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Boilerplate Workflow

Source: R/boilerplate-svm-poly.R
ts_auto_svm_poly.Rd

This is a boilerplate function to automatically create the following:

  • recipe

  • model specification

  • workflow

  • tuned model (grid ect)

  • calibration tibble and plot

Usage

ts_auto_svm_poly(
  .data,
  .date_col,
  .value_col,
  .formula,
  .rsamp_obj,
  .prefix = "ts_svm_poly",
  .tune = TRUE,
  .grid_size = 10,
  .num_cores = 1,
  .cv_assess = 12,
  .cv_skip = 3,
  .cv_slice_limit = 6,
  .best_metric = "rmse",
  .bootstrap_final = FALSE
)

Arguments

.data

The data being passed to the function. The time-series object.

.date_col

The column that holds the datetime.

.value_col

The column that has the value

.formula

The formula that is passed to the recipe like value ~ .

.rsamp_obj

The rsample splits object

.prefix

Default is ts_smooth_es

.tune

Defaults to TRUE, this creates a tuning grid and tuned model.

.grid_size

If .tune is TRUE then the .grid_size is the size of the tuning grid.

.num_cores

How many cores do you want to use. Default is 1

.cv_assess

How many observations for assess. See timetk::time_series_cv()

.cv_skip

How many observations to skip. See timetk::time_series_cv()

.cv_slice_limit

How many slices to return. See timetk::time_series_cv()

.best_metric

Default is "rmse". See modeltime::default_forecast_accuracy_metric_set()

.bootstrap_final

Not yet implemented.

Value

A list

Details

This uses parsnip::svm_poly() and sets the parsnip::engine to kernlab.

See also

https://parsnip.tidymodels.org/reference/svm_poly.html

Other Boiler_Plate: ts_auto_arima_xgboost(), ts_auto_arima(), ts_auto_croston(), ts_auto_exp_smoothing(), ts_auto_glmnet(), ts_auto_lm(), ts_auto_mars(), ts_auto_nnetar(), ts_auto_prophet_boost(), ts_auto_prophet_reg(), ts_auto_smooth_es(), ts_auto_svm_rbf(), ts_auto_theta(), ts_auto_xgboost()

Other SVM: ts_auto_svm_rbf()

Author

Steven P. Sanderson II, MPH

Examples

# \donttest{
library(dplyr)
library(timetk)
library(modeltime)

data <- AirPassengers %>%
  ts_to_tbl() %>%
  select(-index)

splits <- time_series_split(
  data
  , date_col
  , assess = 12
  , skip = 3
  , cumulative = TRUE
)

