使用Caret in R保存并加载catboost模型

Question

我能够用插入符号训练Catboost模型（在Rstudio中）并且效果很好。

my_catboost <- caret::train(x, y, 

              method=catboost.caret, 
              trControl=fitControl, 
              tuneGrid = param,
              metric = "ROC")

如果我使用该模型预测同一会话中的新数据，没有问题，它可以工作：

output <- caret::predict.train(my_catboost, newdata=x_testing, type="prob")

但是，如果我保存模型并稍后加载（或保存它，删除“my_catboost”并加载），函数预测将使R和Rstudio崩溃而没有错误消息，并且在Rstudio日志中找不到任何内容。在加载之后，我可以看到在全局环境中创建的模型，看起来很好。

我尝试了R函数save和load，saveRDS和readRDS，两者都崩溃了

谢谢 ！

Answer 1

你误解了我的评论。以下是使用内置数据集Sonar的答案：

library(caret)
library(catboost)
library(mlbench)
data(Sonar)

创建训练和测试数据集：

set.seed(1)

tr <- createDataPartition(Sonar$Class, p = 0.7, list = FALSE)

trainer <- Sonar[tr,]
tester <- Sonar[-tr,]

火车模型：

fitControl <- trainControl(method = "cv",
                           number = 3,
                           savePredictions = TRUE,
                           summaryFunction = twoClassSummary,
                           classProbs = TRUE)

model <- train(x = trainer[,1:60],
               y = trainer$Class,
               method = catboost.caret, 
               trControl = fitControl, 
               tuneLength = 5,
               metric = "ROC")

使用插入符号预测：

preds1 <- predict(model, tester, type = "prob")

保存最终模型：

catboost::catboost.save_model(model$finalModel, "model")

加载保存的模型：

model2 <- catboost::catboost.load_model("model")

使用保存的模型预测：

preds2 <- catboost.predict(model2,
                           catboost.load_pool(tester),
                           prediction_type = "Probability")

检查预测的平等性

all.equal(preds1[,2], preds2)

编辑：同时：

saveRDS(model, "caret.model.rds")
model3 <- readRDS("caret.model.rds")
preds3 <- predict(model3, tester, type = "prob")

导致R会话崩溃

R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] mlbench_2.1-1        catboost_0.10.3      caret_6.0-80         ggplot2_2.2.1        lattice_0.20-35      RevoUtils_11.0.0    
[7] RevoUtilsMath_11.0.0

loaded via a namespace (and not attached):
 [1] httr_1.3.1         magic_1.5-8        ddalpha_1.3.3      tidyr_0.8.1        sfsmisc_1.1-2      jsonlite_1.5      
 [7] viridisLite_0.3.0  splines_3.5.0      foreach_1.5.0      prodlim_2018.04.18 assertthat_0.2.0   stats4_3.5.0      
[13] DRR_0.0.3          yaml_2.1.19        robustbase_0.93-0  ipred_0.9-6        pillar_1.2.3       glue_1.2.0        
[19] digest_0.6.15      colorspace_1.3-2   recipes_0.1.2      htmltools_0.3.6    Matrix_1.2-14      plyr_1.8.4        
[25] psych_1.8.4        timeDate_3043.102  pkgconfig_2.0.1    CVST_0.2-2         broom_0.4.4        purrr_0.2.4       
[31] scales_0.5.0       gower_0.1.2        lava_1.6.1         tibble_1.4.2       withr_2.1.2        nnet_7.3-12       
[37] lazyeval_0.2.1     mnormt_1.5-5       survival_2.41-3    magrittr_1.5       nlme_3.1-137       MASS_7.3-49       
[43] dimRed_0.1.0       foreign_0.8-70     class_7.3-14       tools_3.5.0        data.table_1.11.4  stringr_1.3.1     
[49] plotly_4.7.1       kernlab_0.9-26     munsell_0.4.3      bindrcpp_0.2.2     compiler_3.5.0     RcppRoll_0.2.2    
[55] rlang_0.2.0        grid_3.5.0         iterators_1.0.10   htmlwidgets_1.2    geometry_0.3-6     gtable_0.2.0      
[61] ModelMetrics_1.1.0 codetools_0.2-15   abind_1.4-5        reshape2_1.4.3     R6_2.2.2           lubridate_1.7.4   
[67] dplyr_0.7.5        bindr_0.1.1        stringi_1.1.7      parallel_3.5.0     Rcpp_0.12.17       rpart_4.1-13      
[73] DEoptimR_1.0-8     tidyselect_0.2.4