This may be a usage misunderstanding, but I expect the following toy example to work. I want to have a lagged predictor in my recipe, but once I include it in the recipe, and try to predict on the same data using a workflow with the recipe, it doesn't recognize the column foo and cannot compute its lag.
Now, I can get this to work if I:
- Pull the fit out of the workflow that has been fit.
- Independently prep and bake the data I want to fit.
Which I code after the failed workflow fit, and it succeeds. According to the documentation, I should be able to put a workflow fit in the predict slot: https://www.tidymodels.org/start/recipes/#predict-workflow
I am probably fundamentally misunderstanding how workflow is supposed to operate. I have what I consider a workaround, but I do not understand why the failed statement isn't working in the way the workaround is. I expected the failed workflow construct to work under the covers like the workaround I have.
In short, if work_df is a dataframe, the_rec is a recipe based off work_df, rf_mod is a model, and you create the workflow rf_workflow, then should I expect the predict() function to work identically in the two predict() calls below?
## Workflow
rf_workflow <-
workflow() %>%
add_model(rf_mod) %>%
add_recipe(the_rec)
## fit
rf_workflow_fit <-
rf_workflow %>%
fit(data = work_df)
## Predict with workflow. I expect since a workflow has a fit model and
## a recipe as part of it, it should know how to do the following:
predict(rf_workflow_fit, work_df)
#> Error: Problem with `mutate()` input `lag_1_foo`.
#> x object 'foo' not found
#> i Input `lag_1_foo` is `dplyr::lag(x = foo, n = 1L, default = NA)`.
## Predict by explicitly prepping and baking the data, and pulling out the
## fit from the workflow:
predict(
rf_workflow_fit %>%
pull_workflow_fit(),
bake(prep(the_rec), work_df))
#> # A tibble: 995 x 1
#> .pred
#> <dbl>
#> 1 2.24
#> 2 0.595
#> 3 0.262
Full reprex example below.
library(tidymodels)
#> -- Attaching packages -------------------------------------------------------------------------------------- tidymodels 0.1.1 --
#> v broom 0.7.1 v recipes 0.1.13
#> v dials 0.0.9 v rsample 0.0.8
#> v dplyr 1.0.2 v tibble 3.0.3
#> v ggplot2 3.3.2 v tidyr 1.1.2
#> v infer 0.5.3 v tune 0.1.1
#> v modeldata 0.0.2 v workflows 0.2.1
#> v parsnip 0.1.3 v yardstick 0.0.7
#> v purrr 0.3.4
#> -- Conflicts ----------------------------------------------------------------------------------------- tidymodels_conflicts() --
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x recipes::step() masks stats::step()
library(dplyr)
set.seed(123)
### Create autocorrelated timeseries: https://stafoo.stackexchange.com/a/29242/17203
work_df <-
tibble(
foo = stats::filter(rnorm(1000), filter=rep(1,5), circular=TRUE) %>%
as.numeric()
)
# plot(work_df$foo)
work_df
#> # A tibble: 1,000 x 1
#> foo
#> <dbl>
#> 1 -0.00375
#> 2 0.589
#> 3 0.968
#> 4 3.24
#> 5 3.93
#> 6 1.11
#> 7 0.353
#> 8 -0.222
#> 9 -0.713
#> 10 -0.814
#> # ... with 990 more rows
## Recipe
the_rec <-
recipe(foo ~ ., data = work_df) %>%
step_lag(foo, lag=1:5) %>%
step_naomit(all_predictors())
the_rec %>% prep() %>% juice()
#> # A tibble: 995 x 6
#> foo lag_1_foo lag_2_foo lag_3_foo lag_4_foo lag_5_foo
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.11 3.93 3.24 0.968 0.589 -0.00375
#> 2 0.353 1.11 3.93 3.24 0.968 0.589
#> 3 -0.222 0.353 1.11 3.93 3.24 0.968
#> 4 -0.713 -0.222 0.353 1.11 3.93 3.24
#> 5 -0.814 -0.713 -0.222 0.353 1.11 3.93
#> 6 0.852 -0.814 -0.713 -0.222 0.353 1.11
#> 7 1.65 0.852 -0.814 -0.713 -0.222 0.353
#> 8 1.54 1.65 0.852 -0.814 -0.713 -0.222
#> 9 2.10 1.54 1.65 0.852 -0.814 -0.713
#> 10 2.24 2.10 1.54 1.65 0.852 -0.814
#> # ... with 985 more rows
## Model
rf_mod <-
rand_forest(
mtry = 4,
trees = 1000,
min_n = 13) %>%
set_mode("regression") %>%
set_engine("ranger")
## Workflow
rf_workflow <-
workflow() %>%
add_model(rf_mod) %>%
add_recipe(the_rec)
## fit
rf_workflow_fit <-
rf_workflow %>%
fit(data = work_df)
## Predict
predict(rf_workflow_fit, work_df)
#> Error: Problem with `mutate()` input `lag_1_foo`.
#> x object 'foo' not found
#> i Input `lag_1_foo` is `dplyr::lag(x = foo, n = 1L, default = NA)`.
## Perhaps I just need to pull off the fit and work with that?... Nope.
predict(
rf_workflow_fit %>%
pull_workflow_fit(),
work_df)
#> Error: Can't subset columns that don't exist.
#> x Columns `lag_1_foo`, `lag_2_foo`, `lag_3_foo`, `lag_4_foo`, and `lag_5_foo` don't exist.
## Maybe I need to bake it first... and that works.
## But doesn't that defeat the purpose of a workflow?
predict(
rf_workflow_fit %>%
pull_workflow_fit(),
bake(prep(the_rec), work_df))
#> # A tibble: 995 x 1
#> .pred
#> <dbl>
#> 1 2.24
#> 2 0.595
#> 3 0.262
#> 4 -0.977
#> 5 -1.24
#> 6 -0.140
#> 7 1.36
#> 8 1.30
#> 9 1.78
#> 10 2.42
#> # ... with 985 more rows
## Session info
sessioninfo::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 3.6.3 (2020-02-29)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz America/Chicago
#> date 2020-10-13
#>
#> - Packages -------------------------------------------------------------------
#> package * version date lib source
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Created on 2020-10-13 by the reprex package (v0.3.0)
The reason you are experiencing an error is that you have created a predictor variable from the outcome. When it comes time to predict on new data, the outcome is not available; we are predicting the outcome for new data, not assuming that it is there already.
This is a fairly strong assumption of the tidymodels framework, for either modeling or preprocessing, to protect against information leakage. You can read about this a bit more here.
It's possible you already know about these resources, but if you are working with time series models, I'd suggest checking out these resources: