plot.ROC() and plot.prROC() fix 🔨 (#38)

## 📚 What? This PR closes #36 and expands the unit-tests to include all associated methods with `ROC()` and `prROC()`. The most important part of the PR is, 1. The `plot`-methods now uses the finite `data.frame` for labelling and plotting. 2. Version bumped to `v0.3-1` 3. NEWS updated
serkor1 · Jan 1, 2025 · c0500dc · c0500dc
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: SLmetrics
 Title: Machine Learning Performance Evaluation on Steroids
-Version: 0.3-0
+Version: 0.3-1
 Authors@R: c(
     person(
       given   = "Serkan", 

diff --git a/NEWS.Rmd b/NEWS.Rmd
@@ -18,6 +18,16 @@ knitr::opts_chunk$set(
 set.seed(1903) 
 ```
 
+# Version 0.3-1
+
+> Version 0.3-1 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.
+
+## :bug: Bug-fixes
+
+* **Plot-method in ROC and prROC (https://github.com/serkor1/SLmetrics/issues/36):** Fixed a bug in  `plot.ROC()` and `plot.prROC()` where if `panels = FALSE` additional lines would be added to the plot.
+
 # Version 0.3-0
 
 > Version 0.3-0 is considered pre-release of {SLmetrics}. We do not
@@ -124,7 +134,7 @@ SLmetrics::weighted.cmatrix(
 )
 ```
 
-## Bug-fixes
+## :bug: Bug-fixes
 
 * **Return named vectors:** The classification metrics when `micro == NULL` were not returning named vectors. This has been fixed. 
 
@@ -194,7 +204,7 @@ try(
 )
 ```
 
-## Bug-fixes
+## :bug: 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 of `Rcpp::IntegerMatrix`.
 
@@ -259,7 +269,7 @@ SLmetrics::accuracy(
 )
 ```
 
-## Bug-fixes
+## :bug: 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.
 

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,17 @@
 
+# Version 0.3-1
+
+> Version 0.3-1 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.
+
+## :bug: Bug-fixes
+
+- **Plot-method in ROC and prROC
+  (<https://github.com/serkor1/SLmetrics/issues/36>):** Fixed a bug in
+  `plot.ROC()` and `plot.prROC()` where if `panels = FALSE` additional
+  lines would be added to the plot.
+
 # Version 0.3-0
 
 > Version 0.3-0 is considered pre-release of {SLmetrics}. We do not
@@ -143,7 +156,7 @@ SLmetrics::weighted.cmatrix(
     #> b 3.506631 5.426818 8.358687
     #> c 6.615661 6.390454 2.233511
 
-## Bug-fixes
+## :bug: Bug-fixes
 
 - **Return named vectors:** The classification metrics when
   `micro == NULL` were not returning named vectors. This has been fixed.
@@ -247,7 +260,7 @@ try(
     #> Error in UseMethod(generic = "weighted.accuracy", object = ..1) : 
     #>   no applicable method for 'weighted.accuracy' applied to an object of class "cmatrix"
 
