Changelog

Version 0.3-4 is considered pre-release of {SLmetrics}. We do not expect any breaking changes, unless a major bug/issue is reported and its nature forces breaking changes.

🔖 Version 0.3-4

This update has been focused on two three things:

1. Optimization of the back-end by using Armadillo instead of Eigen.

2. Streamlining and extending the documentation

3. Making functions more flexible

As an example on the increased flexibility is the introduction of theestimator-argument in classification metrics - the new approach enables new additions of aggregation methods as the field evolves. The “old” approach were limited to three values NULL, TRUE and FALSE. Furthermore the function signatures of the generics have been made more flexible - this will enable possible wrapping packages to freely implement argument names off the generic.

✨ Improvements

• Armadillo backend: All functions have been ported to the C++ Armadillo library, and are heavily templated and Object Oriented. The functions are 5-20x faster than before.

• Streamlined documentation: All documentation have been reworked, and are now using generic {roxygen2} templates. The new structure of the documentation is focused on shared documentation and therefore equal metrics like recall and sensitivity are aliased, and referenced differently - as a result there should be less noise in the documentation. The creating factor has been removed, and all examples are simplified.

• Efficient multi-metric evaluation: The Precision-Recall and Receiver Operator Characteristics functions now accepts an indices argument. The indices takes an integer-matrix of corresponding to the sorted probabilities column-wise. See below:

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Calculate Precision-Recall
stopifnot(
    all.equal(
        target  = SLmetrics::pr.curve(actual_classes, probability_matrix),
        current = SLmetrics::pr.curve(actual_classes, probability_matrix, indices = SLmetrics::preorder(probability_matrix, TRUE))
    )
)

Depending on the system and data, there is a 3x gain in speed. This approach is highly efficient for cases where multiple AUC or curves are to be computed as it avoids sorting the same probability matrix more than once.

🚀 New features

• Hamming Loss: The fraction of the wrong labels to the total number of labels, i.e. , where is the target, is the prediction, and is the “Exclusive, or” operator that returns zero when the target and prediction are identical and one otherwise. The interface tohammingloss() is given below:

set.seed(1903)

## classes
classes <- c("Kebab", "Falafel")

## actual and
## predicted classes
actual    <- factor(sample(classes, 10, TRUE))
predicted <- factor(sample(classes, 10, TRUE))
w         <- runif(n = 10)

## calculate hamming
## loss (weighted and unweighted)
SLmetrics::hammingloss(
    actual,
    predicted
)
#> [1] 1

SLmetrics::weighted.hammingloss(
    actual,
    predicted,
    w = w
)
#> [1] 1

• Tweedie Deviance: The interface to tweedie.deviance() is given below:

## Generate actual
## and predicted values
actual_values <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)

predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.tweedie(
   actual_values, 
   predicted_values
)
#> [1] 0.9976545

• Gamma Deviance: The interface to gamma.deviance() is given below:

## Generate actual
## and predicted values
actual_values <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)

predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.gamma(
   actual_values, 
   predicted_values
)
#> [1] 0.9976545

• Poisson Deviance: The interface to poisson.deviance() is given below:

## Generate actual
## and predicted values
actual_values <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)

predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.poisson(
   actual_values, 
   predicted_values
)
#> [1] 0.3980706

• Mean Arctangent Absolute Error: Seehere for a general description of the implementation. The metric can be calculated as follows:

## Generate actual
## and predicted values
actual_values <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)

predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::maape(
   actual_values, 
   predicted_values
)
#> [1] 0.2499164

• Geometric Mean Squared Error: The function have been implemented with logs and antilogs and is robust to zero-valued vectors. The metric can be calculated as follows:

## Generate actual
## and predicted values
actual_values <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)

predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::gmse(
   actual_values, 
   predicted_values
)
#> [1] 0.03926918

🐛 Bug-fixes

💥 Breaking changes

• Area under the curve: The new interface is given below:

## Generate x and y
## pair
x <- seq(0, pi, length.out = 200)
y <- sin(x)

## 1.1) calculate area
SLmetrics::auc.xy(y = y,  x = x)
#> [1] 1.999958

• Receiver Operating Characteristics: The new interface is given below:

## define classes
## and response probabilities
actual   <- factor(c("Class A", "Class B", "Class A"))
response <- matrix(cbind(
    0.2, 0.8,
    0.8, 0.2,
    0.7, 0.3
),nrow = 3, ncol = 2)

