Package 'validatetools' reference manual

Title:	Checking and Simplifying Validation Rule Sets
Description:	Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with 'validate'.
Authors:	Edwin de Jonge [aut, cre] , Mark van der Loo [aut], Jacco Daalmans [ctb]
Maintainer:	Edwin de Jonge <[email protected]>
License:	MIT + file LICENSE
Version:	0.5.2
Built:	2024-12-11 04:25:58 UTC
Source:	https://github.com/data-cleaning/validatetools

Detect viable domains for categorical variables

Description

Detect viable domains for categorical variables

Usage

detect_boundary_cat(x, ..., as_df = FALSE)
detect_boundary_cat(x, ..., as_df = FALSE)

Arguments

`x`	`validator` object with rules
`...`	not used
`as_df`	return result as data.frame (before 0.4.5)

Value

data.frame with columns $variable, $value, $min, $max. Each row is a category/value of a categorical variable.

Examples

rules <- validator(
  x >= 1,
  x + y <= 10,
  y >= 6
)

detect_boundary_num(rules)

rules <- validator(
  job %in% c("yes", "no"),
  if (job == "no") income == 0,
  income > 0
)

detect_boundary_cat(rules)
rules <- validator(
  x >= 1,
  x + y <= 10,
  y >= 6
)

detect_boundary_num(rules)

rules <- validator(
  job %in% c("yes", "no"),
  if (job == "no") income == 0,
  income > 0
)

detect_boundary_cat(rules)

Detect the range for numerical variables

Description

Detect for each numerical variable in a validation rule set, what its maximum and minimum values are. This allows for manual rule set checking: does rule set x overly constrain numerical values?

Usage

detect_boundary_num(x, eps = 1e-08, ...)
detect_boundary_num(x, eps = 1e-08, ...)

Arguments

`x`	`validator` object, rule set to be checked
`eps`	detected fixed values will have this precission.
`...`	currently not used

Details

This procedure only finds minimum and maximum values, but misses gaps.

Value

data.frame with columns "variable", "lowerbound", "upperbound".

References

Statistical Data Cleaning with R (2017), Chapter 8, M. van der Loo, E. de Jonge

Simplifying constraints in data editing (2015). Technical Report 2015|18, Statistics Netherlands, J. Daalmans

Examples

rules <- validator(
  x >= 1,
  x + y <= 10,
  y >= 6
)

detect_boundary_num(rules)

rules <- validator(
  job %in% c("yes", "no"),
  if (job == "no") income == 0,
  income > 0
)

detect_boundary_cat(rules)
rules <- validator(
  x >= 1,
  x + y <= 10,
  y >= 6
)

detect_boundary_num(rules)

rules <- validator(
  job %in% c("yes", "no"),
  if (job == "no") income == 0,
  income > 0
)

detect_boundary_cat(rules)

Detect fixed variables

Description

Detects variables that have a fixed value in the rule set. To simplify a rule set, these variables can be substituted with their value.

Usage

detect_fixed_variables(x, eps = x$options("lin.eq.eps"), ...)
detect_fixed_variables(x, eps = x$options("lin.eq.eps"), ...)

Arguments

`x`	`validator` object with the validation rules.
`eps`	detected fixed values will have this precission.
`...`	not used.

Examples

library(validate)
rules <- validator( x >= 0
                  , x <= 0
                  )
detect_fixed_variables(rules)
simplify_fixed_variables(rules)

rules <- validator( x1 + x2 + x3 == 0
                  , x1 + x2 >= 0
                  , x3 >= 0
                  )
simplify_fixed_variables(rules)
library(validate)
rules <- validator( x >= 0
                  , x <= 0
                  )
detect_fixed_variables(rules)
simplify_fixed_variables(rules)

rules <- validator( x1 + x2 + x3 == 0
                  , x1 + x2 >= 0
                  , x3 >= 0
                  )
simplify_fixed_variables(rules)

Detect which rules cause infeasibility

Description

Detect which rules cause infeasibility. This methods tries to remove the minimum number of rules to make the system mathematically feasible. Note that this may not result in your desired system, because some rules may be more important to you than others. This can be mitigated by supplying weights for the rules. Default weight is 1.

