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[LTR] RankLib.jar 包介绍

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  • 2019-06-27
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简介一、介绍RankLib.jar是一个学习排名(Learningtorank)算法的库,目前已经实现了如下几种算法:MARTRankNetRankBoostAdaRankCoord

一、介绍

RankLib.jar 是一个学习排名(Learning to rank)算法的库,目前已经实现了如下几种算法:

MARTRankNetRankBoostAdaRankCoordinate AscentLambdaMARTListNetRandom ForestsLinear regression

二、jar 包

Usage: java -jar RankLib.jar <Params>Params: [+] Training (+ tuning and evaluation) # 训练数据 -train <file> Training data # 指定排名算法 -ranker <type> Specify which ranking algorithm to use 0: MART (gradient boosted regression tree) 1: RankNet 2: RankBoost 3: AdaRank 4: Coordinate Ascent 6: LambdaMART 7: ListNet 8: Random Forests 9: Linear regression (L2 regularization) # 特征描述文件,列出要学习的特征,每行一个特征,默认使用所有特征 [ -feature <file> ] Feature description file: list features to be considered by the learner, each on a separate line If not specified, all features will be used. # [ -metric2t <metric> ] Metric to optimize on the training data. Supported: MAP, NDCG@k, DCG@k, P@k, RR@k, ERR@k (default=ERR@10) [ -gmax <label> ] Highest judged relevance label. It affects the calculation of ERR (default=4, i.e. 5-point scale {0,1,2,3,4}) [ -silent ] Do not print progress messages (which are printed by default) # 是否在验证数据集上调整模型 [ -validate <file> ] Specify if you want to tune your system on the validation data (default=unspecified) If specified, the final model will be the one that performs best on the validation data # 训练-验证数据集的分割比例 [ -tvs <x in [0..1]> ] If you don"t have separate validation data, use this to set train-validation split to be (x)(1.0-x) # 学习模型保存到指定文件 [ -save <model> ] Save the model learned (default=not-save) # 是否要在数据上测试训练的模型 [ -test <file> ] Specify if you want to evaluate the trained model on this data (default=unspecified) # 训练-测试数据集的分割比例 [ -tts <x in [0..1]> ] Set train-test split to be (x)(1.0-x). -tts will override -tvs # 默认与 metric2t 一致 [ -metric2T <metric> ] Metric to evaluate on the test data (default to the same as specified for -metric2t) # 归一化特征向量,方法包括求和归一化,均值/标准差归一化,最大值/最小值归一化 [ -norm <method>] Normalize all feature vectors (default=no-normalization). Method can be: sum: normalize each feature by the sum of all its values zscore: normalize each feature by its mean/standard deviation linear: normalize each feature by its min/max values # 在训练数据集上执行交叉验证 [ -kcv <k> ] Specify if you want to perform k-fold cross validation using the specified training data (default=NoCV) -tvs can be used to further reserve a portion of the training data in each fold for validation # 交叉验证训练库模型的目录 [ -kcvmd <dir> ] Directory for models trained via cross-validation (default=not-save) [ -kcvmn <model> ] Name for model learned in each fold. It will be prefix-ed with the fold-number (default=empty) [-] RankNet-specific parameters # 特定参数 # 训练迭代次数 [ -epoch <T> ] The number of epochs to train (default=100) # 隐含层个数 [ -layer <layer> ] The number of hidden layers (default=1) # 每层隐含节点个数 [ -node <node> ] The number of hidden nodes per layer (default=10) # 学习率 [ -lr <rate> ] Learning rate (default=0.00005) [-] RankBoost-specific parameters # 特定参数 # 训练迭代次数 [ -round <T> ] The number of rounds to train (default=300) # 搜索的阈值候选个数 [ -tc <k> ] Number of threshold candidates to search. -1 to use all feature values (default=10) [-] AdaRank-specific parameters # 特定参数 # 训练迭代次数 [ -round <T> ] The number of rounds to train (default=500) # [ -noeq ] Train without enqueuing too-strong features (default=unspecified) # 连续两轮学习之间的误差 [ -tolerance <t> ] Tolerance between two consecutive rounds of learning (default=0.002) # 一个特征可以被连续选择而不改变性能的最大次数 [ -max <times> ] The maximum number of times can a feature be consecutively selected without changing performance (default=5) [-] Coordinate Ascent-specific parameters # 特定参数 [ -r <k> ] The number of random restarts (default=5) [ -i <iteration> ] The number of iterations to search in each dimension (default=25) [ -tolerance <t> ] Performance tolerance between two solutions (default=0.001) [ -reg <slack> ] Regularization parameter (default=no-regularization) [-] {MART, LambdaMART}-specific parameters # 特定参数 # 树的个数 [ -tree <t> ] Number of trees (default=1000) # 一个叶子的样本个数 [ -leaf <l> ] Number of leaves for each tree (default=10) # 学习率 [ -shrinkage <factor> ] Shrinkage, or learning rate (default=0.1) # 树分割时的候选特征个数 [ -tc <k> ] Number of threshold candidates for tree spliting. -1 to use all feature values (default=256) # 一个叶子最少的样本个数 [ -mls <n> ] Min leaf support -- minimum #samples each leaf has to contain (default=1) [ -estop <e> ] Stop early when no improvement is observed on validaton data in e consecutive rounds (default=100) [-] ListNet-specific parameters [ -epoch <T> ] The number of epochs to train (default=1500) [ -lr <rate> ] Learning rate (default=0.00001) [-] Random Forests-specific parameters # 随机森林特定参数 [ -bag <r> ] Number of bags (default=300) # 子集采样率 [ -srate <r> ] Sub-sampling rate (default=1.0) # 特征采样率 [ -frate <r> ] Feature sampling rate (default=0.3) [ -rtype <type> ] Ranker to bag (default=0, i.e. MART) # 树个数 [ -tree <t> ] Number of trees in each bag (default=1) # 每棵树的叶节点个数 [ -leaf <l> ] Number of leaves for each tree (default=100) # 学习率 [ -shrinkage <factor> ] Shrinkage, or learning rate (default=0.1) # 树分割时使用的候选特征阈值个数 [ -tc <k> ] Number of threshold candidates for tree spliting. -1 to use all feature values (default=256) [ -mls <n> ] Min leaf support -- minimum #samples each leaf has to contain (default=1) [-] Linear Regression-specific parameters [ -L2 <reg> ] L2 regularization parameter (default=1.0E-10) [+] Testing previously saved models # 测试已保存的模型 # 加载模型 -load <model> The model to load Multiple -load can be used to specify models from multiple folds (in increasing order), in which case the test/rank data will be partitioned accordingly. # 测试数据 -test <file> Test data to evaluate the model(s) (specify either this or -rank but not both) # 对指定文件中的样本排序,与 -test 不能同时使用 -rank <file> Rank the samples in the specified file (specify either this or -test but not both) [ -metric2T <metric> ] Metric to evaluate on the test data (default=ERR@10) [ -gmax <label> ] Highest judged relevance label. It affects the calculation of ERR (default=4, i.e. 5-point scale {0,1,2,3,4}) [ -score <file>] Store ranker"s score for each object being ranked (has to be used with -rank) # 打印单个排名列表上的性能(必须与 -test 一起使用) [ -idv <file> ] Save model performance (in test metric) on individual ranked lists (has to be used with -test) # 特征归一化 [ -norm ] Normalize feature vectors (similar to -norm for training/tuning)

