使用 tidymodels 预测 GAM 模型时出错
Error while predicting a GAM model using tidymodels
我想要什么： 我正在尝试使用 tidymodels 对给定数据拟合 GAM 模型进行分类。
到目前为止： 我能够拟合一个 logit 模型。
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
df_split <- initial_split(df, prop = 0.75, strata = class)
df_train <- training(df_split)
df_test <- testing(df_split)
log_model <- logistic_reg(mode = "classification",
engine = "glm") %>%
fit(class~duration, data = df_train)
predict(log_model, df_test)
#> # A tibble: 26 × 1
#> .pred_class
#> <fct>
#> 1 good
#> 2 good
#> 3 good
#> 4 bad
#> 5 good
#> 6 good
#> 7 bad
#> 8 bad
#> 9 good
#> 10 bad
#> # … with 16 more rows
我的问题： 令人惊讶的是，当我尝试 GAM 时出现错误。
gen_model <- gen_additive_mod(mode = "classification",
engine = "mgcv") %>%
fit(class~duration, data = df_train)
predict(gen_model, df_test)
#> Error: $ operator is invalid for atomic vectors
数据： 这是 df 数据帧的 dput：
df <- structure(list(class = structure(c(2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L),
.Label = c("bad",
"good"), class = "factor"),
duration = c(42, 31.7869911119342,
18, 24, 12, 18, 10, 9, 12, 24, 10, 27, 14.4910072591156, 12,
48, 24, 30, 18, 6, 6, 12, 48, 10, 18, 6, 12, 24.4157173759304,
18, 48, 60, 18, 15, 9, 60, 24, 24, 9, 21, 26.4959116294049, 12,
5, 12, 12, 48, 18, 48, 12, 17.4877766738646, 36, 9, 15, 39.2811119947582,
27, 21, 24, 10, 6, 12, 12, 24, 39, 18, 24, 15, 48, 12, 24, 26.7659258879721,
36, 24, 27, 9, 12, 48, 28, 21, 6, 24, 24, 24, 18, 36, 36, 30,
8.19771710922942, 36, 18, 12, 13.8241796996444, 26.0928970947862,
10, 36, 12, 12, 24, 21.3157193372026, 18, 21, 24, 24)),
class = c("tbl_df",
"tbl", "data.frame"),
row.names = c(NA, -100L))
由 reprex package (v2.0.1)
于 2022 年 1 月 12 日创建
会话信息
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.2 (2021-11-01)
#> os macOS Big Sur 10.16
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#> ui X11
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#> collate es_ES.UTF-8
#> ctype es_ES.UTF-8
#> tz Europe/Madrid
#> date 2022-01-12
#> pandoc 2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
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#> lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.1.0)
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#> MASS 7.3-54 2021-05-03 [1] CRAN (R 4.1.2)
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#> mgcv 1.8-38 2021-10-06 [1] CRAN (R 4.1.2)
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#> parsnip * 0.1.7 2021-07-21 [1] CRAN (R 4.1.0)
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#> tidyr * 1.1.4 2021-09-27 [1] CRAN (R 4.1.0)
#> tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.1.0)
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#> ──────────────────────────────────────────────────────────────────────────────
此问题已在开发版 {parsnip} (>0.1.7) 中得到解决。您可以通过 运行 remotes::install_github("tidymodels/parsnip").
安装它
library(parsnip)
library(rsample)
df <- structure(list(class = structure(c(2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L),
.Label = c("bad",
"good"), class = "factor"),
duration = c(42, 31.7869911119342,
18, 24, 12, 18, 10, 9, 12, 24, 10, 27, 14.4910072591156, 12,
48, 24, 30, 18, 6, 6, 12, 48, 10, 18, 6, 12, 24.4157173759304,
18, 48, 60, 18, 15, 9, 60, 24, 24, 9, 21, 26.4959116294049, 12,
5, 12, 12, 48, 18, 48, 12, 17.4877766738646, 36, 9, 15, 39.2811119947582,
27, 21, 24, 10, 6, 12, 12, 24, 39, 18, 24, 15, 48, 12, 24, 26.7659258879721,
36, 24, 27, 9, 12, 48, 28, 21, 6, 24, 24, 24, 18, 36, 36, 30,
8.19771710922942, 36, 18, 12, 13.8241796996444, 26.0928970947862,
10, 36, 12, 12, 24, 21.3157193372026, 18, 21, 24, 24)),
class = c("tbl_df",
"tbl", "data.frame"),
row.names = c(NA, -100L))
df_split <- initial_split(df, prop = 0.75, strata = class)
df_train <- training(df_split)
df_test <- testing(df_split)
gen_model <- gen_additive_mod(mode = "classification",
engine = "mgcv") %>%
fit(class~duration, data = df_train)
predict(gen_model, df_test)
#> # A tibble: 26 × 1
#> .pred_class
#> <fct>
#> 1 bad
#> 2 good
#> 3 good
#> 4 good
#> 5 bad
#> 6 good
#> 7 good
#> 8 good
#> 9 good
#> 10 good
#> # … with 16 more rows
由 reprex package (v2.0.1)
创建于 2022-01-12
我想要什么： 我正在尝试使用 tidymodels 对给定数据拟合 GAM 模型进行分类。
到目前为止： 我能够拟合一个 logit 模型。
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
df_split <- initial_split(df, prop = 0.75, strata = class)
df_train <- training(df_split)