## receiver operating curve
SLmetrics::roc.curve(
    actual,
    response
)
#>    threshold level   label fpr tpr
#> 1        Inf     1 Class A 0.0 0.0
#> 2        0.8     1 Class A 1.0 0.0
#> 3        0.8     1 Class A 1.0 0.5
#> 4        0.2     1 Class A 1.0 1.0
#> 5       -Inf     1 Class A 1.0 1.0
#> 6        Inf     2 Class B 0.0 0.0
#> 7        0.7     2 Class B 0.0 1.0
#> 8        0.3     2 Class B 0.5 1.0
#> 9        0.2     2 Class B 1.0 1.0
#> 10      -Inf     2 Class B 1.0 1.0

## area under the receiver operating
## curve
SLmetrics::auc.roc.curve(
    actual,
    response,
    estimator = 0 # 0: class-wise, 1: micro average, 2: macro average 
)
#> Class A Class B 
#>       0       1

• Precision-Recall Curve: The new interface is given below:

## define classes
## and response probabilities
actual   <- factor(c("Class A", "Class B", "Class A"))
response <- matrix(cbind(
    0.2, 0.8,
    0.8, 0.2,
    0.7, 0.3
),nrow = 3, ncol = 2)

## precision-recall curve
SLmetrics::pr.curve(
    actual,
    response
)
#>    threshold level   label recall precision
#> 1        Inf     1 Class A    0.0     1.000
#> 2        0.8     1 Class A    0.0     0.000
#> 3        0.8     1 Class A    0.5     0.500
#> 4        0.2     1 Class A    1.0     0.667
#> 5       -Inf     1 Class A    1.0     0.667
#> 6        Inf     2 Class B    0.0     1.000
#> 7        0.7     2 Class B    1.0     1.000
#> 8        0.3     2 Class B    1.0     0.500
#> 9        0.2     2 Class B    1.0     0.333
#> 10      -Inf     2 Class B    1.0     0.333

## area under the precision-recall
## curve
SLmetrics::auc.pr.curve(
    actual,
    response,
    estimator = 0 # 0: class-wise, 1: micro average, 2: macro average 
)
#>   Class A   Class B 
#> 0.4166667 1.0000000

• Entropy: entropy() has been renamed to shannon.entropy(). The new interface to shannon.entropy() is given below:

## Observed probabilities
pk <- matrix(
  cbind(1/2, 1/2),
  ncol = 2
)

## Shannon Entropy
SLmetrics::shannon.entropy(pk)
#> [1] 0.6931472

• Aggregation in classification metrics: The aggregation flag in the classification functions micro have been replaced with theinteger-argument estimator which falls back to class-wise evaluation if misspecified. The new interface is given below and is applicable to all functions that has this argument:

set.seed(1903)

## classes
classes <- c("Kebab", "Falafel")

## actual and
## predicted classes
actual    <- factor(sample(classes, 10, TRUE))
predicted <- factor(sample(classes, 10, TRUE))

## recall: class-wise
SLmetrics::recall(
    actual,
    predicted,
    estimator = 0
)
#> Falafel   Kebab 
#>       0       0

## recall: micro-averaged
SLmetrics::recall(
    actual,
    predicted,
    estimator = 1
)
#> [1] 0

## recall: macro-averaged
SLmetrics::recall(
    actual,
    predicted,
    estimator = 1
)
#> [1] 0

🔖 Version 0.3-3

✨ Improvements

• Initial CRAN release: The R-package has (finally) been submitted to CRAN and was released on 2025-03-18 with the classic “Thanks, on its way to CRAN” message.

• S3 signatures: All S3-methods now have a generic signature, making it easier to navigate the functions argument-wise.

