SKompiler | compiling trained SKLearn models into other representations

by konstantint Python Version: 0.7 License: MIT

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

kandi X-RAY | SKompiler Summary

SKompiler is a Python library. SKompiler has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install SKompiler' or download it from GitHub, PyPI.

The package provides a tool for transforming trained SKLearn models into other forms, such as SQL queries, Excel formulas, Portable Format for Analytics (PFA) files or Sympy expressions (which, in turn, can be translated to code in a variety of languages, such as C, Javascript, Rust, Julia, etc).

Support

Quality

Security

License

Reuse

Support

SKompiler has a low active ecosystem.

It has 102 star(s) with 7 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 5 have been closed. On average issues are closed in 61 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SKompiler is 0.7

Quality

SKompiler has 0 bugs and 87 code smells.

Security

SKompiler has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SKompiler code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SKompiler is licensed under the MIT License. This license is Permissive.

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

Reuse

SKompiler releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

It has 2391 lines of code, 336 functions and 46 files.

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

Top functions reviewed by kandi - BETA

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

Substitute references
Return a copy of a node with the given keyword arguments
Map a function over a list of nodes
Apply a function to each node
Convert a range of ranges into a range
Return a list of column names
Takes a value from an iterable
Performs MatVec product
Make a copy of the result
Implementation of log regressor
Return a constant expression
LFold operator
Compute the common dtype for this expression
Softmax
Make ast vector
Return ast UnaryFunction node
Parse matrices
Convert a vector
Return the mean of elems
Return a Result
Least operator
Create an AST node
Binary operator
Evaluate IIF
Convert a value to a constant
Return a function that parses a template
Implements Unary Function

Get all kandi verified functions for this library.

SKompiler Key Features

No Key Features are available at this moment for SKompiler.

SKompiler Examples and Code Snippets

No Code Snippets are available at this moment for SKompiler.

Community Discussions

No Community Discussions are available at this moment for SKompiler.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install SKompiler

The simplest way to install the package is via pip:. Note that the [full] option includes the installations of sympy, sqlalchemy and astor, which are necessary if you plan to convert SKompiler's expressions to sympy expressions (which, in turn, can be compiled to many other languages) or to SQLAlchemy expressions (which can be further translated to different SQL dialects) or to Python source code. If you do not need this functionality (say, you only need the raw SKompiler expressions or perhaps only the SQL conversions without the sympy ones), you may avoid the forced installation of all optional dependencies by simply writing. (you are free to install any of the required extra dependencies, via separate calls to pip install, of course).