web Framework
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Introducing Dependency Injections in Robyn with a Twist!
For those who might not be familiar, Robyn is a fast, asynchronous Python backend web framework that operates with a Rust runtime, combining the best of both worlds for efficient and robust web development.
- Robyn: A Fast, Innovator Friendly, and Community Driven Python Web Framework
- Robyn – Web Framework in Rust
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FastHttp for Python (64k requests/s)
If you're comparing web frameworks you might also like to look at robyn https://robyn.tech/, which claims impressive performance. It's always tricky tho' to go from benchmarks to a particular use case.
- Robyn: High-Performance and Community-Driven Python Web Framework
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Robyn passes 1M installs on PyPi.
Robyn's Link - https://github.com/sparckles/robyn
- Robyn v0.38.0 - An improved CLI for create-robyn-app
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Robyn Finds a New Nest: Joining the Sparckles Open-Source Organization
For the unaware, Robyn , is a High-Performance, Community-Driven, and Innovator Friendly Python Web Framework with a Rust runtime.
EconML
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[D] What approach to decide which class is most optimal for recovery?
A good package with many of the tools used for this type of problem as well as pretty good documentation about how it all works is https://github.com/microsoft/EconML
- Getting treatment effects from a random forest
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Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
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EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
Github: https://github.com/microsoft/EconML
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UpliftML: An uplift modeling library that handles web scale datasets
Many libraries have recently emerged that offer implementations of algorithms for heterogeneous treatment effect estimation (or, CATE estimation). The most well-known examples are Microsoft's EconML (https://github.com/microsoft/EconML) and Uber's CausalML (https://github.com/uber/causalml). Existing libraries require all data to fit in memory, which is often a limitation for industry applications on web scale datasets. Booking.com's new library offers similar functionality on top of Spark, enabling web scale uplift modeling.
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[q] before/after test
EconML and CausalLift are pretty good python packages that help you build uplift models. scikit-uplift is a decent sklearn style wrapper package that can be helpful as well. One of the drawbacks of these packages is they only allow for the modeling of a single treatment. mr-uplift is a newer package that allows you to model the multiple treatment effects simultaneously. I haven't used it personally, but it does look fairly interesting.
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[N] Spotify Confidence - open source for analyzing a/b test data
Can't see how this adds to decades of causal inference packages development in stats oriented frameworks like R/Stata/EViews etc and the ongoing effort of porting this to Python. If you want something fancy there's already EconML.
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What are some applications of Data Science in Digital Marketing?
Uplift Modeling - This is a very powerful technique aimed at discovering the customers who are most likely to respond to your marketing efforts. Some good python libraries for this are EconML and mr-uplift
What are some alternatives?
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
causalml - Uplift modeling and causal inference with machine learning algorithms
uvicorn - An ASGI web server, for Python. 🦄
upliftml - UpliftML: A Python Package for Scalable Uplift Modeling
MMM-BurnIn
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
strawberry - A GraphQL library for Python that leverages type annotations 🍓
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
Python-Regex - A port of the Rust regex library to python for super speed linear matching.
tensor-house - A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.