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README

RankLib

Overview

RankLib is a library of learning to rank algorithms. Currently eight popular algorithms have been implemented:

MART (Multiple Additive Regression Trees, a.k.a. Gradient boosted regression tree) [6]
RankNet [1]
RankBoost [2]
AdaRank [3]
Coordinate Ascent [4]
LambdaMART [5]
ListNet [7]
Random Forests [8]

It also implements many retrieval metrics as well as provides many ways to carry out evaluation.

This project forked from The Lemur Project.

Version

Versions in Maven Repository

License

RankLib is available under BSD license.

References

C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton and G. Hullender. Learning to rank using gradient descent. In Proc. of ICML, pages 89-96, 2005.
Y. Freund, R. Iyer, R. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. The Journal of Machine Learning Research, 4: 933-969, 2003.
J. Xu and H. Li. AdaRank: a boosting algorithm for information retrieval. In Proc. of SIGIR, pages 391-398, 2007.
D. Metzler and W.B. Croft. Linear feature-based models for information retrieval. Information Retrieval, 10(3): 257-274, 2007.
Q. Wu, C.J.C. Burges, K. Svore and J. Gao. Adapting Boosting for Information Retrieval Measures. Journal of Information Retrieval, 2007.
J.H. Friedman. Greedy function approximation: A gradient boosting machine. Technical Report, IMS Reitz Lecture, Stanford, 1999; see also Annals of Statistics, 2001.
Z. Cao, T. Qin, T.Y. Liu, M. Tsai and H. Li. Learning to Rank: From Pairwise Approach to Listwise Approach. ICML 2007.
L. Breiman. Random Forests. Machine Learning 45 (1): 5–32, 2001.

Extension points exported contracts — how you extend this code

TransferFunction (Interface)

@author vdang This is the abstract class for implementing transfer functions for neuralnet. [4 implementers]

src/main/java/ciir/umass/edu/learning/neuralnet/TransferFunction.java

LineConsumer (Interface)

(no doc)

src/main/java/ciir/umass/edu/parsing/ModelLineProducer.java

Core symbols most depended-on inside this repo

get

called by 240

src/main/java/ciir/umass/edu/learning/RankList.java

size

called by 237

src/main/java/ciir/umass/edu/learning/RankList.java

get

called by 165

src/main/java/ciir/umass/edu/learning/neuralnet/Layer.java

size

called by 157

src/main/java/ciir/umass/edu/learning/neuralnet/Layer.java

add

called by 121

src/main/java/ciir/umass/edu/learning/tree/Ensemble.java

create

called by 83

src/main/java/ciir/umass/edu/utilities/RankLibError.java

round

called by 57

src/main/java/ciir/umass/edu/utilities/SimpleMath.java

getLabel

called by 53

src/main/java/ciir/umass/edu/learning/DataPoint.java

Shape

Method 497

Class 74

Enum 3

Interface 2

Languages

Java100%

Modules by API surface

src/main/java/ciir/umass/edu/learning/neuralnet/RankNet.java28 symbols

src/main/java/ciir/umass/edu/learning/tree/LambdaMART.java27 symbols

src/main/java/ciir/umass/edu/learning/Ranker.java24 symbols

src/main/java/ciir/umass/edu/learning/tree/Split.java22 symbols

src/main/java/ciir/umass/edu/learning/DataPoint.java21 symbols

src/main/java/ciir/umass/edu/learning/CoorAscent.java19 symbols

src/main/java/ciir/umass/edu/utilities/ExpressionEvaluator.java18 symbols

src/test/java/ciir/umass/edu/eval/EvaluatorTest.java16 symbols

src/main/java/ciir/umass/edu/utilities/FileUtils.java14 symbols

src/main/java/ciir/umass/edu/learning/neuralnet/ListNet.java14 symbols

src/main/java/ciir/umass/edu/learning/boosting/RankBoost.java14 symbols

src/main/java/ciir/umass/edu/eval/Evaluator.java14 symbols

For agents

$ claude mcp add ranklib \
  -- python -m otcore.mcp_server <graph>

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