delete unused algorithm 3

server/website/website/migrations/0006_added_algorithm_selection.py

@@ -16,7 +16,9 @@ class Migration(migrations.Migration):
         migrations.AddField(
             model_name='session',
             name='algorithm',
-            field=models.IntegerField(choices=[(1, 'Ottertune Default'), (2, 'Algorithm 1'), (3, 'Algorithm 2'), (4, 'Algorithm 3')], default=1),
+            field=models.IntegerField(choices=[(1, 'Ottertune Default'),
+                                               (2, 'Deep Deterministic Policy Gradients'),
+                                               (3, 'Deep Neural Network')], default=1),
         ),
         migrations.AlterField(
             model_name='pipelinedata',

server/website/website/types.py

@@ -174,13 +174,11 @@ class LabelStyleType(BaseType):
 
 class AlgorithmType(BaseType):
     OTTERTUNE = 1
-    ALGORITHM1 = 2
+    DDPG = 2
-    ALGORITHM2 = 3
+    DNN = 3
-    ALGORITHM3 = 4
 
     TYPE_NAMES = {
         OTTERTUNE: 'Ottertune Default',
-        ALGORITHM1: 'Algorithm 1',
+        DDPG: 'Deep Deterministic Policy Gradients',
-        ALGORITHM2: 'Algorithm 2',
+        DNN: 'Deep Neural Network',
-        ALGORITHM3: 'Algorithm 3'
     }

server/website/website/utils.py

@@ -110,21 +110,6 @@ class DataUtil(object):
             maxvals.append(maxval)
         return np.array(minvals), np.array(maxvals)
 
-    @staticmethod
-    def denormalize_knob_data(knob_values, knob_labels, session):
-        for i, knob in enumerate(knob_labels):
-            knob_object = KnobCatalog.objects.get(dbms=session.dbms, name=knob, tunable=True)
-            knob_session_object = SessionKnob.objects.filter(knob=knob_object, session=session,
-                                                             tunable=True)
-            if knob_session_object.exists():
-                minval = float(knob_session_object[0].minval)
-                maxval = float(knob_session_object[0].maxval)
-            else:
-                minval = float(knob_object.minval)
-                maxval = float(knob_object.maxval)
-            knob_values[i] = knob_values[i] * (maxval - minval) + minval
-        return knob_values
-
     @staticmethod
     def aggregate_data(results):
         knob_labels = list(JSONUtil.loads(results[0].knob_data.data).keys())