• Exported Data: Three new datasets have been introduced to the package; the Wine Quality-,Obesity- and Banknote Authentication datasets. Each dataset is comes in named list where features and targets are stored separately. Below is an example from the Obesity dataset:

## 1) summarise the
## list
summary(SLmetrics::obesity)
#>          Length Class      Mode
#> features 15     data.frame list
#> target    2     -none-     list

## 2) head the featues
head(SLmetrics::obesity$features)
#>        caec       calc                mtrans family_history_with_overweight
#> 1 sometimes         no public_transportation                              1
#> 2 sometimes  sometimes public_transportation                              1
#> 3 sometimes frequently public_transportation                              1
#> 4 sometimes frequently               walking                              0
#> 5 sometimes  sometimes public_transportation                              0
#> 6 sometimes  sometimes            automobile                              0
#>   favc smoke scc male age height fcvc ncp ch2o faf tue
#> 1    0     0   0    0  21   1.62    2   3    2   0   1
#> 2    0     1   1    0  21   1.52    3   3    3   3   0
#> 3    0     0   0    1  23   1.80    2   3    2   2   1
#> 4    0     0   0    1  27   1.80    3   3    2   2   0
#> 5    0     0   0    1  22   1.78    2   1    2   0   0
#> 6    1     0   0    1  29   1.62    2   3    2   0   0

## 3) head target
## variables
head(SLmetrics::obesity$target$class)
#> [1] Normal_Weight       Normal_Weight       Normal_Weight      
#> [4] Overweight_Level_I  Overweight_Level_II Normal_Weight      
#> 7 Levels: Insufficient_Weight Normal_Weight Obesity_Type_I ... Overweight_Level_II
head(SLmetrics::obesity$target$regression)
#> [1] 64.0 56.0 77.0 87.0 89.8 53.0

🚀 New features

New metrics

• Poisson LogLoss: The logloss for count data has been implemented. This metric shares the method of logloss and can be used as follows:

## 1) define observed integers
## and response probabilities
actual   <- as.integer(factor(c("Class A", "Class B", "Class A")))
weights  <- c(0.3,0.9,1) 
response <- matrix(cbind(
    0.2, 0.8,
    0.8, 0.2,
    0.7, 0.3
),nrow = 3, ncol = 2)

## 2) weighted
## and unweighted poisson
## distributed log-loss
cat(
    "Unweighted Poisson Log Loss:",
    SLmetrics::logloss(
        actual,
        response
    ),
    "Weighted Poisson Log Loss:",
    SLmetrics::weighted.logloss(
        actual   = actual,
        response = response,
        w        = weights
    ),
    sep = "\n"
)
#> Unweighted Poisson Log Loss:
#> 1.590672
#> Weighted Poisson Log Loss:
#> 1.505212

• Area under the Curve: A new set of functions have been introduced which calculates the weighted and unweighted area under the Precision-Recall and Receiver Operator Characteristics curve. See below:

## 1) define observed integers
## and response probabilities
actual   <- factor(c("Class A", "Class B", "Class A"))
weights  <- c(0.3,0.9,1) 
response <- matrix(cbind(
    0.2, 0.8,
    0.8, 0.2,
    0.7, 0.3
),nrow = 3, ncol = 2)

## 2) area under
## the precision-recall curve
SLmetrics::pr.auc(
    actual   = actual,
    response = response
)
#>   Class A   Class B 
#> 0.4166667 1.0000000

Metric tools

A new family of Tools-functions are introduced with this update. This addition introduces unexported functions for constructing fast and memory efficient proprietary metrics. These functions are rewritten built-in functions from {stats} and family.

• Covariance Matrix: A re-written stats::cov.wt(), using Rcpp. Example usage:

## 1) actual and
## predicted values
actual    <- c(1.2,  0.3, 0.56, 0.11, 1.01)
predicted <- c(0.9, 0.22, 0.76, 0.21, 1.1) 

## 2) covariance
## matrix 
SLmetrics:::cov.wt(
    cbind(actual, predicted)
)
#> $cov
#>             actual predicted
#> actual    0.213330  0.169215
#> predicted 0.169215  0.163720
#> 
#> $center
#>    actual predicted 
#>     0.636     0.638 
#> 
#> $n.obs
#> [1] 5

• Area under the curve (AUC): The function calculates the area under the plot for bivariate curves for ordered and unordered x and y pairs. The function assumes that values are ordered and calculates the AUC directly - to control this behaviour use the ordered-argument in the function. Below is an example:

## 0) seed
set.seed(1903)

## 1) Ordered x and y pair
x <- seq(0, pi, length.out = 200)
y <- sin(x)

## 1.1) calculate area
ordered_auc <- SLmetrics::auc(y = y,  x = x)

## 2) Unordered x and y pair
x <- sample(seq(0, pi, length.out = 200))
y <- sin(x)

## 2.1) calculate area
unordered_auc <- SLmetrics::auc(y = y,  x = x)

## 2.2) calculate area with explicit
## ordering
unordered_auc_flag <- SLmetrics::auc(
  y = y,
  x = x,
  ordered = FALSE
)

