tune_grid 中的错误，没有适用于 prep R tidymodels 的方法
Error in tune_grid with no applicable method for prep R tidymodels
我使用我为我的一个包构建的自定义步骤函数，它工作得很好，它在包的本地副本中（尚未提交给 CRAN）link 在这里运行： step_hai_fourier
这是会话信息（我们可以看到 healthyR.ai 0.0.2.9000 已加载）：
> sessionInfo()
R version 4.1.0 (2021-05-18)
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
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] timetk_2.6.1 tidyquant_1.0.3 quantmod_0.4.18
[4] TTR_0.24.2 PerformanceAnalytics_2.0.4 xts_0.12.1
[7] zoo_1.8-9 lubridate_1.8.0 forcats_0.5.1
[10] stringr_1.4.0 readr_2.0.2 tidyverse_1.3.1
[13] janitor_2.1.0 healthyR.ts_0.1.4 __healthyR.ai_0.0.2.9000__
[16] yardstick_0.0.8 workflowsets_0.1.0 workflows_0.2.4
[19] tune_0.1.6 tidyr_1.1.4 tibble_3.1.6
[22] rsample_0.1.1 recipes_0.1.17 purrr_0.3.4
[25] parsnip_0.1.7 modeldata_0.1.1 infer_1.0.0
[28] ggplot2_3.3.5 dplyr_1.0.7 dials_0.0.10
[31] scales_1.1.1 broom_0.7.10 tidymodels_0.1.4
[34] modeltime_1.1.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.3.0 plyr_1.8.6 lazyeval_0.2.2
[5] splines_4.1.0 crosstalk_1.2.0 listenv_0.8.0 digest_0.6.28
[9] foreach_1.5.1 htmltools_0.5.2 fansi_0.5.0 checkmate_2.0.0
[13] magrittr_2.0.1 doParallel_1.0.16 tzdb_0.2.0 globals_0.14.0
[17] modelr_0.1.8 gower_0.2.2 RcppParallel_5.1.4 vroom_1.5.5
[21] hardhat_0.1.6 forecast_8.15 tseries_0.10-48 colorspace_2.0-2
[25] rvest_1.0.2 haven_2.4.3 crayon_1.4.2 jsonlite_1.7.2
[29] survival_3.2-11 iterators_1.0.13 glue_1.5.0 gtable_0.3.0
[33] ipred_0.9-12 Quandl_2.11.0 future.apply_1.8.1 DBI_1.1.1
[37] Rcpp_1.0.7 viridisLite_0.4.0 GPfit_1.0-8 bit_4.0.4
[41] lava_1.6.10 StanHeaders_2.21.0-7 prodlim_2019.11.13 htmlwidgets_1.5.4
[45] httr_1.4.2 ellipsis_0.3.2 pkgconfig_2.0.3 sass_0.4.0
[49] nnet_7.3-16 dbplyr_2.1.1 utf8_1.2.2 tidyselect_1.1.1
[53] labeling_0.4.2 rlang_0.4.12 DiceDesign_1.9 munsell_0.5.0
[57] cellranger_1.1.0 tools_4.1.0 xgboost_1.5.0.1 cli_3.1.0
[61] generics_0.1.1 fastmap_1.1.0 yaml_2.2.1 bit64_4.0.5
[65] fs_1.5.0 future_1.23.0 nlme_3.1-152 xml2_1.3.2
[69] compiler_4.1.0 rstudioapi_0.13 plotly_4.10.0 curl_4.3.2
[73] gt_0.3.1 reprex_2.0.1 lhs_1.1.3 stringi_1.7.5
[77] lattice_0.20-44 Matrix_1.3-4 urca_1.3-0 vctrs_0.3.8
[81] pillar_1.6.4 lifecycle_1.0.1 furrr_0.2.3 lmtest_0.9-39
[85] data.table_1.14.2 R6_2.5.1 parallelly_1.28.1 codetools_0.2-18
[89] MASS_7.3-54 assertthat_0.2.1 withr_2.4.2 fracdiff_1.5-1
[93] hms_1.1.1 quadprog_1.5-8 grid_4.1.0 rpart_4.1-15
[97] timeDate_3043.102 class_7.3-19 snakecase_0.11.0 pROC_1.18.0
这是我的脚本，显示 prep 和“果汁”工作正常：
# Libraries ----
library(modeltime)
library(tidymodels)
library(healthyR.ai)
library(healthyR.ts)
library(parallel)
library(janitor)
library(tidyverse)
library(tidyquant)
library(timetk)
# Data ----
url <- "https://cci30.com/ajax/getIndexHistory.php"
destfile <- "00_data/cci30_OHLCV.csv"
download.file(url, destfile = destfile)
