背景
- 我使用R构建了一个极端梯度提升（XGB）模型
- 我已经使用模型对象对测试集进行了评分
- 但是，我无法使用模型对象对部署集进行评分
加载R库
library(xgboost)library(Matrix)创建虚拟数据
### 训练集 ###train1 <- c("5032","1","66","139","0","9500","12","0")train2 <-c("5031","1","61","34","5078","5100","12","2")train3 <-c("5030","0","72","161","2540","4000","11","2")train4 <-c("5029","1","68","0","6456","10750","12","4")train5 <-c("5028","1","59","86","0","10000","12","0")train6 <-c("5027","0","49","42","1756","4500","12","2")train7 <-c("5026","0","61","14","0","2500","12","0")train8 <-c("5025","0","44","153","0","9000","12","0")train9 <-c("5024","1","79","61","0","5000","12","0")train10 <-c("5023","1","46","139","2121","5600","6","3")train <- rbind.data.frame(train1, train2, train3, train4, train5, train6, train7, train8, train9, train10)names(train) <- c("customer_id","target","v1","v2","v3","v4","v5","v6")for(i in 1:ncol(train)) { train[,i] <- as.character(train[,i])}for(i in 1:ncol(train)) { train[,i] <- as.integer(train[,i])}### 测试集 ###test1 <- c("5021","0","55","64","2891","5000","12","4")test2 <-c("5020","1","57","49","167","3000","12","2")test3 <-c("5019","1","54","55","4352","9000","12","4")test4 <-c("5018","0","70","8","2701","5000","12","3")test5 <-c("5017","0","64","59","52","3000","12","2")test6 <-c("5016","1","57","73","0","4000","12","0")test7 <-c("5015","0","46","28","1187","6000","12","3")test8 <-c("5014","1","57","38","740","4500","12","2")test9 <-c("5013","1","54","159","0","3300","11","0")test10 <-c("5012","0","48","19","690","6500","11","2")test <- rbind.data.frame(test1, test2, test3, test4, test5, test6, test7, test8, test9, test10)names(test) <- c("customer_id","target","v1","v2","v3","v4","v5","v6")for(i in 1:ncol(test)) { test[,i] <- as.character(test[,i])}for(i in 1:ncol(test)) { test[,i] <- as.integer(test[,i])}### 部署集 ###deploy1 <- c("5011","58","5","7897","12000","12","4")deploy2 <- c("5010","60","161","1601","7500","12","2")deploy3 <- c("5009","40","59","0","5000","12","0")deploy4 <- c("5008","57","80","0","3500","12","0")deploy5 <- c("5007","50","70","1056","3000","12","2")deploy6 <- c("5006","65","6","1010","9000","12","3")deploy7 <- c("5005","65","17","1978","4500","12","2")deploy8 <- c("5004","80","103","0","10000","12","0")deploy9 <- c("5003","52","11","2569","3500","12","2")deploy10 <- c("5002","54","81","1905","4000","12","4")deploy <- rbind.data.frame(deploy1, deploy2, deploy3, deploy4, deploy5, deploy6, deploy7, deploy8, deploy9, deploy10)names(deploy) <- c("customer_id","v1","v2","v3","v4","v5","v6")for(i in 1:ncol(deploy)) { deploy[,i] <- as.character(deploy[,i])}for(i in 1:ncol(deploy)) { deploy[,i] <- as.integer(deploy[,i])}转换为矩阵
# 移除客户IDtrain_A <- train %>% select(-customer_id) test_A <- test %>% select(-customer_id) # 将训练集转换为稀疏矩阵train_sparse_matrix<- sparse.model.matrix(target ~.-1, data = train_A)test_sparse_matrix<- sparse.model.matrix(target ~.-1, data = test_A)# 创建目标向量train_target <- as.vector(train_A$target)test_target <- as.vector(test_A$target)# 将训练集转换为dmatrix（xgboost推荐格式）train_dmatrix <- xgboost::xgb.DMatrix(data=train_sparse_matrix, label=train_target)test_dmatrix <- xgboost::xgb.DMatrix(data=test_sparse_matrix, label=test_target)训练模型
hn_xgb <- xgboost(tar_flag ~ ., data = train_dmatrix, max_depth = 6, eta = 0.3, num_parallel_tree = 1, nthread = 2, nround = 100, metrics = 'error', objective = 'binary:logistic')评分测试集
predict(hn_xgb, test_dmatrix)评分部署集
部署集没有目标变量，因为目标事件尚未发生，即部署评分试图预测的正是这个目标事件。
### 转换为矩阵 #### 移除客户IDdeploy_A <- deploy %>% select(-customer_id) # 将部署集转换为稀疏矩阵deploy_sparse_matrix<- sparse.model.matrix(data = deploy_A) ## 错误 !!!返回以下错误：
由于无法创建稀疏矩阵，接下来创建DMatrix的步骤也无法工作…
# 将训练集转换为dmatrix（xgboost推荐格式）deploy_dmatrix <- xgboost::xgb.DMatrix(data=deploy_sparse_matrix)这意味着我无法对我的部署集进行评分…
问题
- 如何将我的部署集转换为稀疏矩阵或DMatrix？
- 您能推荐任何更简单的步骤来评分我的部署集吗？
回答：
我对您的数据进行了一些清理，以便更易读。如果有不明白的地方，请告诉我。