df_test <- testing(df_split)
log_model <- logistic_reg(mode = "classification",
engine = "glm") %>%
fit(class~duration, data = df_train)
predict(log_model, df_test)
#> # A tibble: 26 × 1
#> .pred_class
#> <fct>
#> 1 good
#> 2 good
#> 3 good
#> 4 bad
#> 5 good
#> 6 good
#> 7 bad
#> 8 bad
#> 9 good
#> 10 bad
#> # … with 16 more rows
我的问题： 令人惊讶的是，当我尝试 GAM 时出现错误。
gen_model <- gen_additive_mod(mode = "classification",
engine = "mgcv") %>%
fit(class~duration, data = df_train)
predict(gen_model, df_test)
#> Error: $ operator is invalid for atomic vectors
数据： 这是 df 数据帧的 dput：
df <- structure(list(class = structure(c(2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L),
.Label = c("bad",
"good"), class = "factor"),
duration = c(42, 31.7869911119342,
18, 24, 12, 18, 10, 9, 12, 24, 10, 27, 14.4910072591156, 12,
48, 24, 30, 18, 6, 6, 12, 48, 10, 18, 6, 12, 24.4157173759304,
18, 48, 60, 18, 15, 9, 60, 24, 24, 9, 21, 26.4959116294049, 12,
5, 12, 12, 48, 18, 48, 12, 17.4877766738646, 36, 9, 15, 39.2811119947582,
27, 21, 24, 10, 6, 12, 12, 24, 39, 18, 24, 15, 48, 12, 24, 26.7659258879721,
36, 24, 27, 9, 12, 48, 28, 21, 6, 24, 24, 24, 18, 36, 36, 30,
8.19771710922942, 36, 18, 12, 13.8241796996444, 26.0928970947862,
10, 36, 12, 12, 24, 21.3157193372026, 18, 21, 24, 24)),
class = c("tbl_df",
"tbl", "data.frame"),
row.names = c(NA, -100L))
由 reprex package (v2.0.1)
于 2022 年 1 月 12 日创建 会话信息sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.2 (2021-11-01)
#> os macOS Big Sur 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate es_ES.UTF-8
#> ctype es_ES.UTF-8
#> tz Europe/Madrid
#> date 2022-01-12
#> pandoc 2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
#>
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#> rpart 4.1-15 2019-04-12 [1] CRAN (R 4.1.2)
#> rsample * 0.1.1 2021-11-08 [1] CRAN (R 4.1.0)
#> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0)
#> scales * 1.1.1 2020-05-11 [1] CRAN (R 4.1.0)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.0)
#> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.0)
#> survival 3.2-13 2021-08-24 [1] CRAN (R 4.1.2)
#> tibble * 3.1.6 2021-11-07 [1] CRAN (R 4.1.0)
#> tidymodels * 0.1.4.9000 2022-01-12 [1] Github (tidymodels/tidymodels@8486957)
#> tidyr * 1.1.4 2021-09-27 [1] CRAN (R 4.1.0)
#> tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.1.0)
#> timeDate 3043.102 2018-02-21 [1] CRAN (R 4.1.0)
#> tune * 0.1.6 2021-07-21 [1] CRAN (R 4.1.0)
#> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0)
#> vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0)
#> withr 2.4.3 2021-11-30 [1] CRAN (R 4.1.0)
#> workflows * 0.2.4 2021-10-12 [1] CRAN (R 4.1.0)
#> workflowsets * 0.1.0 2021-07-22 [1] CRAN (R 4.1.0)
#> xfun 0.29 2021-12-14 [1] CRAN (R 4.1.0)
#> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.1.0)
#> yardstick * 0.0.9 2021-11-22 [1] CRAN (R 4.1.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
#>
#> ──────────────────────────────────────────────────────────────────────────────
此问题已在开发版 {parsnip} (>0.1.7) 中得到解决。您可以通过 运行 remotes::install_github("tidymodels/parsnip").
library(parsnip)
library(rsample)
df <- structure(list(class = structure(c(2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L),
.Label = c("bad",
"good"), class = "factor"),
duration = c(42, 31.7869911119342,
18, 24, 12, 18, 10, 9, 12, 24, 10, 27, 14.4910072591156, 12,
48, 24, 30, 18, 6, 6, 12, 48, 10, 18, 6, 12, 24.4157173759304,
18, 48, 60, 18, 15, 9, 60, 24, 24, 9, 21, 26.4959116294049, 12,
5, 12, 12, 48, 18, 48, 12, 17.4877766738646, 36, 9, 15, 39.2811119947582,
27, 21, 24, 10, 6, 12, 12, 24, 39, 18, 24, 15, 48, 12, 24, 26.7659258879721,
36, 24, 27, 9, 12, 48, 28, 21, 6, 24, 24, 24, 18, 36, 36, 30,
8.19771710922942, 36, 18, 12, 13.8241796996444, 26.0928970947862,
10, 36, 12, 12, 24, 21.3157193372026, 18, 21, 24, 24)),
class = c("tbl_df",
"tbl", "data.frame"),
row.names = c(NA, -100L))
df_split <- initial_split(df, prop = 0.75, strata = class)
df_train <- training(df_split)
df_test <- testing(df_split)
gen_model <- gen_additive_mod(mode = "classification",
engine = "mgcv") %>%
fit(class~duration, data = df_train)
predict(gen_model, df_test)
#> # A tibble: 26 × 1
#> .pred_class
#> <fct>
#> 1 bad
#> 2 good
#> 3 good
#> 4 good
#> 5 bad
#> 6 good
#> 7 good
#> 8 good
#> 9 good
#> 10 good
#> # … with 16 more rows
由 reprex package (v2.0.1)
创建于 2022-01-12