feat: weighted metrics (#26)

* feat(confusionmatrix): added weight metric

* feat(confusionmatrix): added more options to `getShortStats()`

src/__tests__/confusionMatrix.js

@@ -150,6 +150,13 @@ test('Predicted Negatives', () => {
   expect(cm.getPredNegative('other')).toStrictEqual(11)
 })
 
+test('Support', () => {
+  const cm = new CM(CATEGORIES, M0)
+  expect(cm.getSupport('bug')).toStrictEqual(6)
+  expect(cm.getSupport('code')).toStrictEqual(3)
+  expect(cm.getSupport('other')).toStrictEqual(11)
+})
+
 describe('Accuracy', () => {
   const cm = new CM(CATEGORIES, M0)
   test('Accuracy', () => {
@@ -158,12 +165,19 @@ describe('Accuracy', () => {
     expect(cm.getAccuracy('other')).toStrictEqual(0.8)
   })
 
-  test('Macro accuracy', () => {
+  test('Macro Accuracy', () => {
     expect(cm.getMacroAccuracy()).toStrictEqual(M[56])
   })
-  test('Micro accuracy', () => {
+
+  test('Micro Accuracy', () => {
     expect(cm.getMicroAccuracy()).toStrictEqual(0.75)
   })
+
+  test('Weighted Accuracy', () => {
+    expect(
+      Math.round(cm.getWeightedAccuracy() * 100000) / 100000,
+    ).toStrictEqual(0.83)
+  })
 })
 
 describe('Recall', () => {
@@ -174,13 +188,19 @@ describe('Recall', () => {
     expect(cm.getRecall('other')).toStrictEqual(M[811]) //.727
   })
 
-  test('Macro recall', () => {
+  test('Macro Recall', () => {
     expect(cm.getMacroRecall()).toStrictEqual((3 / 2 + M[811]) / 3) //~.742
   })
 
-  test('Micro recall', () => {
+  test('Micro Recall', () => {
     expect(cm.getMicroRecall()).toStrictEqual(0.75)
   })
+
+  test('Weighted Recall', () => {
+    expect(Math.round(cm.getWeightedRecall() * 100000) / 100000).toStrictEqual(
+      0.75,
+    )
+  })
 })
 
 describe('Precision', () => {
@@ -191,13 +211,19 @@ describe('Precision', () => {
     expect(cm.getPrecision('other')).toStrictEqual(M[89]) //.889
   })
 
-  test('Macro precision', () => {
+  test('Macro Precision', () => {
     expect(cm.getMacroPrecision()).toStrictEqual((M[56] + 0.4 + M[89]) / 3) //~.707
   })
 
-  test('Micro precision', () => {
+  test('Micro Precision', () => {
     expect(cm.getMicroPrecision()).toStrictEqual(0.75)
   })
+
+  test('Weighted Precision', () => {
+    expect(
+      Math.round(cm.getWeightedPrecision() * 100000) / 100000,
+    ).toStrictEqual(0.79889)
+  })
 })
 
 describe('F1', () => {
@@ -215,6 +241,12 @@ describe('F1', () => {
   test('Micro F1', () => {
     expect(cm.getMicroF1()).toStrictEqual(0.75)
   })
+
+  test('Weighted F1', () => {
+    expect(Math.round(cm.getWeightedF1() * 100000) / 100000).toStrictEqual(
+      0.765,
+    )
+  })
 })
 
 describe('MissRate', () => {
@@ -232,6 +264,12 @@ describe('MissRate', () => {
   test('Micro MissRate', () => {
     expect(cm.getMicroMissRate()).toStrictEqual(0.25)
   })
+
+  test('Weighted MissRate', () => {
+    expect(
+      Math.round(cm.getWeightedMissRate() * 100000) / 100000,
+    ).toStrictEqual(0.25)
+  })
 })
 
 describe('FallOut', () => {
@@ -250,6 +288,12 @@ describe('FallOut', () => {
   test('Micro FallOut', () => {
     expect(cm.getMicroFallOut()).toStrictEqual(0.125)
   })
+
+  test('Weighted FallOut', () => {
+    expect(Math.round(cm.getWeightedFallOut() * 100000) / 100000).toStrictEqual(
+      0.10901,
+    )
+  })
 })
 
 describe('Specificity', () => {
@@ -268,6 +312,12 @@ describe('Specificity', () => {
   test('Micro Specificity', () => {
     expect(cm.getMicroSpecificity()).toStrictEqual(0.875)
   })
+
+  test('Weighted Specificity', () => {
+    expect(
+      Math.round(cm.getWeightedSpecificity() * 100000) / 100000,
+    ).toStrictEqual(0.89099)
+  })
 })
 
 describe('Prevalence', () => {
@@ -285,6 +335,12 @@ describe('Prevalence', () => {
   test('Micro Prevalence', () => {
     expect(cm.getMicroPrevalence()).toStrictEqual(M[13])
   })
+
+  test('Weighted Prevalence', () => {
+    expect(
+      Math.round(cm.getWeightedPrevalence() * 100000) / 100000,
+    ).toStrictEqual(0.415)
+  })
 })
 
 describe('fromData', () => {
@@ -416,16 +472,42 @@ describe('toString', () => {
   })
 })
 
-test('shortStats', () => {
-  const cm = new CM(CATEGORIES, M0)
-  const ss = `Total: 20
+describe('shortStats', () => {
+  test('default', () => {
+    const cm = new CM(CATEGORIES, M0)
+    const ss = `Total: 20
 True: 15
 False: 5
 Accuracy: 75%
 Precision: 75%
 Recall: 75%
 F1: 75%`
-  expect(cm.getShortStats()).toStrictEqual(ss)
+    expect(cm.getShortStats()).toStrictEqual(ss)
+  })
+
+  test('macro', () => {
+    const cm = new CM(CATEGORIES, M0)
+    const ss = `Total: 20
+True: 15
+False: 5
+Accuracy: 83.33333333333334%
+Precision: 70.74074074074073%
+Recall: 74.24242424242425%
+F1: 71.11111111111111%`
+    expect(cm.getShortStats('macro')).toStrictEqual(ss)
+  })
+
+  test('weighted', () => {
+    const cm = new CM(CATEGORIES, M0)
+    const ss = `Total: 20
+True: 15
+False: 5
+Accuracy: 83%
+Precision: 79.88888888888889%
+Recall: 75%
+F1: 76.49999999999999%`
+    expect(cm.getShortStats('weighted')).toStrictEqual(ss)
+  })
 })
 
 describe('Long stats', () => {
@@ -439,6 +521,7 @@ describe('Long stats', () => {
       classes: CATEGORIES,
       microAvg: {},
       macroAvg: {},
+      weightedAvg: {},
       results: {
         bug: {},
         code: {},
@@ -473,6 +556,19 @@ describe('Long stats', () => {
     })
   })
 
+  it('has weighted details', () => {
+    expect(stats.weightedAvg).toMatchObject({
+      accuracy: 0.83 + 1e-16,
+      f1: 0.7649999999999999,
+      fallOut: 0.10901027077497664,
+      missRate: 0.25,
+      precision: 0.7988888888888889,
+      prevalence: 0.41500000000000004,
+      recall: 0.75,
+      specificity: 0.8909897292250232,
+    })
+  })
+
   it('has class details', () => {
     const bugStats = stats.results.bug
     expect(bugStats).toMatchObject({