RankLib | Improving RankLib -

by busarobi Java Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | RankLib Summary

RankLib is a Java library. RankLib has no bugs, it has no vulnerabilities and it has low support. However RankLib build file is not available. You can download it from GitHub.

Improving RankLib

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Quality

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Support

RankLib has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

RankLib has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of RankLib is current.

Quality

RankLib has no bugs reported.

Security

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

License

RankLib 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

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

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

Top functions reviewed by kandi - BETA

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

Main method for testing
Prepares features for the feature file
Read a feature file
Evaluate a feature description
Performs the training
Calculate the Euclidean distance between two vectors
Shuffles the set of features
Initialize the training samples
Reorders the scores
Load the weights from the specified file
Load features from a file
Returns a string representation of the model
Initialize the data structures
Estimates the training error
Initialize the input
Normalizes the data points
Returns the rank of the data point
Compute ERR score at given rank
Gunzip file
Learn the scores
Train the training algorithm
Train the training algorithm
Load network from a file
Normalizes the mean values of a given rank
Load the weights from a file
Simple test code

Get all kandi verified functions for this library.

RankLib Key Features

No Key Features are available at this moment for RankLib.

RankLib Examples and Code Snippets

No Code Snippets are available at this moment for RankLib.

Community Discussions

Trending Discussions on RankLib

How to get the feature importance of ranklib generated random forests model?

Lemur RankLib return code 1 on training

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

QUESTION

How to get the feature importance of ranklib generated random forests model?

Asked 2021-Feb-21 at 21:07

Using ranklib's learning to rank random forests generates an xml-like model. Ranklib has a tool that provides features' frequency which cannot necessarily be considered as feature importance.

How can I get the Gini feature importance or Gini index of random forests generated by ranklib? How to parse the tree generated?

Found in the Sourceforge discussion forum that you need to parse the model file yourself.

...

ANSWER

Answered 2021-Feb-19 at 04:47

I, personally, had a lot of struggles to get the Gini importance of features from a ranklib random forest and finally succeeded. Here I share the Github repository I made to solve the problem.

You can do it by running this command (use python3):

python Gini.py

Please see the repository for more details on how to do it.

Source https://stackoverflow.com/questions/66271935

QUESTION

Lemur RankLib return code 1 on training

Asked 2018-May-05 at 04:29

I am using RankLib for my data (shape: 218279 rows × 1504 columns) using python and getting error code 1 with none output. I am just wondering is there any documentation regarding error codes on RankLib?

I am using Jupyter iPython for my project and run the process using subprocess.run. In case you are wondering, below is my code to train.

...

ANSWER

Answered 2018-May-05 at 04:29

This problem is solved. Apparently, the minimum value of relevance ranking data for list-wise approach is 1 and not 0. Initially I thought 0 would mean the data is not relevant at all.

Source https://stackoverflow.com/questions/50076633

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.

...

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 RankLib

You can download it from GitHub.
You can use RankLib like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the RankLib component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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