Usage

detect_infeasible_rules(x, weight = numeric(), ...)
detect_infeasible_rules(x, weight = numeric(), ...)

Arguments

`x`	`validator` object with rules
`weight`	optional named `numeric` with weights. Unnamed variables in the weight are given the default weight `1`.
`...`	not used

Value

character with the names of the rules that are causing infeasibility.

Examples

rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")
rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")

Detect redundant rules without removing.

Description

Detect redundancies in a rule set.

Usage

detect_redundancy(x, ...)
detect_redundancy(x, ...)

Arguments

`x`	`validator` object with the validation rules.
`...`	not used.

Note

For removal of duplicate rules, simplify

Examples

rules <- validator( rule1 = x > 1
                  , rule2 = x > 2
                  )

# rule1 is superfluous
remove_redundancy(rules)

# rule 1 is implied by rule 2
is_implied_by(rules, "rule1")

rules <- validator( rule1 = x > 2
                  , rule2 = x > 2
)

# standout: rule1 and rule2, oldest rules wins
remove_redundancy(rules)

# Note that detection signifies both rules!
detect_redundancy(rules)


rules <- validator( rule1 = x > 1
                  , rule2 = x > 2
                  )

# rule1 is superfluous
remove_redundancy(rules)

# rule 1 is implied by rule 2
is_implied_by(rules, "rule1")

rules <- validator( rule1 = x > 2
                  , rule2 = x > 2
)

# standout: rule1 and rule2, oldest rules wins
remove_redundancy(rules)

# Note that detection signifies both rules!
detect_redundancy(rules)

expect values

Description

expect values

Usage

expect_values(values, weights, ...)
expect_values(values, weights, ...)

Arguments

`values`	named list of values.
`weights`	named numeric of equal length as values.
`...`	not used

Check if rules are categorical

Description

Check if rules are categorical

Usage

is_categorical(x, ...)
is_categorical(x, ...)

Arguments

`x`	validator object
`...`	not used

Value

logical indicating which rules are purely categorical/logical

Examples

v <- validator( A %in% c("a1", "a2")
              , B %in% c("b1", "b2")
              , if (A == "a1") B == "b1"
              , y > x
              )

is_categorical(v)
v <- validator( A %in% c("a1", "a2")
              , B %in% c("b1", "b2")
              , if (A == "a1") B == "b1"
              , y > x
              )

is_categorical(v)

Check if rules are conditional rules

Description

Check if rules are conditional rules

Usage

is_conditional(rules, ...)
is_conditional(rules, ...)

Arguments

`rules`	validator object containing validation rules
`...`	not used

Value

logical indicating which rules are conditional

Examples

v <- validator( A %in% c("a1", "a2")
              , B %in% c("b1", "b2")
              , if (A == "a1")  x > 1 # conditional
              , if (y > 0) x >= 0 # conditional
              , if (A == "a1") B == "b1" # categorical
              )

is_conditional(v)
v <- validator( A %in% c("a1", "a2")
              , B %in% c("b1", "b2")
              , if (A == "a1")  x > 1 # conditional
              , if (y > 0) x >= 0 # conditional
              , if (A == "a1") B == "b1" # categorical
              )

is_conditional(v)

Find out which rules are conflicting

Description

Find out for a contradicting rule which rules are conflicting. This helps in determining and assessing conflicts in rule sets. Which of the rules should stay and which should go?

Usage

is_contradicted_by(x, rule_name)
is_contradicted_by(x, rule_name)

Arguments

`x`	`validator` object with rules.
`rule_name`	`character` with the names of the rules that are causing infeasibility.

Value

character with conflicting rules.

