Update to pythresh v1

docs/about.rst

@@ -61,7 +61,7 @@ Adam Goodge (PhD Researcher @ National University of Singapore):
 - Joined in 2022 (implemented LUNAR)
 - `LinkedIn (Adam Goodge) <https://www.linkedin.com/in/adam-goodge-33908691/>`_
 
-Daniel Kulik (Machine Learning Developer; MSc Student @ University of the Free State):
+Daniel Kulik (Machine Learning Developer; MSc Astrophysics @ University of the Free State):
 
 - Joined 2022 (implemented integration with PyThresh and more)
 - `LinkedIn (Daniel Kulik) <https://www.linkedin.com/in/daniel-kulik-148256223>`_

docs/requirements.txt

@@ -1,13 +1,12 @@
 combo
 furo
-geomstats
 joblib>=1.5
 matplotlib
 numpy>=1.19
 numba>=0.51
 pyclustering
 pytest
-pythresh>=0.3.1
+pythresh>=1.0.0
 ruptures
 scipy>=1.5.1
 scikit-learn>=0.22.0

pyod/models/base.py

@@ -167,7 +167,7 @@ def predict(self, X, return_confidence=False):
 
         # if this is a PyThresh object
         else:
-            prediction = self.contamination.eval(pred_score)
+            prediction = self.contamination.predict(pred_score)
 
         if return_confidence:
             confidence = self.predict_confidence(X)
@@ -291,7 +291,7 @@ def predict_confidence(self, X):
             prediction = (test_scores > self.threshold_).astype('int').ravel()
         # if this is a PyThresh object
         else:
-            prediction = self.contamination.eval(test_scores)
+            prediction = self.contamination.predict(test_scores)
         np.place(confidence, prediction == 0, 1 - confidence[prediction == 0])
 
         return confidence
@@ -575,7 +575,8 @@ def _process_decision_scores(self):
 
         # if this is a PyThresh object
         else:
-            self.labels_ = self.contamination.eval(self.decision_scores_)
+            self.contamination.fit(self.decision_scores_)
+            self.labels_ = self.contamination.labels_
             self.threshold_ = self.contamination.thresh_
             if not self.threshold_:
                 self.threshold_ = np.sum(self.labels_) / len(self.labels_)