fix: corrections to anomaly evaluation

Author: hmgomes

notebooks/anomaly_detection.ipynb

@@ -92,6 +92,9 @@ def train(self, instance: Instance):
         self._learn_batch(data)
         return
 
+    def __str__(self):
+        return "Online Isolation Forest"
+
     def predict(self, instance: Instance) -> Optional[LabelIndex]:
         pass
 

src/capymoa/anomaly/_online_isolation_forest.py

@@ -559,7 +559,8 @@ def update(self, y_target_index: int, score: float):
     def metrics_header(self):
         performance_measurements = self.moa_evaluator.getPerformanceMeasurements()
         performance_names = [
-            "".join(measurement.getName()) for measurement in performance_measurements
+            _translate_metric_name("".join(measurement.getName()), to='capymoa')
+            for measurement in performance_measurements
         ]
         return performance_names
 
@@ -579,15 +580,14 @@ def metrics_per_window(self):
         return pd.DataFrame(self.result_windows, columns=self.metrics_header())
 
     def auc(self):
-        index = self.metrics_header().index("AUC")
+        index = self.metrics_header().index("auc")
         return self.metrics()[index]
 
     def s_auc(self):
-        index = self.metrics_header().index("sAUC")
+        index = self.metrics_header().index("s_auc")
         return self.metrics()[index]
 
 
-
 class ClassificationWindowedEvaluator(ClassificationEvaluator):
     """
     Uses the ClassificationEvaluator to perform a windowed evaluation.