,
Mark van der Loo [aut]| Title: | Locate Errors with Validation Rules |
|---|---|
| Description: | Errors in data can be located and removed using validation rules from package 'validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, chapter 7. |
| Authors: | Edwin de Jonge [aut, cre] ,
Mark van der Loo [aut] |
| Maintainer: | Edwin de Jonge <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.1 |
| Built: | 2024-11-06 04:57:13 UTC |
| Source: | https://github.com/data-cleaning/errorlocate |
Find errors in data given a set of validation rules.
The errorlocate helps to identify obvious errors in raw datasets.
It works in tandem with the package validate().
With validate you formulate data validation rules to which the data must comply.
For example:
"age cannot be negative": age >= 0
While validate can identify if a record is valid or not, it does not identify
which of the variables are responsible for the invalidation. This may seem a simple task,
but is actually quite tricky: a set of validation rules form a web
of dependent variables: changing the value of an invalid record to repair for rule 1, may invalidate
the record for rule 2.
Errorlocate provides a small framework for record based error detection and implements the Felligi Holt algorithm. This algorithm assumes there is no other information available then the values of a record and a set of validation rules. The algorithm minimizes the (weighted) number of values that need to be adjusted to remove the invalidation.
The errorlocate package translates the validation and error localization problem into
a mixed integer problem and uses a mip solver to find a solution.
Maintainer: Edwin de Jonge [email protected] (ORCID)
Authors:
Mark van der Loo [email protected]
T. De Waal (2003) Processing of Erroneous and Unsafe Data. PhD thesis, University of Rotterdam.
Van der Loo, M., de Jonge, E, Data Cleaning With Applications in R
E. De Jonge and Van der Loo, M. (2012) Error localization as a mixed-integer program in editrules.
lp_solve and Kjell Konis. (2011). lpSolveAPI: R Interface for lp_solve version 5.5.2.0. R package version 5.5.2.0-5. http://CRAN.R-project.org/package=lpSolveAPI
Useful links:
Report bugs at https://github.com/data-cleaning/errorlocate/issues
Utility function to add some small positive noise to weights. This is mainly done to randomly choose between solutions of equal weight. Without adding noise to weights lp solvers may return an identical solution over and over while there are multiple solutions of equal weight. The generated noise is positive to prevent that weights will be zero or negative.
add_noise(x, max_delta = NULL, ...)add_noise(x, max_delta = NULL, ...)
x |
|
max_delta |
when supplied noise will be drawn from |
... |
currently not used |
When no max_delta is supplied, add_noise will use the minimum difference
larger than zero divided by the length(x).
numeric vector/matrix with noise applied.
ErrorLocalizer can be used as a base class to implement a new error localization algorithm.
The derived class must implement two methods: initialize, which is called
before any error localization is done and locate which operates upon data. The
extra parameter ... can used to supply algorithmic specific parameters.
Errorlocation contains the result of a error detection. Errors can record based or variable based.
A record based error is restricted within one observation.
errorlocate() using the Felligi Holt algorithm assumes errors are record based.
A variable based error is a flaw in uni- or multivariate distribution. To correct this error multiple observations or the aggregated number should be adjusted.
Current implementation assumes that errors are record based. The error locations can be retrieved
using the method values() and are a matrix of
rows and columns, with the same dimensions are the data.frame that was checked.
For errors that are purely column based, or dataset based, errorlocations will return a matrix with all
rows or cells set to TRUE.
The values() return NA for missing values.
$errors: matrix indicating which values are erronuous (TRUE),
missing (NA) or valid (FALSE)
$weight: The total weight per record. A weight of 0 means no errors were detected.
$status: The status of the mip solver for this record.
$duration: The number of seconds for processing each record.