-## Bug-fixes
+## :bug: Bug-fixes
 
 - **Floating precision:** Metrics would give different results based on
   the method used. This means that `foo.cmatrix()` and `foo.factor()`
@@ -353,7 +366,7 @@ SLmetrics::accuracy(
 )
 ```
 
-## Bug-fixes
+## :bug: Bug-fixes
 
 - The `plot.prROC()`- and `plot.ROC()`-functions now adds a line to the
   plot when `panels = FALSE`. See Issue

diff --git a/NEWS_files/figure-gfm/unnamed-chunk-10-1.png b/NEWS_files/figure-gfm/unnamed-chunk-10-1.png
diff --git a/NEWS_files/figure-gfm/unnamed-chunk-10-2.png b/NEWS_files/figure-gfm/unnamed-chunk-10-2.png
diff --git a/R/S3_PrecisionRecallCurve.R b/R/S3_PrecisionRecallCurve.R
@@ -124,41 +124,45 @@ plot.prROC <- function(
     x,
     panels = TRUE,
     ...) {
-
-  # 1) Plot options
-  #
-  # All common options for the
-  # plot goes her
-  pformula <- precision ~ recall
-  groups   <- x$label
-  xlab     <- "Recall"
-  ylab     <- "Precision"
-  main     <- "Precision-Recall Curve"
-
-  # 1.1) conditional plotting
-  # statements
-  if (panels) {
-
-    # 1.2) grouped by
-    # label.
-    pformula <- precision ~ recall | factor(label, labels = unique(label))
-
-    # 1.3) disable grouping
-    # if panelwise
-    groups  <- NULL
-
-  }
-
-  roc_plot(
-    formula  = pformula,
-    groups   = groups,
-    xlab     = xlab,
-    ylab     = ylab,
-    main     = main,
-    DT       = x[is.finite(x$threshold), ],
-    add_poly = panels,
-    ...  
-  )
+
+    # 0) exract the finite
+    # data.frame
+    x <- x[is.finite(x$threshold), ]
+
+    # 1) Plot options
+    #
+    # All common options for the
+    # plot goes her
+    pformula <- precision ~ recall
+    groups   <- x$label
+    xlab     <- "Recall"
+    ylab     <- "Precision"
+    main     <- "Precision-Recall Curve"
+
+    # 1.1) conditional plotting
+    # statements
+    if (panels) {
+
+      # 1.2) grouped by
+      # label.
+      pformula <- precision ~ recall | factor(label, labels = unique(label))
+
+      # 1.3) disable grouping
+      # if panelwise
+      groups  <- NULL
+
+    }
+
+    roc_plot(
+      formula  = pformula,
+      groups   = groups,
+      xlab     = xlab,
+      ylab     = ylab,
+      main     = main,
+      DT       = x,
+      add_poly = panels,
+      ...  
+    )
 
 }
 

diff --git a/R/S3_RecieverOperatorCharacteristics.R b/R/S3_RecieverOperatorCharacteristics.R
@@ -130,44 +130,46 @@ plot.ROC <- function(
     x,
     panels = TRUE,
     ...) {
-
-
-
-  # 1) Plot options
-  #
-  # All common options for the
-  # plot goes her
-  pformula <- tpr ~ fpr
-  groups   <- x$label
-  xlab     <- "False Positive Rate (FPR)"
-  ylab     <- "True Positive Rate (TPR)"
-  main     <- "Reciever Operator Characteristics"
-
-  # 1.1) conditional plotting
-  # statements
-  if (panels) {
-
-    # 1.2) grouped by
-    # label.
-    pformula <- tpr ~ fpr | factor(label, labels = unique(label))
-
-    # 1.3) disable grouping
-    # if panelwise
-    groups  <- NULL
-
-  }
-
-
-  roc_plot(
-    formula  = pformula,
-    groups   = groups,
-    xlab     = xlab,
-    ylab     = ylab,
-    main     = main,
-    DT       = x[is.finite(x$threshold), ],
-    add_poly = panels,
-    ...  
-  )
+
+    # 0) exract the finite
+    # data.frame
+    x <- x[is.finite(x$threshold), ]
+
+    # 1) Plot options
+    #
+    # All common options for the
+    # plot goes her
+    pformula <- tpr ~ fpr
+    groups   <- x$label
+    xlab     <- "False Positive Rate (FPR)"
+    ylab     <- "True Positive Rate (TPR)"
+    main     <- "Reciever Operator Characteristics"
+
+    # 1.1) conditional plotting
+    # statements
+    if (panels) {
+
+      # 1.2) grouped by
+      # label.
+      pformula <- tpr ~ fpr | factor(label, labels = unique(label))
+
+      # 1.3) disable grouping
+      # if panelwise
+      groups  <- NULL
+
+    }
+
+
+    roc_plot(
+      formula  = pformula,
+      groups   = groups,
+      xlab     = xlab,
+      ylab     = ylab,
+      main     = main,
+      DT       = x,
+      add_poly = panels,
+      ...  
+    )
 
 }
 

diff --git a/tests/testthat/test-ROC.R b/tests/testthat/test-ROC.R
@@ -71,12 +71,22 @@ testthat::test_that(
         # 2.3) Test that methods
         # works as expected
         testthat::expect_no_condition(
-          object = invisible(capture.output(SLmetrics:::print.ROC(score))),
+          object = invisible(capture.output(print(score))),
           message = info
         )
 
         testthat::expect_no_condition(
-          object  = SLmetrics:::plot.ROC(score),
+          object = invisible(capture.output(summary(score))),
+          message = info
+        )
+
+        testthat::expect_no_condition(
+          object  = plot(score, panels = FALSE),
+          message = info
+        )
+
+        testthat::expect_no_condition(
+          object  = plot(score, panels = TRUE),
           message = info
         )
 

diff --git a/tests/testthat/test-prROC.R b/tests/testthat/test-prROC.R
@@ -72,12 +72,22 @@ testthat::test_that(
         # 2.3) Test that methods
         # works as expected
         testthat::expect_no_condition(
-          object = invisible(capture.output(SLmetrics:::print.prROC(score))),
+          object = invisible(capture.output(print(score))),
           message = info
         )
 
         testthat::expect_no_condition(
-          object  = SLmetrics:::plot.prROC(score),
+          object = invisible(capture.output(summary(score))),
+          message = info
+        )
+
+        testthat::expect_no_condition(
+          object  = plot(score, panels = FALSE),
+          message = info
+        )
+
+        testthat::expect_no_condition(
+          object  = plot(score, panels = TRUE),
           message = info
         )