cci_index_tbl <- read_csv("00_data/cci30_OHLCV.csv") %>%
clean_names()
# * Daily Log Returns ----
time_param <- "weekly"
log_returns_tbl <- cci_index_tbl %>%
tq_transmute(
select = close
, mutate_fun = periodReturn
, period = time_param
, type = "log"
, col_rename = "value"
) %>%
set_names("date_col", "value")
# * Train/Test ----
splits <- log_returns_tbl %>%
time_series_split(
date_var = date_col
, assess = "12 weeks"
, cumulative = TRUE
)
splits %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(
.date_var = date_col
, .value = value
, .title = paste0(
"CCI30 ", stringr::str_to_title(time_param), " Log Returns"
)
)
n_cores <- detectCores() - 1
# Recipe ----
recipe_base <- recipe(value ~ ., data = training(splits)) %>%
step_hai_fourier(value, scale_type = "sincos", period = 12, order = 1)
recipe_base %>% prep() %>% juice()
控制台说：
> recipe_base %>% prep() %>% juice()
# A tibble: 347 x 3
date_col value value_sincos
<date> <dbl> <dbl>
1 2015-01-04 -0.196 -0.102
2 2015-01-11 -0.00811 -0.00425
3 2015-01-18 -0.209 -0.108
4 2015-01-25 0.185 0.0961
5 2015-02-01 -0.141 -0.0736
6 2015-02-08 -0.0231 -0.0121
7 2015-02-15 0.0208 0.0109
8 2015-02-22 -0.0180 -0.00941
9 2015-03-01 0.0841 0.0440
10 2015-03-08 -0.0182 -0.00955
# ... with 337 more rows
到目前为止很完美，这是脚本的其余部分：
# Model ----
# Boosted Auto ARIMA
model_spec_arima_boosted <- arima_boost(
min_n = 2
, learn_rate = 0.015
) %>%
set_engine(engine = "auto_arima_xgboost")
# Workflowset ----
wfsets <- workflow_set(
preproc = list(
base = recipe_base
),
models = list(
model_spec_arima_boosted
),
cross = TRUE
)
parallel_start(n_cores)
wf_fits <- wfsets %>%
modeltime_fit_workflowset(
data = training(splits)
, control = control_fit_workflowset(
allow_par = TRUE
, verbose = TRUE
)
)
parallel_stop()
wf_fits <- wf_fits %>%
filter(.model != "NULL")
# Model Table -------------------------------------------------------------
models_tbl <- wf_fits
# Calibrate Model Testing -------------------------------------------------
parallel_start(n_cores)
calibration_tbl <- models_tbl %>%
modeltime_calibrate(new_data = testing(splits))
parallel_stop()
calibration_tbl
# Testing Accuracy --------------------------------------------------------
parallel_start(n_cores)
calibration_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = log_returns_tbl
) %>%
plot_modeltime_forecast(
.legend_max_width = 25,
.interactive = TRUE,
.conf_interval_show = FALSE
)
parallel_stop()
calibration_tbl %>%
modeltime_accuracy() %>%
drop_na() %>%
arrange(desc(rsq)) %>%
table_modeltime_accuracy(.interactive = FALSE)
# Model Tuning ----
# Get Model
plucked_model <- calibration_tbl %>%
modeltime::pluck_modeltime_model(1)
training_data <- rsample::training(splits)
tscv <- timetk::time_series_cv(
data = training_data,
date_var = date_col,
cumulative = TRUE,
assess = "26 weeks",
skip = "4 weeks",
slice_limit = 6
)