Examples

rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")
rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")

Find which rule(s) make rule_name redundant

Description

Find out which rules are causing rule_name(s) to be redundant.

Usage

is_implied_by(x, rule_name, ...)
is_implied_by(x, rule_name, ...)

Arguments

`x`	`validator` object with rule
`rule_name`	`character` with the names of the rules to be checked
`...`	not used

Value

character with the names of the rule that cause the implication.

Examples

rules <- validator( rule1 = x > 1
                  , rule2 = x > 2
                  )

# rule1 is superfluous
remove_redundancy(rules)

# rule 1 is implied by rule 2
is_implied_by(rules, "rule1")

rules <- validator( rule1 = x > 2
                  , rule2 = x > 2
)

# standout: rule1 and rule2, oldest rules wins
remove_redundancy(rules)

# Note that detection signifies both rules!
detect_redundancy(rules)


rules <- validator( rule1 = x > 1
                  , rule2 = x > 2
                  )

# rule1 is superfluous
remove_redundancy(rules)

# rule 1 is implied by rule 2
is_implied_by(rules, "rule1")

rules <- validator( rule1 = x > 2
                  , rule2 = x > 2
)

# standout: rule1 and rule2, oldest rules wins
remove_redundancy(rules)

# Note that detection signifies both rules!
detect_redundancy(rules)

Check the feasibility of a rule set

Description

An infeasible rule set cannot be satisfied by any data because of internal contradictions. This function checks whether the record-wise linear, categorical and conditional rules in a rule set are consistent.

Usage

is_infeasible(x, ...)
is_infeasible(x, ...)

Arguments

`x`	`validator` object with validation rules.
`...`	not used

Value

TRUE or FALSE

Examples

rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")
rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")

Check which rules are linear rules.

Description

Check which rules are linear rules.

Usage

is_linear(x, ...)
is_linear(x, ...)

Arguments

`x`	`validator` object containing data validation rules
`...`	not used

Value

logical indicating which rules are (purely) linear.

Make an infeasible system feasible.

Description

Make an infeasible system feasible, by removing the minimum (weighted) number of rules, such that the remaining rules are not conflicting. This function uses detect_infeasible_rules for determining the rules to be removed.

Usage

make_feasible(x, ...)
make_feasible(x, ...)

Arguments

`x`	`validator` object with the validation rules.
`...`	passed to `detect_infeasible_rules`

Value

validator object with feasible rules.

Examples

rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")
rules <- validator( x > 0)

is_infeasible(rules)

rules <- validator( rule1 = x > 0
                  , rule2 = x < 0
                  )

is_infeasible(rules)

detect_infeasible_rules(rules)
make_feasible(rules)

# find out the conflict with this rule
is_contradicted_by(rules, "rule1")

Remove redundant rules

Description

Simplify a rule set by removing redundant rules

Usage

remove_redundancy(x, ...)
remove_redundancy(x, ...)

Arguments

`x`	`validator` object with validation rules.
`...`	not used

Value

simplified validator object, in which redundant rules are removed.

Examples

rules <- validator( rule1 = x > 1
                  , rule2 = x > 2
                  )

# rule1 is superfluous
remove_redundancy(rules)

# rule 1 is implied by rule 2
is_implied_by(rules, "rule1")

rules <- validator( rule1 = x > 2
                  , rule2 = x > 2
)

# standout: rule1 and rule2, oldest rules wins
remove_redundancy(rules)

# Note that detection signifies both rules!
detect_redundancy(rules)


rules <- validator( rule1 = x > 1
                  , rule2 = x > 2
                  )

# rule1 is superfluous
remove_redundancy(rules)

# rule 1 is implied by rule 2
is_implied_by(rules, "rule1")

rules <- validator( rule1 = x > 2
                  , rule2 = x > 2
)

# standout: rule1 and rule2, oldest rules wins
remove_redundancy(rules)

# Note that detection signifies both rules!
detect_redundancy(rules)

Simplify conditional statements

Description

Conditional rules may be constrained by the others rules in a validation rule set. This procedure tries to simplify conditional statements.