ts_auto_poly <- ts_auto_svm_poly(
  .data = data,
  .num_cores = 2,
  .date_col = date_col,
  .value_col = value,
  .rsamp_obj = splits,
  .formula = value ~ .,
  .grid_size = 3,
  .tune = FALSE
)
#> Warning: Column(s) have zero variance so scaling cannot be used: `date_col_day`, `date_col_hour`, `date_col_minute`, `date_col_second`, `date_col_hour12`, `date_col_am.pm`, `date_col_mday`, `date_col_mday7`, `date_col_locale_DE`, `date_col_CH_BerchtoldsDay`, `date_col_JP_BankHolidayJan2`, `date_col_JP_BankHolidayJan3`, `date_col_World_Epiphany`, `date_col_IT_Epiphany`, `date_col_JP_ComingOfAgeDay`, `date_col_JP_SeijinNoHi`, `date_col_US_MLKingsBirthday`, `date_col_US_InaugurationDay`, `date_col_World_PresentationOfLord`, `date_col_JP_KenkokuKinenNoHi`, `date_col_JP_NatFoundationDay`, `date_col_US_LincolnsBirthday`, `date_col_CA_FamilyDay`, `date_col_US_PresidentsDay`, `date_col_US_WashingtonsBirthday`, `date_col_World_Quinquagesima`, `date_col_World_AshWednesday`, `date_col_JP_VernalEquinox`, `date_col_World_Annunciation`, `date_col_World_PalmSunday`, `date_col_World_GoodFriday`, `date_col_US_GoodFriday`, `date_col_CH_Sechselaeuten`, `date_col_World_EasterMonday`, `date_col_IT_LiberationDay`, `date_col_JP_GreeneryDay`, `date_col_JP_MidoriNoHi`, `date_col_JP_ConstitutionDay`, `date_col_JP_KenpouKinenBi`, `date_col_JP_KokuminNoKyujitu`, `date_col_JP_NationHoliday`, `date_col_JP_ChildrensDay`, `date_col_JP_KodomoNoHi`, `date_col_FR_FetDeLaVictoire1945`, `date_col_World_RogationSunday`, `date_col_CA_VictoriaDay`, `date_col_World_Ascension`, `date_col_CH_Ascension`, `date_col_DE_Ascension`, `date_col_FR_Ascension`, `date_col_GB_SpringBankHoliday`, `date_col_US_DecorationMemorialDay`, `date_col_US_MemorialDay`, `date_col_World_PentecostMonday`, `date_col_World_CorpusChristi`, `date_col_DE_CorpusChristi`, `date_col_US_JuneteenthNationalIndependenceDay`, `date_col_US_IndependenceDay`, `date_col_FR_BastilleDay`, `date_col_JP_MarineDay`, `date_col_JP_UmiNoHi`, `date_col_World_TransfigurationOfLord`, `date_col_World_AssumptionOfMary`, `date_col_FR_AssumptionVirginMary`, `date_col_IT_AssumptionOfVirginMary`, `date_col_GB_SummerBankHoliday`, `date_col_World_BirthOfVirginMary`, `date_col_CH_Knabenschiessen`, `date_col_World_CelebrationOfHolyCross`, `date_col_JP_KeirouNOhi`, `date_col_JP_RespectForTheAgedDay`, `date_col_JP_AutumnalEquinox`, `date_col_JP_ShuubunNoHi`, `date_col_World_MassOfArchangels`, `date_col_DE_GermanUnity`, `date_col_CA_ThanksgivingDay`, `date_col_JP_HealthandSportsDay`, `date_col_JP_TaiikuNoHi`, `date_col_US_ColumbusDay`, `date_col_World_AllSouls`, `date_col_JP_BunkaNoHi`, `date_col_JP_NationalCultureDay`, `date_col_US_ElectionDay`, `date_col_World_CaRemembranceDay`, `date_col_FR_ArmisticeDay`, `date_col_US_VeteransDay`, `date_col_World_ChristTheKing`, `date_col_JP_EmperorsBirthday`, `date_col_JP_KinrouKanshaNoHi`, `date_col_JP_TennouTanjyouBi`, `date_col_JP_ThanksgivingDay`, `date_col_US_ThanksgivingDay`, `date_col_World_Advent2nd`, `date_col_IT_StAmrose`, `date_col_IT_ImmaculateConception`, `date_col_World_Advent3rd`, `date_col_World_Advent4th`, `date_col_World_ChristmasEve`, `date_col_DE_ChristmasEve`, `date_col_World_ChristmasDay`, `date_col_US_ChristmasDay`, `date_col_World_BoxingDay`, `date_col_DE_NewYearsEve`, `date_col_JP_BankHolidayDec31`, `date_col_GB_MilleniumDay`, `date_col_month.lbl_13` and `date_col_wday.lbl_8`. Consider using `step_zv()` to remove those columns before normalizing
#>  Setting default kernel parameters  
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `.nested.col = purrr::map2(...)`.
#> Caused by warning:
