Fix docstring examples, typo

VectorInstitute · Mar 4, 2024 · ba5f70f · ba5f70f
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Showing 3 changed files with 13 additions and 13 deletions.
diff --git a/cyclops/evaluate/metrics/experimental/functional/roc.py b/cyclops/evaluate/metrics/experimental/functional/roc.py
@@ -130,7 +130,7 @@ def binary_roc(
     -------
     ROCCurve
         A named tuple containing the false positive rate (FPR), true positive rate
-        (TPR) and thresholds. The FPR and TPR are arrays of of shape
+        (TPR) and thresholds. The FPR and TPR are arrays of shape
         `(num_thresholds + 1,)` and the thresholds are an array of shape
         `(num_thresholds,)`.
 

diff --git a/cyclops/evaluate/metrics/experimental/precision_recall_curve.py b/cyclops/evaluate/metrics/experimental/precision_recall_curve.py
@@ -55,14 +55,14 @@ class BinaryPrecisionRecallCurve(Metric, registry_key="binary_precision_recall_c
     >>> preds = anp.asarray([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
     >>> metric = BinaryPrecisionRecallCurve(thresholds=None)
     >>> metric(target, preds)
-    (Array([0.5      , 0.6      , 0.5      , 0.6666667,
-           0.5      , 1.       , 1.       ], dtype=float32), Array([1.        , 1.        , 0.6666667 , 0.6666667 ,
-           0.33333334, 0.33333334, 0.        ], dtype=float32), Array([0.11, 0.22, 0.33, 0.73, 0.84, 0.92], dtype=float64))
+    PRCurve(precision=Array([0.5      , 0.6      , 0.5      , 0.6666667,
+           0.5      , 1.       , 1.       ], dtype=float32), recall=Array([1.        , 1.        , 0.6666667 , 0.6666667 ,
+           0.33333334, 0.33333334, 0.        ], dtype=float32), thresholds=Array([0.11, 0.22, 0.33, 0.73, 0.84, 0.92], dtype=float64))
     >>> metric = BinaryPrecisionRecallCurve(thresholds=5)
     >>> metric(target, preds)
-    (Array([0.5      , 0.5      , 0.6666667, 0.5      ,
-           0.       , 1.       ], dtype=float32), Array([1.        , 0.6666667 , 0.6666667 , 0.33333334,
-           0.        , 0.        ], dtype=float32), Array([0.  , 0.25, 0.5 , 0.75, 1.  ], dtype=float32))
+    PRCurve(precision=Array([0.5      , 0.5      , 0.6666667, 0.5      ,
+           0.       , 1.       ], dtype=float32), recall=Array([1.        , 0.6666667 , 0.6666667 , 0.33333334,
+           0.        , 0.        ], dtype=float32), thresholds=Array([0.  , 0.25, 0.5 , 0.75, 1.  ], dtype=float32))
 
     """  # noqa: W505
 

diff --git a/cyclops/evaluate/metrics/experimental/roc.py b/cyclops/evaluate/metrics/experimental/roc.py
@@ -43,14 +43,14 @@ class BinaryROC(BinaryPrecisionRecallCurve, registry_key="binary_roc_curve"):
     >>> preds = anp.asarray([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
     >>> metric = BinaryROC(thresholds=None)
     >>> metric(target, preds)
-    (Array([0.        , 0.        , 0.33333334, 0.33333334,
-           0.6666667 , 0.6666667 , 1.        ], dtype=float32), Array([0.        , 0.33333334, 0.33333334, 0.6666667 ,
-           0.6666667 , 1.        , 1.        ], dtype=float32), Array([1.  , 0.92, 0.84, 0.73, 0.33, 0.22, 0.11], dtype=float64))
+    ROCCurve(fpr=Array([0.        , 0.        , 0.33333334, 0.33333334,
+           0.6666667 , 0.6666667 , 1.        ], dtype=float32), tpr=Array([0.        , 0.33333334, 0.33333334, 0.6666667 ,
+           0.6666667 , 1.        , 1.        ], dtype=float32), thresholds=Array([1.  , 0.92, 0.84, 0.73, 0.33, 0.22, 0.11], dtype=float64))
     >>> metric = BinaryROC(thresholds=5)
     >>> metric(target, preds)
-    (Array([0.        , 0.33333334, 0.33333334, 0.6666667 ,
-           1.        ], dtype=float32), Array([0.        , 0.33333334, 0.6666667 , 0.6666667 ,
-           1.        ], dtype=float32), Array([1.  , 0.75, 0.5 , 0.25, 0.  ], dtype=float32))
+    ROCCurve(fpr=Array([0.        , 0.33333334, 0.33333334, 0.6666667 ,
+           1.        ], dtype=float32), tpr=Array([0.        , 0.33333334, 0.6666667 , 0.6666667 ,
+           1.        ], dtype=float32), thresholds=Array([1.  , 0.75, 0.5 , 0.25, 0.  ], dtype=float32))
 
     """  # noqa: W505