Question

我遇到了xgboost和for循环的错误，我得到的错误如下;

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有人问过类似的问题，here

该软件包的创建者建议如下;

这意味着您的一些培训数据或评估数据包含所有数据 1或全0作为标签

我的问题是二进制分类问题，Error in xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval) : [23:48:27] amalgamation/../src/metric/rank_metric.cc:135: Check failed: !auc_error AUC: the dataset only contains pos or neg samples。

我的代码如下;

0, 1,

我遇到上面的错误......但是当我从all <- NULL for(i in 1:length(splitxgb)){ xgbdata <- splitxgb[[i]] smp_size <- floor(0.75 * nrow(xgbdata)) train_ind <- sample(seq_len(nrow(xgbdata)), size = smp_size) train <- xgbdata[train_ind, ] test <- xgbdata[-train_ind, ] ids <- sample(nrow(train)) nfolds <- 5 #TAKE this out of the forloop score <- data.table() result <- data.table() x_train <- train %>% select(-BvD.ID.number, -Major.sectors, -Region.in.country, -Major.sectors.id, -Region.in.country.id, -status) x_test <- test %>% select(-BvD.ID.number, -Major.sectors, -Region.in.country, -Major.sectors.id, -Region.in.country.id, -status) y_train <- train$status y_test <- test$status nrounds <- 12 #take out of the for loop early_stopping_round <- NULL # take out of the for loop dtrain <- xgb.DMatrix(data = as.matrix(x_train), label = y_train, missing=NaN) dtest <- xgb.DMatrix(data = as.matrix(x_test), missing=NaN) watchlist <- list(train = dtrain) params <- list("eta" = 0.01, "max_deptch" = 10, # take out of the for loop "colsample_bytree" = 0.50, "min_child_weight" = 0.75, "subsample" = 0.5, "objective" = "reg:logistic", #should this be reg_log, binary:log etc. "eval_metric" = "auc") model_xgb <- xgb.train(params = params, data = dtrain, maximize = TRUE, nrounds = nrounds, watchlist = watchlist, early_stopping_rounds = early_stopping_round, print_every_n = 1) pred <- predict(model_xgb, dtest) result <- cbind(test %>% select(BvD.ID.number), status = round(pred, 0), pred) compare <- merge(x = result, y = test[ , c("BvD.ID.number", "status", "Region.in.country", "Major.sectors")], by = "BvD.ID.number", all.x=TRUE) all[[i]] <- compare }中取出所有内容并单独运行时，例如以下内容;

for loop

我分别为每个i <-165 xgbdata <- splitxgb[[i]] smp_size <- floor(0.75 * nrow(xgbdata)) train_ind <- sample(seq_len(nrow(xgbdata)), size = smp_size) train <- xgbdata[train_ind, ] test <- xgbdata[-train_ind, ] ids <- sample(nrow(train)) nfolds <- 5 #TAKE this out of the forloop score <- data.table() result <- data.table() x_train <- train %>% select(-BvD.ID.number, -Major.sectors, -Region.in.country, -Major.sectors.id, -Region.in.country.id, -status) x_test <- test %>% select(-BvD.ID.number, -Major.sectors, -Region.in.country, -Major.sectors.id, -Region.in.country.id, -status) y_train <- train$status y_test <- test$status nrounds <- 12 #take out of the for loop early_stopping_round <- NULL # take out of the for loop dtrain <- xgb.DMatrix(data = as.matrix(x_train), label = y_train, missing=NaN) dtest <- xgb.DMatrix(data = as.matrix(x_test), missing=NaN) watchlist <- list(train = dtrain) params <- list("eta" = 0.01, "max_deptch" = 10, # take out of the for loop "colsample_bytree" = 0.50, "min_child_weight" = 0.75, "subsample" = 0.5, "objective" = "reg:logistic", #should this be reg_log, binary:log etc. "eval_metric" = "auc") model_xgb <- xgb.train(params = params, data = dtrain, maximize = TRUE, nrounds = nrounds, watchlist = watchlist, early_stopping_rounds = early_stopping_round, print_every_n = 1) pred <- predict(model_xgb, dtest) result <- cbind(test %>% select(BvD.ID.number), status = round(pred, 0), pred) compare <- merge(x = result, y = test[ , c("BvD.ID.number", "status", "Region.in.country", "Major.sectors")], by = "BvD.ID.number", all.x=TRUE) all[[i]] <- compare运行此操作...我没有收到任何错误，

网上有一些信息，但我遇到的问题没有什么特别的，为什么我在循环中获得错误但不是单独的？

Answer 1

看起来您的分割有时会分割数据，无论是训练还是测试，因此所有标签都是1或0。

尝试打印（或写入CSV）所有部门，看看是否正确。

如果是这样，您希望确保每个分区（火车和测试）的每个标签至少有一行数据。

您可以通过重复拆分直到存在这种情况，或者在代码中以您选择的任何其他方式强制执行此操作。

我建议重新采样，直到存在这种情况。

for循环中的xgboost错误，但在独立运行xgboost时正常工作

1 个答案: