使用GBM的插入符号错误，但并非没有插入符号 [英] Caret error using GBM, but not without caret

问题描述

我一直在通过插入符号使用 gbm ，但是没有问题，但是从数据框中删除一些变量时，它开始失败.我已经尝试过上述软件包的github版本和cran版本.

I've been using gbm through caret without problems, but when removing some variables from my dataframe it started to fail. I've tried with both github and cran versions of the mentioned packages.

这是错误:

> fitRF = train(my_data[trainIndex,vars_for_clust], clusterAssignment[trainIndex], method = "gbm", verbose=T)
Something is wrong; all the Accuracy metric values are missing:
    Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :9     NA's   :9    
Error in train.default(my_data[trainIndex, vars_for_clust], clusterAssignment[trainIndex],  : 
  Stopping
In addition: There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In eval(expr, envir, enclos) :
  model fit failed for Resample01: shrinkage=0.1, interaction.depth=1, n.minobsinnode=10, n.trees=150 Error in gbm.fit(x = structure(list(relatedness_cottle = c(0, 0, 8, 6,  : 
  unused arguments (x = list(relatedness_cottle = c(0, 0, 8, 6, 0, 6, 8, 10, 10, 6, 6, 4, 4, 4, 0, 0, 0, 0, 18, 18, 18, 0, 0, 6, 6, 0, 18, 12, 0, 4, 4, 4, 0, 0, 0, 18, 18, 6, 4, 4, 4, 6, 8, 6, 6, 0, 14, 2, 0, 8, 6, 6, 0, 4, 0, 0, 0, 0, 0, 4, 8, 8, 8, 4, 18, 0, 0, 4, 10, 18, 6, 0, 0, 18, 10, 10, 6, 2, 4, 4, 10, 10, 10, 2, 8, 0, 0, 0, 0, 10, 6, 6, 0, 4, 4, 0, 0, 0, 0, 8, 0, 0, 4, 4, 6, 6, 10, 6, 0, 0, 6, 4, 4, 8, 0, 12, 6, 2, 2, 8, 8, 4, 4, 4, 4, 6, 2, 2, 4, 0, 6, 0, 0, 0, 12, 18, 8, 0, 0, 4, 4, 2, 0, 0, 0, 0, 18, 
12, 6, 6, 4, 4, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 18, 0, 0, 18, 6, 4, 2, 2, 0, 0, 10, 0, 0, 0, 12, 4, 4, 4, 4, 4, 8, 18, 6, 18, 18, 12, 12, 12, 0, 0, 0, 0, 10, 12, 12, 12, 12, 12, 4, 4, 4, 6, 6, 6, 6, 12, 0, 6, 0, 0, 4, 4, 18, 18, 18, 0, 0, 4, 6, 6, 0, 0, 2, 0, 0, 0, 18, 12, 12, 0, 0, 0, 0, 0, 0, 18 [... truncated]

没有缺失值，响应是4级因子，输入如下:

There are no missing values, the response is a 4 level factor and inputs are the following:

 Classes ‘tbl_df’, ‘tbl’ and 'data.frame':  1165 obs. of  14 variables:
 $ relatedness_cottle       : num  0 0 8 8 0 6 0 6 6 0 ...
 $ dominance_cottle         : int  4 6 0 6 6 6 6 4 4 4 ...
 $ time_spent               : num  26832 20822 18893 13107 25406 ...
 $ num_color_changes        : num  3.33 2.33 1.33 1 1 ...
 $ num_selects              : num  1 0.667 2 0.667 1.667 ...
 $ show_select_match        : num  1 0.667 0.333 1 1 ...
 $ default_size             : num  0.667 0 0.667 0 0 ...
 $ select_order             : Factor w/ 6 levels "future_past_present",..: 1 4 4 2 5 1 4 6 6 4 ...
 $ order_x                  : Factor w/ 6 levels "future_past_present",..: 4 4 4 4 4 3 4 4 4 4 ...
 $ color_past               : Factor w/ 8 levels "black","blue",..: 5 1 6 8 5 7 1 6 6 5 ...
 $ color_present            : Factor w/ 8 levels "black","blue",..: 1 4 4 4 6 8 4 4 1 4 ...
 $ color_future             : Factor w/ 8 levels "black","blue",..: 2 2 2 2 2 2 1 2 8 2 ...
 $ dominance_cottle_future  : int  0 4 0 4 2 0 4 2 2 0 ...
 $ relatedness_cottle_future: int  0 2 4 4 0 4 0 2 4 0 ...

但是，如果我直接通过数据框调用 gbm ，它会起作用:

But if I call gbm directly with the dataframe, it works:

summary(gbm(clusterAssignment[trainIndex] ~ ., data = my_data[trainIndex,vars_for_clust]))
Distribution not specified, assuming multinomial ...
                                                var   rel.inf
color_present                         color_present 33.533673
dominance_cottle                   dominance_cottle 33.170138
default_size                           default_size 25.321566
dominance_cottle_future     dominance_cottle_future  5.674563
color_future                           color_future  2.300060
relatedness_cottle               relatedness_cottle  0.000000
time_spent                               time_spent  0.000000
num_color_changes                 num_color_changes  0.000000
num_selects                             num_selects  0.000000
show_select_match                 show_select_match  0.000000
select_order                           select_order  0.000000
order_x                                     order_x  0.000000
color_past                               color_past  0.000000
relatedness_cottle_future relatedness_cottle_future  0.000000

编辑:要重现，运行此处找到的脚本.

推荐答案

现在，使用as.data.frame()将数据帧从plyr/dplyr转换为普通数据帧即可解决此问题.

For now, casting a dataframe from plyr/dplyr to a normal dataframe with as.data.frame() fixes the problem.

train(as.data.frame(issueDataframe), issueResponse, method="gbm")

请参见此问题.

这篇关于使用GBM的插入符号错误，但并非没有插入符号的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！