# lightgbm_learning_to_rank **Repository Path**: Samuelcoding/lightgbm_learning_to_rank ## Basic Information - **Project Name**: lightgbm_learning_to_rank - **Description**: 使用lightgbm实现L2R - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 1 - **Created**: 2020-12-17 - **Last Updated**: 2024-12-06 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 利用lightgbm做learning to rank 排序，主要包括： - 数据预处理 - 模型训练 - 模型决策可视化 - 预测 - ndcg评估 - 特征重要度 - SHAP特征贡献度解释 - 样本的叶结点输出 (要求安装lightgbm、graphviz、shap等) ## 一.data format (raw data -> (feats.txt, group.txt)) ###### python lgb_ltr.py -process ##### 1.raw_train.txt 0 qid:10002 1:0.007477 2:0.000000 ... 45:0.000000 46:0.007042 #docid = GX008-86-4444840 inc = 1 prob = 0.086622 0 qid:10002 1:0.603738 2:0.000000 ... 45:0.333333 46:1.000000 #docid = GX037-06-11625428 inc = 0.0031586555555558 prob = 0.0897452 ... ##### 2.feats.txt: 0 1:0.007477 2:0.000000 ... 45:0.000000 46:0.007042 0 1:0.603738 2:0.000000 ... 45:0.333333 46:1.000000 ... ##### 3.group.txt: 8 8 8 8 8 16 8 118 16 8 ... ## 二.model train (feats.txt, group.txt) -> train -> model.mod ###### python lgb_ltr.py -train train params = { 'task': 'train', # 执行的任务类型 'boosting_type': 'gbrt', # 基学习器 'objective': 'lambdarank', # 排序任务(目标函数) 'metric': 'ndcg', # 度量的指标(评估函数) 'max_position': 10, # @NDCG 位置优化 'metric_freq': 1, # 每隔多少次输出一次度量结果 'train_metric': True, # 训练时就输出度量结果 'ndcg_at': [10], 'max_bin': 255, # 一个整数，表示最大的桶的数量。默认值为 255。lightgbm 会根据它来自动压缩内存。如max_bin=255 时，则lightgbm 将使用uint8 来表示特征的每一个值。 'num_iterations': 200, # 迭代次数，即生成的树的棵数 'learning_rate': 0.01, # 学习率 'num_leaves': 31, # 叶子数 'max_depth':6, 'tree_learner': 'serial', # 用于并行学习，‘serial’： 单台机器的tree learner 'min_data_in_leaf': 30, # 一个叶子节点上包含的最少样本数量 'verbose': 2 # 显示训练时的信息 } - docs:7796 - groups:380 - consume time : 4 seconds - training's ndcg@10: 0.940891 ##### 1.model.mod(model的格式在data/model/mode.mod) 训练时的输出： - [LightGBM] [Info] Total Bins 9171 - [LightGBM] [Info] Number of data: 7796, number of used features: 40 - [LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 9 - [1] training's ndcg@10: 0.791427 - [LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 12 - [2] training's ndcg@10: 0.828608 - [LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 10 - ... - ... - ... - [198] training's ndcg@10: 0.941018 - [LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 11 - [199] training's ndcg@10: 0.941038 - [LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 11 - [200] training's ndcg@10: 0.940891 - consume time : 4 seconds ## 三.模型决策过程的可视化生成 可指定树的索引进行可视化生成，便于分析决策过程。 ###### python lgb_ltr.py -plottree ![image](https://raw.githubusercontent.com/jiangnanboy/learning_to_rank/master/data/plot/tree_plot.png) ## 四.predict 数据格式如feats.txt，当然可以在每行后面加一个标识(如文档编号，商品编码等)作为排序的输出,这里我直接从test.txt中得到feats与comment作为predict ###### python lgb_ltr.py -predict ##### 1.predict results - ['docid = GX252-32-5579630 inc = 1 prob = 0.190849' - 'docid = GX108-43-5342284 inc = 0.188670948386237 prob = 0.103576' - 'docid = GX039-85-6430259 inc = 1 prob = 0.300191' ..., - 'docid = GX009-50-15026058 inc = 1 prob = 0.082903' - 'docid = GX065-08-0661325 inc = 0.012907717401617 prob = 0.0312699' - 'docid = GX012-13-5603768 inc = 1 prob = 0.0961297'] ## 五.validate ndcg 数据来自test.txt(data from test.txt) ###### python lgb_ltr.py -ndcg all qids average ndcg: 0.761044123343 ## 六.features 打印特征重要度(features importance) ###### python lgb_ltr.py -feature 模型中的特征是"Column_number",这里打印重要度时可以映射到真实的特征名，比如本测试用例是46个feature ##### 1.features importance - feat0name : 228 : 0.038 - feat1name : 22 : 0.0036666666666666666 - feat2name : 27 : 0.0045 - feat3name : 11 : 0.0018333333333333333 - feat4name : 198 : 0.033 - feat10name : 160 : 0.02666666666666667 - ... - ... - ... - feat37name : 188 : 0.03133333333333333 - feat38name : 434 : 0.07233333333333333 - feat39name : 286 : 0.04766666666666667 - feat40name : 169 : 0.028166666666666666 - feat41name : 348 : 0.058 - feat43name : 304 : 0.050666666666666665 - feat44name : 283 : 0.04716666666666667 - feat45name : 220 : 0.03666666666666667 ## 七.利用SHAP值解析模型中特征重要度 ###### python lgb_ltr.py -shap 这里不同于六中特征重要度的计算，而是利用博弈论的方法--SHAP（SHapley Additive exPlanations）来解析模型。 利用SHAP可以进行特征总体分析、多维特征交叉分析以及单特征分析等。 ##### 1.总体分析 ![image](https://raw.githubusercontent.com/jiangnanboy/learning_to_rank/master/data/plot/all_feature_importance_1.png) ![image](https://raw.githubusercontent.com/jiangnanboy/learning_to_rank/master/data/plot/all_feature_importance_2.png) ##### 2.多维特征交叉分析 ![image](https://raw.githubusercontent.com/jiangnanboy/learning_to_rank/master/data/plot/multi-dimension_feature_importance.png) ##### 3.单特征分析 ![image](https://raw.githubusercontent.com/jiangnanboy/learning_to_rank/master/data/plot/single_feature_importance.png) ## 八.利用模型得到样本叶结点的one-hot表示，可以用于像gbdt+lr这种模型的训练 ###### python lgb_ltr.py -leaf 这里测试用例是test/leaf.txt 5个样本 [ - [ 0. 1. 0. ..., 0. 0. 1.] - [ 1. 0. 0. ..., 0. 0. 0.] - [ 0. 0. 1. ..., 0. 0. 1.] - [ 0. 1. 0. ..., 0. 1. 0.] - [ 0. 0. 0. ..., 1. 0. 0.] ] ## 九.REFERENCES https://github.com/microsoft/LightGBM https://github.com/jma127/pyltr https://github.com/slundberg/shap