Fix typos and docstrings

VectorInstitute · Dec 19, 2023 · 7742ecf · 7742ecf
1 parent 5f32388
commit 7742ecf
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Showing 4 changed files with 6 additions and 6 deletions.
diff --git a/cyclops/evaluate/metrics/experimental/functional/specificity.py b/cyclops/evaluate/metrics/experimental/functional/specificity.py
@@ -315,7 +315,7 @@ def multilabel_specificity(
     average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
     ignore_index: Optional[int] = None,
 ) -> Array:
-    """Measure the proportion of positive predictions that are true positive.
+    """Measure how well a classifier identifies negative samples.
 
     Parameters
     ----------

diff --git a/cyclops/evaluate/metrics/experimental/negative_predictive_value.py b/cyclops/evaluate/metrics/experimental/negative_predictive_value.py
@@ -12,7 +12,7 @@
 
 
 class BinaryNPV(_AbstractBinaryStatScores, registry_key="binary_npv"):
-    """The proportion of predictive negatives that are true negatives.
+    """The proportion of negative predictions that are true negatives.
 
     Parameters
     ----------
@@ -54,7 +54,7 @@ class MulticlassNPV(
     _AbstractMulticlassStatScores,
     registry_key="multiclass_npv",
 ):
-    """The proportion of predictive negatives that are true negatives.
+    """The proportion of negative predictions that are true negatives.
 
     Parameters
     ----------
@@ -122,7 +122,7 @@ class MultilabelNPV(
     _AbstractMultilabelStatScores,
     registry_key="multilabel_npv",
 ):
-    """The proportion of actual negatives that are correctly identified.
+    """The proportion of negative predictions that are true negatives.
 
     Parameters
     ----------

diff --git a/cyclops/evaluate/metrics/f_beta.py b/cyclops/evaluate/metrics/f_beta.py
@@ -178,7 +178,7 @@ class MultilabelFbetaScore(MultilabelStatScores, registry_key="multilabel_fbeta_
     num_labels : int
         Number of labels for the task.
     threshold : float, default=0.5
-        Threshold for deciding the positive class if predicitions are logits
+        Threshold for deciding the positive class if predictions are logits
         or probability scores. Logits will be converted to probabilities using
         the sigmoid function.
     top_k : int, optional

diff --git a/tests/cyclops/evaluate/metrics/experimental/test_specificity.py b/tests/cyclops/evaluate/metrics/experimental/test_specificity.py
@@ -1,4 +1,4 @@
-"""Test specificity recall metrics."""
+"""Test specificity."""
 from functools import partial
 from typing import Literal, Optional