# Working with ImplicationSets

# Introduction

In this vignette, we present the main functionalities and data structures of the `fcaR` package when working with formal contexts and concepts, in FCA.

We load the `fcaR` package by:

    library(fcaR)

# Datasets

We are going to work with two datasets, a crisp one and a fuzzy one.

The classical (binary) dataset is the well-known `planets` formal context, presented in

> Wille R (1982). “Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts.” In Ordered Sets, pp. 445–470. Springer.

    knitr::kable(planets, format = "html", booktabs = TRUE)

| small | medium | large | near | far | moon | no_moon
--- | --- | --- | --- | --- | --- | --- | ---
Mercury | 1 | 0 | 0 | 1 | 0 | 0 | 1
Venus | 1 | 0 | 0 | 1 | 0 | 0 | 1
Earth | 1 | 0 | 0 | 1 | 0 | 1 | 0
Mars | 1 | 0 | 0 | 1 | 0 | 1 | 0
Jupiter | 0 | 0 | 1 | 0 | 1 | 1 | 0
Saturn | 0 | 0 | 1 | 0 | 1 | 1 | 0
Uranus | 0 | 1 | 0 | 0 | 1 | 1 | 0
Neptune | 0 | 1 | 0 | 0 | 1 | 1 | 0
Pluto | 1 | 0 | 0 | 0 | 1 | 1 | 0

The other formal context is fuzzy and is defined by the following matrix I:

    knitr::kable(I, format = "html", booktabs = TRUE)

| P1 | P2 | P3 | P4 | P5 | P6
--- | --- | --- | --- | --- | --- | ---
O1 | 0.0 | 0.0 | 0.5 | 0.5 | 1.0 | 0
O2 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0
O3 | 0.5 | 0.5 | 0.0 | 0.0 | 0.0 | 1
O4 | 0.0 | 0.0 | 0.0 | 1.0 | 0.5 | 0
O5 | 0.0 | 0.0 | 1.0 | 0.5 | 0.0 | 0
O6 | 0.5 | 0.5 | 0.0 | 0.0 | 0.0 | 1

# Working with ImplicationSets

Although `ImplicationSet` objects can be created ad hoc, the usual way to get implications is by the application of the NextClosure algorithm to a `FormalContext` object.

Thus, let us create different formal contexts with the previous datasets:

    fc_planets <- FormalContext$new(planets)
    fc_I <- FormalContext$new(I)

## Extraction of the canonical basis of implications

The function `find_implications()` use the NextClosure algorithm in a formal context to extract the canonical basis of implications:

    fc_planets$find_implications()
    fc_I$find_implications()

We can inspect the implications by doing:

    fc_planets$implications
    #> Implication set with 10 implications.
    #> Rule 1: {no_moon} -> {small, near}
    #> Rule 2: {far} -> {moon}
    #> Rule 3: {near} -> {small}
    #> Rule 4: {large} -> {far, moon}
    #> Rule 5: {medium} -> {far, moon}
    #> Rule 6: {medium, large, far, moon} -> {small, near, no_moon}
    #> Rule 7: {small, near, moon, no_moon} -> {medium, large, far}
    #> Rule 8: {small, near, far, moon} -> {medium, large, no_moon}
    #> Rule 9: {small, large, far, moon} -> {medium, near, no_moon}
    #> Rule 10: {small, medium, far, moon} -> {large, near, no_moon}
    fc_I$implications
    #> Implication set with 12 implications.
    #> Rule 1: {P6 [0.5]} -> {P1 [0.5], P2 [0.5], P6}
    #> Rule 2: {P5 [0.5]} -> {P4 [0.5]}
    #> Rule 3: {P3 [0.5], P4 [0.5], P5 [0.5]} -> {P5}
    #> Rule 4: {P3 [0.5], P4} -> {P3}
    #> Rule 5: {P3, P4 [0.5], P5} -> {P4}
    #> Rule 6: {P2 [0.5]} -> {P1 [0.5]}
    #> Rule 7: {P1 [0.5]} -> {P2 [0.5]}
    #> Rule 8: {P1 [0.5], P2 [0.5], P4 [0.5]} -> {P1, P2, P3, P4, P5, P6}
    #> Rule 9: {P1 [0.5], P2 [0.5], P3 [0.5]} -> {P1, P2, P3}
    #> Rule 10: {P1 [0.5], P2} -> {P1}
    #> Rule 11: {P1, P2 [0.5]} -> {P2}
    #> Rule 12: {P1, P2, P3, P6} -> {P4, P5}

Internally, an `ImplicationSet` consists of two matrices (one for the left-hand sides and the other for the right-hand sides of the rules). We can get these (sparse) matrices as:

    fc_planets$implications$get_LHS_matrix()
    #> 7 x 10 sparse Matrix of class "dgCMatrix"
    #>    [[ suppressing 10 column names '1', '2', '3' ... ]]
    #>
    #> small   . . . . . . 1 1 1 1
    #> medium  . . . . 1 1 . . . 1
    #> large   . . . 1 . 1 . . 1 .
    #> near    . . 1 . . . 1 1 . .
    #> far     . 1 . . . 1 . 1 1 1
    #> moon    . . . . . 1 1 1 1 1
    #> no_moon 1 . . . . . 1 . . .
    fc_planets$implications$get_RHS_matrix()
    #> 7 x 10 sparse Matrix of class "dgCMatrix"
    #>    [[ suppressing 10 column names '1', '2', '3' ... ]]
    #>
    #> small   1 . 1 . . 1 . . . .
    #> medium  . . . . . . 1 1 1 .
    #> large   . . . . . . 1 1 . 1
    #> near    1 . . . . 1 . . 1 1
    #> far     . . . 1 1 . 1 . . .
    #> moon    . 1 . 1 1 . . . . .
    #> no_moon . . . . . 1 . 1 1 1