## 3) display result
cat(
  "AUC (ordered x and y pair)", ordered_auc,
  "AUC (unordered x and y pair)", unordered_auc,
  "AUC (unordered x and y pair, with unordered flag)", unordered_auc_flag,
  sep = "\n"
)
#> AUC (ordered x and y pair)
#> 1.999958
#> AUC (unordered x and y pair)
#> -1.720771
#> AUC (unordered x and y pair, with unordered flag)
#> -1.720771

• Sorting algorithms: A set of sorting and ordering algorithms applicable to matrices have been implemented. The use-case is currently limited to auc.foo, ROC and prROC functions. The algorithms can be used as follows:

## 1) generate a 4x4 matrix
## with random values to be sorted
set.seed(1903)
X <- matrix(
  data = cbind(sample(16:1)),
  nrow = 4
)

## 2) sort matrix
## in decreasing order
SLmetrics::presort(X)
#>      [,1] [,2] [,3] [,4]
#> [1,]    3    2    6    1
#> [2,]    4    5   10    7
#> [3,]    9    8   15   11
#> [4,]   13   14   16   12

## 3) get indices 
## for sorted matrix
SLmetrics::preorder(X)
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    1    2    4
#> [2,]    2    3    3    2
#> [3,]    3    2    1    1
#> [4,]    4    4    4    3

💥 Breaking changes

• Logloss: The argument pk has been replaced by response.

🔖 Version 0.3-2

✨ Improvements

• Regression metrics (See PR https://github.com/serkor1/SLmetrics/pull/64): All regression metrics have had their back-end optimized and are now 2-10 times faster than prior versions.

• LAPACK/BLAS Support (https://github.com/serkor1/SLmetrics/pull/65): Added LAPACK/BLAS support for efficient matrix-operations.

• OpenMP: Enabling/disabling OpenMP is now handled on the R-side and obeys suppressMessages(). See below:

## suppress OpenMP messages
suppressMessages(
  SLmetrics::openmp.off()
)

🚀 New features

• Available threads: The available number of threads can be retrieved using the openmp.threads(). See below:

## number of available
## threads
SLmetrics::openmp.threads()
#> [1] 16

🐛 Bug-fixes

• Diagnostic Odds Ratio: The dor() is now returning a single<[numeric]>-value instead of k number of identical<[numeric]>-values.

💥 Breaking Changes

• OpenMP Interface: The interface to enabling/disabling OpenMP support has been reworked and has a more natural flow. The new interface is described below:

## enable OpenMP
SLmetrics::openmp.on()
#> OpenMP enabled!

## disable OpenMP
SLmetrics::openmp.off()
#> OpenMP disabled!

To set the number of threads use the openmp.threads() as follows:

## set number of threads
SLmetrics::openmp.threads(3)
#> Using 3 threads.

🔖 Version 0.3-1

✨ Improvements

• OpenMP Support (PR https://github.com/serkor1/SLmetrics/pull/40): {SLmetrics} now supports parallelization through OpenMP. The OpenMP can be utilized as follows:

set.seed(1903)

## 1) probability distribution
## function
rand.sum <- function(n){
    x <- sort(runif(n-1))
    c(x,1) - c(0,x)
  }

## 2) generate probability
## matrix
pk <- t(replicate(
    n = 100,
    expr = rand.sum(1e3)
    )
)

## 3) calulate entropy
## with and without OpenMP
SLmetrics::setUseOpenMP(TRUE)
#> OpenMP usage set to: enabled
system.time(SLmetrics::entropy(pk))
#>    user  system elapsed 
#>   0.202   0.000   0.014

SLmetrics::setUseOpenMP(FALSE)
#> OpenMP usage set to: disabled
system.time(SLmetrics::entropy(pk))
#>    user  system elapsed 
#>   0.001   0.001   0.001

• Entropy with soft labels (https://github.com/serkor1/SLmetrics/issues/37): entropy(),cross.entropy() and relative.entropy() have been introduced. These functions are heavily inspired by {scipy}. The functions can be used as follows:

