我正在尝试使用以下方法并行学习随机森林中的部分图：
cl <- makeCluster(detectCores()-1)
registerDoParallel(cl)
tmpp <- pdp::partial(rf_full, pred.var = c("Date", "AREA"),
plot = T, plot.engine = "ggplot2",
parallel = T)
stopCluster(cl)
但是，我有这样的消息错误：
Error in summary.connection(connection) : invalid connection
如果它可以帮助，这里是错误消息的回溯如下：
16.
summary.connection(connection)
15.
summary(connection)
14.
serialize(data, node$con)
13.
sendData.SOCKnode(con, list(type = type, data = value, tag = tag))
12.
sendData(con, list(type = type, data = value, tag = tag))
11.
postNode(con, "EXEC", list(fun = fun, args = args, return = return, tag = tag))
10.
sendCall(cl[[i]], fun, list(...))
9.
clusterCall(cl, workerInit, c.expr, exportenv, pkgname, packages, attachExportEnv)
8.
e$fun(obj, substitute(ex), parent.frame(), e$data)
7.
foreach::`%dopar%`(fe, do.ply(i))
6.
llply(.data = .data, .fun = .fun, ..., .progress = .progress, .inform = .inform, .parallel = .parallel, .paropts = .paropts)
5.
ldply(.data = pieces, .fun = .fun, ..., .progress = .progress, .inform = .inform, .parallel = .parallel, .paropts = .paropts, .id = .id)
4.
plyr::adply(pred.grid, .margins = 1, .progress = progress, .parallel = parallel, .paropts = paropts, .fun = function(x) { temp <- train temp[, pred.var] <- x ...
3.
getParDepReg(object, pred.var = pred.var, pred.grid = pred.grid, inv.link = inv.link, ice = ice, train = train, progress = progress, parallel = parallel, paropts = paropts, ...)
2.
partial.default(rf_full, pred.var = c("Date", "AREA"), plot = T, plot.engine = "ggplot2", parallel = T, paropts = list(.packages = "randomForest"))
1.
pdp::partial(rf_full, pred.var = c("Date", "AREA"), plot = T, plot.engine = "ggplot2", parallel = T, paropts = list(.packages = "randomForest")
注意：我在使用这种方式进行并行化之前学习了我的 RF，并且它可以工作。
这是我的会话信息：
> sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
system code page: 1250
attached base packages:
[1] parallel stats graphics grDevices datasets utils methods base
other attached packages:
[1] doParallel_1.0.16 iterators_1.0.13 foreach_1.5.1 ggpubr_0.4.0
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
[9] readr_1.3.1 tidyr_1.1.0 tibble_3.0.4 ggplot2_3.3.2
[13] tidyverse_1.3.0 pdp_0.7.0 MLmetrics_1.1.1 rpart_4.1-15
[17] randomForest_4.6-14
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.1 carData_3.0-4 modelr_0.1.8
[5] assertthat_0.2.1 blob_1.2.1 renv_0.12.3 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.6 backports_1.1.8 lattice_0.20-41
[13] glue_1.4.2 digest_0.6.27 ggsignif_0.6.0 rvest_0.3.6
[17] colorspace_1.4-1 plyr_1.8.6 cowplot_1.1.1 htmltools_0.5.1.1
[21] pkgconfig_2.0.3 broom_0.7.0 haven_2.3.1 bookdown_0.20
[25] scales_1.1.1 openxlsx_4.2.3 rio_0.5.16 farver_2.0.3
[29] generics_0.0.2 car_3.0-10 ellipsis_0.3.1 withr_2.3.0
[33] cli_2.1.0 magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.14 fs_1.4.2 fansi_0.4.1 rstatix_0.6.0
[41] xml2_1.3.2 foreign_0.8-80 tools_4.0.4 data.table_1.13.6
[45] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
[49] zip_2.1.1 compiler_4.0.4 tinytex_0.26 rlang_0.4.10
[53] grid_4.0.4 rstudioapi_0.11 labeling_0.4.2 rmarkdown_2.5
[57] codetools_0.2-16 gtable_0.3.0 abind_1.4-5 DBI_1.1.0
[61] curl_4.3 R6_2.4.1 gridExtra_2.3 lubridate_1.7.9
[65] knitr_1.30 stringi_1.4.6 Rcpp_1.0.5 vctrs_0.3.4
[69] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.18