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R6 class for an Implication Set

Source: R/implication_set.R
ImplicationSet.Rd

This class implements an implication set (LHS -> RHS) in the framework of Formal Concept Analysis (FCA). It inherits from RuleSet and adds FCA-specific methods such as closure computation, simplification, and basis transformation.

Super class

fcaR::RuleSet -> ImplicationSet

Methods

Public methods

Inherited methods

Method new()

Initialize an ImplicationSet

Usage

ImplicationSet$new(...)

Arguments

...

A rules object (from arules) or named arguments: name (string), attributes (character vector), lhs and rhs (sparse matrices), I (incidence matrix).

Returns

A new ImplicationSet object.

Method add()

Add a precomputed implication set

Usage

ImplicationSet$add(...)

Arguments

...

An ImplicationSet object, a rules object, or a pair lhs, rhs of Set objects or dgCMatrix. The implications to add to this formal context.

Returns

Nothing, just updates the internal implications field.

Method to_arules()

Convert to arules format

Usage

ImplicationSet$to_arules(quality = TRUE)

Arguments

quality

(logical) Compute the interest measures for each rule?

Returns

A rules object as used by package arules.

Method print()

Print all implications to text

Usage

ImplicationSet$print()

Returns

A string with all the implications in the set.

Method support()

Compute support of each implication

Usage

ImplicationSet$support()

Returns

A vector with the support of each implication

Method closure()

Compute the semantic closure of a fuzzy set with respect to the implication set

Usage

ImplicationSet$closure(S, reduce = FALSE, verbose = FALSE)

Arguments

S

(a Set object) Fuzzy set to compute its closure. Use class Set to build it.

reduce

(logical) Reduce the implications using simplification logic?

verbose

(logical) Show verbose output?

Returns

If reduce == FALSE, the output is a fuzzy set corresponding to the closure of S. If reduce == TRUE, a list with two components: closure, with the closure as above, and implications, the reduced set of implications.

Method recommend()

Generate a recommendation for a subset of the attributes

Usage

ImplicationSet$recommend(S, attribute_filter)

Arguments

S

(a vector) Vector with the grades of each attribute (a fuzzy set).

attribute_filter

(character vector) Names of the attributes to get recommendation for.

Returns

A fuzzy set describing the values of the attributes in attribute_filter within the closure of S.

Method apply_rules()

Apply rules to remove redundancies

Usage

ImplicationSet$apply_rules(
  rules = c("composition", "generalization"),
  batch_size = 25000L,
  parallelize = FALSE,
  reorder = FALSE
)

Arguments

rules

(character vector) Names of the rules to use. See details.

batch_size

(integer) If the number of rules is large, apply the rules by batches of this size.

parallelize

(logical) If possible, should we parallelize the computation among different batches?

reorder

(logical) Should the rules be randomly reordered previous to the computation?

Details

Currently, the implemented rules are "generalization", "simplification", "reduction" and "composition".

Returns

Nothing, just updates the internal matrices for LHS and RHS.

Method to_basis()

Convert Implications to Canonical Basis

Usage

ImplicationSet$to_basis()

Returns

The canonical basis of implications obtained from the current ImplicationSet

Method to_direct_optimal()

Compute the Direct Optimal Basis using optimized C++ algorithms.

Usage

ImplicationSet$to_direct_optimal(
  method = c("do_sp", "direct_optimal", "final_ts", "monotonic", "priority"),
  verbose = FALSE
)

Arguments

method

(character) The specific algorithm to run:

  • "direct_optimal": (Default) The Direct Optimal Saturation-Pruning algorithm.

  • "final_ts": Computes Transitive Closure then Prunes (Standard approach).

  • "monotonic": Incremental algorithm maintaining monotonicity.

  • "priority": Priority-based refinement algorithm.

verbose

(logical) Print verbose output from the C++ backend.

Returns

Nothing, updates the ImplicationSet in place with the new basis.

Method use_logic()

Sets the logic to use

Usage

ImplicationSet$use_logic(name = available_logics())

Arguments

name

The name of the logic to use. To see the available names, run available_logics().

Method get_logic()

Gets the logic used

Usage

ImplicationSet$get_logic()

Returns

A string with the name of the logic.

Method get_standard_context()

Get the standard formal context associated with the implication set.

Usage

ImplicationSet$get_standard_context()

Details

The standard formal context \(K = (J, M, I)\) associated to a set of implications on \(M\) is such that its concept lattice is isomorphic to the lattice of closed sets of the implication set. The objects \(J\) of this context are the meet-irreducible closed sets of the implication set.

Returns

A FormalContext object where the objects are the meet-irreducible closed sets and the attributes are the attributes of the implication set.

Method use_hedge()

Sets the hedge to use when computing closures

Usage

ImplicationSet$use_hedge(name = c("globalization", "identity"))

Arguments

name

The name of the hedge to use. Only "globalization" and "identity" are allowed.

Method get_hedge()

Gets the hedge used to compute closures

Usage

ImplicationSet$get_hedge()

Returns

A string with the name of the hedge

Method to_json()

Export the implication set to JSON

Usage

ImplicationSet$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 implication set, or a list if return_list is TRUE.

Method clone()

The objects of this class are cloneable with this method.

Usage

ImplicationSet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.