scream: Scream

In tidymodels/hardhat: Construct Modeling Packages

Scream

Description

scream() ensures that the structure of data is the same as prototype, ptype. Under the hood, vctrs::vec_cast() is used, which casts each column of data to the same type as the corresponding column in ptype.

This casting enforces a number of important structural checks, including but not limited to:

• Data Classes - Checks that the class of each column in data is the same as the corresponding column in ptype.

• Novel Levels - Checks that the factor columns in data don't have any new levels when compared with the ptype columns. If there are new levels, a warning is issued and they are coerced to NA. This check is optional, and can be turned off with allow_novel_levels = TRUE.

• Level Recovery - Checks that the factor columns in data aren't missing any factor levels when compared with the ptype columns. If there are missing levels, then they are restored.

Usage

scream(data, ptype, allow_novel_levels = FALSE)

Arguments

data	A data frame containing the new data to check the structure of.
ptype	A data frame prototype to cast data to. This is commonly a 0-row slice of the training set.
allow_novel_levels	Should novel factor levels in data be allowed? The safest approach is the default, which throws a warning when novel levels are found, and coerces them to NA values. Setting this argument to TRUE will ignore all novel levels. This argument does not apply to ordered factors. Novel levels are not allowed in ordered factors because the level ordering is a critical part of the type.

Details

scream() is called by forge() after shrink() but before the actual processing is done. Generally, you don't need to call scream() directly, as forge() will do it for you.

If scream() is used as a standalone function, it is good practice to call shrink() right before it as there are no checks in scream() that ensure that all of the required column names actually exist in data. Those checks exist in shrink().

Value

A tibble containing the required columns after any required structural modifications have been made.

Factor Levels

scream() tries to be helpful by recovering missing factor levels and warning about novel levels. The following graphic outlines how scream() handles factor levels when coercing from a column in data to a column in ptype.

Note that ordered factor handing is much stricter than factor handling. Ordered factors in data must have exactly the same levels as ordered factors in ptype.

Examples

# ---------------------------------------------------------------------------
# Setup

train <- iris[1:100, ]
test <- iris[101:150, ]

# mold() is run at model fit time
# and a formula preprocessing blueprint is recorded
x <- mold(log(Sepal.Width) ~ Species, train)

# Inside the result of mold() are the prototype tibbles
# for the predictors and the outcomes
ptype_pred <- x$blueprint$ptypes$predictors
ptype_out <- x$blueprint$ptypes$outcomes

# ---------------------------------------------------------------------------
# shrink() / scream()

# Pass the test data, along with a prototype, to
# shrink() to extract the prototype columns
test_shrunk <- shrink(test, ptype_pred)

# Now pass that to scream() to perform validation checks
# If no warnings / errors are thrown, the checks were
# successful!
scream(test_shrunk, ptype_pred)

# ---------------------------------------------------------------------------
# Outcomes

# To also extract the outcomes, use the outcome prototype
test_outcome <- shrink(test, ptype_out)
scream(test_outcome, ptype_out)

# ---------------------------------------------------------------------------
# Casting

# scream() uses vctrs::vec_cast() to intelligently convert
# new data to the prototype automatically. This means
# it can automatically perform certain conversions, like
# coercing character columns to factors.
test2 <- test
test2$Species <- as.character(test2$Species)

test2_shrunk <- shrink(test2, ptype_pred)
scream(test2_shrunk, ptype_pred)

# It can also recover missing factor levels.
# For example, it is plausible that the test data only had the
# "virginica" level
test3 <- test
test3$Species <- factor(test3$Species, levels = "virginica")

test3_shrunk <- shrink(test3, ptype_pred)
test3_fixed <- scream(test3_shrunk, ptype_pred)

# scream() recovered the missing levels
levels(test3_fixed$Species)

# ---------------------------------------------------------------------------
# Novel levels

# When novel levels with any data are present in `data`, the default
# is to coerce them to `NA` values with a warning.
test4 <- test
test4$Species <- as.character(test4$Species)
test4$Species[1] <- "new_level"

test4$Species <- factor(
  test4$Species,
  levels = c(levels(test$Species), "new_level")
)

test4 <- shrink(test4, ptype_pred)

# Warning is thrown
test4_removed <- scream(test4, ptype_pred)

# Novel level is removed
levels(test4_removed$Species)

# No warning is thrown
test4_kept <- scream(test4, ptype_pred, allow_novel_levels = TRUE)

# Novel level is kept
levels(test4_kept$Species)

tidymodels/hardhat documentation built on Dec. 14, 2024, 11:11 a.m.