added precision, recall, f1_score

NAMESPACE

@@ -10,6 +10,7 @@ export(auc)
 export(bias)
 export(ce)
 export(f1)
+export(fbeta_score)
 export(ll)
 export(logLoss)
 export(mae)
@@ -20,7 +21,9 @@ export(mdae)
 export(mse)
 export(msle)
 export(percent_bias)
+export(precision)
 export(rae)
+export(recall)
 export(rmse)
 export(rmsle)
 export(rrse)

R/binary_classification.R

@@ -76,3 +76,65 @@ ll <- function(actual, predicted) {
 logLoss <- function(actual, predicted) {
     return(mean(ll(actual, predicted)))
 }
+
+
+
+#' Precision
+#' 
+#' \code{precision} computes proportion of observations predicted to be in the
+#'                  positive class (i.e. the element in \code{predicted} equals 1)
+#'                  that actually belong to the positive class (i.e. the element 
+#'                  in \code{actual} equals 1)
+#'     
+#' @inheritParams params_binary
+#' @export
+#' @seealso \code{\link{recall}} \code{\link{fbeta_score}}
+#' @examples 
+#' actual <- c(1, 1, 1, 0, 0, 0)
+#' predicted <- c(1, 1, 1, 1, 1, 1)
+#' precision(actual, predicted)
+precision <- function(actual, predicted) {
+    return(mean(actual[predicted == 1]))
+}
+
+#' Recall
+#' 
+#' \code{recall} computes proportion of observations in the positive class
+#'               (i.e. the element in \code{actual} equals 1) that are predicted
+#'               to be in the positive class (i.e. the element in \code{predicted}
+#'               equals 1)
+#'     
+#' @inheritParams params_binary
+#' @export
+#' @seealso \code{\link{precision}} \code{\link{fbeta_score}}
+#' @examples 
+#' actual <- c(1, 1, 1, 0, 0, 0)
+#' predicted <- c(1, 0, 1, 1, 1, 1)
+#' recall(actual, predicted)
+recall <- function(actual, predicted) {
+    return(mean(predicted[actual == 1]))
+}
+
+#' F-beta Score
+#' 
+#' \code{fbeta_score} computes a weighted harmonic mean of Precision and Recall.
+#'                    The \code{beta} parameter controls the weighting.
+#'                    
+#' @inheritParams params_binary
+#' @param beta A non-negative real number controlling how close the F-beta score is to 
+#'             either Precision or Recall. When \code{beta} is at the default of 1, 
+#'             the F-beta Score is exactly an equally weighted harmonic mean.
+#'             The F-beta score will weight toward Precision when \code{beta} is less 
+#'             than one.  The F-beta score will weight toward Recall when \code{beta} is
+#'             greater than one.
+#' @export
+#' @seealso \code{\link{precision}} \code{\link{recall}}
+#' @examples 
+#' actual <- c(1, 1, 1, 0, 0, 0)
+#' predicted <- c(1, 0, 1, 1, 1, 1)
+#' recall(actual, predicted)
+fbeta_score <- function(actual, predicted, beta = 1) {
+    prec <- precision(actual, predicted)
+    rec <- recall(actual, predicted)
+    return((1 + beta^2) * prec * rec / ((beta^2 * prec) + rec))
+}

README.md

@@ -43,3 +43,6 @@ All functions in the **Metrics** package take at least two arguments: `actual` a
 | binary classification | Area Under ROC Curve | auc | ![equation](https://latex.codecogs.com/gif.latex?%5Cdpi%7B150%7D%20%5Cint_0%5E1%20%5B1%20-%20G_1%28G%5E%7B-1%7D_0%281%20-%20v%29%29%5D%20dv). `help(auc)` for details. |
 | binary classification | Log Loss | ll | ![equation](https://latex.codecogs.com/gif.latex?%5Cdpi%7B150%7D%20x_i%20*%20%5Cln%28y_i%29%20+%20%281%20-%20x_i%29%20*%20%5Cln%281%20-%20y_i%29) |
 | binary classification | Mean Log Loss | logloss | ![equation](https://latex.codecogs.com/gif.latex?%5Cdpi%7B150%7D%20%5Cfrac%7B1%7D%7Bn%7D%20%5Csum_%7Bi%3D1%7D%5En%20x_i%20*%20%5Cln%28y_i%29%20+%20%281%20-%20x_i%29%20*%20%5Cln%281%20-%20y_i%29) |
+| binary classification | Precision | precision | ![equation](https://latex.codecogs.com/gif.latex?%5Cdpi%7B150%7D%20%5Cfrac%7B1%7D%7B%5Csum%20I%28y_i%20%3D%201%29%7D%5Csum_%7Bi%3D1%7D%20%5E%7Bn%7D%20I%28y_i%20%3D%201%29x_i) |
+| binary classification | Recall | recall | ![equation](https://latex.codecogs.com/gif.latex?%5Cdpi%7B150%7D%20%5Cfrac%7B1%7D%7B%5Csum%20I%28x_i%20%3D%201%29%7D%5Csum_%7Bi%3D1%7D%20%5E%7Bn%7D%20I%28x_i%20%3D%201%29y_i) |
+| binary classification | F-beta Score | fbeta_score | ![equation](https://latex.codecogs.com/gif.latex?%5Cdpi%7B150%7D%20%281%20+%20%5Cbeta%5E2%29%20%5Cfrac%7B%5Ctext%7Bprecision%7D%20*%20%5Ctext%7Brecall%7D%7D%7B%20%28%5Cbeta%5E2%20*%20%5Ctext%7Bprecision%7D%29%20+%20%5Ctext%7Brecall%7D%7D) |

tests/testthat/test-binary_classification.R

@@ -21,3 +21,22 @@ test_that('mean los loss is calculated correctly', {
     expect_equal(logLoss(c(1,1,1,0,0,0),c(.5,.1,.01,.9,.75,.001)), 1.881797068998267)
 })
 
+test_that('precision is calculated correctly', {
+    expect_equal(precision(c(1,1,0,0),c(1,1,0,0)), 1)
+    expect_equal(precision(c(0,0,1,1),c(1,1,0,0)), 0)
+    expect_equal(precision(c(1,1,0,0),c(1,1,1,1)), 1/2)
+})
+
+test_that('recall is calculated correctly', {
+  expect_equal(recall(c(1,1,0,0),c(1,1,0,0)), 1)
+  expect_equal(recall(c(0,0,1,1),c(1,1,0,0)), 0)
+  expect_equal(recall(c(1,1,1,1),c(1,0,0,1)), 1/2)
+})
+
+test_that('f-beta score is calculated correctly',{
+  expect_equal(fbeta_score(c(1,1,0,0),c(1,1,0,0)), 1)
+  expect_equal(fbeta_score(c(0,0,1,1),c(1,1,1,0)), 2/5)
+  expect_equal(fbeta_score(c(1,1,1,1),c(1,0,0,1)), 2/3)
+  expect_equal(fbeta_score(c(1,1,0,0),c(1,1,1,1),beta=0), 1/2)
+})
+