sklearn-porter | Transpile trained scikit-learn estimators | Machine Learning library

by nok Python Version: 0.7.4 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | sklearn-porter Summary

sklearn-porter is a Python library typically used in Artificial Intelligence, Machine Learning applications. sklearn-porter has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install sklearn-porter' or download it from GitHub, PyPI.

Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

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Quality

Security

License

Reuse

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sklearn-porter has a medium active ecosystem.

It has 1211 star(s) with 167 fork(s). There are 33 watchers for this library.

It had no major release in the last 12 months.

There are 34 open issues and 34 have been closed. On average issues are closed in 167 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sklearn-porter is 0.7.4

Quality

sklearn-porter has 0 bugs and 0 code smells.

Security

sklearn-porter has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

sklearn-porter code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sklearn-porter is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

sklearn-porter releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

sklearn-porter saves you 2485 person hours of effort in developing the same functionality from scratch.

It has 5407 lines of code, 385 functions and 174 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed sklearn-porter and discovered the below as its top functions. This is intended to give you an instant insight into sklearn-porter implemented functionality, and help decide if they suit your requirements.

Predict the model .
Parse command line arguments .
Export the estimator model .
Main function .
Recursively create branches .
Generate a template from a given name .
Create embedded methods .
Creates method or method embedded method .
Load the contents of a meta file .
Exports the model .

Get all kandi verified functions for this library.

sklearn-porter Key Features

No Key Features are available at this moment for sklearn-porter.

sklearn-porter Examples and Code Snippets

No Code Snippets are available at this moment for sklearn-porter.

Community Discussions

Trending Discussions on sklearn-porter

How to export sklearn RandomForestClassifier python code to C code with sklearn_porter?

QUESTION

How to export sklearn RandomForestClassifier python code to C code with sklearn_porter?

Asked 2021-Mar-19 at 06:14

I'm trying to use sklearn_porter to train a Random Forest Modell in python which then should be exported to C code.

This is my code:

...

ANSWER

Answered 2021-Mar-18 at 13:55

The code works Using scikit-learn version 0.22.2. and Python 3.7.9!

Source https://stackoverflow.com/questions/66675001

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install sklearn-porter

You can install using 'pip install sklearn-porter' or download it from GitHub, PyPI.
You can use sklearn-porter like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.