Update ISCO-08 Hierarchical Accuracy Measure documentation

isco_ham.py

@@ -37,8 +37,8 @@ _CITATION = """
 _DESCRIPTION = """
 The ISCO-08 Hierarchical Accuracy Measure is an implementation 
 of the measure described in [Functional Annotation of Genes Using Hierarchical Text Categorization](https://www.researchgate.net/publication/44046343_Functional_Annotation_of_Genes_Using_Hierarchical_Text_Categorization) 
-(Kiritchenko, Svetlana and Famili, Fazel. 2005) with the ISCO-08 taxonomy by the International Labour Organization.\n
-\n
+(Kiritchenko, Svetlana and Famili, Fazel. 2005) with the ISCO-08 taxonomy by the International Labour Organization.
+
 1. The measure gives credit to partially correct classification, 
 e.g. misclassification into node $I$ (ISCO unit group "1120") 
 when the correct category is $G$ (ISCO unit group "1111") 
@@ -47,24 +47,24 @@ should be penalized less than misclassification into node $D$
 as $G$ and $D$ is not.
 2. The measure punishes distant errors more heavily:
     1. the measure gives higher evaluation for correctly classifying one level down compared to staying at the parent node, e.g. classification into node $E$ (ISCO minor group "111") is better than classification into its parent $C$ (ISCO sub-major group "11") since $E$ is closer to the correct category $G$; 
-    2. the measure gives lower evaluation for incorrectly classifying one level down comparing to staying at the parent node, e.g. classification into node $F$ (ISCO minor group "112") is worse than classification into its parent $C$ since $F$ is farther away from $G$.\n
-\n
-The features described are accomplished by pairing hierarchical variants of precision ($hP$) and recall ($hR$) to form a hierarchical F1 (hF_β) score where each sample belongs not only to its class (e.g., a unit group level code), but also to all ancestors of the class in a hierarchical graph (i.e., the minor, sub-major, and major group level codes).\n
-\n
-Hierarchical precision can be computed with:\n
-$hP = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}^′_i |} = \frac{1}{2}$\n
-\n
-Hierarchical recall can be computed with:\n
-$hR = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}_i |} = \frac{1}{2}$\n
-\n
-Combining the two values $hP$ and $hR$ into one hF-measure:\n
-hF_β = \frac{(β^2 + 1) · hP · hR}{(β^2 · hP + hR)}, β ∈ [0, +∞)\n
-\n
-Note:\n
-**TP**: True positive\n
-**TN**: True negative\n
-**FP**: False positive\n
-**FN**: False negative\n
+    2. the measure gives lower evaluation for incorrectly classifying one level down comparing to staying at the parent node, e.g. classification into node $F$ (ISCO minor group "112") is worse than classification into its parent $C$ since $F$ is farther away from $G$.
+
+The features described are accomplished by pairing hierarchical variants of precision ($hP$) and recall ($hR$) to form a hierarchical F1 (hF_β) score where each sample belongs not only to its class (e.g., a unit group level code), but also to all ancestors of the class in a hierarchical graph (i.e., the minor, sub-major, and major group level codes).
+
+Hierarchical precision can be computed with:
+$hP = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}^′_i |} = \frac{1}{2}$
+
+Hierarchical recall can be computed with:
+$hR = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}_i |} = \frac{1}{2}$
+
+Combining the two values $hP$ and $hR$ into one hF-measure:
+hF_β = \frac{(β^2 + 1) · hP · hR}{(β^2 · hP + hR)}, β ∈ [0, +∞)
+
+Note:
+**TP**: True positive
+**TN**: True negative
+**FP**: False positive
+**FN**: False negative
 """
 
 _KWARGS_DESCRIPTION = """

metric_template_1.py

@@ -37,8 +37,8 @@ _CITATION = """
 _DESCRIPTION = """
 The ISCO-08 Hierarchical Accuracy Measure is an implementation 
 of the measure described in [Functional Annotation of Genes Using Hierarchical Text Categorization](https://www.researchgate.net/publication/44046343_Functional_Annotation_of_Genes_Using_Hierarchical_Text_Categorization) 
-(Kiritchenko, Svetlana and Famili, Fazel. 2005) with the ISCO-08 taxonomy by the International Labour Organization.\n
-\n
+(Kiritchenko, Svetlana and Famili, Fazel. 2005) with the ISCO-08 taxonomy by the International Labour Organization.
+
 1. The measure gives credit to partially correct classification, 
 e.g. misclassification into node $I$ (ISCO unit group "1120") 
 when the correct category is $G$ (ISCO unit group "1111") 
@@ -47,24 +47,24 @@ should be penalized less than misclassification into node $D$
 as $G$ and $D$ is not.
 2. The measure punishes distant errors more heavily:
     1. the measure gives higher evaluation for correctly classifying one level down compared to staying at the parent node, e.g. classification into node $E$ (ISCO minor group "111") is better than classification into its parent $C$ (ISCO sub-major group "11") since $E$ is closer to the correct category $G$; 
-    2. the measure gives lower evaluation for incorrectly classifying one level down comparing to staying at the parent node, e.g. classification into node $F$ (ISCO minor group "112") is worse than classification into its parent $C$ since $F$ is farther away from $G$.\n
-\n
-The features described are accomplished by pairing hierarchical variants of precision ($hP$) and recall ($hR$) to form a hierarchical F1 (hF_β) score where each sample belongs not only to its class (e.g., a unit group level code), but also to all ancestors of the class in a hierarchical graph (i.e., the minor, sub-major, and major group level codes).\n
-\n
-Hierarchical precision can be computed with:\n
-$hP = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}^′_i |} = \frac{1}{2}$\n
-\n
-Hierarchical recall can be computed with:\n
-$hR = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}_i |} = \frac{1}{2}$\n
-\n
-Combining the two values $hP$ and $hR$ into one hF-measure:\n
-hF_β = \frac{(β^2 + 1) · hP · hR}{(β^2 · hP + hR)}, β ∈ [0, +∞)\n
-\n
-Note:\n
-**TP**: True positive\n
-**TN**: True negative\n
-**FP**: False positive\n
-**FN**: False negative\n
+    2. the measure gives lower evaluation for incorrectly classifying one level down comparing to staying at the parent node, e.g. classification into node $F$ (ISCO minor group "112") is worse than classification into its parent $C$ since $F$ is farther away from $G$.
+
+The features described are accomplished by pairing hierarchical variants of precision ($hP$) and recall ($hR$) to form a hierarchical F1 (hF_β) score where each sample belongs not only to its class (e.g., a unit group level code), but also to all ancestors of the class in a hierarchical graph (i.e., the minor, sub-major, and major group level codes).
+
+Hierarchical precision can be computed with:
+$hP = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}^′_i |} = \frac{1}{2}$
+
+Hierarchical recall can be computed with:
+$hR = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}_i |} = \frac{1}{2}$
+
+Combining the two values $hP$ and $hR$ into one hF-measure:
+hF_β = \frac{(β^2 + 1) · hP · hR}{(β^2 · hP + hR)}, β ∈ [0, +∞)
+
+Note:
+**TP**: True positive
+**TN**: True negative
+**FP**: False positive
+**FN**: False negative
 """
 
 _KWARGS_DESCRIPTION = """