DSN random state fix and tests

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
@@ -42,7 +42,7 @@ repos:
         name: Sort imports
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.1.4
+    rev: v0.1.7
     hooks:
       - id: ruff
         args: [--exit-non-zero-on-fix, --fix, --line-length=180]

README.rst

@@ -293,7 +293,7 @@ smallest uncertainty about its mean and is the most robust (best least
 accurate prediction). However, for interpretability and general
 performance the ``FILTER`` thresholder is a good fit.
 
-Further utilities are available for assiting in the selection of the
+Further utilities are available for assisting in the selection of the
 most optimal outlier detection and thresholding methods `ranking
 <https://pythresh.readthedocs.io/en/latest/ranking.html>`_ as well as
 determining the confidence with regards to the selected thresholding

docs/index.rst

@@ -100,7 +100,7 @@ provided the smallest uncertainty about its mean and is the most robust
 (best least accurate prediction). However, for interpretability and
 general performance the ``FILTER`` thresholder is a good fit.
 
-Further utilities are available for assiting in the selection of the
+Further utilities are available for assisting in the selection of the
 most optimal outlier detection and thresholding methods `ranking
 <https://pythresh.readthedocs.io/en/latest/ranking.html>`_ as well as
 determining the confidence with regards to the selected thresholding

pythresh/test/test_filter.py

@@ -45,15 +45,17 @@ def setUp(self):
         self.methods = ['gaussian', 'savgol', 'hilbert', 'wiener', 'medfilt',
                         'decimate', 'detrend', 'resample']
 
-        self.sigma = 'auto'
+        pre_sig = len(scores)
+
+        self.sigmas = ['auto', int(pre_sig**0.6), int(pre_sig**75)]
 
     def test_prediction_labels(self):
 
-        params = product(self.all_scores, self.methods)
+        params = product(self.all_scores, self.methods, self.sigmas)
 
-        for scores, method in params:
+        for scores, method, sigma in params:
 
-            self.thres = FILTER(method=method, sigma=self.sigma)
+            self.thres = FILTER(method=method, sigma=sigma)
             pred_labels = self.thres.eval(scores)
             assert (self.thres.thresh_ is not None)
 

pythresh/thresholds/dsn.py

@@ -217,7 +217,8 @@ def _MAH_metric(self):
         """Calculate the Mahalanobis distance."""
 
         # fit a Minimum Covariance Determinant (MCD) robust estimator to data
-        robust_cov = MinCovDet().fit(np.array([self.val_norm]).T)
+        robust_cov = MinCovDet(random_state=self.random_state).fit(
+            np.array([self.val_norm]).T)
 
         # Get the Mahalanobis distance
         dist = robust_cov.mahalanobis(np.array([self.val_data]).T)