Merge pull request #10 from MalikAly/factor

Add scalar factor support and tests for MAD and ZSCORE

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
         name: Format docstrings
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.18.0
+    rev: v3.19.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
@@ -42,8 +42,9 @@ repos:
         name: Sort imports
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.7.0
+    rev: v0.7.2
     hooks:
       - id: ruff
         args: [--exit-non-zero-on-fix, --fix, --line-length=180]
+        exclude: "\\.ipynb$"
         name: Lint code

pythresh/test/test_mad.py

@@ -1,5 +1,6 @@
 import sys
 import unittest
+from itertools import product
 from os.path import dirname as up
 
 # noinspection PyProtectedMember
@@ -41,11 +42,15 @@ def setUp(self):
 
         self.all_scores = [scores, multiple_scores]
 
-        self.thres = MAD()
+        self.factors = [0.5, 1, 2]
 
     def test_prediction_labels(self):
 
-        for scores in self.all_scores:
+        params = product(self.all_scores, self.factors)
+
+        for scores, factor in params:
+
+            self.thres = MAD(factor=factor)
 
             pred_labels = self.thres.eval(scores)
             assert (self.thres.thresh_ is not None)

pythresh/test/test_zscore.py

@@ -1,5 +1,6 @@
 import sys
 import unittest
+from itertools import product
 from os.path import dirname as up
 
 # noinspection PyProtectedMember
@@ -41,11 +42,15 @@ def setUp(self):
 
         self.all_scores = [scores, multiple_scores]
 
-        self.thres = ZSCORE()
+        self.factors = [0.5, 1, 2]
 
     def test_prediction_labels(self):
 
-        for scores in self.all_scores:
+        params = product(self.all_scores, self.factors)
+
+        for scores, factor in params:
+
+            self.thres = ZSCORE(factor=factor)
 
             pred_labels = self.thres.eval(scores)
             assert (self.thres.thresh_ is not None)

pythresh/thresholds/mad.py

@@ -17,6 +17,9 @@ class MAD(BaseThresholder):
        Parameters
        ----------
 
+       factor : int, optional (default=1)
+            The factor to multiply the MAD by to set the threshold.
+            The default is 1.
        random_state : int, optional (default=1234)
             Random seed for the random number generators of the thresholders. Can also
             be set to None.
@@ -48,8 +51,9 @@ class MAD(BaseThresholder):
 
     """
 
-    def __init__(self, random_state=1234):
+    def __init__(self, factor=1, random_state=1234):
 
+        self.factor = factor
         self.random_state = random_state
 
     def eval(self, decision):
@@ -78,8 +82,8 @@ def eval(self, decision):
 
         # Set limit
         mean = np.mean(decision)
-        limit = mean + \
-            stats.median_abs_deviation(decision, scale=np.std(decision))
+        mad = stats.median_abs_deviation(decision, scale=np.std(decision))
+        limit = mean + self.factor * mad
 
         self.thresh_ = limit
 

pythresh/thresholds/zscore.py

@@ -15,7 +15,9 @@ class ZSCORE(BaseThresholder):
 
        Parameters
        ----------
-
+       factor : int, optional (default=1)
+            The factor to multiply the zscore by to set the threshold.
+            The default is 1.
        random_state : int, optional (default=1234)
             Random seed for the random number generators of the thresholders. Can also
             be set to None.
@@ -43,8 +45,9 @@ class ZSCORE(BaseThresholder):
 
     """
 
-    def __init__(self, random_state=1234):
+    def __init__(self, factor=1, random_state=1234):
 
+        self.factor = factor
         self.random_state = random_state
 
     def eval(self, decision):
@@ -74,9 +77,9 @@ def eval(self, decision):
         # Get the zscore of the decision scores
         zscore = stats.zscore(decision)
 
-        # Set the limit to where the zscore is 1
+        # Set the limit to where the zscore is greater than the factor
         labels = np.zeros(len(decision), dtype=int)
-        mask = np.where(zscore >= 1.0)
+        mask = np.where(zscore >= self.factor)
         labels[mask] = 1
 
         self.thresh_ = np.min(labels[labels == 1])