feat: [TTA] add algo for dropping Features in adjust()

Tries to drop the lowest-scoring Features until the target time (range) is
reached. This is not optimised and a relatively naïve approach- there are many
inputs which would result in a non-ideal pruning.

pipeline/adjusters.py

@@ -84,7 +84,7 @@ class TargetTimeAdjuster(Adjuster):
         self.strategy = strategy
 
     def adjust(self) -> list:
-        """Drop Features until the target time within the margin is reached.
+        """Drop Features until the target time within the margin is reached. Prioritise dropping lower scoring Features.
 
         Approach:
 
@@ -111,4 +111,36 @@ class TargetTimeAdjuster(Adjuster):
             return self.features
 
         # sort list of Features by score (primary) and by duration (secondary)
-        sorted_features = sorted(self.features, key=lambda x: (x.score, x.time))
+        sorted_features = self._sort_by_score_time(self.features)
+        drop_indices = [] # indices of Features to drop
+
+        # first pass- drop lowest scoring Features until we are within the target time
+        for i in range(len(sorted_features)):
+            # check if dropping this Feature would put us in the target range:
+            # if so, drop it and return
+            if (total_time - sorted_features[i].interval.duration >= target_time_min and
+               total_time - sorted_features[i].interval.duration <= target_time_max):
+                drop_indices.append(i)
+                break
+
+            elif (total_time - sorted_features[i].interval.duration > target_time_max):
+                drop_indices.append(i)
+                total_time -= sorted_features[i].interval.duration
+
+        for i in drop_indices:
+            self.features.remove(sorted_features[i])
+
+        # if we are now within the target time, return the Features
+        total_time = self._features_total_time(features=self.features)
+        if total_time <= target_time_max:
+            return self.features
+
+        # else: we are still over the target time
+        # so drop the lowest scoring Features until we are UNDER the target time
+        for i in range(len(sorted_features)):
+            self.features.remove(sorted_features[i])
+            total_time -= sorted_features[i].interval.duration
+            if total_time <= target_time_max:
+                break
+
+        return self.features