For a recipe with at least one preprocessing operation, estimate the required parameters from a training set that can be later applied to other data sets.
Usage
prep(x, ...)
# S3 method for class 'recipe'
prep(
x,
training = NULL,
fresh = FALSE,
verbose = FALSE,
retain = TRUE,
log_changes = FALSE,
strings_as_factors = TRUE,
...
)
Arguments
- x
an object
- ...
further arguments passed to or from other methods (not currently used).
- training
A data frame, tibble, or sparse matrix from the
Matrix
package, that will be used to estimate parameters for preprocessing. See sparse_data for more information about use of sparse data.- fresh
A logical indicating whether already trained operation should be re-trained. If
TRUE
, you should pass in a data set to the argumenttraining
.- verbose
A logical that controls whether progress is reported as operations are executed.
- retain
A logical: should the preprocessed training set be saved into the
template
slot of the recipe after training? This is a good idea if you want to add more steps later but want to avoid re-training the existing steps. Also, it is advisable to useretain = TRUE
if any steps use the optionskip = FALSE
. Note that this can make the final recipe size large. Whenverbose = TRUE
, a message is written with the approximate object size in memory but may be an underestimate since it does not take environments into account.- log_changes
A logical for printing a summary for each step regarding which (if any) columns were added or removed during training.
- strings_as_factors
A logical: should character columns be converted to factors? This affects the preprocessed training set (when
retain = TRUE
) as well as the results ofbake.recipe
.
Value
A recipe whose step objects have been updated with the required
quantities (e.g. parameter estimates, model objects, etc). Also, the
term_info
object is likely to be modified as the operations are
executed.
Details
Given a data set, this function estimates the required quantities and
statistics needed by any operations. prep()
returns an updated recipe
with the estimates. If you are using a recipe as a preprocessor for modeling,
we highly recommend that you use a workflow()
instead of manually
estimating a recipe (see the example in recipe()
).
Note that missing data is handled in the steps; there is no global
na.rm
option at the recipe level or in prep()
.
Also, if a recipe has been trained using prep()
and then steps
are added, prep()
will only update the new operations. If
fresh = TRUE
, all of the operations will be (re)estimated.
As the steps are executed, the training
set is updated. For example,
if the first step is to center the data and the second is to scale the
data, the step for scaling is given the centered data.
Examples
data(ames, package = "modeldata")
library(dplyr)
ames <- mutate(ames, Sale_Price = log10(Sale_Price))
ames_rec <-
recipe(
Sale_Price ~ Longitude + Latitude + Neighborhood + Year_Built + Central_Air,
data = ames
) %>%
step_other(Neighborhood, threshold = 0.05) %>%
step_dummy(all_nominal()) %>%
step_interact(~ starts_with("Central_Air"):Year_Built) %>%
step_ns(Longitude, Latitude, deg_free = 5)
prep(ames_rec, verbose = TRUE)
#> oper 1 step other [training]
#> oper 2 step dummy [training]
#> oper 3 step interact [training]
#> oper 4 step ns [training]
#> The retained training set is ~ 0.48 Mb in memory.
#>
#>
#> ── Recipe ────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 5
#>
#> ── Training information
#> Training data contained 2930 data points and no incomplete rows.
#>
#> ── Operations
#> • Collapsing factor levels for: Neighborhood | Trained
#> • Dummy variables from: Neighborhood and Central_Air | Trained
#> • Interactions with: Central_Air_Y:Year_Built | Trained
#> • Natural splines on: Longitude and Latitude | Trained
prep(ames_rec, log_changes = TRUE)
#> step_other (other_JgwxH): same number of columns
#>
#> step_dummy (dummy_Fe2kG):
#> new (9): Neighborhood_College_Creek, Neighborhood_Old_Town, ...
#> removed (2): Neighborhood, Central_Air
#>
#> step_interact (interact_LGGmr):
#> new (1): Central_Air_Y_x_Year_Built
#>
#> step_ns (ns_lWKqm):
#> new (10): Longitude_ns_1, Longitude_ns_2, Longitude_ns_3, ...
#> removed (2): Longitude, Latitude
#>
#>
#> ── Recipe ────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 5
#>
#> ── Training information
#> Training data contained 2930 data points and no incomplete rows.
#>
#> ── Operations
#> • Collapsing factor levels for: Neighborhood | Trained
#> • Dummy variables from: Neighborhood and Central_Air | Trained
#> • Interactions with: Central_Air_Y:Year_Built | Trained
#> • Natural splines on: Longitude and Latitude | Trained