pycall.rb
catboost
pycall.rb | catboost | |
---|---|---|
6 | 8 | |
1,030 | 7,753 | |
- | 0.8% | |
6.0 | 9.9 | |
7 months ago | 3 days ago | |
C | Python | |
MIT License | Apache License 2.0 |
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pycall.rb
- Call Python functions from the Ruby language
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RFC: Run Python from Ruby and Ruby from Python
Yeah, I know there are few libraries that do similar things: pycall.rb and rb_call, and there is also rubypython, but it's not supported and doesn't work with Python 3. I used pycall to create matplotlib charts from Ruby, it's great, and I'm gonna use part of its code, type conversion implementation, for example. But I don't think it's enough, it's like a one way bridge, I want more, I want to call Python from Ruby and Ruby from Python at the same time: create an Airfow PythonOperator, invoke Ruby code inside, store some value into XCom. What about rb_call, I don't like how it's implemented at all, it starts a separate process and serializes data using MessagePack RPC, so you can't use callbacks. It's not even possible to pass a Python object as an argument or call Ruby method that requires a block. And of course it's not effective.
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Building an app around a LLM, Rails + Python or just Python?
I have build a rails app that uses openai gem and it's working very well. For more advanced things I am exploring Pycall: https://github.com/mrkn/pycall.rb to call python functions. Don't have any experience though.
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What's the easiest way to interface my Rails app with a Python library?
I have use this before cool and easy also could run in heroku ()( for my case https://github.com/mrkn/pycall.rb
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Help!
From docs. I was able to install matplotlib (without --pre):
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Tips for collaborating with datascience teams
We use https://github.com/mrkn/pycall.rb extensively to interface with python libraries. So far, the only problem we have is memory leaks in python, but we mitigated the problem by isolating the leaking parts in a separate process.
catboost
- CatBoost: Open-source gradient boosting library
- Boosting Algorithms
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What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.
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Writing the fastest GBDT libary in Rust
Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.
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Data Science toolset summary from 2021
Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/
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CatBoost Quickstart — ML Classification
CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.
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[D] What are your favorite Random Forest implementations that support categoricals
If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.
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CatBoost and Water Pumps
The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.
What are some alternatives?
hashpling - hashpling allows you to use shebang on non-UNIX platform
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
data-science-with-ruby - Practical Data Science with Ruby based tools.
Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)
soloud - Free, easy, portable audio engine for games
Keras - Deep Learning for humans
ruby-openai - OpenAI API + Ruby! 🤖❤️ Now with Assistants v2, Batches & Ollama/Groq 🚀
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
boxcars - Building applications with composability using Boxcars with LLM's. Inspired by LangChain.
vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
rubypython - An in-process between Ruby and Python. Soon changing repo address.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more