SLRC | Short-term and Life-time Repeat Consumption Model | Recommender System library

by THUwangcy Python Version: Current License: No License

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

kandi X-RAY | SLRC Summary

SLRC is a Python library typically used in Artificial Intelligence, Recommender System, Deep Learning, Pytorch applications. SLRC has no bugs, it has no vulnerabilities and it has low support. However SLRC build file is not available. You can download it from GitHub.

Short-term and Life-time Repeat Consumption (SLRC) Model repeat consuming behavior with combination of Collaborative Filtering (CF) and Hawkes Process.

Support

Quality

Security

License

Reuse

Support

SLRC has a low active ecosystem.

It has 23 star(s) with 5 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 3 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SLRC is current.

Quality

SLRC has no bugs reported.

Security

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

License

SLRC does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

SLRC 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.

Top functions reviewed by kandi - BETA

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

Load data
Load dev test data
Loads the book
Fill repeat_info
Load training data
Filter out noise based on given frequency
Assign user id and item ids
Generate test data
Train the model
Validate EVA termination
Evaluate the model
Predict clicks
Parse command line arguments
Create a model
Save a split
Print the best validation result

Get all kandi verified functions for this library.

SLRC Key Features

No Key Features are available at this moment for SLRC.

SLRC Examples and Code Snippets

No Code Snippets are available at this moment for SLRC.

Community Discussions

Trending Discussions on SLRC

Plotting only a table based on groupby data from a dataframe?

QUESTION

Plotting only a table based on groupby data from a dataframe?

Asked 2021-Jan-10 at 11:07

I ultimately would like to write a table based on "groupby" of a DataFrame to a pdf file.

In order to do so, creating a "plot" of the table seems to be a way to achieve it.

I use the code below. The table is shown nicely using print(), but generates the error when trying to plot the table:

...

ANSWER

Answered 2021-Jan-10 at 10:30

You can use reset_index(). Your groupby aggregation with .sum() returns a pandas series, while the plotting function expects a dataframe (or similar 2D string structure). When printing, a Multiindex dataframe looks similar to a series, so it is easy to assume, you generated a new dataframe for the plot. However, you might have noticed that the printout of your aggregation series does not have a column name, the name Holding is instead printed below.

Source https://stackoverflow.com/questions/65647518

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

Vulnerabilities

No vulnerabilities reported

Install SLRC

You can download it from GitHub.
You can use SLRC 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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