## 1) Define actual
## and observed probabilities

## 1.1) actual probabilies
pk <- matrix(
  cbind(1/2, 1/2),
  ncol = 2
)

## 1.2) observed (estimated) probabilites
qk <- matrix(
  cbind(9/10, 1/10), 
  ncol = 2
)

## 2) calculate entropy
cat(
  "Entropy", SLmetrics::entropy(pk),
  "Relative Entropy", SLmetrics::relative.entropy(pk, qk),
  "Cross Entropy", SLmetrics::cross.entropy(pk, qk),
  sep = "\n"
)
#> Entropy
#> 0.6931472
#> Relative Entropy
#> 0.5108256
#> Cross Entropy
#> 1.203973

🐛 Bug-fixes

• Plot-method in ROC and prROC (https://github.com/serkor1/SLmetrics/issues/36): Fixed a bug inplot.ROC() and plot.prROC() where if panels = FALSE additional lines would be added to the plot.

💥 Breaking changes

• logloss: The argument response have ben renamed to qk as in the entropy()-family to maintain some degree of consistency.

• entropy.factor(): The function have been deleted and is no more. This was mainly due to avoid the documentation from being too large. The logloss()-function replaces it.

🔖 Version 0.3-0

✨ Improvements

New features

• Relative Root Mean Squared Error: The function normalizes the Root Mean Squared Error by a factor. There is no official way of normalizing it - and in {SLmetrics} the RMSE can be normalized using three options; mean-, range- and IQR-normalization. It can be used as follows,

## 1) define actual and
## predicted values
actual <- rnorm(50)
predicted <- actual + rnorm(50)

## 2) calculate rrse
## with normalization
## 0: mean
## 1: range
## 2: iqr
cat(
  "Mean Relative Root Mean Squared Error", SLmetrics::rrmse(
    actual        = actual,
    predicted     = predicted,
    normalization = 0
  ),
  "Range Relative Root Mean Squared Error", SLmetrics::rrmse(
    actual        = actual,
    predicted     = predicted,
    normalization = 1
  ),
  "IQR Relative Root Mean Squared Error", SLmetrics::rrmse(
    actual        = actual,
    predicted     = predicted,
    normalization = 2
  ),
  sep = "\n"
)
#> Mean Relative Root Mean Squared Error
#> 2.724473
#> Range Relative Root Mean Squared Error
#> 0.2288034
#> IQR Relative Root Mean Squared Error
#> 0.6317316

• Log Loss: Weighted and unweighted Log Loss, with and without normalization. The function can be used as follows,

## 1) define actual
## values and estimated
## probabilities
actual   <- factor(c("Class A", "Class B", "Class A"))
weights  <- c(0.3,0.9,1) 
response <- matrix(cbind(
    0.2, 0.8,
    0.8, 0.2,
    0.7, 0.3
),nrow = 3, ncol = 2)

## 2) weighted and unweighted
## log-loss
cat(
    "Unweighted Log Loss:",
    SLmetrics::logloss(
        actual,
        response
    ),
    "Weighted log Loss:",
    SLmetrics::weighted.logloss(
        actual,
        response,       
        weights
    ),
    sep = "\n"
)
#> Unweighted Log Loss:
#> 0.7297521
#> Weighted log Loss:
#> 0.4668102

• Weighted Receiver Operator Characteristics: weighted.ROC(), the function calculates the weighted True Positive and False Positive Rates for each threshold.

• Weighted Precision-Recall Curve: weighted.prROC(), the function calculates the weighted Recall and Precision for each threshold.

🐛 Bug-fixes

• Return named vectors: The classification metrics whenmicro == NULL were not returning named vectors. This has been fixed.

💥 Breaking Changes

• Weighted Confusion Matrix: The w-argument in cmatrix() has been removed in favor of the more verbose weighted confusion matrix call weighted.cmatrix()-function. See below,

Prior to version 0.3-0 the weighted confusion matrix were a part of the cmatrix()-function and were called as follows,

SLmetrics::cmatrix(
    actual    = actual,
    predicted = predicted,
    w         = weights
)

This solution, although simple, were inconsistent with the remaining implementation of weighted metrics in {SLmetrics}. To regain consistency and simplicity the weighted confusion matrix are now retrieved as follows,

## 1) define actual
## and predicted values
## with sample weights
actual    <- factor(sample(letters[1:3], 50, replace = TRUE))
predicted <- factor(sample(letters[1:3], 50, replace = TRUE))
weights   <- runif(length(actual))

## 2) unweighted confusion
## matrix
SLmetrics::cmatrix(
    actual    = actual,
    predicted = predicted
)
#>    a  b  c
#> a  5  5  5
#> b  6  4  2
#> c  7  6 10