Other error finding:
errors_removed(),
expand_weights(),
locate_errors(),
replace_errors()
errors_removed retrieves the errors detected by replace_errors()
errors_removed(x, ...)errors_removed(x, ...)
x |
|
... |
not used |
errorlocation-class() object
Other error finding:
errorlocation-class,
expand_weights(),
locate_errors(),
replace_errors()
rules <- validator( profit + cost == turnover , cost - 0.6*turnover >= 0 , cost>= 0 , turnover >= 0 ) data <- data.frame(profit=755, cost=125, turnover=200) data_no_error <- replace_errors(data,rules) # faulty data was replaced with NA data_no_error errors_removed(data_no_error) # a bit more control, you can supply the result of locate_errors # to replace_errors, which is a good thing, otherwise replace_errors will call # locate_errors internally. error_locations <- locate_errors(data, rules) replace_errors(data, error_locations)rules <- validator( profit + cost == turnover , cost - 0.6*turnover >= 0 , cost>= 0 , turnover >= 0 ) data <- data.frame(profit=755, cost=125, turnover=200) data_no_error <- replace_errors(data,rules) # faulty data was replaced with NA data_no_error errors_removed(data_no_error) # a bit more control, you can supply the result of locate_errors # to replace_errors, which is a good thing, otherwise replace_errors will call # locate_errors internally. error_locations <- locate_errors(data, rules) replace_errors(data, error_locations)
Expands a weight specification into a weight matrix to be used
by locate_errors and replace_errors. Weights allow for "guiding" the
errorlocalization process, so that less reliable values/variables with less
weight are selected first. See details on the specification.
expand_weights(dat, weight = NULL, as.data.frame = FALSE, ...)expand_weights(dat, weight = NULL, as.data.frame = FALSE, ...)
dat |
|
weight |
weight specification, see details. |
as.data.frame |
if |
... |
unused |
If weight fine tuning is needed,
a possible scenario is to generate a weight data.frame using expand_weights and
adjust it before executing locate_errors() or replace_errors().
The following specifications for weight are supported:
NULL: generates a weight matrix with 1's
a named numeric, unmentioned columns will have weight 1
a unnamed numeric with a length equal to ncol(dat)
a data.frame with same number of rows as dat
a matrix with same number of rows as dat
Inf, NA weights will be interpreted as that those variables must not be
changed and are fixated. Inf weights perform much better than setting a weight
to a large number.
matrix or data.frame of same dimensions as dat
Other error finding:
errorlocation-class,
errors_removed(),
locate_errors(),
replace_errors()
dat <- read.csv(text= "age,country 49, NL 23, DE ", strip.white=TRUE) weight <- c(age = 2, country = 1) expand_weights(dat, weight) weight <- c(2, 1) expand_weights(dat, weight, as.data.frame = TRUE) # works too weight <- c(country=5) expand_weights(dat, weight) # specify a per row weight for country weight <- data.frame(country=c(1,5)) expand_weights(dat, weight) # country should not be changed! weight <- c(country = Inf) expand_weights(dat, weight)dat <- read.csv(text= "age,country 49, NL 23, DE ", strip.white=TRUE) weight <- c(age = 2, country = 1) expand_weights(dat, weight) weight <- c(2, 1) expand_weights(dat, weight, as.data.frame = TRUE) # works too weight <- c(country=5) expand_weights(dat, weight) # specify a per row weight for country weight <- data.frame(country=c(1,5)) expand_weights(dat, weight) # country should not be changed! weight <- c(country = Inf) expand_weights(dat, weight)
Implementation of the Feligi-Holt algorithm using the ErrorLocalizer base class.
Given a set of validation rules and a dataset the Feligi-Holt algorithm finds for each record
the smallest (weighted) combination of variables that are erroneous (if any).
Most users do not need this class and can use locate_errors().
errorlocalizer implements feligi holt using a MIP-solver. For problems in which
coefficients of the validation rules or the data are too different, you should consider scaling
the data.
Utility function to inspect the mip problem for a record. inspect_mip can
be used as a "drop-in" replacement for locate_errors(), but works on the
first record.
inspect_mip(data, x, weight, ...)inspect_mip(data, x, weight, ...)
data |
data to be checked |
x |
validation rules or errorlocalizer object to be used for finding possible errors. |
weight |
|
... |
optional parameters that are passed to |
It may sometimes be handy to find out what is happening exactly with a record.