# * Tune Spec ----
# Model Spec
model_spec <- plucked_model %>% parsnip::extract_spec_parsnip()
model_spec_engine <- model_spec[["engine"]]
model_spec_tuner <- healthyR.ts::ts_model_spec_tune_template(model_spec_engine)
# * Grid Spec ----
grid_spec <- dials::grid_latin_hypercube(
tune::parameters(model_spec_tuner),
size = 30
)
# * Tune Model ----
wflw_tune_spec <- plucked_model %>%
workflows::update_model(model_spec_tuner)
# * Run Tuning Grid ----
modeltime::parallel_start(n_cores)
# THIS FAILS ----
tune_results <- wflw_tune_spec %>%
tune::tune_grid(
resamples = tscv,
grid = grid_spec,
metrics = modeltime::default_forecast_accuracy_metric_set(),
control = tune::control_grid(
verbose = TRUE,
save_pred = TRUE
)
)
> tune_results$.notes[[1]]
# A tibble: 1 x 1
.notes
<chr>
1 "preprocessor 1/1: Error in UseMethod(\"prep\"): no applicable method for 'prep' applied to an ob~
不确定为什么会这样，也不确定这是属于这里还是食谱，我把它放在这里是因为它在 tun_grid.
中失败了
这是通过添加函数 .onLoad 来注册 s3 方法来解决的
提交：https://github.com/spsanderson/healthyR.ai/commit/89671ff138e61d07a5dbdfcd7e0a694144aa3e08
.onLoad = function(libname, pkgname) {
maybe_register_s3_methods()
}
# nocov start
maybe_register_s3_methods <- function() {
ns <- asNamespace("healthyR.ai")
names <- names(ns)
if (rlang::is_installed("tune") && utils::packageVersion("tune") >= "0.1.1.9000") {
req_pkgs_names <- grep("^required_pkgs\.", names, value = TRUE)
req_pkgs_classes <- gsub("required_pkgs.", "", req_pkgs_names)
for (i in seq_along(req_pkgs_names)) {
class <- req_pkgs_classes[[i]]
s3_register("tune::required_pkgs", class)
}
}
# ----------------------------------------------------------------------------
invisible()
}
# vctrs:::s3_register()
s3_register <- function(generic, class, method = NULL) {
stopifnot(is.character(generic), length(generic) == 1)
stopifnot(is.character(class), length(class) == 1)
pieces <- strsplit(generic, "::")[[1]]
stopifnot(length(pieces) == 2)
package <- pieces[[1]]
generic <- pieces[[2]]
caller <- parent.frame()
get_method_env <- function() {
top <- topenv(caller)
if (isNamespace(top)) {
asNamespace(environmentName(top))
} else {
caller
}
}
get_method <- function(method, env) {
if (is.null(method)) {
get(paste0(generic, ".", class), envir = get_method_env())
} else {
method
}
}
method_fn <- get_method(method)
stopifnot(is.function(method_fn))
# Always register hook in case package is later unloaded & reloaded
setHook(
packageEvent(package, "onLoad"),
function(...) {
ns <- asNamespace(package)
# Refresh the method, it might have been updated by `devtools::load_all()`
method_fn <- get_method(method)
registerS3method(generic, class, method_fn, envir = ns)
}
)
# Avoid registration failures during loading (pkgload or regular)
if (!isNamespaceLoaded(package)) {
return(invisible())
}
envir <- asNamespace(package)
# Only register if generic can be accessed
if (exists(generic, envir)) {
