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>
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
- RankNet
- RankBoost
- AdaRank
- Coordinate Ascent
- LambdaMART
- ListNet
- Random Forests
- Linear regression
3、-feature <file>
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 <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.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
...