[LTR] RankLib.jar 包介绍

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：

• MART
• RankNet
• RankBoost
• AdaRank
• Coordinate Ascent
• LambdaMART
• ListNet
• Random Forests
• Linear regression

二、jar 包

Usage: java -jar RankLib.jar 
Params:
  [+] Training (+ tuning and evaluation)
        # 训练数据
        -train            Training data
        # 指定排名算法
        -ranker           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  ]     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 to optimize on the training data. Supported: MAP, NDCG@k, DCG@k, P@k, RR@k, ERR@k (default=ERR@10)
        [ -gmax  ]       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  ]    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  ] 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  ]       Save the model learned (default=not-save)
        # 是否要在数据上测试训练的模型
        [ -test  ]        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  ] Set train-test split to be (x)(1.0-x). -tts will override -tvs
        # 默认与 metric2t 一致
        [ -metric2T  ]  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 ]       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  ]            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  ]        Directory for models trained via cross-validation (default=not-save)
        
        [ -kcvmn  ]      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  ]          The number of epochs to train (default=100)
        # 隐含层个数
        [ -layer  ]      The number of hidden layers (default=1)
        # 每层隐含节点个数
        [ -node  ]        The number of hidden nodes per layer (default=10)
        # 学习率
        [ -lr  ]          Learning rate (default=0.00005)

    [-] RankBoost-specific parameters # 特定参数
        # 训练迭代次数
        [ -round  ]          The number of rounds to train (default=300)
        # 搜索的阈值候选个数
        [ -tc  ]             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  ]          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  ]      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  ]        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  ]              The number of random restarts (default=5)
        [ -i  ]      The number of iterations to search in each dimension (default=25)
        [ -tolerance  ]      Performance tolerance between two solutions (default=0.001)
        [ -reg  ]        Regularization parameter (default=no-regularization)

    [-] {MART, LambdaMART}-specific parameters # 特定参数
        # 树的个数
        [ -tree  ]           Number of trees (default=1000)
        # 一个叶子的样本个数
        [ -leaf  ]           Number of leaves for each tree (default=10)
        # 学习率
        [ -shrinkage  ] Shrinkage, or learning rate (default=0.1)
        # 树分割时的候选特征个数
        [ -tc  ]             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  ]            Min leaf support -- minimum #samples each leaf has to contain (default=1)
        [ -estop  ]          Stop early when no improvement is observed on validaton data in e consecutive rounds (default=100)

    [-] ListNet-specific parameters
        [ -epoch  ]          The number of epochs to train (default=1500)
        [ -lr  ]          Learning rate (default=0.00001)

    [-] Random Forests-specific parameters # 随机森林特定参数
        [ -bag  ]            Number of bags (default=300)
        # 子集采样率
        [ -srate  ]          Sub-sampling rate (default=1.0)
        # 特征采样率
        [ -frate  ]          Feature sampling rate (default=0.3)
        [ -rtype  ]       Ranker to bag (default=0, i.e. MART)
        # 树个数
        [ -tree  ]           Number of trees in each bag (default=1)
        # 每棵树的叶节点个数
        [ -leaf  ]           Number of leaves for each tree (default=100)
        # 学习率
        [ -shrinkage  ] Shrinkage, or learning rate (default=0.1)
        # 树分割时使用的候选特征阈值个数
        [ -tc  ]             Number of threshold candidates for tree spliting. -1 to use all feature values (default=256)
        [ -mls  ]            Min leaf support -- minimum #samples each leaf has to contain (default=1)

    [-] Linear Regression-specific parameters
        [ -L2  ]           L2 regularization parameter (default=1.0E-10)

  [+] Testing previously saved models # 测试已保存的模型
        # 加载模型
        -load            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             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             Rank the samples in the specified file (specify either this or -test but not both)
        [ -metric2T  ]  Metric to evaluate on the test data (default=ERR@10)
        [ -gmax  ]       Highest judged relevance label. It affects the calculation of ERR (default=4, i.e. 5-point scale {0,1,2,3,4})
        [ -score ]        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  ]         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

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

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
• RankNet
• RankBoost
• AdaRank
• Coordinate Ascent
• LambdaMART
• ListNet
• Random Forests
• Linear regression

3、-feature

指定样本的特征定义文件，格式如下：

feature1
feature2
...
# featureK(该特征不参与分析)

4、-metric2t

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.txt
java -jar bin/RankLib.jar -load ca.model.txt -test MQ2008/Fold1/test.txt -metric2T NDCG@10 -idv output/ca.ndcg.txt
java -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   0.0
NDCG@10   176   0.6722390270733757
NDCG@10   177   0.4772656487866462
NDCG@10   178   0.539003131276382
NDCG@10   185   0.6131471927654585
NDCG@10   189   1.0
NDCG@10   191   0.6309297535714574
NDCG@10   192   1.0
NDCG@10   194   0.2532778777010656
NDCG@10   197   1.0
NDCG@10   200   0.6131471927654585
NDCG@10   204   0.4772656487866462
NDCG@10   207   0.0
NDCG@10   209   0.123151194370365
NDCG@10   221   0.39038004999210174
NDCG@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   -7.528650760650635
1   1   2.9022061824798584
1   2   -0.700125515460968
1   3   2.376657485961914
1   4   -0.29666265845298767
1   5   -2.038628101348877
1   6   -5.267711162567139
1   7   -2.022146463394165
1   8   0.6741248369216919
...

参考

RankLib wiki

转载于:https://www.cnblogs.com/memento/p/9398047.html