registerS3method(generic, class, method_fn, envir = envir)
}
invisible()
}
我使用我为我的一个包构建的自定义步骤函数，它工作得很好，它在包的本地副本中（尚未提交给 CRAN）link 在这里运行： step_hai_fourier
这是会话信息（我们可以看到 healthyR.ai 0.0.2.9000 已加载）：
> sessionInfo()
R version 4.1.0 (2021-05-18)
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
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] timetk_2.6.1 tidyquant_1.0.3 quantmod_0.4.18
[4] TTR_0.24.2 PerformanceAnalytics_2.0.4 xts_0.12.1
[7] zoo_1.8-9 lubridate_1.8.0 forcats_0.5.1
[10] stringr_1.4.0 readr_2.0.2 tidyverse_1.3.1
[13] janitor_2.1.0 healthyR.ts_0.1.4 __healthyR.ai_0.0.2.9000__
[16] yardstick_0.0.8 workflowsets_0.1.0 workflows_0.2.4
[19] tune_0.1.6 tidyr_1.1.4 tibble_3.1.6
[22] rsample_0.1.1 recipes_0.1.17 purrr_0.3.4
[25] parsnip_0.1.7 modeldata_0.1.1 infer_1.0.0
[28] ggplot2_3.3.5 dplyr_1.0.7 dials_0.0.10
[31] scales_1.1.1 broom_0.7.10 tidymodels_0.1.4
[34] modeltime_1.1.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.3.0 plyr_1.8.6 lazyeval_0.2.2
[5] splines_4.1.0 crosstalk_1.2.0 listenv_0.8.0 digest_0.6.28
[9] foreach_1.5.1 htmltools_0.5.2 fansi_0.5.0 checkmate_2.0.0
[13] magrittr_2.0.1 doParallel_1.0.16 tzdb_0.2.0 globals_0.14.0
[17] modelr_0.1.8 gower_0.2.2 RcppParallel_5.1.4 vroom_1.5.5
[21] hardhat_0.1.6 forecast_8.15 tseries_0.10-48 colorspace_2.0-2
[25] rvest_1.0.2 haven_2.4.3 crayon_1.4.2 jsonlite_1.7.2
[29] survival_3.2-11 iterators_1.0.13 glue_1.5.0 gtable_0.3.0
[33] ipred_0.9-12 Quandl_2.11.0 future.apply_1.8.1 DBI_1.1.1
[37] Rcpp_1.0.7 viridisLite_0.4.0 GPfit_1.0-8 bit_4.0.4
[41] lava_1.6.10 StanHeaders_2.21.0-7 prodlim_2019.11.13 htmlwidgets_1.5.4
[45] httr_1.4.2 ellipsis_0.3.2 pkgconfig_2.0.3 sass_0.4.0
[49] nnet_7.3-16 dbplyr_2.1.1 utf8_1.2.2 tidyselect_1.1.1
[53] labeling_0.4.2 rlang_0.4.12 DiceDesign_1.9 munsell_0.5.0
[57] cellranger_1.1.0 tools_4.1.0 xgboost_1.5.0.1 cli_3.1.0
[61] generics_0.1.1 fastmap_1.1.0 yaml_2.2.1 bit64_4.0.5
[65] fs_1.5.0 future_1.23.0 nlme_3.1-152 xml2_1.3.2
[69] compiler_4.1.0 rstudioapi_0.13 plotly_4.10.0 curl_4.3.2
[73] gt_0.3.1 reprex_2.0.1 lhs_1.1.3 stringi_1.7.5
[77] lattice_0.20-44 Matrix_1.3-4 urca_1.3-0 vctrs_0.3.8
[81] pillar_1.6.4 lifecycle_1.0.1 furrr_0.2.3 lmtest_0.9-39
[85] data.table_1.14.2 R6_2.5.1 parallelly_1.28.1 codetools_0.2-18
[89] MASS_7.3-54 assertthat_0.2.1 withr_2.4.2 fracdiff_1.5-1
[93] hms_1.1.1 quadprog_1.5-8 grid_4.1.0 rpart_4.1-15
[97] timeDate_3043.102 class_7.3-19 snakecase_0.11.0 pROC_1.18.0
这是我的脚本，显示 prep 和“果汁”工作正常：
# Libraries ----
library(modeltime)
library(tidymodels)
library(healthyR.ai)
library(healthyR.ts)
library(parallel)
library(janitor)
library(tidyverse)
library(tidyquant)
library(timetk)
# Data ----
url <- "https://cci30.com/ajax/getIndexHistory.php"
destfile <- "00_data/cci30_OHLCV.csv"
download.file(url, destfile = destfile)
cci_index_tbl <- read_csv("00_data/cci30_OHLCV.csv") %>%
clean_names()
