lkpy | Python recommendation toolkit | Recommender System library

by lenskit Python Version: 0.14.2 License: MIT

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

kandi X-RAY | lkpy Summary

lkpy is a Python library typically used in Artificial Intelligence, Recommender System applications. lkpy has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However lkpy build file is not available. You can install using 'pip install lkpy' or download it from GitHub, PyPI.

Python recommendation toolkit

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Quality

Security

License

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Support

lkpy has a low active ecosystem.

It has 235 star(s) with 57 fork(s). There are 9 watchers for this library.

It had no major release in the last 12 months.

There are 28 open issues and 72 have been closed. On average issues are closed in 136 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of lkpy is 0.14.2

Quality

lkpy has 0 bugs and 0 code smells.

Security

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

lkpy code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

lkpy 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

lkpy releases are available to install and integrate.

Deployable package is available in PyPI.

lkpy has no build file. You will be need to create the build yourself to build the component from source.

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

lkpy saves you 4396 person hours of effort in developing the same functionality from scratch.

It has 7983 lines of code, 711 functions and 85 files.

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

Top functions reviewed by kandi - BETA

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

Predict ratings for a given user
Calculate the sum of the predicted scores
Count the number of reachable targets that are reachable
Predict the weighted average weighted average score
Sample rows from data
Disjoint dataset
Raise an error if the index is not unique
Generate TTP pair
Prepare data for training
Predict given pairs
Recommend scores for a given user
Predict for given items
Predicts the score for a given user
Returns the candidates for a given user
Perform the measure analysis
Predict scores for a given user
Save a model into shared memory
Check the runtime environment
Predict similarity for a given user
Mean absolute error
Compute the distance between two CSR
Determine the DCG
Compute the covariance of the covariance matrix
Compute the linear gradient of the sparse matrix
Root mean squared error
Compute the implicit LU decomposition for the given matrices

Get all kandi verified functions for this library.

lkpy Key Features

No Key Features are available at this moment for lkpy.

lkpy Examples and Code Snippets

No Code Snippets are available at this moment for lkpy.

Community Discussions

Trending Discussions on lkpy

Problems writing a query to match records between tables with complex matching criteria

QUESTION

Problems writing a query to match records between tables with complex matching criteria

Asked 2019-Oct-23 at 22:02

I have a matching algorithm that I need to build on a large dataset in a SQL Server 2008 database. This is a minimal example of the kind of thing I need.

Say I have table1 and table2 each with just four columns, unique_id, col1, col2 and col3.

...

ANSWER

Answered 2019-Oct-23 at 22:02

You can get the results you want by JOINing the tables on matching values on each column while also allowing for NULL values to act as a match, and then counting the number of actual matches and requiring that to be at least 2:

Source https://stackoverflow.com/questions/58521880

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

Vulnerabilities

No vulnerabilities reported

Install lkpy

You can install using 'pip install lkpy' or download it from GitHub, PyPI.
You can use lkpy 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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