#> ! Column(s) have zero variance so scaling cannot be used: `date_col_day`, `date_col_hour`, `date_col_minute`, `date_col_second`, `date_col_hour12`, `date_col_am.pm`, `date_col_mday`, `date_col_mday7`, `date_col_locale_DE`, `date_col_CH_BerchtoldsDay`, `date_col_JP_BankHolidayJan2`, `date_col_JP_BankHolidayJan3`, `date_col_World_Epiphany`, `date_col_IT_Epiphany`, `date_col_JP_ComingOfAgeDay`, `date_col_JP_SeijinNoHi`, `date_col_US_MLKingsBirthday`, `date_col_US_InaugurationDay`, `date_col_World_PresentationOfLord`, `date_col_JP_KenkokuKinenNoHi`, `date_col_JP_NatFoundationDay`, `date_col_US_LincolnsBirthday`, `date_col_CA_FamilyDay`, `date_col_US_PresidentsDay`, `date_col_US_WashingtonsBirthday`, `date_col_World_Quinquagesima`, `date_col_World_AshWednesday`, `date_col_JP_VernalEquinox`, `date_col_World_Annunciation`, `date_col_World_PalmSunday`, `date_col_World_GoodFriday`, `date_col_US_GoodFriday`, `date_col_CH_Sechselaeuten`, `date_col_World_EasterMonday`, `date_col_IT_LiberationDay`, `date_col_JP_GreeneryDay`, `date_col_JP_MidoriNoHi`, `date_col_JP_ConstitutionDay`, `date_col_JP_KenpouKinenBi`, `date_col_JP_KokuminNoKyujitu`, `date_col_JP_NationHoliday`, `date_col_JP_ChildrensDay`, `date_col_JP_KodomoNoHi`, `date_col_FR_FetDeLaVictoire1945`, `date_col_World_RogationSunday`, `date_col_CA_VictoriaDay`, `date_col_World_Ascension`, `date_col_CH_Ascension`, `date_col_DE_Ascension`, `date_col_FR_Ascension`, `date_col_GB_SpringBankHoliday`, `date_col_US_DecorationMemorialDay`, `date_col_US_MemorialDay`, `date_col_World_PentecostMonday`, `date_col_World_CorpusChristi`, `date_col_DE_CorpusChristi`, `date_col_US_JuneteenthNationalIndependenceDay`, `date_col_US_IndependenceDay`, `date_col_FR_BastilleDay`, `date_col_JP_MarineDay`, `date_col_JP_UmiNoHi`, `date_col_World_TransfigurationOfLord`, `date_col_World_AssumptionOfMary`, `date_col_FR_AssumptionVirginMary`, `date_col_IT_AssumptionOfVirginMary`, `date_col_GB_SummerBankHoliday`, `date_col_World_BirthOfVirginMary`, `date_col_CH_Knabenschiessen`, `date_col_World_CelebrationOfHolyCross`, `date_col_JP_KeirouNOhi`, `date_col_JP_RespectForTheAgedDay`, `date_col_JP_AutumnalEquinox`, `date_col_JP_ShuubunNoHi`, `date_col_World_MassOfArchangels`, `date_col_DE_GermanUnity`, `date_col_CA_ThanksgivingDay`, `date_col_JP_HealthandSportsDay`, `date_col_JP_TaiikuNoHi`, `date_col_US_ColumbusDay`, `date_col_World_AllSouls`, `date_col_JP_BunkaNoHi`, `date_col_JP_NationalCultureDay`, `date_col_US_ElectionDay`, `date_col_World_CaRemembranceDay`, `date_col_FR_ArmisticeDay`, `date_col_US_VeteransDay`, `date_col_World_ChristTheKing`, `date_col_JP_EmperorsBirthday`, `date_col_JP_KinrouKanshaNoHi`, `date_col_JP_TennouTanjyouBi`, `date_col_JP_ThanksgivingDay`, `date_col_US_ThanksgivingDay`, `date_col_World_Advent2nd`, `date_col_IT_StAmrose`, `date_col_IT_ImmaculateConception`, `date_col_World_Advent3rd`, `date_col_World_Advent4th`, `date_col_World_ChristmasEve`, `date_col_DE_ChristmasEve`, `date_col_World_ChristmasDay`, `date_col_US_ChristmasDay`, `date_col_World_BoxingDay`, `date_col_DE_NewYearsEve`, `date_col_JP_BankHolidayDec31`, `date_col_GB_MilleniumDay`, `date_col_month.lbl_13` and `date_col_wday.lbl_8`. Consider using `step_zv()` to remove those columns before normalizing
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `.nested.col = purrr::map2(...)`.
#> Caused by warning:
#> ! Column(s) have zero variance so scaling cannot be used: `date_col_day`, `date_col_hour`, `date_col_minute`, `date_col_second`, `date_col_hour12`, `date_col_am.pm`, `date_col_mday`, `date_col_mday7`, `date_col_locale_DE`, `date_col_CH_BerchtoldsDay`, `date_col_JP_BankHolidayJan2`, `date_col_JP_BankHolidayJan3`, `date_col_World_Epiphany`, `date_col_IT_Epiphany`, `date_col_JP_ComingOfAgeDay`, `date_col_JP_SeijinNoHi`, `date_col_US_MLKingsBirthday`, `date_col_US_InaugurationDay`, `date_col_World_PresentationOfLord`, `date_col_JP_KenkokuKinenNoHi`, `date_col_JP_NatFoundationDay`, `date_col_US_LincolnsBirthday`, `date_col_CA_FamilyDay`, `date_col_US_PresidentsDay`, `date_col_US_WashingtonsBirthday`, `date_col_World_Quinquagesima`, `date_col_World_AshWednesday`, `date_col_JP_VernalEquinox`, `date_col_World_Annunciation`, `date_col_World_PalmSunday`, `date_col_World_GoodFriday`, `date_col_US_GoodFriday`, `date_col_CH_Sechselaeuten`, `date_col_World_EasterMonday`, `date_col_IT_LiberationDay`, `date_col_JP_GreeneryDay`, `date_col_JP_MidoriNoHi`, `date_col_JP_ConstitutionDay`, `date_col_JP_KenpouKinenBi`, `date_col_JP_KokuminNoKyujitu`, `date_col_JP_NationHoliday`, `date_col_JP_ChildrensDay`, `date_col_JP_KodomoNoHi`, `date_col_FR_FetDeLaVictoire1945`, `date_col_World_RogationSunday`, `date_col_CA_VictoriaDay`, `date_col_World_Ascension`, `date_col_CH_Ascension`, `date_col_DE_Ascension`, `date_col_FR_Ascension`, `date_col_GB_SpringBankHoliday`, `date_col_US_DecorationMemorialDay`, `date_col_US_MemorialDay`, `date_col_World_PentecostMonday`, `date_col_World_CorpusChristi`, `date_col_DE_CorpusChristi`, `date_col_US_JuneteenthNationalIndependenceDay`, `date_col_US_IndependenceDay`, `date_col_FR_BastilleDay`, `date_col_JP_MarineDay`, `date_col_JP_UmiNoHi`, `date_col_World_TransfigurationOfLord`, `date_col_World_AssumptionOfMary`, `date_col_FR_AssumptionVirginMary`, `date_col_IT_AssumptionOfVirginMary`, `date_col_GB_SummerBankHoliday`, `date_col_World_BirthOfVirginMary`, `date_col_CH_Knabenschiessen`, `date_col_World_CelebrationOfHolyCross`, `date_col_JP_KeirouNOhi`, `date_col_JP_RespectForTheAgedDay`, `date_col_JP_AutumnalEquinox`, `date_col_JP_ShuubunNoHi`, `date_col_World_MassOfArchangels`, `date_col_DE_GermanUnity`, `date_col_CA_ThanksgivingDay`, `date_col_JP_HealthandSportsDay`, `date_col_JP_TaiikuNoHi`, `date_col_US_ColumbusDay`, `date_col_World_AllSouls`, `date_col_JP_BunkaNoHi`, `date_col_JP_NationalCultureDay`, `date_col_US_ElectionDay`, `date_col_World_CaRemembranceDay`, `date_col_FR_ArmisticeDay`, `date_col_US_VeteransDay`, `date_col_World_ChristTheKing`, `date_col_JP_EmperorsBirthday`, `date_col_JP_KinrouKanshaNoHi`, `date_col_JP_TennouTanjyouBi`, `date_col_JP_ThanksgivingDay`, `date_col_US_ThanksgivingDay`, `date_col_World_Advent2nd`, `date_col_IT_StAmrose`, `date_col_IT_ImmaculateConception`, `date_col_World_Advent3rd`, `date_col_World_Advent4th`, `date_col_World_ChristmasEve`, `date_col_DE_ChristmasEve`, `date_col_World_ChristmasDay`, `date_col_US_ChristmasDay`, `date_col_World_BoxingDay`, `date_col_DE_NewYearsEve`, `date_col_JP_BankHolidayDec31`, `date_col_GB_MilleniumDay`, `date_col_month.lbl_13` and `date_col_wday.lbl_8`. Consider using `step_zv()` to remove those columns before normalizing

ts_auto_poly$recipe_info
#> $recipe_call
#> recipe(.data = data, .date_col = date_col, .value_col = value, 
#>     .formula = value ~ ., .rsamp_obj = splits, .tune = FALSE, 
#>     .grid_size = 3, .num_cores = 2)
#> 
#> $recipe_syntax
#> [1] "ts_svm_poly_recipe <-"                                                                                                                                             
#> [2] "\n  recipe(.data = data, .date_col = date_col, .value_col = value, .formula = value ~ \n    ., .rsamp_obj = splits, .tune = FALSE, .grid_size = 3, .num_cores = 2)"
#> 
#> $rec_obj
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 1
#> 
#> ── Operations 
#> • Timeseries signature features from: date_col
#> • Holiday signature features from: date_col
#> • Novel factor level assignment for: recipes::all_nominal_predictors()
#> • Variable mutation for: tidyselect::vars_select_helpers$where(is.character)
#> • Dummy variables from: recipes::all_nominal()
#> • Centering and scaling for: recipes::all_numeric_predictors(), ...
#> • Sparse, unbalanced variable filter on: recipes::all_predictors(), ...
#> • Correlation filter on: recipes::all_numeric_predictors()
#> 
# }