1. -train <file>

指定训练数据的文件,训练数据格式:

label qid:$id $featureid:$featurevalue $featureid:$featurevalue ... # description

每行代表一个样本,相同查询请求的样本的 qid 相同,label 表示该样本和该查询请求的相关程度,description 描述信息,不参与训练计算。

2、-ranker <type>

指定排名算法

MART(Multiple Additive Regression Tree)多重增量回归树GBDT(Gradient Boosting Decision Tree)梯度渐进决策树GBRT(Gradient Boosting Regression Tree)梯度渐进回归树TreeNet 决策树网络RankNetRankBoostAdaRankCoordinate AscentLambdaMARTListNetRandom ForestsLinear regression

3、-feature <file>

指定样本的特征定义文件,格式如下:

feature1feature2...# featureK(该特征不参与分析)

4、-metric2t <metric>

指定信息检索中的评价指标,包括:MAP, NDCG@k, DCG@k, P@k, RR@k, ERR@k

5、Example

java -jar bin/RankLib.jar -train MQ2008/Fold1/train.txt -test MQ2008/Fold1/test.txt -validate MQ2008/Fold1/vali.txt -ranker 6 -metric2t NDCG@10 -metric2T ERR@10 -save mymodel.txt

命令解释 >>>训练数据:MQ2008/Fold1/train.txt测试数据:MQ2008/Fold1/test.txt验证数据:MQ2008/Fold1/vali.txt排名算法:6,LambdaMART评估指标:NDCG,取排名前 10 个数据进行计算测试数据评估指标:ERR,取排名前 10 个数据进行计算保存模型:mymodel.txt

