Refactor sad.py file.

river/anomaly/sad.py

@@ -21,7 +21,7 @@ class StandardAbsoluteDeviation(anomaly.base.AnomalyDetector):
     Parameters
     ----------
     sub_stat
-        The statistic to be substracted, then divided by the standard deviation for scoring.
+        The statistic to be subtracted, then divided by the standard deviation for scoring.
         This parameter must be either "mean" or "median".
 
     References
@@ -58,29 +58,32 @@ class StandardAbsoluteDeviation(anomaly.base.AnomalyDetector):
 
     """
 
-    def __init__(self, subtracted_statistic: str = "mean"):
-        if subtracted_statistic == "mean":
-            self.subtracted_statistic = stats.Mean()
-        elif subtracted_statistic == "median":
-            self.subtracted_statistic = stats.Quantile(q=0.5)
+    def __init__(self, sub_stat: str = "mean"):
+        self.variance = stats.Var()
+        self.sub_stat = sub_stat
+
+        if self.sub_stat == "mean":
+            self.subtracted_statistic_estimator = stats.Mean()
+        elif self.sub_stat == "median":
+            self.subtracted_statistic_estimator = stats.Quantile(q=0.5)
         else:
             raise ValueError(
-                f"Unknown subtracted statistic {subtracted_statistic}, expected one of median, mean."
+                f"Unknown subtracted statistic {self.sub_stat}, expected one of median, mean."
             )
 
-        self.variance = stats.Var()
-
     def learn_one(self, x):
         assert len(x) == 1
         ((x_key, x_value),) = x.items()
 
         self.variance.update(x_value)
-        self.subtracted_statistic.update(x_value)
+        self.subtracted_statistic_estimator.update(x_value)
 
     def score_one(self, x):
         assert len(x) == 1
         ((x_key, x_value),) = x.items()
 
-        score = (x_value - self.subtracted_statistic.get()) / (self.variance.get() ** 0.5 + 1e-10)
+        score = (x_value - self.subtracted_statistic_estimator.get()) / (
+            self.variance.get() ** 0.5 + 1e-10
+        )
 
         return abs(score)