RankNet | slightly modified ) Keras implementation | Machine Learning library

by airalcorn2 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | RankNet Summary

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

My (slightly modified) Keras implementation of RankNet (as described here) and PyTorch implementation of LambdaRank (as described here). See here for a tutorial demonstating how to to train a model that can be used with Solr.

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RankNet has a low active ecosystem.

It has 217 star(s) with 44 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 5 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of RankNet is current.

Quality

RankNet has 0 bugs and 0 code smells.

Security

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

RankNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

RankNet 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

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

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

RankNet saves you 28 person hours of effort in developing the same functionality from scratch.

It has 77 lines of code, 1 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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RankNet Key Features

No Key Features are available at this moment for RankNet.

RankNet Examples and Code Snippets

No Code Snippets are available at this moment for RankNet.

Community Discussions

Trending Discussions on RankNet

How can I implement pairwise query-dependent learning-to-rank in Azure Search?

What metrics can I use to validate and test RankNet in the RankLib library in the Lemur Project?

QUESTION

How can I implement pairwise query-dependent learning-to-rank in Azure Search?

Asked 2019-Mar-23 at 20:00

The problem:

I am setting up a product that utilizes Azure Search, and one of the requirements is that the results of a search conduct multi-stage learning-to-rank where the final stage involves a pairwise query-dependent machine-learned model such as RankNet.

Is there any existing support in Azure Search for this? If not, where in the Azure Search pipeline would you recommend I start?

What I have tried:

I had been hoping to find something similar to the ElasticSearch LTR Plugin but have not been able to.

The only option I can currently think of is to set-up a server which forwards the query from the front-end to Azure Search, re-ranks the search results my pairwise LTR methods, reconstructs the re-ranked search results, and sends those to the front-end.

However, I am very apprehensive about the inefficiency of this option and it would be unnecessary if there is an existing way for me to do this.

Language / Libraries

If relevant: I am coding primarily in C# and would be using CNTK for machine-learning.

...

ANSWER

Answered 2019-Mar-23 at 20:00

At this time, your suggestion is the way to go. Azure Search does not currently offer a way to inject a custom ranker within the search pipeline. You would need to config your query to return a large amount of results and then re-rank yourself. Sorry we do not have a better answer than this right now. If you have time, it would be great if you could cast your vote for this here as we are hearing this more often lately.

Source https://stackoverflow.com/questions/55309021

QUESTION

What metrics can I use to validate and test RankNet in the RankLib library in the Lemur Project?

Asked 2018-Mar-28 at 03:17

I am currently using the RankLib implementation of the RankNet algorithm (-ranker 4) with a held-out set. I am using the jar file in terminal to run this.

The documentation stipulates:

metric2t (e.g. NDCG, ERR, etc) only applies to list-wise algorithms (AdaRank, Coordinate Ascent and LambdaMART). Point-wise and pair-wise techniques (MART, RankNet, RankBoost), due to their nature, always use their internal RMSE / pair-wise loss as the optimisation criteria.

However, when I set the 'metrics2t' to ERR@10 or NDCG@10, it starts to train and validate on my chosen metric rather that 'RMSE'.

This is part of the table outputted when I run RankNet with ERR@10.

Is there something that I am missing as this seems to be a contradiction to me.

Thanks.

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ANSWER

Answered 2018-Mar-28 at 03:17

I am not sure, but, I think even if it prints the result for those metrics, it is not optimizing for them.

The library's developers simply left it there, as for other methods it is common to use one of those metrics for validation. And there is no option to simply turn of the computing off the metrics during training.

Right now I am training a RankNet model, and it seems that ERR@10 for training and validation data is actually increasing, while the "% mis-ordered pairs" is decreasing.

Source https://stackoverflow.com/questions/43089917

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

Vulnerabilities

No vulnerabilities reported

Install RankNet

You can download it from GitHub.
You can use RankNet 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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