参数 -validate 是可选的,但可以更好的模型结果,对于 RankNet/MART/LambdaMART 非常重要。-metric2t 仅应用于 list-wise 算法(AdaRank、Coordinate Ascent 和 LambdaMART);point-wise 和 Pair-wise 算法(MART、RankNet、RankBoost)是使用自己内部的 RMSE/pair-wise loss 作为评价指标。ListNet 虽然是 list-wise 算法,但是也不用 metric2t 指定评价指标。

6、k-fold cross validation

顺序分区

java -jar bin/RankLib.jar -train MQ2008/Fold1/train.txt -ranker 4 -kcv 5 -kcvmd models/ -kcvmn ca -metric2t NDCG@10 -metric2T ERR@10

按顺序将训练数据拆分5等份,第 i 份数据作为第 i 折叠的测试数据,第 i 折叠的训练数据则是由其他折叠的数据组成。

随机分区

java -cp bin/RankLib.jar ciir.umass.edu.features.FeatureManager -input MQ2008/Fold1/train.txt -output mydata/ -shuffle

将训练数据 train.txt 重新洗牌存储在 mydata/ 目录下 train.txt.shuffled

获取每个折叠中的数据

java -cp bin/RankLib.jar ciir.umass.edu.features.FeatureManager -input MQ2008/Fold1/train.txt.shuffled -output mydata/ -k 5

7、评估已训练的模型

java -jar bin/RankLib.jar -load mymodel.txt -test MQ2008/Fold1/test.txt -metric2T ERR@10

8、模型对比

java -jar bin/RankLib.jar -test MQ2008/Fold1/test.txt -metric2T NDCG@10 -idv output/baseline.ndcg.txtjava -jar bin/RankLib.jar -load ca.model.txt -test MQ2008/Fold1/test.txt -metric2T NDCG@10 -idv output/ca.ndcg.txtjava -jar bin/RankLib.jar -load lm.model.txt -test MQ2008/Fold1/test.txt -metric2T NDCG@10 -idv output/lm.ndcg.txt

输出文件中包含了每条查询的 NDCG@10 指标值,以及所有查询的综合指标,例如:

NDCG@10 170 0.0NDCG@10 176 0.6722390270733757NDCG@10 177 0.4772656487866462NDCG@10 178 0.539003131276382NDCG@10 185 0.6131471927654585NDCG@10 189 1.0NDCG@10 191 0.6309297535714574NDCG@10 192 1.0NDCG@10 194 0.2532778777010656NDCG@10 197 1.0NDCG@10 200 0.6131471927654585NDCG@10 204 0.4772656487866462NDCG@10 207 0.0NDCG@10 209 0.123151194370365NDCG@10 221 0.39038004999210174NDCG@10 all 0.5193204478059303

然后再进行对比:

java -cp RankLib.jar ciir.umass.edu.eval.Analyzer -all output/ -base baseline.ndcg.txt > analysis.txt

对比结果 analysis.txt 如下:

Overall comparison ------------------------------------------------------------------------ System Performance Improvement Win Loss p-value baseline_ndcg.txt [baseline] 0.093 LM_ndcg.txt 0.2863 +0.1933 (+207.8%) 9 1 0.03 CA_ndcg.txt 0.5193 +0.4263 (+458.26%) 12 0 0.0 Detailed break down ------------------------------------------------------------------------ [ < -100%) [-100%,-75%) [-75%,-50%) [-50%,-25%) [-25%,0%) (0%,+25%] (+25%,+50%] (+50%,+75%] (+75%,+100%] ( > +100%] LM_ndcg.txt 0 0 1 0 0 4 2 2 1 0 CA_ndcg.txt 0 0 0 0 0 1 6 2 3 0

9、利用训练模型重排名

java -jar RankLib.jar -load mymodel.txt -rank myResultLists.txt -score myScoreFile.txt

myScoreFile.txt 文件中只是增加了一列,表示重新计算的排名评分,需要自己另外根据该评分排序获取新的排名顺序。

1 0 -7.5286507606506351 1 2.90220618247985841 2 -0.7001255154609681 3 2.3766574859619141 4 -0.296662658452987671 5 -2.0386281013488771 6 -5.2677111625671391 7 -2.0221464633941651 8 0.6741248369216919...

参考

RankLib wiki

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