Kuhns{III-VII} metrics Added

CHANGELOG.md

@@ -6,6 +6,12 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ## [Unreleased]
 ### Added
+- 5 new distance/similarity
+	1. KuhnsIII
+	2. KuhnsIV
+	3. KuhnsV
+	4. KuhnsVI
+	5. KuhnsVII
 ### Changed
 - PyPI badge in `README.md`
 - GitHub actions are limited to the `dev` and `master` branches

Document/Distance.ipynb

@@ -3045,6 +3045,291 @@
     "</ul>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuhns III"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kuhns III correlation [[40]](#ref40)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$corr_{KuhnsIII} =\n",
+    "\\frac{\\delta(TP + FP, TP + FN)}\n",
+    "{(1-\\frac{TP}{2 \\times TP + FP + FN})(2 \\times TP + FP + FN-\\frac{(TP + FP)(TP + FN)}{n})}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\delta(TP + FP, TP + FN) = TP - \\frac{(TP + FP) \\times (TP + FN)}{N}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.4148148148148148, 1: 0.1388888888888889, 2: 0.08088235294117647}"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm.distance(metric=DistanceType.KuhnsIII)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    "    <li><span style=\"color:red;\">Notice </span> :  new in <span style=\"color:red;\">version 4.2</span> </li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuhns IV"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kuhns IV correlation [[40]](#ref40)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$corr_{KuhnsIV} =\n",
+    "\\frac{\\delta(TP + FP, TP + FN)}\n",
+    "{\\min(TP + FP, TP + FN)}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\delta(TP + FP, TP + FN) = TP - \\frac{(TP + FP) \\times (TP + FN)}{N}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.5833333333333334, 1: 0.25, 2: 0.1}"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm.distance(metric=DistanceType.KuhnsIV)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    "    <li><span style=\"color:red;\">Notice </span> :  new in <span style=\"color:red;\">version 4.2</span> </li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuhns V"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kuhns V correlation [[40]](#ref40)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$corr_{KuhnsV} =\n",
+    "\\frac{\\delta(TP + FP, TP + FN)}\n",
+    "{\\max((TP+FP)(1-\\frac{TP+FP}{n}), (TP+FN)(1-\\frac{TP+FN}{n}))}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\delta(TP + FP, TP + FN) = TP - \\frac{(TP + FP) \\times (TP + FN)}{N}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.6000000000000001, 1: 0.2222222222222222, 2: 0.16666666666666666}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm.distance(metric=DistanceType.KuhnsV)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    "    <li><span style=\"color:red;\">Notice </span> :  new in <span style=\"color:red;\">version 4.2</span> </li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuhns VI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kuhns VI correlation [[40]](#ref40)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$corr_{KuhnsVI} =\n",
+    "\\frac{\\delta(TP + FP, TP + FN)}\n",
+    "{\\min((TP+FP)(1-\\frac{TP+FP}{n}), (TP+FN)(1-\\frac{TP+FN}{n}))}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\delta(TP + FP, TP + FN) = TP - \\frac{(TP + FP) \\times (TP + FN)}{N}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.7777777777777778, 1: 0.3, 2: 0.17142857142857146}"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm.distance(metric=DistanceType.KuhnsVI)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    "    <li><span style=\"color:red;\">Notice </span> :  new in <span style=\"color:red;\">version 4.2</span> </li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuhns VII"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kuhns VII correlation [[40]](#ref40)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$corr_{KuhnsVII} =\n",
+    "\\frac{\\delta(TP + FP, TP + FN)}\n",
+    "{\\sqrt{(TP + FP) \\times (TP + FN)}}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\delta(TP + FP, TP + FN) = TP - \\frac{(TP + FP) \\times (TP + FN)}{N}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0.45184805705753195, 1: 0.20412414523193154, 2: 0.09128709291752768}"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm.distance(metric=DistanceType.KuhnsVII)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    "    <li><span style=\"color:red;\">Notice </span> :  new in <span style=\"color:red;\">version 4.2</span> </li>\n",
+    "</ul>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3140,7 +3425,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -3154,7 +3439,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,

Test/verified_test.py

@@ -416,6 +416,16 @@
 >>> assert isclose(cm2.distance(metric=DistanceType.KuhnsI)[1], 0.005004425239483548, abs_tol=ABS_TOL, rel_tol=REL_TOL)
 >>> assert isclose(cm1.distance(metric=DistanceType.KuhnsII)[1], 0.49489795918367346, abs_tol=ABS_TOL, rel_tol=REL_TOL)
 >>> assert isclose(cm2.distance(metric=DistanceType.KuhnsII)[1], 0.32695578231292516, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm1.distance(metric=DistanceType.KuhnsIII)[1], 0.3307757885763001, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm2.distance(metric=DistanceType.KuhnsIII)[1], 0.21873141468207793, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm1.distance(metric=DistanceType.KuhnsIV)[1], 0.49489795918367346, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm2.distance(metric=DistanceType.KuhnsIV)[1], 0.3923469387755102, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm1.distance(metric=DistanceType.KuhnsV)[1], 0.497435897435897, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm2.distance(metric=DistanceType.KuhnsV)[1], 0.329477292202228, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm1.distance(metric=DistanceType.KuhnsVI)[1], 0.497435897435897, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm2.distance(metric=DistanceType.KuhnsVI)[1], 0.394865211810013, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm1.distance(metric=DistanceType.KuhnsVII)[1], 0.49489795918367346, abs_tol=ABS_TOL, rel_tol=REL_TOL)
+>>> assert isclose(cm2.distance(metric=DistanceType.KuhnsVII)[1], 0.3581621145590755, abs_tol=ABS_TOL, rel_tol=REL_TOL)
 >>> mlcm = MultiLabelCM(actual_vector=[{"cat", "bird"}, {"dog"}], predict_vector=[{"cat"}, {"dog", "bird"}], classes=["cat", "dog", "bird"]) # Verified Case -- (http://bitly.ws/GNq2)
 >>> mlcm.actual_vector_multihot
 [[1, 0, 1], [0, 1, 0]]