Usage

simplify_conditional(x, ...)
simplify_conditional(x, ...)

Arguments

`x`	`validator` object with the validation rules.
`...`	not used.

Value

validator simplified rule set.

References

TODO non-constraining, non-relaxing

Examples

library(validate)

# non-relaxing clause
rules <- validator( r1 = if (x > 1) y > 3
                  , r2 = y < 2
                  )
# y > 3 is always FALSE so r1 can be simplified
simplify_conditional(rules)

# non-constraining clause
rules <- validator( r1 = if (x > 0) y > 0
                  , r2 = if (x < 1) y > 1
                  )
simplify_conditional(rules)

rules <- validator( r1 = if (A == "a1") x > 0
                  , r2 = if (A == "a2") x > 1
                  , r3 = A == "a1"
                  )
simplify_conditional(rules)
library(validate)

# non-relaxing clause
rules <- validator( r1 = if (x > 1) y > 3
                  , r2 = y < 2
                  )
# y > 3 is always FALSE so r1 can be simplified
simplify_conditional(rules)

# non-constraining clause
rules <- validator( r1 = if (x > 0) y > 0
                  , r2 = if (x < 1) y > 1
                  )
simplify_conditional(rules)

rules <- validator( r1 = if (A == "a1") x > 0
                  , r2 = if (A == "a2") x > 1
                  , r3 = A == "a1"
                  )
simplify_conditional(rules)

Simplify fixed variables

Description

Detect variables of which the values are restricted to a single value by the rule set. Simplify the rule set by replacing fixed variables with these values.

Usage

simplify_fixed_variables(x, eps = 1e-08, ...)
simplify_fixed_variables(x, eps = 1e-08, ...)

Arguments

`x`	`validator` object with validation rules
`eps`	detected fixed values will have this precission.
`...`	passed to `substitute_values`.

Value

validator object in which

Examples

library(validate)
rules <- validator( x >= 0
                  , x <= 0
                  )
detect_fixed_variables(rules)
simplify_fixed_variables(rules)

rules <- validator( x1 + x2 + x3 == 0
                  , x1 + x2 >= 0
                  , x3 >= 0
                  )
simplify_fixed_variables(rules)
library(validate)
rules <- validator( x >= 0
                  , x <= 0
                  )
detect_fixed_variables(rules)
simplify_fixed_variables(rules)

rules <- validator( x1 + x2 + x3 == 0
                  , x1 + x2 >= 0
                  , x3 >= 0
                  )
simplify_fixed_variables(rules)

Simplify a rule set

Description

Simplifies a rule set set by applying different simplification methods. This is a convenience function that works in common cases. The following simplification methods are executed:

substitute_values: filling in any parameters that are supplied via .values or ....
simplify_fixed_variables: find out if there are fixed values. If this is the case, they are substituted.
simplify_conditional: Simplify conditional statements, by removing clauses that are superfluous.
remove_redundancy: remove redundant rules.

For more control, these methods can be called separately.

Usage

simplify_rules(.x, .values = list(...), ...)
simplify_rules(.x, .values = list(...), ...)

Arguments

`.x`	`validator` object with the rules to be simplified.
`.values`	optional named list with values that will be substituted.
`...`	parameters that will be used to substitute values.

Examples

rules <- validator( x > 0
                  , if (x > 0) y == 1
                  , A %in% c("a1", "a2")
                  , if (A == "a1") y > 1
                  )

simplify_rules(rules)

rules <- validator( x > 0
                  , if (x > 0) y == 1
                  , A %in% c("a1", "a2")
                  , if (A == "a1") y > 1
                  )

simplify_rules(rules)

substitute a value in a rule set

Description

Substitute values into expression, thereby simplifying the rule set. Rules that evaluate to TRUE because of the substitution are removed.