See the example section for finding out what to do with inspect_mip. See
vignette("inspect_mip") for more details.
Other Mixed Integer Problem:
MipRules-class
rules <- validator(x > 1) data <- list(x = 0) weight <- c(x = 1) mip <- inspect_mip(data, rules) print(mip) # inspect the lp problem (prior to solving it with lpsolveAPI) lp <- mip$to_lp() print(lp) # for large problems write the lp problem to disk for inspection # lpSolveAPI::write.lp(lp, "my_problem.lp") # solve the mip system / find a solution res <- mip$execute() names(res) # lpSolveAPI status of finding a solution res$s # lp problem after solving (often simplified version of first lp) res$lp # records that are deemed "faulty" res$errors # values of variables used in the mip formulation. Also contains a valid solution # for "faulty" variables res$values # see the derived mip rules and objective function, used in the construction of # lp problem mip$mip_rules() mip$objectiverules <- validator(x > 1) data <- list(x = 0) weight <- c(x = 1) mip <- inspect_mip(data, rules) print(mip) # inspect the lp problem (prior to solving it with lpsolveAPI) lp <- mip$to_lp() print(lp) # for large problems write the lp problem to disk for inspection # lpSolveAPI::write.lp(lp, "my_problem.lp") # solve the mip system / find a solution res <- mip$execute() names(res) # lpSolveAPI status of finding a solution res$s # lp problem after solving (often simplified version of first lp) res$lp # records that are deemed "faulty" res$errors # values of variables used in the mip formulation. Also contains a valid solution # for "faulty" variables res$values # see the derived mip rules and objective function, used in the construction of # lp problem mip$mip_rules() mip$objective
Check if rules are categorical
is_categorical(x, ...)is_categorical(x, ...)
x |
validator or expression object |
... |
not used |
#' @note errorlocate supports linear,
categorical and conditional rules to be used in finding errors. Other rule types
are ignored during error finding.
logical indicating which rules are purely categorical/logical
Other rule type:
is_conditional(),
is_linear()
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
is_conditional(rules, ...)is_conditional(rules, ...)
rules |
validator object containing validation rules |
... |
not used |
logical indicating which rules are conditional
errorlocate supports linear,
categorical and conditional rules to be used in finding errors. Other rule types
are ignored during error finding.
Other rule type:
is_categorical(),
is_linear()
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)
Check which rules are linear rules.
is_linear(x, ...)is_linear(x, ...)
x |
|
... |
not used |
logical indicating which rules are (purely) linear.
errorlocate supports linear,
categorical and conditional rules to be used in finding errors. Other rule types
are ignored during error finding.
Other rule type:
is_categorical(),
is_conditional()
Find out which fields in a data.frame are "faulty" using validation rules
This method returns found errors, according to the specified method x.
Use method replace_errors(), to automatically remove these errors.
'
locate_errors( data, x, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), timeout = 60 ) ## S4 method for signature 'data.frame,validator' locate_errors( data, x, weight = NULL, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), timeout = 60 ) ## S4 method for signature 'data.frame,ErrorLocalizer' locate_errors( data, x, weight = NULL, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), timeout = 60 )locate_errors( data, x, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), timeout = 60 ) ## S4 method for signature 'data.frame,validator' locate_errors( data, x, weight = NULL, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), timeout = 60 ) ## S4 method for signature 'data.frame,ErrorLocalizer' locate_errors( data, x, weight = NULL, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), timeout = 60 )
data |
data to be checked |
x |
validation rules or errorlocalizer object to be used for finding possible errors. |
... |
optional parameters that are passed to |
cl |
optional parallel / cluster. |
Ncpus |
number of nodes to use. See details |
timeout |
maximum number of seconds that the localizer should use per record. |
weight |
|
ref |
|
Use an Inf weight specification to fixate variables that can not be changed.