# * Daily Log Returns ----
time_param <- "weekly"
log_returns_tbl <- cci_index_tbl %>%
tq_transmute(
select = close
, mutate_fun = periodReturn
, period = time_param
, type = "log"
, col_rename = "value"
) %>%
set_names("date_col", "value")
# * Train/Test ----
splits <- log_returns_tbl %>%
time_series_split(
date_var = date_col
, assess = "12 weeks"
, cumulative = TRUE
)
splits %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(
.date_var = date_col
, .value = value
, .title = paste0(
"CCI30 ", stringr::str_to_title(time_param), " Log Returns"
)
)
n_cores <- detectCores() - 1
# Recipe ----
recipe_base <- recipe(value ~ ., data = training(splits)) %>%
step_hai_fourier(value, scale_type = "sincos", period = 12, order = 1)
recipe_base %>% prep() %>% juice()
控制台说：
> recipe_base %>% prep() %>% juice()
# A tibble: 347 x 3
date_col value value_sincos
<date> <dbl> <dbl>
1 2015-01-04 -0.196 -0.102
2 2015-01-11 -0.00811 -0.00425
3 2015-01-18 -0.209 -0.108
4 2015-01-25 0.185 0.0961
5 2015-02-01 -0.141 -0.0736
6 2015-02-08 -0.0231 -0.0121
7 2015-02-15 0.0208 0.0109
8 2015-02-22 -0.0180 -0.00941
9 2015-03-01 0.0841 0.0440
10 2015-03-08 -0.0182 -0.00955
# ... with 337 more rows
到目前为止很完美，这是脚本的其余部分：
# Model ----
# Boosted Auto ARIMA
model_spec_arima_boosted <- arima_boost(
min_n = 2
, learn_rate = 0.015
) %>%
set_engine(engine = "auto_arima_xgboost")
# Workflowset ----
wfsets <- workflow_set(
preproc = list(
base = recipe_base
),
models = list(
model_spec_arima_boosted
),
cross = TRUE
)
parallel_start(n_cores)
wf_fits <- wfsets %>%
modeltime_fit_workflowset(
data = training(splits)
, control = control_fit_workflowset(
allow_par = TRUE
, verbose = TRUE
)
)
parallel_stop()
wf_fits <- wf_fits %>%
filter(.model != "NULL")
# Model Table -------------------------------------------------------------
models_tbl <- wf_fits
# Calibrate Model Testing -------------------------------------------------
parallel_start(n_cores)
calibration_tbl <- models_tbl %>%
modeltime_calibrate(new_data = testing(splits))
parallel_stop()
calibration_tbl
# Testing Accuracy --------------------------------------------------------
parallel_start(n_cores)
calibration_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = log_returns_tbl
) %>%
plot_modeltime_forecast(
.legend_max_width = 25,
.interactive = TRUE,
.conf_interval_show = FALSE
)
parallel_stop()
calibration_tbl %>%
modeltime_accuracy() %>%
drop_na() %>%
arrange(desc(rsq)) %>%
table_modeltime_accuracy(.interactive = FALSE)
# Model Tuning ----
# Get Model
plucked_model <- calibration_tbl %>%
modeltime::pluck_modeltime_model(1)
training_data <- rsample::training(splits)
tscv <- timetk::time_series_cv(
data = training_data,
date_var = date_col,
cumulative = TRUE,
assess = "26 weeks",
skip = "4 weeks",
slice_limit = 6
)
# * Tune Spec ----
# Model Spec
model_spec <- plucked_model %>% parsnip::extract_spec_parsnip()
