Refactor ISCOHAM class name to ISCO_Hierarchical_Accuracy

isco_ham.py

@@ -35,36 +35,7 @@ _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.
-
-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") 
-should be penalized less than misclassification into node $D$ 
-(e.g., ISCO unit group "1211") since $I$ is in the same subgraph (ISCO sub-major group "11")
-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 $G$.
-
-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
+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) and adapted for the ISCO-08 classification scheme by the International Labour Organization.
 """
 
 _KWARGS_DESCRIPTION = """
@@ -103,7 +74,7 @@ ILO_ISCO_CSV_URL = (
 
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class ISCOHAM(evaluate.Metric):
+class ISCO_Hierarchical_Accuracy(evaluate.Metric):
     """The ISCO-08 Hierarchical Accuracy Measure"""
 
     def _info(self):

metric_template_1.py

@@ -74,7 +74,7 @@ ILO_ISCO_CSV_URL = (
 
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class ISCOHAM(evaluate.Metric):
+class ISCO_Hierarchical_Accuracy(evaluate.Metric):
     """The ISCO-08 Hierarchical Accuracy Measure"""
 
     def _info(self):