OptimalBinningWoE (version 1.0.8)

ob_check_distincts: Check Distinct Length

Description

Internal utility function that counts the number of distinct (unique) values in a feature vector. Used for preprocessing validation before applying optimal binning algorithms to determine if the feature has sufficient variability.

Usage

ob_check_distincts(x, target)

Value

Integer scalar representing the number of unique values in x, excluding NA values. Returns 0 if x is empty or contains only

NA values.

Arguments

x

Vector (numeric, character, or factor) whose unique values are to be counted. Accepts any R vector type that can be compared for equality.

target

Integer vector of binary target values (0/1). Must have the same length as x. While not used in the distinct count calculation, it is required for interface consistency and may be used for future extensions (e.g., counting distinct values per class).

Details

This function is typically used internally by optimal binning algorithms to:

• Validate that the feature has at least 2 distinct values (required for binning).

• Determine if special handling is needed for low-cardinality features (e.g., \(\le 2\) unique values).

• Decide between binning strategies (continuous vs categorical).

Handling of Missing Values: NA, NaN, and Inf values are excluded from the count. To include missing values as a distinct category, preprocess x by converting missings to a placeholder (e.g., "-999" for numeric, "Missing" for character).

Examples

# \donttest{
# Continuous feature with many unique values
x_continuous <- rnorm(1000)
target <- sample(0:1, 1000, replace = TRUE)
ob_check_distincts(x_continuous, target)
# Returns: ~1000 (approximately all unique due to floating point)

# Low-cardinality feature
x_binary <- sample(c("Yes", "No"), 1000, replace = TRUE)
ob_check_distincts(x_binary, target)
# Returns: 2

# Feature with missing values
x_with_na <- c(1, 2, NA, 2, 3, NA, 1)
target_short <- c(1, 0, 1, 0, 1, 0, 1)
ob_check_distincts(x_with_na, target_short)
# Returns: 3 (counts: 1, 2, 3; NAs excluded)

# Empty or all-NA feature
x_empty <- rep(NA, 100)
ob_check_distincts(x_empty, sample(0:1, 100, replace = TRUE))
# Returns: 0
# }