rename matthews_corrcoef to mcc

Signed-off-by: Zhiyuan Chen <[email protected]>

danling/metrics/__init__.py

@@ -22,7 +22,7 @@
 from .average_meter import AverageMeter, AverageMeters, MultiTaskAverageMeters
 
 with try_import() as lazy_import:
-    from .functional import accuracy, auprc, auroc, matthews_corrcoef, pearson, r2_score, rmse, spearman
+    from .functional import accuracy, auprc, auroc, mcc, pearson, r2_score, rmse, spearman
     from .metrics import Metrics, MultiTaskMetrics
 
 __all__ = [
@@ -40,12 +40,12 @@
 
 def binary_metrics(**kwargs):
     lazy_import.check()
-    return Metrics(auroc=auroc, auprc=auprc, acc=accuracy, mcc=matthews_corrcoef, **kwargs)
+    return Metrics(auroc=auroc, auprc=auprc, acc=accuracy, mcc=mcc, **kwargs)
 
 
 def multiclass_metrics(num_classes: int, **kwargs):
     lazy_import.check()
-    p_mcc = partial(matthews_corrcoef, num_classes=num_classes)
+    p_mcc = partial(mcc, num_classes=num_classes)
     p_auroc = partial(auroc, num_classes=num_classes)
     p_auprc = partial(auprc, num_classes=num_classes)
     p_acc = partial(accuracy, num_classes=num_classes)
@@ -54,7 +54,7 @@ def multiclass_metrics(num_classes: int, **kwargs):
 
 def multilabel_metrics(num_labels: int, **kwargs):
     lazy_import.check()
-    p_mcc = partial(matthews_corrcoef, num_labels=num_labels)
+    p_mcc = partial(mcc, num_labels=num_labels)
     p_auroc = partial(auroc, num_labels=num_labels)
     p_auprc = partial(auprc, num_labels=num_labels)
     p_acc = partial(accuracy, num_labels=num_labels)

danling/metrics/functional.py

@@ -102,7 +102,7 @@ def accuracy(
     raise ValueError("Could not infer the type of the task. Only one of `num_labels`, `num_classes` is allowed.")
 
 
-def matthews_corrcoef(
+def mcc(
     input: Tensor | NestedTensor,
     target: Tensor | NestedTensor,
     threshold: float = 0.5,

danling/metrics/metrics.py

@@ -456,7 +456,7 @@ def score_name(self, name) -> None:
 class MultiTaskMetrics(MultiTaskDict):
     r"""
     Examples:
-        >>> from danling.metrics.functional import auroc, auprc, pearson, spearman, accuracy, matthews_corrcoef
+        >>> from danling.metrics.functional import auroc, auprc, pearson, spearman, accuracy, mcc
         >>> metrics = MultiTaskMetrics()
         >>> metrics.dataset1.cls = Metrics(auroc=auroc, auprc=auprc)
         >>> metrics.dataset1.reg = Metrics(pearson=pearson, spearman=spearman)