The main practical use of an `ImplicationSet` is to compute the closure of a set of attributes, by using the `closure()` function:

    # Let us build a set of attributes
    S <- SparseSet$new(attributes = fc_planets$attributes)
    S$assign(large = 1, far = 1)
    S
    #> {large, far}

    fc_planets$implications$closure(S)$closure
    #> {large, far, moon}

## Cardinality, size and support of the implication set

Some quantities can be computed for an `ImplicationSet`:

- Cardinality: the number of implications in the set

    fc_planets$implications$cardinality()
    #> [1] 10

- Size: The number of attributes in the LHS and the RHS of each implication

    sizes <- fc_planets$implications$size()
    # Total number of attributes in the LHS and the RHS
    colSums(sizes)
    #> LHS RHS
    #>  25  23

- Support: The proportion of objects in the formal context whose attributes contain the LHS of a particular rule

    fc_planets$implications$support()
    #>  [1] 0.2222222 0.5555556 0.4444444 0.2222222 0.2222222 0.0000000 0.0000000
    #>  [8] 0.0000000 0.0000000 0.0000000

## Export to LaTeX

A nice feature is the ability to export an `ImplicationSet` to LaTeX format:

    fc_planets$implications$to_latex()
    #> \begin{longtable}{rrcl}
    #> 1: &\ensuremath{\left\{\mathrm{no_moon}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{small},\, \mathrm{near}\right\}}\\
    #> 2: &\ensuremath{\left\{\mathrm{far}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{moon}\right\}}\\
    #> 3: &\ensuremath{\left\{\mathrm{near}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{small}\right\}}\\
    #> 4: &\ensuremath{\left\{\mathrm{large}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{far},\, \mathrm{moon}\right\}}\\
    #> 5: &\ensuremath{\left\{\mathrm{medium}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{far},\, \mathrm{moon}\right\}}\\
    #> 6: &\ensuremath{\left\{\mathrm{medium},\, \mathrm{large},\, \mathrm{far},\, \mathrm{moon}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{small},\, \mathrm{near},\, \mathrm{no_moon}\right\}}\\
    #> 7: &\ensuremath{\left\{\mathrm{small},\, \mathrm{near},\, \mathrm{moon},\, \mathrm{no_moon}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{medium},\, \mathrm{large},\, \mathrm{far}\right\}}\\
    #> 8: &\ensuremath{\left\{\mathrm{small},\, \mathrm{near},\, \mathrm{far},\, \mathrm{moon}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{medium},\, \mathrm{large},\, \mathrm{no_moon}\right\}}\\
    #> 9: &\ensuremath{\left\{\mathrm{small},\, \mathrm{large},\, \mathrm{far},\, \mathrm{moon}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{medium},\, \mathrm{near},\, \mathrm{no_moon}\right\}}\\
    #> 10: &\ensuremath{\left\{\mathrm{small},\, \mathrm{medium},\, \mathrm{far},\, \mathrm{moon}\right\}}&\ensuremath{\Rightarrow}&\ensuremath{\left\{\mathrm{large},\, \mathrm{near},\, \mathrm{no_moon}\right\}}\\
    #> \end{longtable}

## Filtering of implications

Sometimes it is needed to work with a subset of the implications, using only the implications that fulfill certain conditions:

    # Implications with P1 and P2 in the LHS and P5 in the RHS
    fc_I$implications$filter(lhs = c("P1", "P2"),
                             rhs = "P5")
    #> Implication set with 2 implications.
    #> Rule 1: {P1 [0.5], P2 [0.5], P4 [0.5]} -> {P1, P2, P3, P4, P5, P6}
    #> Rule 2: {P1, P2, P3, P6} -> {P4, P5}

## Simplification Logic

In this package, we have implemented logic tools to operate on the implications.

First, some simplification rules have been developed, named reduction, composition, generalization and simplification, that can be applied using the `apply_rules()` function:

    fc_I$implications$apply_rules(rules = c("composition",
                                            "simplification"))
    #> Processing batch
    #> --> composition: from 12 to 12 in 0.002 secs.
    #> --> simplification: from 12 to 12 in 0.053 secs.
    #> Batch took 0.058 secs.

This enables the reduction of the cardinality and/or the size of the `ImplicationSet`.

In addition, the “simplification” rule to remove redundancies can be used in the computation of the closure of a set, to provide a reduced set of implications that is inferred from the set of attributes:

    # Let us build a set of attributes
    S <- SparseSet$new(attributes = fc_planets$attributes)
    S$assign(large = 1, far = 1)
    S
    #> {large, far}

    fc_planets$implications$closure(S, reduce = TRUE)
    #> $closure
    #> {large, far, moon}
    #> $implications
    #> Implication set with 7 implications.
    #> Rule 1: {medium} -> {small, near, no_moon}
    #> Rule 2: {small, near, no_moon} -> {medium}
    #> Rule 3: {small, near} -> {medium, no_moon}
    #> Rule 4: {small} -> {medium, near, no_moon}
    #> Rule 5: {small, medium} -> {near, no_moon}
    #> Rule 6: {no_moon} -> {small, near}
    #> Rule 7: {near} -> {small}

## Recommendations

One of the final applications of an `ImplicationSet` is the easy development of a recommendation system where, from an attribute set, the system would infer the value to other attribute. This is done by the `recommend()` function, which internally computes the closure of the attribute set:

    S <- SparseSet$new(attributes = fc_I$attributes)
    S$assign(P1 = 1, P4 = 0.5)

    fc_I$implications$recommend(S, attribute_filter = c("P3", "P5"))
    #> P3 P5
    #>  1  1