Usage

substitute_values(.x, .values = list(...), ..., .add_constraints = TRUE)
substitute_values(.x, .values = list(...), ..., .add_constraints = TRUE)

Arguments

`.x`	`validator` object with rules
`.values`	(optional) named list with values for variables to substitute
`...`	alternative way of supplying values for variables (see examples).
`.add_constraints`	`logical`, should values be added as constraints to the resulting validator object?

Examples

library(validate)
rules <- validator( rule1 = z > 1
                  , rule2 = y > z
                  )
# rule1 is dropped, since it always is true
substitute_values(rules, list(z=2))

# you can also supply the values as separate parameters
substitute_values(rules, z = 2)

# you can choose to not add substituted values as a constraint
substitute_values(rules, z = 2, .add_constraints = FALSE)

rules <- validator( rule1 = if (gender == "male") age >= 18 )
substitute_values(rules, gender="male")
substitute_values(rules, gender="female")
library(validate)
rules <- validator( rule1 = z > 1
                  , rule2 = y > z
                  )
# rule1 is dropped, since it always is true
substitute_values(rules, list(z=2))

# you can also supply the values as separate parameters
substitute_values(rules, z = 2)

# you can choose to not add substituted values as a constraint
substitute_values(rules, z = 2, .add_constraints = FALSE)

rules <- validator( rule1 = if (gender == "male") age >= 18 )
substitute_values(rules, gender="male")
substitute_values(rules, gender="female")

translate linear rules into an lp problem

Description

translate linear rules into an lp problem

Usage

translate_mip_lp(rules, objective = NULL, eps = 0.001)
translate_mip_lp(rules, objective = NULL, eps = 0.001)

Arguments

`rules`	mip rules
`objective`	function
`eps`	accuracy for equality/inequality

Tools for validation rules

Description

validatetools is a utility package for managing validation rule sets that are defined with validate. In production systems validation rule sets tend to grow organically and accumulate redundant or (partially) contradictory rules. 'validatetools' helps to identify problems with large rule sets and includes simplification methods for resolving issues.

Problem detection

The following methods allow for problem detection:

is_infeasible checks a rule set for feasibility. An infeasible system must be corrected to be useful.
detect_boundary_num shows for each numerical variable the allowed range of values.
detect_boundary_cat shows for each categorical variable the allowed range of values.
detect_fixed_variables shows variables whose value is fixated by the rule set.
detect_redundancy shows which rules are already implied by other rules.

Simplifying rule set

The following methods detect possible simplifications and apply them to a rule set.

substitute_values: replace variables with constants.
simplify_fixed_variables: substitute the fixed variables with their values in a rule set.
simplify_conditional: remove redundant (parts of) conditional rules.
remove_redundancy: remove redundant rules.

References

Statistical Data Cleaning with Applications in R, Mark van der Loo and Edwin de Jonge, ISBN: 978-1-118-89715-7

Package 'validatetools'

Help Index

Detect viable domains for categorical variables

Description

Usage

Arguments

Value

See Also

Examples

Detect the range for numerical variables

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Detect fixed variables

Description

Usage

Arguments

See Also

Examples

Detect which rules cause infeasibility

Description

Usage

Arguments

Value

See Also

Examples

Detect redundant rules without removing.

Description

Usage

Arguments

Note

See Also

Examples

expect values

Description

Usage

Arguments

Check if rules are categorical

Description

Usage

Arguments

Value

Examples

Check if rules are conditional rules

Description

Usage

Arguments

Value

Examples

Find out which rules are conflicting

Description

Usage

Arguments

Value

See Also

Examples

Find which rule(s) make rule_name redundant

Description

Usage

Arguments

Value

See Also

Examples

Check the feasibility of a rule set

Description

Usage

Arguments

Value

See Also

Examples

Check which rules are linear rules.

Description

Usage

Arguments

Value