## 3) weighted confusion
## matrix
SLmetrics::weighted.cmatrix(
    actual    = actual,
    predicted = predicted,
    w         = weights
)
#>          a        b        c
#> a 2.938218 3.273240 1.795141
#> b 2.787594 2.946578 1.089021
#> c 3.708406 3.255066 3.477562

🔖 Version 0.2-0

:hammer_and_wrench: General

• documentation: The documentation has gotten some extra love, and now all functions have their formulas embedded, the details section have been freed from a general description of [factor] creation. This will make room for future expansions on the various functions where more details are required.

• Unit-testing: All functions are now being tested for edge-cases in balanced and imbalanced classification problems, and regression problems, individually. This will enable a more robust development process and prevent avoidable bugs.

✨ Improvements

• weighted classification metrics: The cmatrix()-function now accepts the argument w which is the sample weights; if passed the respective method will return the weighted metric. Below is an example using sample weights for the confusion matrix,

## 1) define actual and 
## predicted values with
## sample weights
actual    <- factor(sample(letters[1:3], 50, replace = TRUE))
predicted <- factor(sample(letters[1:3], 50, replace = TRUE))
weights   <- runif(length(actual))

## 2) compute weighted
## and unweighted confusion
## matrix
SLmetrics::cmatrix(
    actual    = actual,
    predicted = predicted
)
#>   a b c
#> a 3 7 5
#> b 7 3 6
#> c 9 4 6

SLmetrics::cmatrix(
    actual    = actual,
    predicted = predicted,
    w         = weights
)
#>          a        b        c
#> a 1.361853 3.060079 1.914518
#> b 4.210749 2.250443 1.847300
#> c 3.534448 1.696727 2.587295

Calculating weighted metrics using the <factor>- or<cmatrix>-method,

## 1) weigthed confusion matrix
## and weighted accuray
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual,
    predicted = predicted,
    w         = weights
)

## 2) weighted accuracy
## using <cmatrix> method
SLmetrics::accuracy(
    confusion_matrix
)
#> [1] 0.2759862

## 2) weighted accuracy
## using <factor> method
SLmetrics::weighted.accuracy(
    actual    = actual,
    predicted = predicted,
    w         = weights
)
#> [1] 0.2759862

Please note, however, that it is not possible to pass cmatrix()-intoweighted.accuracy(). See below:

try(
    SLmetrics::weighted.accuracy(
        confusion_matrix
    )
)
#> Error in UseMethod(generic = "weighted.accuracy", object = ..1) : 
#>   no applicable method for 'weighted.accuracy' applied to an object of class "cmatrix"

🐛 Bug-fixes

• Floating precision: Metrics would give different results based on the method used. This means that foo.cmatrix() and foo.factor() would produce different results (See Issue https://github.com/serkor1/SLmetrics/issues/16). This has been fixed by using higher precision Rcpp::NumericMatrix instead ofRcpp::IntegerMatrix.

• Miscalculation of Confusion Matrix elements: An error in how FN,TN, FP and TP were calculated have been fixed. No issue has been raised for this bug. This was not something that was caught by the unit-tests, as the total samples were too high to spot this error. It has, however, been fixed now. This means that all metrics that uses these explicitly are now stable, and produces the desired output.

• Calculation Error in Fowlks Mallows Index: A bug in the calculation of the fmi()-function has been fixed. Thefmi()-function now correctly calculates the measure.

• Calculation Error in Pinball Deviance and Concordance Correlation Coefficient: See issue https://github.com/serkor1/SLmetrics/issues/19. Switched to unbiased variance calculation in ccc()-function. The pinball()-function were missing a weighted quantile function. The issue is now fixed.

• Calculation Error in Balanced Accuracy: See issue https://github.com/serkor1/SLmetrics/issues/24. The function now correctly adjusts for random chance, and the result matches that of {scikit-learn}

• Calculation Error in F-beta Score: See issue https://github.com/serkor1/SLmetrics/issues/23. The function werent respecting na.rm and micro, this has been fixed accordingly.

• Calculation Error in Relative Absolute Error: The function was incorrectly calculating means, instead of sums. This has been fixed.

