RuleSet: R6 class for a Rule Set

In fcaR: Formal Concept Analysis

R6 class for a Rule Set

Description

This class implements a generic rule set (LHS -> RHS), serving as the parent class for ImplicationSet. It provides common functionality for managing, filtering, and exporting rules.

Methods

Public methods

• RuleSet$new()

• RuleSet$get_attributes()

• RuleSet$[()

• RuleSet$to_arules()

• RuleSet$add()

• RuleSet$cardinality()

• RuleSet$is_empty()

• RuleSet$size()

• RuleSet$total_size()

• RuleSet$print()

• RuleSet$get_quality()

• RuleSet$to_latex()

• RuleSet$get_LHS_matrix()

• RuleSet$get_RHS_matrix()

• RuleSet$filter()

• RuleSet$get_implications()

• RuleSet$support()

• RuleSet$confidence()

• RuleSet$to_json()

• RuleSet$clone()

---

Method new()

Initialize a RuleSet

Usage

RuleSet$new(...)

Arguments

...

A rules object (from arules) or named arguments: name (string), attributes (character vector), lhs and rhs (sparse matrices), I (incidence matrix), quality (data.frame), confidence (numeric vector, backward compat).

Returns

A new RuleSet object.

---

Method get_attributes()

Get the names of the attributes

Usage

RuleSet$get_attributes()

Returns

A character vector with the names of the attributes used in the rules.

---

Method [()

Get a subset of the rule set

Usage

RuleSet$[(idx)

Arguments

idx

(integer or logical vector) Indices of the rules to extract or remove. If logical vector, only TRUE elements are retained and the rest discarded.

Returns

A new RuleSet with only the rules given by the idx indices.

---

Method to_arules()

Convert to arules format

Usage

RuleSet$to_arules(quality = TRUE)

Arguments

quality

(logical) Compute/include the interest measures for each rule?

Returns

A rules object as used by package arules.

---

Method add()

Add a precomputed rule set

Usage

RuleSet$add(...)

Arguments

...

A RuleSet object, or a pair lhs, rhs of dgCMatrix.

Returns

Nothing, just updates the internal field.

---

Method cardinality()

Cardinality: Number of rules in the set

Usage

RuleSet$cardinality()

Returns

The cardinality of the rule set.

---

Method is_empty()

Empty set

Usage

RuleSet$is_empty()

Returns

TRUE if the set of rules is empty, FALSE otherwise.

---

Method size()

Size: number of attributes in each of LHS and RHS

Usage

RuleSet$size()

Returns

A matrix with two columns: the number of attributes present in each of the LHS and RHS of each rule.

---

Method total_size()

Total Size: cumulative sum of attributes in LHS and RHS

Usage

RuleSet$total_size()

Returns

A named numeric vector of length 2: LHS and RHS sizes.

---

Method print()

Print all rules to text

Usage

RuleSet$print()

Returns

A string with all the rules in the set.

---

Method get_quality()

Get quality metrics

Usage

RuleSet$get_quality()

Returns

A data.frame with the quality metrics for each rule.

---

Method to_latex()

Export to LaTeX

Usage

RuleSet$to_latex(
  print = TRUE,
  ncols = 1,
  numbered = TRUE,
  numbers = seq_len(self$cardinality())
)

Arguments

print

(logical) Print to output?

ncols

(integer) Number of columns for the output.

numbered

(logical) If TRUE (default), implications will be numbered in the output.

numbers

(vector) If numbered, use these elements to enumerate the implications. The default is to enumerate 1, 2, ..., but can be changed.

Returns

A string in LaTeX format that prints nicely all the rules.

---

Method get_LHS_matrix()

Get internal LHS matrix

Usage

RuleSet$get_LHS_matrix()

Returns

A sparse matrix representing the LHS of the rules in the set.

---

Method get_RHS_matrix()

Get internal RHS matrix

Usage

RuleSet$get_RHS_matrix()

Returns

A sparse matrix representing the RHS of the rules in the set.

---

Method filter()

Filter rules by attributes in LHS and RHS

Usage

RuleSet$filter(
  lhs = NULL,
  not_lhs = NULL,
  rhs = NULL,
  not_rhs = NULL,
  drop = FALSE
)

Arguments

lhs

(character vector) Names of the attributes to filter the LHS by. If NULL, no filtering is done on the LHS.

not_lhs

(character vector) Names of the attributes to not include in the LHS. If NULL (the default), it is not considered at all.

rhs

(character vector) Names of the attributes to filter the RHS by. If NULL, no filtering is done on the RHS.

not_rhs

(character vector) Names of the attributes to not include in the RHS. If NULL (the default), it is not considered at all.

drop

(logical) Remove the rest of attributes in RHS?

Returns

A RuleSet (or subclass) that is a subset of the current set, only with those rules which have the attributes in lhs and rhs in their LHS and RHS, respectively.

---

Method get_implications()

Extract the implications with confidence 1

Usage

RuleSet$get_implications()

Returns

An ImplicationSet object containing only the rules with confidence 1.

---

Method support()

Compute support of each rule

Usage

RuleSet$support()

Returns

A vector with the support of each rule.

---

Method confidence()

Compute the confidence of each rule

Usage

RuleSet$confidence()

Returns

A numeric vector with the confidence of each rule.

---

Method to_json()

Export the rule set to JSON

Usage

RuleSet$to_json(file = NULL, return_list = FALSE)

Arguments

file

(character) The path of the file to save the JSON to.

return_list

(logical) If TRUE, returns the list representation instead of the JSON string.

Returns

A JSON string representing the rule set, or a list if return_list is TRUE.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

RuleSet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

fcaR documentation built on July 27, 2026, 5:06 p.m.