model_spec_engine <- model_spec[["engine"]]
model_spec_tuner <- healthyR.ts::ts_model_spec_tune_template(model_spec_engine)
# * Grid Spec ----
grid_spec <- dials::grid_latin_hypercube(
tune::parameters(model_spec_tuner),
size = 30
)
# * Tune Model ----
wflw_tune_spec <- plucked_model %>%
workflows::update_model(model_spec_tuner)
# * Run Tuning Grid ----
modeltime::parallel_start(n_cores)
# THIS FAILS ----
tune_results <- wflw_tune_spec %>%
tune::tune_grid(
resamples = tscv,
grid = grid_spec,
metrics = modeltime::default_forecast_accuracy_metric_set(),
control = tune::control_grid(
verbose = TRUE,
save_pred = TRUE
)
)
> tune_results$.notes[[1]]
# A tibble: 1 x 1
.notes
<chr>
1 "preprocessor 1/1: Error in UseMethod(\"prep\"): no applicable method for 'prep' applied to an ob~
不确定为什么会这样，也不确定这是属于这里还是食谱，我把它放在这里是因为它在 tun_grid.
中失败了这是通过添加函数 .onLoad 来注册 s3 方法来解决的
提交：https://github.com/spsanderson/healthyR.ai/commit/89671ff138e61d07a5dbdfcd7e0a694144aa3e08
.onLoad = function(libname, pkgname) {
maybe_register_s3_methods()
}
# nocov start
maybe_register_s3_methods <- function() {
ns <- asNamespace("healthyR.ai")
names <- names(ns)
if (rlang::is_installed("tune") && utils::packageVersion("tune") >= "0.1.1.9000") {
req_pkgs_names <- grep("^required_pkgs\.", names, value = TRUE)
req_pkgs_classes <- gsub("required_pkgs.", "", req_pkgs_names)
for (i in seq_along(req_pkgs_names)) {
class <- req_pkgs_classes[[i]]
s3_register("tune::required_pkgs", class)
}
}
# ----------------------------------------------------------------------------
invisible()
}
# vctrs:::s3_register()
s3_register <- function(generic, class, method = NULL) {
stopifnot(is.character(generic), length(generic) == 1)
stopifnot(is.character(class), length(class) == 1)
pieces <- strsplit(generic, "::")[[1]]
stopifnot(length(pieces) == 2)
package <- pieces[[1]]
generic <- pieces[[2]]
caller <- parent.frame()
get_method_env <- function() {
top <- topenv(caller)
if (isNamespace(top)) {
asNamespace(environmentName(top))
} else {
caller
}
}
get_method <- function(method, env) {
if (is.null(method)) {
get(paste0(generic, ".", class), envir = get_method_env())
} else {
method
}
}
method_fn <- get_method(method)
stopifnot(is.function(method_fn))
# Always register hook in case package is later unloaded & reloaded
setHook(
packageEvent(package, "onLoad"),
function(...) {
ns <- asNamespace(package)
# Refresh the method, it might have been updated by `devtools::load_all()`
method_fn <- get_method(method)
registerS3method(generic, class, method_fn, envir = ns)
}
)
# Avoid registration failures during loading (pkgload or regular)
if (!isNamespaceLoaded(package)) {
return(invisible())
}
envir <- asNamespace(package)
# Only register if generic can be accessed
if (exists(generic, envir)) {
registerS3method(generic, class, method_fn, envir = envir)
}
invisible()
}