Updated max thresholder limit

Author: KulikDM

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
         name: Format docstrings
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.19.1
+    rev: v3.20.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
@@ -42,7 +42,7 @@ repos:
         name: Sort imports
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.11.5
+    rev: v0.11.13
     hooks:
       - id: ruff
         args: [--exit-non-zero-on-fix, --fix, --line-length=180]

CHANGES.txt

@@ -87,12 +87,13 @@ v<0.3.7>, <08/18/2024> -- Updated time complexity benchmarks
 v<0.3.8>, <11/21/2024> -- Added factor arg to MAD and ZSCORE, contribution by @MalikAly
 v<0.3.8>, <12/15/2024> -- Removed matplotlib as core dependency
 v<1.0.0>, <12/19/2024> -- Fixed low contamination issue in RANK
-v<1.0.0>, <01/27/2024> -- Added numpy random seed to all thresholders
-v<1.0.0>, <01/27/2024> -- Added `fit` and `predict` methods to all thresholders
-v<1.0.0>, <01/27/2024> -- Aligned MTT alpha arg with standard value
-v<1.0.0>, <01/27/2024> -- Aligned all thresholders to be sklearn compatible
-v<1.0.0>, <01/27/2024> -- Added new example notebooks
-v<1.0.0>, <01/27/2024> -- Updated all thresholder tests
-v<1.0.0>, <01/27/2024> -- Updated all thresholder examples
-v<1.0.0>, <01/27/2024> -- Updated docs with shift to V1
-v<1.0.0>, <01/27/2024> -- Updated docs with datatables
+v<1.0.0>, <01/27/2025> -- Added numpy random seed to all thresholders
+v<1.0.0>, <01/27/2025> -- Added `fit` and `predict` methods to all thresholders
+v<1.0.0>, <01/27/2025> -- Aligned MTT alpha arg with standard value
+v<1.0.0>, <01/27/2025> -- Aligned all thresholders to be sklearn compatible
+v<1.0.0>, <01/27/2025> -- Added new example notebooks
+v<1.0.0>, <01/27/2025> -- Updated all thresholder tests
+v<1.0.0>, <01/27/2025> -- Updated all thresholder examples
+v<1.0.0>, <01/27/2025> -- Updated docs with shift to V1
+v<1.0.0>, <01/27/2025> -- Updated docs with datatables
+v<1.0.1>, <06/14/2025> -- Updated max thresholder limit

pythresh/thresholds/aucp.py

@@ -93,7 +93,9 @@ def eval(self, decision):
 
         # Apply the limit to where the area is less than that limit percentage
         # of the total area under the curve
-        limit = 1
+        eps = np.finfo(decision.dtype).eps
+        limit = 1.0 + eps
+
         for i in range(len(dat_range)):
 
             splt_area = auc(dat_range[i:], val[i:])

pythresh/thresholds/fgd.py

@@ -82,8 +82,11 @@ def eval(self, decision):
                 if count == 2:
                     break
 
+        eps = np.finfo(decision.dtype).eps
+
         limit = ((dat_range[ind[0]]+dat_range[ind[1]])/2 if
-                 len(ind) > 1 else 1.1)
+                 len(ind) > 1 else 1.0 + eps)
+
         self.thresh_ = limit
 
         return cut(decision, limit)

pythresh/thresholds/fwfm.py

@@ -74,7 +74,8 @@ def eval(self, decision):
         base_width = peak_widths(val, peaks, rel_height=0.99)[0]
 
         # Normalize and set limit
-        limit = base_width[0]/len(val) if len(base_width) > 0 else 1.1
+        eps = np.finfo(decision.dtype).eps
+        limit = base_width[0]/len(val) if len(base_width) > 0 else 1.0 + eps
 
         self.thresh_ = limit
 

pythresh/thresholds/gesd.py

@@ -106,7 +106,8 @@ def eval(self, decision):
 
         arr = decision.copy()
 
-        limit = 1.1
+        eps = np.finfo(decision.dtype).eps
+        limit = 1.0 + eps
 
         if self.max_outliers == 'auto':
             self.max_outliers = len(decision) // 2

pythresh/thresholds/mcst.py

@@ -135,7 +135,9 @@ def eval(self, decision):
                 p_std = p_check
                 povr.append(rnd[i])
 
-        limit = np.min(povr) if povr else 1.1
+        eps = np.finfo(decision.dtype).eps
+        limit = np.min(povr) if povr else 1.0 + eps
+
         self.thresh_ = limit
 
         return cut(decision, limit)

pythresh/thresholds/mtt.py

@@ -78,7 +78,8 @@ def eval(self, decision):
 
         arr = np.sort(decision.copy())
 
-        limit = 1.1
+        eps = np.finfo(decision.dtype).eps
+        limit = 1.0 + eps
 
         while True: