scikit-learn-intelex
eland
| scikit-learn-intelex | eland | |
|---|---|---|
| 3 | 4 | |
| 1,161 | 611 | |
| 1.1% | 0.8% | |
| 9.5 | 8.5 | |
| 2 days ago | about 13 hours ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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.
scikit-learn-intelex
- Machine Learning with PyTorch and Scikit-Learn – The *New* Python ML Book
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Improving xgb prediction times on a single core
I can recommend https://github.com/intel/scikit-learn-intelex. We have been using this and it works great. The prediction time is greatly reduced and it has been running very stable. It's super easy to install and convert the trained XGB models to this Intel format.
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Intel Extension for Scikit-Learn
Looks like they are responding to https://github.com/intel/scikit-learn-intelex#-acceleration
I completely agree. I hope some Intel competitor funds a scikit-learn developer to read this code and extract all the portable performance improvements.
eland
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I'm getting elasticsearch.BadRequestError: BadRequestError(400, 'illegal_argument_exception', "specified fields can't be null or empty") using Eland library
We have a fix for this issue reported here merged and pending a release. Hopefully that release will happen in the next few days, then you can upgrade and the default experience for everyone won't be as confusing :)
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is it possible to use log data from elastic search and visualise it to a custom made dashboard in python?
Another option depending on what sort of data you want, and if you want to use python, is to use Eland: https://github.com/elastic/eland, together with for example Jupyter notebooks you can create super quick visualizations :)
- hey, any idea about how to automatically extract data from kibana to a panda data frame in order to be analyzed?
- Explore and analyze Elasticsearch data with Pandas-Compatible API
What are some alternatives?
cuml - cuML - RAPIDS Machine Learning Library
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
pandastable - Table analysis in Tkinter using pandas DataFrames.
xgb_vs_lightgbm - comparison of prediction times
modin - Modin: Scale your Pandas workflows by changing a single line of code
AlgorithmsAndDataStructure - Algorithms And DataStructure Implemented In Python, Java & CPP, Give a Star 🌟If it helps you
kangas - 🦘 Explore multimedia datasets at scale
scikit-learn - scikit-learn: machine learning in Python
nixtla - Python SDK for TimeGPT, a foundational time series model
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
Solomon - Data Exploration tool.