💥 Breaking changes

• All regression metrics have had na.rm- and w-arguments removed. All weighted regression metrics have a separate function on theweighted.foo() to increase consistency across all metrics. The new function call is given below:

## 1) define actual and
## predicted values
actual    <- rnorm(n = 50)
predicted <- actual + rnorm(n = 50)
w         <- runif(n = 50)

## 2) weighted and unweighted
## root mean squared error
SLmetrics::rmse(actual, predicted)
#> [1] 0.9944228
SLmetrics::weighted.rmse(actual, predicted, w = w)
#> [1] 0.9904456

• The rrmse()-function have been removed in favor of therrse()-function. This function was incorrectly specified and described in the package.

🔖 Version 0.1-1

:hammer_and_wrench: General

• Backend changes: All pair-wise metrics are moved from{Rcpp} to C++, this have reduced execution time by half. All pair-wise metrics are now faster.

✨ Improvements

• NA-controls: All pair-wise metrics that don’t have amicro-argument were handling missing values as according to C++ and {Rcpp} internals. SeeIssue. Thank you @EmilHvitfeldt for pointing this out. This has now been fixed so functions use an na.rm-argument to explicitly control for this. See below,

## 1) define actual and
## predicted classes
actual    <- factor(c("no", "yes", "yes"))
predicted <- factor(c(NA, "no", "yes"))

## 2) calculate
## accuracy with
## and without na.rm
SLmetrics::accuracy(
    actual    = actual,
    predicted = predicted,
    na.rm     = TRUE
)
#> [1] 0.5

SLmetrics::accuracy(
    actual    = actual,
    predicted = predicted,
    na.rm     = FALSE
)
#> [1] NaN

🐛 Bug-fixes

• The plot.prROC()- and plot.ROC()-functions now adds a line to the plot when panels = FALSE. See Issue https://github.com/serkor1/SLmetrics/issues/9.

## 1) define actual classes
## and response probabilities
actual <- factor(
    sample(letters[1:3], size = 50, replace = TRUE)
)

response <- rbeta(
    n = 50,
    shape1 = 20,
    shape2 = 2
)

## 2) define ROC and
## prROC objects
roc_obj <- SLmetrics::ROC(
    actual   = actual,
    response = response 
)

pr_obj <- SLmetrics::prROC(
    actual   = actual,
    response = response 
)

## set plot grid
par(mfrow = c(1,2))

## plot data
## with panels = FALSE
plot(roc_obj, panels = FALSE)

plot(pr_obj, panels = FALSE)

📦 {SLmetrics} Version 0.1-0

{SLmetrics} is a collection of Machine Learning performance evaluation functions for supervised learning written in C++ with{Rcpp}. Visit the online documentation on Github pages.

ℹ️ Basic usage

Classification metrics

## 1) define actual and
## predicted classes
actual <- factor(
    sample(letters[1:3], size = 10, replace = TRUE)
)

predicted <- factor(
    sample(letters[1:3], size = 10, replace = TRUE)
)

## 2) print values
print(actual)
#>  [1] c c c a a b a a a c
#> Levels: a b c

## 1) compute and summarise the
## the confusion matrix
summary(
    confusion_matrix <- SLmetrics::cmatrix(
        actual    = actual,
        predicted = predicted
    )
)
#> Confusion Matrix (3 x 3) 
#> ================================================================================
#>   a b c
#> a 1 1 3
#> b 0 1 0
#> c 0 4 0
#> ================================================================================
#> Overall Statistics (micro average)
#>  - Accuracy:          0.20
#>  - Balanced Accuracy: 0.40
#>  - Sensitivity:       0.20
#>  - Specificity:       0.60
#>  - Precision:         0.20

## 1) false positive rate
## using <cmatrix> method
SLmetrics::fpr(confusion_matrix)
#>         a         b         c 
#> 0.0000000 0.5555556 0.5000000

## 2) false positive rate
## using <factor> method
SLmetrics::fpr(
    actual    = actual, 
    predicted = predicted
)
#>         a         b         c 
#> 0.0000000 0.5555556 0.5000000

Regression metrics

## 1) define actual and
## predicted values
actual <- rnorm(n = 10)
predicted <- actual + rnorm(n = 10)

## 1) calculate Huber Loss and
## Root Mean Squared Error
SLmetrics::huberloss(
    actual    = actual,
    predicted = predicted
)
#> [1] 0.4204029

SLmetrics::rmse(
    actual    = actual,
    predicted = predicted
)
#> [1] 0.9548376