See expand_weights() for more details.
locate_errors uses lpSolveAPI to formulate and solves a mixed integer problem.
For details see the vignettes.
This solver has many options: lpSolveAPI::lp.control.options. Noteworthy
options to be used are:
timeout: restricts the time the solver spends on a record (seconds)
break.at.value: set this to minimum weight + 1 to improve speed.
presolve: default for errorlocate is "rows". Set to "none" when you have
solutions where all variables are deemed wrong.
locate_errors can be run on multiple cores using R package parallel.
The easiest way to use the parallel option is to set Ncpus to the number of
desired cores, @seealso parallel::detectCores().
Alternatively one can create a cluster object (parallel::makeCluster())
and use cl to pass the cluster object.
Or set cl to an integer which results in parallel::mclapply(), which only works
on non-windows.
errorlocation-class() object describing the errors found.
Other error finding:
errorlocation-class,
errors_removed(),
expand_weights(),
replace_errors()
rules <- validator( profit + cost == turnover , cost >= 0.6 * turnover # cost should be at least 60% of turnover , turnover >= 0 # can not be negative. ) data <- data.frame( profit = 755 , cost = 125 , turnover = 200 ) le <- locate_errors(data, rules) print(le) summary(le) v_categorical <- validator( branch %in% c("government", "industry") , tax %in% c("none", "VAT") , if (tax == "VAT") branch == "industry" ) data <- read.csv(text= " branch, tax government, VAT industry , VAT ", strip.white = TRUE) locate_errors(data, v_categorical)$errors v_logical <- validator( citizen %in% c(TRUE, FALSE) , voted %in% c(TRUE, FALSE) , if (voted == TRUE) citizen == TRUE ) data <- data.frame(voted = TRUE, citizen = FALSE) locate_errors(data, v_logical, weight=c(2,1))$errors # try a condinational rule v <- validator( married %in% c(TRUE, FALSE) , if (married==TRUE) age >= 17 ) data <- data.frame( married = TRUE, age = 16) locate_errors(data, v, weight=c(married=1, age=2))$errors # different weights per row data <- read.csv(text= "married, age TRUE, 16 TRUE, 14 ", strip.white = TRUE) weight <- read.csv(text= "married, age 1, 2 2, 1 ", strip.white = TRUE) locate_errors(data, v, weight = weight)$errors # fixate / exclude a variable from error localiziation # using an Inf weight weight <- c(age = Inf) locate_errors(data, v, weight = weight)$errorsrules <- validator( profit + cost == turnover , cost >= 0.6 * turnover # cost should be at least 60% of turnover , turnover >= 0 # can not be negative. ) data <- data.frame( profit = 755 , cost = 125 , turnover = 200 ) le <- locate_errors(data, rules) print(le) summary(le) v_categorical <- validator( branch %in% c("government", "industry") , tax %in% c("none", "VAT") , if (tax == "VAT") branch == "industry" ) data <- read.csv(text= " branch, tax government, VAT industry , VAT ", strip.white = TRUE) locate_errors(data, v_categorical)$errors v_logical <- validator( citizen %in% c(TRUE, FALSE) , voted %in% c(TRUE, FALSE) , if (voted == TRUE) citizen == TRUE ) data <- data.frame(voted = TRUE, citizen = FALSE) locate_errors(data, v_logical, weight=c(2,1))$errors # try a condinational rule v <- validator( married %in% c(TRUE, FALSE) , if (married==TRUE) age >= 17 ) data <- data.frame( married = TRUE, age = 16) locate_errors(data, v, weight=c(married=1, age=2))$errors # different weights per row data <- read.csv(text= "married, age TRUE, 16 TRUE, 14 ", strip.white = TRUE) weight <- read.csv(text= "married, age 1, 2 2, 1 ", strip.white = TRUE) locate_errors(data, v, weight = weight)$errors # fixate / exclude a variable from error localiziation # using an Inf weight weight <- c(age = Inf) locate_errors(data, v, weight = weight)$errors
Create a mip object from validator() object.
This is a utility class that translates a validor object into a mixed integer problem that
can be solved.
Most users should use locate_errors() which will handle all translation and execution
automatically. This class is provided so users can implement or derive an alternative solution.
The MipRules class contains the following methods:
$execute() calls the mip solver to execute the rules.
$to_lp(): transforms the object into a lp_solve object
$is_infeasible Checks if the current system of mixed integer rules is feasible.
$set_values: set values and weights for variables (determines the objective function).
Other Mixed Integer Problem:
inspect_mip()
rules <- validator(x > 1) mr <- miprules(rules) mr$to_lp() mr$set_values(c(x=0), weights=c(x=1)) mr$execute()rules <- validator(x > 1) mr <- miprules(rules) mr$to_lp() mr$set_values(c(x=0), weights=c(x=1)) mr$execute()
Find erroneous fields using locate_errors() and replace these
fields automatically with NA or a suggestion that is provided by the error detection algorithm.
replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), value = c("NA", "suggestion") ) ## S4 method for signature 'data.frame,validator' replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), value = c("NA", "suggestion") ) ## S4 method for signature 'data.frame,ErrorLocalizer' replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), value = c("NA", "suggestion") ) ## S4 method for signature 'data.frame,errorlocation' replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = 1, value = c("NA", "suggestion") )replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), value = c("NA", "suggestion") ) ## S4 method for signature 'data.frame,validator' replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), value = c("NA", "suggestion") ) ## S4 method for signature 'data.frame,ErrorLocalizer' replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = getOption("Ncpus", 1), value = c("NA", "suggestion") ) ## S4 method for signature 'data.frame,errorlocation' replace_errors( data, x, ref = NULL, ..., cl = NULL, Ncpus = 1, value = c("NA", "suggestion") )
data |
data to be checked |
x |
|
ref |
optional reference data set |
... |
these parameters are handed over to |
cl |
optional cluster for parallel execution (see details) |
Ncpus |
number of nodes to use. (see details) |
value |
|
Note that you can also use the result of locate_errors() with replace_errors.
When the procedure takes a long time and locate_errors was called previously
this is the preferred way, because otherwise locate_errors will be executed again.
The errors that were removed from the data.frame can be retrieved with the function
errors_removed(). For more control over error localization see locate_errors().
replace_errors has the same parallelization options as locate_errors() (see there).
data with erroneous values removed.
In general it is better to replace the erroneous fields with NA and apply a proper
imputation method. Suggested values from the error localization method may introduce an undesired bias.
Other error finding:
errorlocation-class,
errors_removed(),
expand_weights(),
locate_errors()
rules <- validator( profit + cost == turnover , cost - 0.6*turnover >= 0 , cost>= 0 , turnover >= 0 ) data <- data.frame(profit=755, cost=125, turnover=200) data_no_error <- replace_errors(data,rules) # faulty data was replaced with NA data_no_error errors_removed(data_no_error) # a bit more control, you can supply the result of locate_errors # to replace_errors, which is a good thing, otherwise replace_errors will call # locate_errors internally. error_locations <- locate_errors(data, rules) replace_errors(data, error_locations)rules <- validator( profit + cost == turnover , cost - 0.6*turnover >= 0 , cost>= 0 , turnover >= 0 ) data <- data.frame(profit=755, cost=125, turnover=200) data_no_error <- replace_errors(data,rules) # faulty data was replaced with NA data_no_error errors_removed(data_no_error) # a bit more control, you can supply the result of locate_errors # to replace_errors, which is a good thing, otherwise replace_errors will call # locate_errors internally. error_locations <- locate_errors(data, rules) replace_errors(data, error_locations)
translate linear rules into an lp problem
translate_mip_lp(rules, objective = NULL, eps = 0.001, ...)translate_mip_lp(rules, objective = NULL, eps = 0.001, ...)
rules |
mip rules |
objective |
function |
eps |
accuracy for equality/inequality |
... |
additional |